diff --git a/.Rbuildignore b/.Rbuildignore
index 0c15d76..1d00090 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -3,3 +3,4 @@
 ^\.travis\.yml$
 ^netlify\.toml$
 ^appveyor\.yml$
+^LICENSE\.md$
diff --git a/.gitignore b/.gitignore
index 5b6a065..7b732e7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,4 @@
 .Rhistory
 .RData
 .Ruserdata
+.DS_Store
diff --git a/02-sampling.Rmd b/02-sampling.Rmd
index e1f9967..c46bfc7 100644
--- a/02-sampling.Rmd
+++ b/02-sampling.Rmd
@@ -114,7 +114,7 @@ aged 50 years or older” or “persons aged 55 years or older”.
 
 The age distribution of the population and the survey eligibility criteria will affect the sample design in terms of the number of communities that you will need to sample ($m$) and the number of older persons ($n$) that can be sampled from each community.
 
-The overall sample size for a RAM-OP sample should be at least $n = 192$ usually collected as $n = 12$ eligible subjects sampled from $m = 16$ communities. If older people make up a very small proportion (i.e. much less than 5%) of the total population and / or the average population of communities is small then you will usually need to sample more than m = 16 communities in order to get about $n = 192$ older people in the overall sample. This is likely to occur when there are fewer than 20 to 25 older people in a community of average size.
+The overall sample size for a RAM-OP sample should be at least $n = 192$ usually collected as $n = 12$ eligible subjects sampled from $m = 16$ communities. If older people make up a very small proportion (i.e. much less than 5%) of the total population and / or the average population of communities is small then you will usually need to sample more than $m = 16$ communities in order to get about $n = 192$ older people in the overall sample. This is likely to occur when there are fewer than 20 to 25 older people in a community of average size.
 
 You can calculate the number of older people that you would expect to be living in a community of average size using the following formula:
 
@@ -164,7 +164,7 @@ RANDBETWEEN(1, 4)
 
  
 
-**Step 3:** Using the *random starting point* and the *sampling interval*, select the sampling villages from a list of all villages organised/sorted by a **non-overalapping** spatial factor such as district or sub-district.
+**Step 3:** Using the *random starting point* and the *sampling interval*, select the sampling villages from a list of all villages organised/sorted by a **non-overlapping** spatial factor such as district or sub-district.
 
 \newpage
 
diff --git a/03-indicators.Rmd b/03-indicators.Rmd
index f0e69b7..7c38636 100644
--- a/03-indicators.Rmd
+++ b/03-indicators.Rmd
@@ -447,7 +447,7 @@ These are standard case definitions for acute malnutrition in adults and recomme
 
 \newpage
 
-```{block2, type = "rmdbox"}
+```{block2, type = "rmdnote"}
 An estimate of GAM prevalence can be made using a classic estimator:
 
 $$\text{prevalence} = \frac{\text{number of respondents with MUAC < 210 mm}}{\text{total number of respondents}}$$
diff --git a/06-practical.Rmd b/06-practical.Rmd
index 60f81ba..187677d 100644
--- a/06-practical.Rmd
+++ b/06-practical.Rmd
@@ -276,7 +276,7 @@ Here is a typical timetable of survey activities:
 * Review logistics arrangements with the trainees
 
 
-**At the end of the trainin**
+**At the end of the training**
 
 * Provide each trainee with a certificate of attendance
 * Pay the training incentives
diff --git a/07-ram_op_software.Rmd b/07-ram_op_software.Rmd
index 78f27b6..43e001f 100644
--- a/07-ram_op_software.Rmd
+++ b/07-ram_op_software.Rmd
@@ -19,13 +19,13 @@ This manual covers analysing your data using the **RAnalyticFlow** workflow. An
 To use the **RAnalyticFlow** workflow you must install:
 
 * **The *R Language for Data-Analysis and Graphics* (R)** : This is the *“engine”* which does all the
-work of analysing your data. You can get the R installation program from: [http://cran.r-project.org](http://cran.r-project.org). Following are links to download operating sofware-specific versions of R:
+work of analysing your data. You can get the R installation program from: [http://cran.r-project.org](http://cran.r-project.org). Following are links to download operating software-specific versions of R:
 
    * [Download R for Linux](https://cran.r-project.org/bin/linux/)
 
    * [Download R for (Mac) OS X](https://cran.r-project.org/bin/macosx/)
 
-   * [Download R for Windowx](https://cran.r-project.org/bin/windows/)
+   * [Download R for Windows](https://cran.r-project.org/bin/windows/)
 
 * **R packages** (libraries of functions needed to work with the **RAnalyticFlow** workflow) : You can install these from within **R** using the Package Installer function within R. The libraries needed are:
 
@@ -55,7 +55,7 @@ install.packages(c("rJava", "JavaGD", "codetools",
                    repos = "https://cloud.r-project.org/")
 ```
 
-The `repos` argument in the R command above specifies the CRAN mirror from which you to download the package/s you want to install. Here we specify the cloud-based mirror for CRAN provided by RStudio. If unspecified, the installiation process will prompt you to select a mirror from which to download packages from. If you already know the URL of the CRAN mirror you want to use, specify this in the `repos` argument. 
+The `repos` argument in the R command above specifies the CRAN mirror from which you to download the package/s you want to install. Here we specify the cloud-based mirror for CRAN provided by RStudio. If unspecified, the installation process will prompt you to select a mirror from which to download packages from. If you already know the URL of the CRAN mirror you want to use, specify this in the `repos` argument. 
 
 Note that **RAnalyticFlow** may require you to have **Java** installed. Check the instructions on the **RAnalyticFlow** [download page](http://r.analyticflow.com/en/download/) and on this [starter guide](http://download.ef-prime.com/ranalyticflow/3.1.5/readme.html).
 
@@ -98,7 +98,7 @@ Before you start work you will need to create a project for your survey:
 
 2. Give your project a useful (i.e. descriptive and memorable) name. This might be a name that describes the survey. For example, if the survey was done in the Kereinik locality of West Darfur in December 2015 you might use the name **WD.Kereinik.Dec2015.RAMOP**
 
-3. Give the location of your project directory or project folder. This is the directory or folder which contains your survey data file, your PSU file, the RAM-OP language file, and a copy of **RAMOP.rflow** (see previous page). The location of the project directory or project folder (labeled “Path” by the software) that **RAnalyticFlow** selects automatically will almost always be wrong. You need to specify this manually.
+3. Give the location of your project directory or project folder. This is the directory or folder which contains your survey data file, your PSU file, the RAM-OP language file, and a copy of **RAMOP.rflow** (see previous page). The location of the project directory or project folder (labelled “Path” by the software) that **RAnalyticFlow** selects automatically will almost always be wrong. You need to specify this manually.
 
 4. Click the **OK** button
 
@@ -120,7 +120,7 @@ knitr::include_graphics("figures/runWorkflowRAF.png")
 
 Once this is done, you should:
 
-1. Retrieve your survey data. This can be in EpiDat (REC) format or CSV format. Select and run the
+1. Retrieve your survey data. This can be in EpiData (REC) format or CSV format. Select and run the
 appropriate **Survey Data** node and select the survey data file.
 
 2. Retrieve the PSU date data. Select and run the **PSU Data** node and select your PSU file.
diff --git a/DESCRIPTION b/DESCRIPTION
index 744763e..06ac294 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: ramOPmanual
 Title: Rapid Assessment Method for Older People (RAM-OP): The Manual
-Version: 0.1.0
+Version: 0.1.1
 Authors@R: c(
     person("Pascale", "Fritsch", role = "aut"),
     person("Ernest", "Guevarra", email = "ernest@guevarra.io", role = c("aut", "cre")),
@@ -14,7 +14,8 @@ Description: HelpAge International <http://www.helpage.org., Valid International
     needs of older people. The method uses simple procedures, in a short time 
     frame (i.e. about two weeks including training, data collection, data entry, 
     and data analysis), and at considerably lower cost than other methods.
+License: GPL-3
 Depends: R (>= 3.0.1)
 Imports:
     bookdown
-License: MIT + file LICENSE
\ No newline at end of file
+
diff --git a/LICENSE.md b/LICENSE.md
new file mode 100644
index 0000000..882ddf1
--- /dev/null
+++ b/LICENSE.md
@@ -0,0 +1,595 @@
+GNU General Public License
+==========================
+
+_Version 3, 29 June 2007_  
+_Copyright © 2007 Free Software Foundation, Inc. &lt;<http://fsf.org/>&gt;_
+
+Everyone is permitted to copy and distribute verbatim copies of this license
+document, but changing it is not allowed.
+
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+indicate your acceptance of this License to do so.
+
+### 10. Automatic Licensing of Downstream Recipients
+
+Each time you convey a covered work, the recipient automatically receives a license
+from the original licensors, to run, modify and propagate that work, subject to this
+License. You are not responsible for enforcing compliance by third parties with this
+License.
+
+An “entity transaction” is a transaction transferring control of an
+organization, or substantially all assets of one, or subdividing an organization, or
+merging organizations. If propagation of a covered work results from an entity
+transaction, each party to that transaction who receives a copy of the work also
+receives whatever licenses to the work the party's predecessor in interest had or
+could give under the previous paragraph, plus a right to possession of the
+Corresponding Source of the work from the predecessor in interest, if the predecessor
+has it or can get it with reasonable efforts.
+
+You may not impose any further restrictions on the exercise of the rights granted or
+affirmed under this License. For example, you may not impose a license fee, royalty,
+or other charge for exercise of rights granted under this License, and you may not
+initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging
+that any patent claim is infringed by making, using, selling, offering for sale, or
+importing the Program or any portion of it.
+
+### 11. Patents
+
+A “contributor” is a copyright holder who authorizes use under this
+License of the Program or a work on which the Program is based. The work thus
+licensed is called the contributor's “contributor version”.
+
+A contributor's “essential patent claims” are all patent claims owned or
+controlled by the contributor, whether already acquired or hereafter acquired, that
+would be infringed by some manner, permitted by this License, of making, using, or
+selling its contributor version, but do not include claims that would be infringed
+only as a consequence of further modification of the contributor version. For
+purposes of this definition, “control” includes the right to grant patent
+sublicenses in a manner consistent with the requirements of this License.
+
+Each contributor grants you a non-exclusive, worldwide, royalty-free patent license
+under the contributor's essential patent claims, to make, use, sell, offer for sale,
+import and otherwise run, modify and propagate the contents of its contributor
+version.
+
+In the following three paragraphs, a “patent license” is any express
+agreement or commitment, however denominated, not to enforce a patent (such as an
+express permission to practice a patent or covenant not to sue for patent
+infringement). To “grant” such a patent license to a party means to make
+such an agreement or commitment not to enforce a patent against the party.
+
+If you convey a covered work, knowingly relying on a patent license, and the
+Corresponding Source of the work is not available for anyone to copy, free of charge
+and under the terms of this License, through a publicly available network server or
+other readily accessible means, then you must either **(1)** cause the Corresponding
+Source to be so available, or **(2)** arrange to deprive yourself of the benefit of the
+patent license for this particular work, or **(3)** arrange, in a manner consistent with
+the requirements of this License, to extend the patent license to downstream
+recipients. “Knowingly relying” means you have actual knowledge that, but
+for the patent license, your conveying the covered work in a country, or your
+recipient's use of the covered work in a country, would infringe one or more
+identifiable patents in that country that you have reason to believe are valid.
+
+If, pursuant to or in connection with a single transaction or arrangement, you
+convey, or propagate by procuring conveyance of, a covered work, and grant a patent
+license to some of the parties receiving the covered work authorizing them to use,
+propagate, modify or convey a specific copy of the covered work, then the patent
+license you grant is automatically extended to all recipients of the covered work and
+works based on it.
+
+A patent license is “discriminatory” if it does not include within the
+scope of its coverage, prohibits the exercise of, or is conditioned on the
+non-exercise of one or more of the rights that are specifically granted under this
+License. You may not convey a covered work if you are a party to an arrangement with
+a third party that is in the business of distributing software, under which you make
+payment to the third party based on the extent of your activity of conveying the
+work, and under which the third party grants, to any of the parties who would receive
+the covered work from you, a discriminatory patent license **(a)** in connection with
+copies of the covered work conveyed by you (or copies made from those copies), or **(b)**
+primarily for and in connection with specific products or compilations that contain
+the covered work, unless you entered into that arrangement, or that patent license
+was granted, prior to 28 March 2007.
+
+Nothing in this License shall be construed as excluding or limiting any implied
+license or other defenses to infringement that may otherwise be available to you
+under applicable patent law.
+
+### 12. No Surrender of Others' Freedom
+
+If conditions are imposed on you (whether by court order, agreement or otherwise)
+that contradict the conditions of this License, they do not excuse you from the
+conditions of this License. If you cannot convey a covered work so as to satisfy
+simultaneously your obligations under this License and any other pertinent
+obligations, then as a consequence you may not convey it at all. For example, if you
+agree to terms that obligate you to collect a royalty for further conveying from
+those to whom you convey the Program, the only way you could satisfy both those terms
+and this License would be to refrain entirely from conveying the Program.
+
+### 13. Use with the GNU Affero General Public License
+
+Notwithstanding any other provision of this License, you have permission to link or
+combine any covered work with a work licensed under version 3 of the GNU Affero
+General Public License into a single combined work, and to convey the resulting work.
+The terms of this License will continue to apply to the part which is the covered
+work, but the special requirements of the GNU Affero General Public License, section
+13, concerning interaction through a network will apply to the combination as such.
+
+### 14. Revised Versions of this License
+
+The Free Software Foundation may publish revised and/or new versions of the GNU
+General Public License from time to time. Such new versions will be similar in spirit
+to the present version, but may differ in detail to address new problems or concerns.
+
+Each version is given a distinguishing version number. If the Program specifies that
+a certain numbered version of the GNU General Public License “or any later
+version” applies to it, you have the option of following the terms and
+conditions either of that numbered version or of any later version published by the
+Free Software Foundation. If the Program does not specify a version number of the GNU
+General Public License, you may choose any version ever published by the Free
+Software Foundation.
+
+If the Program specifies that a proxy can decide which future versions of the GNU
+General Public License can be used, that proxy's public statement of acceptance of a
+version permanently authorizes you to choose that version for the Program.
+
+Later license versions may give you additional or different permissions. However, no
+additional obligations are imposed on any author or copyright holder as a result of
+your choosing to follow a later version.
+
+### 15. Disclaimer of Warranty
+
+THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW.
+EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
+PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER
+EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE
+QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE
+DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+### 16. Limitation of Liability
+
+IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY
+COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS
+PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL,
+INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE
+PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE
+OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE
+WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGES.
+
+### 17. Interpretation of Sections 15 and 16
+
+If the disclaimer of warranty and limitation of liability provided above cannot be
+given local legal effect according to their terms, reviewing courts shall apply local
+law that most closely approximates an absolute waiver of all civil liability in
+connection with the Program, unless a warranty or assumption of liability accompanies
+a copy of the Program in return for a fee.
+
+_END OF TERMS AND CONDITIONS_
+
+## How to Apply These Terms to Your New Programs
+
+If you develop a new program, and you want it to be of the greatest possible use to
+the public, the best way to achieve this is to make it free software which everyone
+can redistribute and change under these terms.
+
+To do so, attach the following notices to the program. It is safest to attach them
+to the start of each source file to most effectively state the exclusion of warranty;
+and each file should have at least the “copyright” line and a pointer to
+where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) 2019 HelpAge International,Valid International
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+If the program does terminal interaction, make it output a short notice like this
+when it starts in an interactive mode:
+
+    ramOPmanual Copyright (C) 2019 HelpAge International,Valid International
+    This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type 'show c' for details.
+
+The hypothetical commands `show w` and `show c` should show the appropriate parts of
+the General Public License. Of course, your program's commands might be different;
+for a GUI interface, you would use an “about box”.
+
+You should also get your employer (if you work as a programmer) or school, if any, to
+sign a “copyright disclaimer” for the program, if necessary. For more
+information on this, and how to apply and follow the GNU GPL, see
+&lt;<http://www.gnu.org/licenses/>&gt;.
+
+The GNU General Public License does not permit incorporating your program into
+proprietary programs. If your program is a subroutine library, you may consider it
+more useful to permit linking proprietary applications with the library. If this is
+what you want to do, use the GNU Lesser General Public License instead of this
+License. But first, please read
+&lt;<http://www.gnu.org/philosophy/why-not-lgpl.html>&gt;.
diff --git a/README.md b/README.md
index 016848c..bd00790 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# Rapid Assessment Method for Older People (RAM-OP): The Manual <img src="figures/op_neg.png" align="right" />
+# Rapid Assessment Method for Older People (RAM-OP): The Manual <img src="figures/ramOP.png" width="200px" align="right" />
 
 [![Travis-CI Build Status](https://travis-ci.org/rapidsurveys/ramOPmanual.svg?branch=master)](https://travis-ci.org/rapidsurveys/ramOPmanual)
 [![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/rapidsurveys/ramOPmanual?branch=master&svg=true)](https://ci.appveyor.com/project/rapidsurveys/ramOPmanual)
diff --git a/_bookdown.yml b/_bookdown.yml
index 460a872..0fab082 100644
--- a/_bookdown.yml
+++ b/_bookdown.yml
@@ -1,6 +1,10 @@
 book_filename: "ramOPmanual"
+repo: https://github.com/rapidsurveys/ramOPmanual
+output_dir: "docs"
 delete_merged_file: true
 language:
+  label:
+    fig: "Figure "
+    tab: "Table "
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-output_dir: "docs"
+    chapter_name: ""
diff --git a/_output.yml b/_output.yml
index 3e4ed13..92700d7 100644
--- a/_output.yml
+++ b/_output.yml
@@ -3,6 +3,8 @@ bookdown::gitbook:
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   fig_caption: yes
   split_by: chapter
+  #includes:
+  #  in_header: google_analytics.html
   config:
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@@ -11,6 +13,10 @@ bookdown::gitbook:
       after: |
         <li></li>
     download: ["pdf", "epub"]
+    sharing:
+      github: yes
+      facebook: no
+      twitter: no
 bookdown::pdf_book:
   includes:
     in_header: preamble.tex
@@ -18,4 +24,8 @@ bookdown::pdf_book:
   citation_package: natbib
   keep_tex: yes
   fig_caption: yes
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+  toc_depth: 3
+  toc_unnumbered: no
+  toc_appendix: yes
+bookdown::epub_book:
+  stylesheet: style.css
\ No newline at end of file
diff --git a/before_body.tex b/before_body.tex
new file mode 100644
index 0000000..dc41495
--- /dev/null
+++ b/before_body.tex
@@ -0,0 +1 @@
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diff --git a/docs/conclusion.html b/docs/conclusion.html
index d2c653f..d4defba 100644
--- a/docs/conclusion.html
+++ b/docs/conclusion.html
@@ -1,40 +1,40 @@
 <!DOCTYPE html>
-<html >
-
+<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
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-  <meta charset="UTF-8">
-  <meta http-equiv="X-UA-Compatible" content="IE=edge">
-  <title>Rapid Assessment Method for Older People (RAM-OP): The Manual</title>
-  <meta name="description" content="Rapid assessment method for older people (RAM-OP) manual">
-  <meta name="generator" content="bookdown 0.7 and GitBook 2.6.7">
+  <meta charset="utf-8" />
+  <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+  <title>7 Conclusion | Rapid Assessment Method for Older People (RAM-OP): The Manual</title>
+  <meta name="description" content="Rapid assessment method for older people (RAM-OP) manual" />
+  <meta name="generator" content="bookdown 0.11 and GitBook 2.6.7" />
 
-  <meta property="og:title" content="Rapid Assessment Method for Older People (RAM-OP): The Manual" />
+  <meta property="og:title" content="7 Conclusion | Rapid Assessment Method for Older People (RAM-OP): The Manual" />
   <meta property="og:type" content="book" />
-  <meta property="og:url" content="https://validmeasures.org/ramOPmanual" />
-  <meta property="og:image" content="https://validmeasures.org/ramOPmanualfigures/coverImage.jpg" />
+  <meta property="og:url" content="https://rapidsurveys.io/ramOPmanual" />
+  <meta property="og:image" content="https://rapidsurveys.io/ramOPmanualfigures/coverImage.jpg" />
   <meta property="og:description" content="Rapid assessment method for older people (RAM-OP) manual" />
-  <meta name="github-repo" content="validmeasures/ramOPmanual" />
+  <meta name="github-repo" content="rapidsurveys/ramOPmanual" />
 
   <meta name="twitter:card" content="summary" />
-  <meta name="twitter:title" content="Rapid Assessment Method for Older People (RAM-OP): The Manual" />
+  <meta name="twitter:title" content="7 Conclusion | Rapid Assessment Method for Older People (RAM-OP): The Manual" />
   
   <meta name="twitter:description" content="Rapid assessment method for older people (RAM-OP) manual" />
-  <meta name="twitter:image" content="https://validmeasures.org/ramOPmanualfigures/coverImage.jpg" />
+  <meta name="twitter:image" content="https://rapidsurveys.io/ramOPmanualfigures/coverImage.jpg" />
 
-<meta name="author" content="Pascale Fritsch, Ernest Guevarra, Katja Siling, Mark Myatt">
+<meta name="author" content="Pascale Fritsch, Ernest Guevarra, Katja Siling, Mark Myatt" />
 
 
 
-  <meta name="viewport" content="width=device-width, initial-scale=1">
-  <meta name="apple-mobile-web-app-capable" content="yes">
-  <meta name="apple-mobile-web-app-status-bar-style" content="black">
+  <meta name="viewport" content="width=device-width, initial-scale=1" />
+  <meta name="apple-mobile-web-app-capable" content="yes" />
+  <meta name="apple-mobile-web-app-status-bar-style" content="black" />
   
   
 <link rel="prev" href="software.html">
 
 <script src="libs/jquery-2.2.3/jquery.min.js"></script>
 <link href="libs/gitbook-2.6.7/css/style.css" rel="stylesheet" />
+<link href="libs/gitbook-2.6.7/css/plugin-table.css" rel="stylesheet" />
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@@ -49,20 +49,26 @@
 
 
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@@ -208,7 +215,7 @@ <h1>
 
             <section class="normal" id="section-">
 <div id="conclusion" class="section level1">
-<h1><span class="header-section-number">Chapter 7</span> Conclusion</h1>
+<h1><span class="header-section-number">7</span> Conclusion</h1>
 <p>We live in an ageing world, where people aged 60 or over will be 2 billion or about 22% of the world’s population by 2050.</p>
 <p>Currently, two in three people aged 60 years or older live in developing countries. By 2050, nearly four in five older people will be living in the developing world.</p>
 <p>The changing demographics of ageing combined with the increasing number of disasters will exert a disproportionate impact on the world’s oldest and poorest.</p>
@@ -237,13 +244,13 @@ <h1><span class="header-section-number">Chapter 7</span> Conclusion</h1>
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 },
+"history": {
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@@ -270,7 +281,7 @@ <h1><span class="header-section-number">Chapter 7</span> Conclusion</h1>
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diff --git a/docs/datasets.html b/docs/datasets.html
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@@ -1,40 +1,40 @@
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-  <title>Rapid Assessment Method for Older People (RAM-OP): The Manual</title>
-  <meta name="description" content="Rapid assessment method for older people (RAM-OP) manual">
-  <meta name="generator" content="bookdown 0.7 and GitBook 2.6.7">
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@@ -208,7 +215,7 @@ <h1>
 
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 <p>This section details the RAM-OP datasets. The information presented here is of most use if you decide not to use the RAM-OP data entry and data checking software. You might, for example, decide to enter survey data using spreadsheet software such as Microsoft Excel. If you do this and want to use the RAM-OP data analysis software then you will need to export the data as a comma-separated-value (CSV) file with the same variable names, variable types and lengths, and using the same codes as shown in the tables in this section. For the main RAM-OP survey dataset these are the same variable names, variable types, variable lengths, codes, and in the same order as shown on the standard RAM-OP questionnaire.</p>
 <p>There are <strong>two</strong> RAM-OP datasets:</p>
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@@ -209,8 +216,8 @@ <h1>
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-<h4 class="author"><em>Pascale Fritsch, Ernest Guevarra, Katja Siling, Mark Myatt</em></h4>
-<h4 class="date"><em>21/12/2015</em></h4>
+<p class="author"><em>Pascale Fritsch, Ernest Guevarra, Katja Siling, Mark Myatt</em></p>
+<p class="date"><em>21/12/2015</em></p>
 </div>
 <div id="the-ram-op-manual" class="section level1 unnumbered">
 <h1>The RAM-OP Manual</h1>
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@@ -208,7 +215,7 @@ <h1>
 
             <section class="normal" id="section-">
 <div id="indicators" class="section level1">
-<h1><span class="header-section-number">Chapter 2</span> Indicators</h1>
+<h1><span class="header-section-number">2</span> Indicators</h1>
 <div id="the-ram-op-indicator-set" class="section level2">
 <h2><span class="header-section-number">2.1</span> The RAM-OP indicator set</h2>
 <p>RAM-OP surveys collect and report on data for a broad range of indicators relevant to older people.</p>
@@ -528,7 +535,7 @@ <h3><span class="header-section-number">2.1.14</span> Anthropometry and screenin
 <p><strong>Note</strong> : MUAC in adults should be measured on the non-dominant arm. This is usually the left arm. The importance of high levels of accuracy and precision at the individual level is of lesser importance in survey work compared to case-finding or diagnosis in clinical contexts, for example. This means that a simple rule such as “Always measure MUAC on the left arm” may be used.</p>
 
 
-<div class="rmdbox">
+<div class="rmdnote">
 <p>An estimate of GAM prevalence can be made using a classic estimator:</p>
 <p><span class="math display">\[\text{prevalence} = \frac{\text{number of respondents with MUAC &lt; 210 mm}}{\text{total number of respondents}}\]</span></p>
 <p>The estimate of GAM prevalence made from the RAM-OP survey data is made using a PROBIT estimator. The PROBIT function is also known as the <em>inverse cumulative distribution</em> function. This function converts parameters of the distribution of an indicator (e.g. the mean and standard deviation of a <em>normally</em> distributed variable) into cumulative percentiles. This means that it is possible to use the normal PROBIT function with estimates of the mean and standard deviation of indicator values in a survey sample to predict (or estimate) the proportion of the population falling below a given threshold. For example, for data with a mean MUAC of 256 mm and a standard deviation of 28 mm the output of the normal PROBIT function for a threshold of 210 mm is 0.0502 meaning that 5.02% of the population are <em>predicted</em> (or <em>estimated</em>) to fall below the 210 mm threshold.</p>
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-<meta name="author" content="Pascale Fritsch, Ernest Guevarra, Katja Siling, Mark Myatt">
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@@ -213,7 +220,7 @@ <h1>Introduction</h1>
 <p>Despite these potential vulnerabilities in humanitarian situations, older people are rarely identified as a group in need of specific nutritional or food assistance. Surveys and assessments almost always focus on children, and sometimes on pregnant and lactating women. Humanitarian workers argue that assessing the nutritional status and needs of older people is both costly and complicated. As a consequence, the nutritional status and needs of older people in crisis go unidentified and unaddressed.</p>
 <p>HelpAge International, VALID International, and Brixton Health, with financial assistance from the Humanitarian Innovation Fund (HIF), have developed a Rapid Assessment Method for Older People (RAM-OP) that provides accurate and reliable estimates of the needs of older people. The method uses simple procedures, in a short time frame (i.e. about two weeks including training, data collection, data entry, and data analysis), and at considerably lower cost than other methods. The RAM-OP method is based on the following principles:</p>
 <ul>
-<li><p>Use of a familiar “household survey” design employing a two-stage cluster sample design optimised to allow the use of a small primary sample ( m ≥ 16 clusters) and a small overall ( n ≥ 192) sample.</p></li>
+<li><p>Use of a familiar “household survey” design employing a two-stage cluster sample design optimised to allow the use of a small primary sample (<span class="math inline">\(m ≥ 16\)</span> clusters) and a small overall (<span class="math inline">\(n ≥ 192\)</span>) sample.</p></li>
 <li>Assessment of multiple dimensions of need in older people (including prevalence of global, moderate and severe acute malnutrition) using, whenever possible, standard and well-tested indicators and question sets.</li>
 <li><p>Data analysis performed using modern computer-intensive methods to allow estimates of indicator levels to be made with useful precision using a small sample size.</p></li>
 </ul>
@@ -247,13 +254,13 @@ <h1>Introduction</h1>
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diff --git a/docs/libs/gitbook-2.6.7/js/app.min.js b/docs/libs/gitbook-2.6.7/js/app.min.js
index 9ace197..d302000 100644
--- a/docs/libs/gitbook-2.6.7/js/app.min.js
+++ b/docs/libs/gitbook-2.6.7/js/app.min.js
@@ -1,6 +1 @@
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data=cache.data,result=typeof value=="string"||isObject(value)?data.set.has(value):data.hash[value];return result?0:-1}function cachePush(value){var data=this.data;if(typeof value=="string"||isObject(value)){data.set.add(value)}else{data.hash[value]=true}}function arrayConcat(array,other){var index=-1,length=array.length,othIndex=-1,othLength=other.length,result=Array(length+othLength);while(++index<length){result[index]=array[index]}while(++othIndex<othLength){result[index++]=other[othIndex]}return result}function arrayCopy(source,array){var index=-1,length=source.length;array||(array=Array(length));while(++index<length){array[index]=source[index]}return array}function arrayEach(array,iteratee){var index=-1,length=array.length;while(++index<length){if(iteratee(array[index],index,array)===false){break}}return array}function arrayEachRight(array,iteratee){var length=array.length;while(length--){if(iteratee(array[length],length,array)===false){break}}return array}function arrayEvery(array,predicate){var index=-1,length=array.length;while(++index<length){if(!predicate(array[index],index,array)){return false}}return true}function arrayExtremum(array,iteratee,comparator,exValue){var index=-1,length=array.length,computed=exValue,result=computed;while(++index<length){var value=array[index],current=+iteratee(value);if(comparator(current,computed)){computed=current;result=value}}return result}function arrayFilter(array,predicate){var index=-1,length=array.length,resIndex=-1,result=[];while(++index<length){var value=array[index];if(predicate(value,index,array)){result[++resIndex]=value}}return result}function arrayMap(array,iteratee){var index=-1,length=array.length,result=Array(length);while(++index<length){result[index]=iteratee(array[index],index,array)}return result}function arrayPush(array,values){var index=-1,length=values.length,offset=array.length;while(++index<length){array[offset+index]=values[index]}return array}function arrayReduce(array,iteratee,accumulator,initFromArray){var index=-1,length=array.length;if(initFromArray&&length){accumulator=array[++index]}while(++index<length){accumulator=iteratee(accumulator,array[index],index,array)}return accumulator}function arrayReduceRight(array,iteratee,accumulator,initFromArray){var length=array.length;if(initFromArray&&length){accumulator=array[--length]}while(length--){accumulator=iteratee(accumulator,array[length],length,array)}return accumulator}function arraySome(array,predicate){var index=-1,length=array.length;while(++index<length){if(predicate(array[index],index,array)){return true}}return false}function arraySum(array,iteratee){var length=array.length,result=0;while(length--){result+=+iteratee(array[length])||0}return result}function assignDefaults(objectValue,sourceValue){return objectValue===undefined?sourceValue:objectValue}function assignOwnDefaults(objectValue,sourceValue,key,object){return objectValue===undefined||!hasOwnProperty.call(object,key)?sourceValue:objectValue}function assignWith(object,source,customizer){var index=-1,props=keys(source),length=props.length;while(++index<length){var key=props[index],value=object[key],result=customizer(value,source[key],key,object,source);if((result===result?result!==value:value===value)||value===undefined&&!(key in object)){object[key]=result}}return object}function baseAssign(object,source){return source==null?object:baseCopy(source,keys(source),object)}function baseAt(collection,props){var index=-1,isNil=collection==null,isArr=!isNil&&isArrayLike(collection),length=isArr?collection.length:0,propsLength=props.length,result=Array(propsLength);while(++index<propsLength){var key=props[index];if(isArr){result[index]=isIndex(key,length)?collection[key]:undefined}else{result[index]=isNil?undefined:collection[key]}}return result}function baseCopy(source,props,object){object||(object={});var index=-1,length=props.length;while(++index<length){var key=props[index];object[key]=source[key]}return object}function baseCallback(func,thisArg,argCount){var type=typeof func;if(type=="function"){return thisArg===undefined?func:bindCallback(func,thisArg,argCount)}if(func==null){return identity}if(type=="object"){return baseMatches(func)}return thisArg===undefined?property(func):baseMatchesProperty(func,thisArg)}function baseClone(value,isDeep,customizer,key,object,stackA,stackB){var result;if(customizer){result=object?customizer(value,key,object):customizer(value)}if(result!==undefined){return result}if(!isObject(value)){return value}var isArr=isArray(value);if(isArr){result=initCloneArray(value);if(!isDeep){return arrayCopy(value,result)}}else{var tag=objToString.call(value),isFunc=tag==funcTag;if(tag==objectTag||tag==argsTag||isFunc&&!object){result=initCloneObject(isFunc?{}:value);if(!isDeep){return baseAssign(result,value)}}else{return cloneableTags[tag]?initCloneByTag(value,tag,isDeep):object?value:{}}}stackA||(stackA=[]);stackB||(stackB=[]);var length=stackA.length;while(length--){if(stackA[length]==value){return stackB[length]}}stackA.push(value);stackB.push(result);(isArr?arrayEach:baseForOwn)(value,function(subValue,key){result[key]=baseClone(subValue,isDeep,customizer,key,value,stackA,stackB)});return result}var baseCreate=function(){function object(){}return function(prototype){if(isObject(prototype)){object.prototype=prototype;var result=new object;object.prototype=undefined}return result||{}}}();function baseDelay(func,wait,args){if(typeof func!="function"){throw new TypeError(FUNC_ERROR_TEXT)}return setTimeout(function(){func.apply(undefined,args)},wait)}function baseDifference(array,values){var length=array?array.length:0,result=[];if(!length){return result}var index=-1,indexOf=getIndexOf(),isCommon=indexOf==baseIndexOf,cache=isCommon&&values.length>=LARGE_ARRAY_SIZE?createCache(values):null,valuesLength=values.length;if(cache){indexOf=cacheIndexOf;isCommon=false;values=cache}outer:while(++index<length){var value=array[index];if(isCommon&&value===value){var valuesIndex=valuesLength;while(valuesIndex--){if(values[valuesIndex]===value){continue outer}}result.push(value)}else if(indexOf(values,value,0)<0){result.push(value)}}return result}var baseEach=createBaseEach(baseForOwn);var baseEachRight=createBaseEach(baseForOwnRight,true);function baseEvery(collection,predicate){var result=true;baseEach(collection,function(value,index,collection){result=!!predicate(value,index,collection);return result});return result}function baseExtremum(collection,iteratee,comparator,exValue){var computed=exValue,result=computed;baseEach(collection,function(value,index,collection){var current=+iteratee(value,index,collection);if(comparator(current,computed)||current===exValue&&current===result){computed=current;result=value}});return result}function baseFill(array,value,start,end){var length=array.length;start=start==null?0:+start||0;if(start<0){start=-start>length?0:length+start}end=end===undefined||end>length?length:+end||0;if(end<0){end+=length}length=start>end?0:end>>>0;start>>>=0;while(start<length){array[start++]=value}return array}function baseFilter(collection,predicate){var result=[];baseEach(collection,function(value,index,collection){if(predicate(value,index,collection)){result.push(value)}});return result}function baseFind(collection,predicate,eachFunc,retKey){var result;eachFunc(collection,function(value,key,collection){if(predicate(value,key,collection)){result=retKey?key:value;return false}});return result}function baseFlatten(array,isDeep,isStrict,result){result||(result=[]);var index=-1,length=array.length;while(++index<length){var value=array[index];if(isObjectLike(value)&&isArrayLike(value)&&(isStrict||isArray(value)||isArguments(value))){if(isDeep){baseFlatten(value,isDeep,isStrict,result)}else{arrayPush(result,value)}}else if(!isStrict){result[result.length]=value}}return result}var baseFor=createBaseFor();var baseForRight=createBaseFor(true);function baseForIn(object,iteratee){return baseFor(object,iteratee,keysIn)}function baseForOwn(object,iteratee){return baseFor(object,iteratee,keys)}function baseForOwnRight(object,iteratee){return baseForRight(object,iteratee,keys)}function baseFunctions(object,props){var index=-1,length=props.length,resIndex=-1,result=[];while(++index<length){var key=props[index];if(isFunction(object[key])){result[++resIndex]=key}}return result}function baseGet(object,path,pathKey){if(object==null){return}if(pathKey!==undefined&&pathKey in toObject(object)){path=[pathKey]}var index=0,length=path.length;while(object!=null&&index<length){object=object[path[index++]]}return index&&index==length?object:undefined}function baseIsEqual(value,other,customizer,isLoose,stackA,stackB){if(value===other){return true}if(value==null||other==null||!isObject(value)&&!isObjectLike(other)){return value!==value&&other!==other}return baseIsEqualDeep(value,other,baseIsEqual,customizer,isLoose,stackA,stackB)}function baseIsEqualDeep(object,other,equalFunc,customizer,isLoose,stackA,stackB){var objIsArr=isArray(object),othIsArr=isArray(other),objTag=arrayTag,othTag=arrayTag;if(!objIsArr){objTag=objToString.call(object);if(objTag==argsTag){objTag=objectTag}else if(objTag!=objectTag){objIsArr=isTypedArray(object)}}if(!othIsArr){othTag=objToString.call(other);if(othTag==argsTag){othTag=objectTag}else if(othTag!=objectTag){othIsArr=isTypedArray(other)}}var objIsObj=objTag==objectTag,othIsObj=othTag==objectTag,isSameTag=objTag==othTag;if(isSameTag&&!(objIsArr||objIsObj)){return equalByTag(object,other,objTag)}if(!isLoose){var objIsWrapped=objIsObj&&hasOwnProperty.call(object,"__wrapped__"),othIsWrapped=othIsObj&&hasOwnProperty.call(other,"__wrapped__");if(objIsWrapped||othIsWrapped){return equalFunc(objIsWrapped?object.value():object,othIsWrapped?other.value():other,customizer,isLoose,stackA,stackB)}}if(!isSameTag){return false}stackA||(stackA=[]);stackB||(stackB=[]);var length=stackA.length;while(length--){if(stackA[length]==object){return stackB[length]==other}}stackA.push(object);stackB.push(other);var result=(objIsArr?equalArrays:equalObjects)(object,other,equalFunc,customizer,isLoose,stackA,stackB);stackA.pop();stackB.pop();return result}function baseIsMatch(object,matchData,customizer){var index=matchData.length,length=index,noCustomizer=!customizer;if(object==null){return!length}object=toObject(object);while(index--){var data=matchData[index];if(noCustomizer&&data[2]?data[1]!==object[data[0]]:!(data[0]in object)){return false}}while(++index<length){data=matchData[index];var key=data[0],objValue=object[key],srcValue=data[1];if(noCustomizer&&data[2]){if(objValue===undefined&&!(key in object)){return false}}else{var result=customizer?customizer(objValue,srcValue,key):undefined;if(!(result===undefined?baseIsEqual(srcValue,objValue,customizer,true):result)){return false}}}return true}function baseMap(collection,iteratee){var index=-1,result=isArrayLike(collection)?Array(collection.length):[];baseEach(collection,function(value,key,collection){result[++index]=iteratee(value,key,collection)});return result}function baseMatches(source){var matchData=getMatchData(source);if(matchData.length==1&&matchData[0][2]){var key=matchData[0][0],value=matchData[0][1];return function(object){if(object==null){return false}return object[key]===value&&(value!==undefined||key in toObject(object))}}return function(object){return baseIsMatch(object,matchData)}}function baseMatchesProperty(path,srcValue){var isArr=isArray(path),isCommon=isKey(path)&&isStrictComparable(srcValue),pathKey=path+"";path=toPath(path);return function(object){if(object==null){return false}var key=pathKey;object=toObject(object);if((isArr||!isCommon)&&!(key in object)){object=path.length==1?object:baseGet(object,baseSlice(path,0,-1));if(object==null){return false}key=last(path);object=toObject(object)}return object[key]===srcValue?srcValue!==undefined||key in object:baseIsEqual(srcValue,object[key],undefined,true)}}function baseMerge(object,source,customizer,stackA,stackB){if(!isObject(object)){return object}var isSrcArr=isArrayLike(source)&&(isArray(source)||isTypedArray(source)),props=isSrcArr?undefined:keys(source);arrayEach(props||source,function(srcValue,key){if(props){key=srcValue;srcValue=source[key]}if(isObjectLike(srcValue)){stackA||(stackA=[]);stackB||(stackB=[]);baseMergeDeep(object,source,key,baseMerge,customizer,stackA,stackB)}else{var value=object[key],result=customizer?customizer(value,srcValue,key,object,source):undefined,isCommon=result===undefined;if(isCommon){result=srcValue}if((result!==undefined||isSrcArr&&!(key in object))&&(isCommon||(result===result?result!==value:value===value))){object[key]=result}}});return object}function baseMergeDeep(object,source,key,mergeFunc,customizer,stackA,stackB){var length=stackA.length,srcValue=source[key];while(length--){if(stackA[length]==srcValue){object[key]=stackB[length];return}}var value=object[key],result=customizer?customizer(value,srcValue,key,object,source):undefined,isCommon=result===undefined;if(isCommon){result=srcValue;if(isArrayLike(srcValue)&&(isArray(srcValue)||isTypedArray(srcValue))){result=isArray(value)?value:isArrayLike(value)?arrayCopy(value):[]}else 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length=array.length;array.sort(comparer);while(length--){array[length]=array[length].value}return array}function baseSortByOrder(collection,iteratees,orders){var callback=getCallback(),index=-1;iteratees=arrayMap(iteratees,function(iteratee){return callback(iteratee)});var result=baseMap(collection,function(value){var criteria=arrayMap(iteratees,function(iteratee){return iteratee(value)});return{criteria:criteria,index:++index,value:value}});return baseSortBy(result,function(object,other){return compareMultiple(object,other,orders)})}function baseSum(collection,iteratee){var result=0;baseEach(collection,function(value,index,collection){result+=+iteratee(value,index,collection)||0});return result}function baseUniq(array,iteratee){var index=-1,indexOf=getIndexOf(),length=array.length,isCommon=indexOf==baseIndexOf,isLarge=isCommon&&length>=LARGE_ARRAY_SIZE,seen=isLarge?createCache():null,result=[];if(seen){indexOf=cacheIndexOf;isCommon=false}else{isLarge=false;seen=iteratee?[]:result}outer:while(++index<length){var value=array[index],computed=iteratee?iteratee(value,index,array):value;if(isCommon&&value===value){var seenIndex=seen.length;while(seenIndex--){if(seen[seenIndex]===computed){continue outer}}if(iteratee){seen.push(computed)}result.push(value)}else if(indexOf(seen,computed,0)<0){if(iteratee||isLarge){seen.push(computed)}result.push(value)}}return result}function baseValues(object,props){var index=-1,length=props.length,result=Array(length);while(++index<length){result[index]=object[props[index]]}return result}function baseWhile(array,predicate,isDrop,fromRight){var length=array.length,index=fromRight?length:-1;while((fromRight?index--:++index<length)&&predicate(array[index],index,array)){}return isDrop?baseSlice(array,fromRight?0:index,fromRight?index+1:length):baseSlice(array,fromRight?index+1:0,fromRight?length:index)}function baseWrapperValue(value,actions){var result=value;if(result instanceof LazyWrapper){result=result.value()}var index=-1,length=actions.length;while(++index<length){var action=actions[index];result=action.func.apply(action.thisArg,arrayPush([result],action.args))}return result}function binaryIndex(array,value,retHighest){var low=0,high=array?array.length:low;if(typeof value=="number"&&value===value&&high<=HALF_MAX_ARRAY_LENGTH){while(low<high){var mid=low+high>>>1,computed=array[mid];if((retHighest?computed<=value:computed<value)&&computed!==null){low=mid+1}else{high=mid}}return high}return binaryIndexBy(array,value,identity,retHighest)}function binaryIndexBy(array,value,iteratee,retHighest){value=iteratee(value);var low=0,high=array?array.length:0,valIsNaN=value!==value,valIsNull=value===null,valIsUndef=value===undefined;while(low<high){var mid=nativeFloor((low+high)/2),computed=iteratee(array[mid]),isDef=computed!==undefined,isReflexive=computed===computed;if(valIsNaN){var setLow=isReflexive||retHighest}else if(valIsNull){setLow=isReflexive&&isDef&&(retHighest||computed!=null);
-}else if(valIsUndef){setLow=isReflexive&&(retHighest||isDef)}else if(computed==null){setLow=false}else{setLow=retHighest?computed<=value:computed<value}if(setLow){low=mid+1}else{high=mid}}return nativeMin(high,MAX_ARRAY_INDEX)}function bindCallback(func,thisArg,argCount){if(typeof func!="function"){return identity}if(thisArg===undefined){return func}switch(argCount){case 1:return function(value){return func.call(thisArg,value)};case 3:return function(value,index,collection){return func.call(thisArg,value,index,collection)};case 4:return function(accumulator,value,index,collection){return func.call(thisArg,accumulator,value,index,collection)};case 5:return function(value,other,key,object,source){return func.call(thisArg,value,other,key,object,source)}}return function(){return func.apply(thisArg,arguments)}}function bufferClone(buffer){var result=new ArrayBuffer(buffer.byteLength),view=new Uint8Array(result);view.set(new Uint8Array(buffer));return result}function composeArgs(args,partials,holders){var holdersLength=holders.length,argsIndex=-1,argsLength=nativeMax(args.length-holdersLength,0),leftIndex=-1,leftLength=partials.length,result=Array(leftLength+argsLength);while(++leftIndex<leftLength){result[leftIndex]=partials[leftIndex]}while(++argsIndex<holdersLength){result[holders[argsIndex]]=args[argsIndex]}while(argsLength--){result[leftIndex++]=args[argsIndex++]}return result}function composeArgsRight(args,partials,holders){var holdersIndex=-1,holdersLength=holders.length,argsIndex=-1,argsLength=nativeMax(args.length-holdersLength,0),rightIndex=-1,rightLength=partials.length,result=Array(argsLength+rightLength);while(++argsIndex<argsLength){result[argsIndex]=args[argsIndex]}var offset=argsIndex;while(++rightIndex<rightLength){result[offset+rightIndex]=partials[rightIndex]}while(++holdersIndex<holdersLength){result[offset+holders[holdersIndex]]=args[argsIndex++]}return result}function createAggregator(setter,initializer){return function(collection,iteratee,thisArg){var result=initializer?initializer():{};iteratee=getCallback(iteratee,thisArg,3);if(isArray(collection)){var index=-1,length=collection.length;while(++index<length){var value=collection[index];setter(result,value,iteratee(value,index,collection),collection)}}else{baseEach(collection,function(value,key,collection){setter(result,value,iteratee(value,key,collection),collection)})}return result}}function createAssigner(assigner){return restParam(function(object,sources){var index=-1,length=object==null?0:sources.length,customizer=length>2?sources[length-2]:undefined,guard=length>2?sources[2]:undefined,thisArg=length>1?sources[length-1]:undefined;if(typeof customizer=="function"){customizer=bindCallback(customizer,thisArg,5);length-=2}else{customizer=typeof thisArg=="function"?thisArg:undefined;length-=customizer?1:0}if(guard&&isIterateeCall(sources[0],sources[1],guard)){customizer=length<3?undefined:customizer;length=1}while(++index<length){var source=sources[index];if(source){assigner(object,source,customizer)}}return object})}function createBaseEach(eachFunc,fromRight){return function(collection,iteratee){var length=collection?getLength(collection):0;if(!isLength(length)){return eachFunc(collection,iteratee)}var index=fromRight?length:-1,iterable=toObject(collection);while(fromRight?index--:++index<length){if(iteratee(iterable[index],index,iterable)===false){break}}return collection}}function createBaseFor(fromRight){return function(object,iteratee,keysFunc){var iterable=toObject(object),props=keysFunc(object),length=props.length,index=fromRight?length:-1;while(fromRight?index--:++index<length){var key=props[index];if(iteratee(iterable[key],key,iterable)===false){break}}return object}}function createBindWrapper(func,thisArg){var Ctor=createCtorWrapper(func);function wrapper(){var fn=this&&this!==root&&this instanceof wrapper?Ctor:func;return fn.apply(thisArg,arguments)}return wrapper}function createCache(values){return nativeCreate&&Set?new SetCache(values):null}function createCompounder(callback){return function(string){var index=-1,array=words(deburr(string)),length=array.length,result="";while(++index<length){result=callback(result,array[index],index)}return result}}function createCtorWrapper(Ctor){return function(){var args=arguments;switch(args.length){case 0:return new Ctor;case 1:return new Ctor(args[0]);case 2:return new Ctor(args[0],args[1]);case 3:return new Ctor(args[0],args[1],args[2]);case 4:return new Ctor(args[0],args[1],args[2],args[3]);case 5:return new Ctor(args[0],args[1],args[2],args[3],args[4]);case 6:return new Ctor(args[0],args[1],args[2],args[3],args[4],args[5]);case 7:return new Ctor(args[0],args[1],args[2],args[3],args[4],args[5],args[6])}var thisBinding=baseCreate(Ctor.prototype),result=Ctor.apply(thisBinding,args);return isObject(result)?result:thisBinding}}function createCurry(flag){function curryFunc(func,arity,guard){if(guard&&isIterateeCall(func,arity,guard)){arity=undefined}var result=createWrapper(func,flag,undefined,undefined,undefined,undefined,undefined,arity);result.placeholder=curryFunc.placeholder;return result}return curryFunc}function createDefaults(assigner,customizer){return restParam(function(args){var object=args[0];if(object==null){return object}args.push(customizer);return assigner.apply(undefined,args)})}function createExtremum(comparator,exValue){return function(collection,iteratee,thisArg){if(thisArg&&isIterateeCall(collection,iteratee,thisArg)){iteratee=undefined}iteratee=getCallback(iteratee,thisArg,3);if(iteratee.length==1){collection=isArray(collection)?collection:toIterable(collection);var result=arrayExtremum(collection,iteratee,comparator,exValue);if(!(collection.length&&result===exValue)){return result}}return baseExtremum(collection,iteratee,comparator,exValue)}}function createFind(eachFunc,fromRight){return function(collection,predicate,thisArg){predicate=getCallback(predicate,thisArg,3);if(isArray(collection)){var index=baseFindIndex(collection,predicate,fromRight);return index>-1?collection[index]:undefined}return baseFind(collection,predicate,eachFunc)}}function createFindIndex(fromRight){return function(array,predicate,thisArg){if(!(array&&array.length)){return-1}predicate=getCallback(predicate,thisArg,3);return baseFindIndex(array,predicate,fromRight)}}function createFindKey(objectFunc){return function(object,predicate,thisArg){predicate=getCallback(predicate,thisArg,3);return baseFind(object,predicate,objectFunc,true)}}function createFlow(fromRight){return function(){var wrapper,length=arguments.length,index=fromRight?length:-1,leftIndex=0,funcs=Array(length);while(fromRight?index--:++index<length){var func=funcs[leftIndex++]=arguments[index];if(typeof func!="function"){throw new TypeError(FUNC_ERROR_TEXT)}if(!wrapper&&LodashWrapper.prototype.thru&&getFuncName(func)=="wrapper"){wrapper=new LodashWrapper([],true)}}index=wrapper?-1:length;while(++index<length){func=funcs[index];var funcName=getFuncName(func),data=funcName=="wrapper"?getData(func):undefined;if(data&&isLaziable(data[0])&&data[1]==(ARY_FLAG|CURRY_FLAG|PARTIAL_FLAG|REARG_FLAG)&&!data[4].length&&data[9]==1){wrapper=wrapper[getFuncName(data[0])].apply(wrapper,data[3])}else{wrapper=func.length==1&&isLaziable(func)?wrapper[funcName]():wrapper.thru(func)}}return function(){var args=arguments,value=args[0];if(wrapper&&args.length==1&&isArray(value)&&value.length>=LARGE_ARRAY_SIZE){return wrapper.plant(value).value()}var index=0,result=length?funcs[index].apply(this,args):value;while(++index<length){result=funcs[index].call(this,result)}return result}}}function createForEach(arrayFunc,eachFunc){return function(collection,iteratee,thisArg){return typeof iteratee=="function"&&thisArg===undefined&&isArray(collection)?arrayFunc(collection,iteratee):eachFunc(collection,bindCallback(iteratee,thisArg,3))}}function createForIn(objectFunc){return function(object,iteratee,thisArg){if(typeof iteratee!="function"||thisArg!==undefined){iteratee=bindCallback(iteratee,thisArg,3)}return objectFunc(object,iteratee,keysIn)}}function createForOwn(objectFunc){return function(object,iteratee,thisArg){if(typeof iteratee!="function"||thisArg!==undefined){iteratee=bindCallback(iteratee,thisArg,3)}return objectFunc(object,iteratee)}}function createObjectMapper(isMapKeys){return function(object,iteratee,thisArg){var result={};iteratee=getCallback(iteratee,thisArg,3);baseForOwn(object,function(value,key,object){var mapped=iteratee(value,key,object);key=isMapKeys?mapped:key;value=isMapKeys?value:mapped;result[key]=value});return result}}function createPadDir(fromRight){return function(string,length,chars){string=baseToString(string);return(fromRight?string:"")+createPadding(string,length,chars)+(fromRight?"":string)}}function createPartial(flag){var partialFunc=restParam(function(func,partials){var holders=replaceHolders(partials,partialFunc.placeholder);return createWrapper(func,flag,undefined,partials,holders)});return partialFunc}function createReduce(arrayFunc,eachFunc){return function(collection,iteratee,accumulator,thisArg){var initFromArray=arguments.length<3;return typeof iteratee=="function"&&thisArg===undefined&&isArray(collection)?arrayFunc(collection,iteratee,accumulator,initFromArray):baseReduce(collection,getCallback(iteratee,thisArg,4),accumulator,initFromArray,eachFunc)}}function createHybridWrapper(func,bitmask,thisArg,partials,holders,partialsRight,holdersRight,argPos,ary,arity){var isAry=bitmask&ARY_FLAG,isBind=bitmask&BIND_FLAG,isBindKey=bitmask&BIND_KEY_FLAG,isCurry=bitmask&CURRY_FLAG,isCurryBound=bitmask&CURRY_BOUND_FLAG,isCurryRight=bitmask&CURRY_RIGHT_FLAG,Ctor=isBindKey?undefined:createCtorWrapper(func);function wrapper(){var length=arguments.length,index=length,args=Array(length);while(index--){args[index]=arguments[index]}if(partials){args=composeArgs(args,partials,holders)}if(partialsRight){args=composeArgsRight(args,partialsRight,holdersRight)}if(isCurry||isCurryRight){var placeholder=wrapper.placeholder,argsHolders=replaceHolders(args,placeholder);length-=argsHolders.length;if(length<arity){var newArgPos=argPos?arrayCopy(argPos):undefined,newArity=nativeMax(arity-length,0),newsHolders=isCurry?argsHolders:undefined,newHoldersRight=isCurry?undefined:argsHolders,newPartials=isCurry?args:undefined,newPartialsRight=isCurry?undefined:args;bitmask|=isCurry?PARTIAL_FLAG:PARTIAL_RIGHT_FLAG;bitmask&=~(isCurry?PARTIAL_RIGHT_FLAG:PARTIAL_FLAG);if(!isCurryBound){bitmask&=~(BIND_FLAG|BIND_KEY_FLAG)}var newData=[func,bitmask,thisArg,newPartials,newsHolders,newPartialsRight,newHoldersRight,newArgPos,ary,newArity],result=createHybridWrapper.apply(undefined,newData);if(isLaziable(func)){setData(result,newData)}result.placeholder=placeholder;return result}}var thisBinding=isBind?thisArg:this,fn=isBindKey?thisBinding[func]:func;if(argPos){args=reorder(args,argPos)}if(isAry&&ary<args.length){args.length=ary}if(this&&this!==root&&this instanceof wrapper){fn=Ctor||createCtorWrapper(func)}return fn.apply(thisBinding,args)}return wrapper}function createPadding(string,length,chars){var strLength=string.length;length=+length;if(strLength>=length||!nativeIsFinite(length)){return""}var padLength=length-strLength;chars=chars==null?" ":chars+"";return repeat(chars,nativeCeil(padLength/chars.length)).slice(0,padLength)}function createPartialWrapper(func,bitmask,thisArg,partials){var isBind=bitmask&BIND_FLAG,Ctor=createCtorWrapper(func);function wrapper(){var argsIndex=-1,argsLength=arguments.length,leftIndex=-1,leftLength=partials.length,args=Array(leftLength+argsLength);while(++leftIndex<leftLength){args[leftIndex]=partials[leftIndex]}while(argsLength--){args[leftIndex++]=arguments[++argsIndex]}var fn=this&&this!==root&&this instanceof wrapper?Ctor:func;return fn.apply(isBind?thisArg:this,args)}return wrapper}function createRound(methodName){var func=Math[methodName];return function(number,precision){precision=precision===undefined?0:+precision||0;if(precision){precision=pow(10,precision);return func(number*precision)/precision}return func(number)}}function createSortedIndex(retHighest){return function(array,value,iteratee,thisArg){var callback=getCallback(iteratee);return iteratee==null&&callback===baseCallback?binaryIndex(array,value,retHighest):binaryIndexBy(array,value,callback(iteratee,thisArg,1),retHighest)}}function createWrapper(func,bitmask,thisArg,partials,holders,argPos,ary,arity){var isBindKey=bitmask&BIND_KEY_FLAG;if(!isBindKey&&typeof func!="function"){throw new TypeError(FUNC_ERROR_TEXT)}var length=partials?partials.length:0;if(!length){bitmask&=~(PARTIAL_FLAG|PARTIAL_RIGHT_FLAG);partials=holders=undefined}length-=holders?holders.length:0;if(bitmask&PARTIAL_RIGHT_FLAG){var partialsRight=partials,holdersRight=holders;partials=holders=undefined}var data=isBindKey?undefined:getData(func),newData=[func,bitmask,thisArg,partials,holders,partialsRight,holdersRight,argPos,ary,arity];if(data){mergeData(newData,data);bitmask=newData[1];arity=newData[9]}newData[9]=arity==null?isBindKey?0:func.length:nativeMax(arity-length,0)||0;if(bitmask==BIND_FLAG){var result=createBindWrapper(newData[0],newData[2])}else 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equalByTag(object,other,tag){switch(tag){case boolTag:case dateTag:return+object==+other;case errorTag:return object.name==other.name&&object.message==other.message;case numberTag:return object!=+object?other!=+other:object==+other;case regexpTag:case stringTag:return object==other+""}return false}function equalObjects(object,other,equalFunc,customizer,isLoose,stackA,stackB){var objProps=keys(object),objLength=objProps.length,othProps=keys(other),othLength=othProps.length;if(objLength!=othLength&&!isLoose){return false}var index=objLength;while(index--){var key=objProps[index];if(!(isLoose?key in other:hasOwnProperty.call(other,key))){return false}}var skipCtor=isLoose;while(++index<objLength){key=objProps[index];var objValue=object[key],othValue=other[key],result=customizer?customizer(isLoose?othValue:objValue,isLoose?objValue:othValue,key):undefined;if(!(result===undefined?equalFunc(objValue,othValue,customizer,isLoose,stackA,stackB):result)){return false}skipCtor||(skipCtor=key=="constructor")}if(!skipCtor){var objCtor=object.constructor,othCtor=other.constructor;if(objCtor!=othCtor&&("constructor"in object&&"constructor"in other)&&!(typeof objCtor=="function"&&objCtor instanceof objCtor&&typeof othCtor=="function"&&othCtor instanceof othCtor)){return false}}return true}function getCallback(func,thisArg,argCount){var result=lodash.callback||callback;result=result===callback?baseCallback:result;return argCount?result(func,thisArg,argCount):result}var getData=!metaMap?noop:function(func){return metaMap.get(func)};function getFuncName(func){var result=func.name,array=realNames[result],length=array?array.length:0;while(length--){var data=array[length],otherFunc=data.func;if(otherFunc==null||otherFunc==func){return data.name}}return result}function getIndexOf(collection,target,fromIndex){var result=lodash.indexOf||indexOf;result=result===indexOf?baseIndexOf:result;return collection?result(collection,target,fromIndex):result}var 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isIndex(value,length){value=typeof value=="number"||reIsUint.test(value)?+value:-1;length=length==null?MAX_SAFE_INTEGER:length;return value>-1&&value%1==0&&value<length}function isIterateeCall(value,index,object){if(!isObject(object)){return false}var type=typeof index;if(type=="number"?isArrayLike(object)&&isIndex(index,object.length):type=="string"&&index in object){var other=object[index];return value===value?value===other:other!==other}return false}function isKey(value,object){var type=typeof value;if(type=="string"&&reIsPlainProp.test(value)||type=="number"){return true}if(isArray(value)){return false}var result=!reIsDeepProp.test(value);return result||object!=null&&value in toObject(object)}function isLaziable(func){var funcName=getFuncName(func);if(!(funcName in LazyWrapper.prototype)){return false}var other=lodash[funcName];if(func===other){return true}var data=getData(other);return!!data&&func===data[0]}function isLength(value){return typeof value=="number"&&value>-1&&value%1==0&&value<=MAX_SAFE_INTEGER}function isStrictComparable(value){return value===value&&!isObject(value)}function mergeData(data,source){var bitmask=data[1],srcBitmask=source[1],newBitmask=bitmask|srcBitmask,isCommon=newBitmask<ARY_FLAG;var isCombo=srcBitmask==ARY_FLAG&&bitmask==CURRY_FLAG||srcBitmask==ARY_FLAG&&bitmask==REARG_FLAG&&data[7].length<=source[8]||srcBitmask==(ARY_FLAG|REARG_FLAG)&&bitmask==CURRY_FLAG;if(!(isCommon||isCombo)){return data}if(srcBitmask&BIND_FLAG){data[2]=source[2];newBitmask|=bitmask&BIND_FLAG?0:CURRY_BOUND_FLAG}var value=source[3];if(value){var 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result={};baseForIn(object,function(value,key,object){if(predicate(value,key,object)){result[key]=value}});return result}function reorder(array,indexes){var arrLength=array.length,length=nativeMin(indexes.length,arrLength),oldArray=arrayCopy(array);while(length--){var index=indexes[length];array[length]=isIndex(index,arrLength)?oldArray[index]:undefined}return array}var setData=function(){var count=0,lastCalled=0;return function(key,value){var stamp=now(),remaining=HOT_SPAN-(stamp-lastCalled);lastCalled=stamp;if(remaining>0){if(++count>=HOT_COUNT){return key}}else{count=0}return baseSetData(key,value)}}();function shimKeys(object){var props=keysIn(object),propsLength=props.length,length=propsLength&&object.length;var allowIndexes=!!length&&isLength(length)&&(isArray(object)||isArguments(object));var index=-1,result=[];while(++index<propsLength){var key=props[index];if(allowIndexes&&isIndex(key,length)||hasOwnProperty.call(object,key)){result.push(key)}}return result}function toIterable(value){if(value==null){return[]}if(!isArrayLike(value)){return values(value)}return isObject(value)?value:Object(value)}function toObject(value){return isObject(value)?value:Object(value)}function toPath(value){if(isArray(value)){return value}var result=[];baseToString(value).replace(rePropName,function(match,number,quote,string){result.push(quote?string.replace(reEscapeChar,"$1"):number||match)});return result}function wrapperClone(wrapper){return wrapper instanceof LazyWrapper?wrapper.clone():new LodashWrapper(wrapper.__wrapped__,wrapper.__chain__,arrayCopy(wrapper.__actions__))}function chunk(array,size,guard){if(guard?isIterateeCall(array,size,guard):size==null){size=1}else{size=nativeMax(nativeFloor(size)||1,1)}var index=0,length=array?array.length:0,resIndex=-1,result=Array(nativeCeil(length/size));while(index<length){result[++resIndex]=baseSlice(array,index,index+=size)}return result}function compact(array){var index=-1,length=array?array.length:0,resIndex=-1,result=[];while(++index<length){var value=array[index];if(value){result[++resIndex]=value}}return result}var difference=restParam(function(array,values){return isObjectLike(array)&&isArrayLike(array)?baseDifference(array,baseFlatten(values,false,true)):[]});function drop(array,n,guard){var length=array?array.length:0;if(!length){return[]}if(guard?isIterateeCall(array,n,guard):n==null){n=1}return baseSlice(array,n<0?0:n)}function dropRight(array,n,guard){var length=array?array.length:0;if(!length){return[]}if(guard?isIterateeCall(array,n,guard):n==null){n=1}n=length-(+n||0);return baseSlice(array,0,n<0?0:n)}function dropRightWhile(array,predicate,thisArg){return array&&array.length?baseWhile(array,getCallback(predicate,thisArg,3),true,true):[]}function dropWhile(array,predicate,thisArg){return array&&array.length?baseWhile(array,getCallback(predicate,thisArg,3),true):[]}function fill(array,value,start,end){var 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fromIndex=="number"){index=(fromIndex<0?nativeMax(length+fromIndex,0):nativeMin(fromIndex||0,length-1))+1}else if(fromIndex){index=binaryIndex(array,value,true)-1;var other=array[index];if(value===value?value===other:other!==other){return index}return-1}if(value!==value){return indexOfNaN(array,index,true)}while(index--){if(array[index]===value){return index}}return-1}function pull(){var args=arguments,array=args[0];if(!(array&&array.length)){return array}var index=0,indexOf=getIndexOf(),length=args.length;while(++index<length){var fromIndex=0,value=args[index];while((fromIndex=indexOf(array,value,fromIndex))>-1){splice.call(array,fromIndex,1)}}return array}var pullAt=restParam(function(array,indexes){indexes=baseFlatten(indexes);var result=baseAt(array,indexes);basePullAt(array,indexes.sort(baseCompareAscending));return result});function remove(array,predicate,thisArg){var result=[];if(!(array&&array.length)){return result}var index=-1,indexes=[],length=array.length;predicate=getCallback(predicate,thisArg,3);while(++index<length){var value=array[index];if(predicate(value,index,array)){result.push(value);indexes.push(index)}}basePullAt(array,indexes);return result}function rest(array){return drop(array,1)}function slice(array,start,end){var length=array?array.length:0;if(!length){return[]}if(end&&typeof end!="number"&&isIterateeCall(array,start,end)){start=0;end=length}return baseSlice(array,start,end)}var sortedIndex=createSortedIndex();var sortedLastIndex=createSortedIndex(true);function take(array,n,guard){var length=array?array.length:0;if(!length){return[]}if(guard?isIterateeCall(array,n,guard):n==null){n=1}return baseSlice(array,0,n<0?0:n)}function takeRight(array,n,guard){var length=array?array.length:0;if(!length){return[]}if(guard?isIterateeCall(array,n,guard):n==null){n=1}n=length-(+n||0);return baseSlice(array,n<0?0:n)}function takeRightWhile(array,predicate,thisArg){return 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result=Array(length);while(++index<length){result[index]=arrayMap(array,baseProperty(index))}return result}function unzipWith(array,iteratee,thisArg){var length=array?array.length:0;if(!length){return[]}var result=unzip(array);if(iteratee==null){return result}iteratee=bindCallback(iteratee,thisArg,4);return arrayMap(result,function(group){return arrayReduce(group,iteratee,undefined,true)})}var without=restParam(function(array,values){return isArrayLike(array)?baseDifference(array,values):[]});function xor(){var index=-1,length=arguments.length;while(++index<length){var array=arguments[index];if(isArrayLike(array)){var result=result?arrayPush(baseDifference(result,array),baseDifference(array,result)):array}}return result?baseUniq(result):[]}var zip=restParam(unzip);function zipObject(props,values){var index=-1,length=props?props.length:0,result={};if(length&&!values&&!isArray(props[0])){values=[]}while(++index<length){var key=props[index];if(values){result[key]=values[index]}else 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result,parent=this;while(parent instanceof baseLodash){var clone=wrapperClone(parent);if(result){previous.__wrapped__=clone}else{result=clone}var previous=clone;parent=parent.__wrapped__}previous.__wrapped__=value;return result}function wrapperReverse(){var value=this.__wrapped__;var interceptor=function(value){return wrapped&&wrapped.__dir__<0?value:value.reverse()};if(value instanceof LazyWrapper){var wrapped=value;if(this.__actions__.length){wrapped=new LazyWrapper(this)}wrapped=wrapped.reverse();wrapped.__actions__.push({func:thru,args:[interceptor],thisArg:undefined});return new LodashWrapper(wrapped,this.__chain__)}return this.thru(interceptor)}function wrapperToString(){return this.value()+""}function wrapperValue(){return baseWrapperValue(this.__wrapped__,this.__actions__)}var at=restParam(function(collection,props){return baseAt(collection,baseFlatten(props))});var countBy=createAggregator(function(result,value,key){hasOwnProperty.call(result,key)?++result[key]:result[key]=1});function every(collection,predicate,thisArg){var func=isArray(collection)?arrayEvery:baseEvery;if(thisArg&&isIterateeCall(collection,predicate,thisArg)){predicate=undefined}if(typeof predicate!="function"||thisArg!==undefined){predicate=getCallback(predicate,thisArg,3)}return func(collection,predicate)}function filter(collection,predicate,thisArg){var func=isArray(collection)?arrayFilter:baseFilter;predicate=getCallback(predicate,thisArg,3);return func(collection,predicate)}var find=createFind(baseEach);var findLast=createFind(baseEachRight,true);function findWhere(collection,source){return find(collection,baseMatches(source))}var forEach=createForEach(arrayEach,baseEach);var forEachRight=createForEach(arrayEachRight,baseEachRight);
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func=isFunc?path:isProp&&value!=null?value[path]:undefined;result[++index]=func?func.apply(value,args):invokePath(value,path,args)});return result});function map(collection,iteratee,thisArg){var func=isArray(collection)?arrayMap:baseMap;iteratee=getCallback(iteratee,thisArg,3);return func(collection,iteratee)}var partition=createAggregator(function(result,value,key){result[key?0:1].push(value)},function(){return[[],[]]});function pluck(collection,path){return map(collection,property(path))}var reduce=createReduce(arrayReduce,baseEach);var reduceRight=createReduce(arrayReduceRight,baseEachRight);function reject(collection,predicate,thisArg){var func=isArray(collection)?arrayFilter:baseFilter;predicate=getCallback(predicate,thisArg,3);return func(collection,function(value,index,collection){return!predicate(value,index,collection)})}function sample(collection,n,guard){if(guard?isIterateeCall(collection,n,guard):n==null){collection=toIterable(collection);var length=collection.length;return length>0?collection[baseRandom(0,length-1)]:undefined}var index=-1,result=toArray(collection),length=result.length,lastIndex=length-1;n=nativeMin(n<0?0:+n||0,length);while(++index<n){var rand=baseRandom(index,lastIndex),value=result[rand];result[rand]=result[index];result[index]=value}result.length=n;return result}function shuffle(collection){return sample(collection,POSITIVE_INFINITY)}function size(collection){var length=collection?getLength(collection):0;return isLength(length)?length:keys(collection).length}function some(collection,predicate,thisArg){var func=isArray(collection)?arraySome:baseSome;if(thisArg&&isIterateeCall(collection,predicate,thisArg)){predicate=undefined}if(typeof predicate!="function"||thisArg!==undefined){predicate=getCallback(predicate,thisArg,3)}return func(collection,predicate)}function sortBy(collection,iteratee,thisArg){if(collection==null){return[]}if(thisArg&&isIterateeCall(collection,iteratee,thisArg)){iteratee=undefined}var index=-1;iteratee=getCallback(iteratee,thisArg,3);var result=baseMap(collection,function(value,key,collection){return{criteria:iteratee(value,key,collection),index:++index,value:value}});return baseSortBy(result,compareAscending)}var sortByAll=restParam(function(collection,iteratees){if(collection==null){return[]}var guard=iteratees[2];if(guard&&isIterateeCall(iteratees[0],iteratees[1],guard)){iteratees.length=1}return baseSortByOrder(collection,baseFlatten(iteratees),[])});function sortByOrder(collection,iteratees,orders,guard){if(collection==null){return[]}if(guard&&isIterateeCall(iteratees,orders,guard)){orders=undefined}if(!isArray(iteratees)){iteratees=iteratees==null?[]:[iteratees]}if(!isArray(orders)){orders=orders==null?[]:[orders]}return baseSortByOrder(collection,iteratees,orders)}function where(collection,source){return filter(collection,baseMatches(source))}var now=nativeNow||function(){return(new Date).getTime()};function after(n,func){if(typeof func!="function"){if(typeof n=="function"){var temp=n;n=func;func=temp}else{throw new TypeError(FUNC_ERROR_TEXT)}}n=nativeIsFinite(n=+n)?n:0;return function(){if(--n<1){return func.apply(this,arguments)}}}function ary(func,n,guard){if(guard&&isIterateeCall(func,n,guard)){n=undefined}n=func&&n==null?func.length:nativeMax(+n||0,0);return createWrapper(func,ARY_FLAG,undefined,undefined,undefined,undefined,n)}function before(n,func){var result;if(typeof func!="function"){if(typeof n=="function"){var temp=n;n=func;func=temp}else{throw new TypeError(FUNC_ERROR_TEXT)}}return function(){if(--n>0){result=func.apply(this,arguments)}if(n<=1){func=undefined}return result}}var bind=restParam(function(func,thisArg,partials){var bitmask=BIND_FLAG;if(partials.length){var holders=replaceHolders(partials,bind.placeholder);bitmask|=PARTIAL_FLAG}return createWrapper(func,bitmask,thisArg,partials,holders)});var bindAll=restParam(function(object,methodNames){methodNames=methodNames.length?baseFlatten(methodNames):functions(object);var 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cancel(){if(timeoutId){clearTimeout(timeoutId)}if(maxTimeoutId){clearTimeout(maxTimeoutId)}lastCalled=0;maxTimeoutId=timeoutId=trailingCall=undefined}function complete(isCalled,id){if(id){clearTimeout(id)}maxTimeoutId=timeoutId=trailingCall=undefined;if(isCalled){lastCalled=now();result=func.apply(thisArg,args);if(!timeoutId&&!maxTimeoutId){args=thisArg=undefined}}}function delayed(){var remaining=wait-(now()-stamp);if(remaining<=0||remaining>wait){complete(trailingCall,maxTimeoutId)}else{timeoutId=setTimeout(delayed,remaining)}}function maxDelayed(){complete(trailing,timeoutId)}function debounced(){args=arguments;stamp=now();thisArg=this;trailingCall=trailing&&(timeoutId||!leading);if(maxWait===false){var leadingCall=leading&&!timeoutId}else{if(!maxTimeoutId&&!leading){lastCalled=stamp}var 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set(object,path,value){if(object==null){return object}var pathKey=path+"";path=object[pathKey]!=null||isKey(path,object)?[pathKey]:toPath(path);var index=-1,length=path.length,lastIndex=length-1,nested=object;while(nested!=null&&++index<length){var key=path[index];if(isObject(nested)){if(index==lastIndex){nested[key]=value}else if(nested[key]==null){nested[key]=isIndex(path[index+1])?[]:{}}}nested=nested[key]}return object}function transform(object,iteratee,accumulator,thisArg){var isArr=isArray(object)||isTypedArray(object);iteratee=getCallback(iteratee,thisArg,4);if(accumulator==null){if(isArr||isObject(object)){var Ctor=object.constructor;if(isArr){accumulator=isArray(object)?new Ctor:[]}else{accumulator=baseCreate(isFunction(Ctor)?Ctor.prototype:undefined)}}else{accumulator={}}}(isArr?arrayEach:baseForOwn)(object,function(value,index,object){return iteratee(accumulator,value,index,object)});return accumulator}function values(object){return baseValues(object,keys(object))}function valuesIn(object){return baseValues(object,keysIn(object))}function inRange(value,start,end){start=+start||0;if(end===undefined){end=start;start=0}else{end=+end||0}return value>=nativeMin(start,end)&&value<nativeMax(start,end)}function random(min,max,floating){if(floating&&isIterateeCall(min,max,floating)){max=floating=undefined}var noMin=min==null,noMax=max==null;if(floating==null){if(noMax&&typeof min=="boolean"){floating=min;min=1}else if(typeof max=="boolean"){floating=max;noMax=true}}if(noMin&&noMax){max=1;noMax=false}min=+min||0;if(noMax){max=min;min=0}else{max=+max||0}if(floating||min%1||max%1){var rand=nativeRandom();return nativeMin(min+rand*(max-min+parseFloat("1e-"+((rand+"").length-1))),max)}return baseRandom(min,max)}var camelCase=createCompounder(function(result,word,index){word=word.toLowerCase();return result+(index?word.charAt(0).toUpperCase()+word.slice(1):word)});function capitalize(string){string=baseToString(string);return string&&string.charAt(0).toUpperCase()+string.slice(1)}function deburr(string){string=baseToString(string);return string&&string.replace(reLatin1,deburrLetter).replace(reComboMark,"")}function endsWith(string,target,position){string=baseToString(string);target=target+"";var length=string.length;position=position===undefined?length:nativeMin(position<0?0:+position||0,length);position-=target.length;return position>=0&&string.indexOf(target,position)==position}function escape(string){string=baseToString(string);return string&&reHasUnescapedHtml.test(string)?string.replace(reUnescapedHtml,escapeHtmlChar):string}function escapeRegExp(string){string=baseToString(string);return string&&reHasRegExpChars.test(string)?string.replace(reRegExpChars,escapeRegExpChar):string||"(?:)"}var kebabCase=createCompounder(function(result,word,index){return result+(index?"-":"")+word.toLowerCase()});function pad(string,length,chars){string=baseToString(string);length=+length;var 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_getKeyInfo(combination,action){var keys;var key;var i;var modifiers=[];keys=_keysFromString(combination);for(i=0;i<keys.length;++i){key=keys[i];if(_SPECIAL_ALIASES[key]){key=_SPECIAL_ALIASES[key]}if(action&&action!="keypress"&&_SHIFT_MAP[key]){key=_SHIFT_MAP[key];modifiers.push("shift")}if(_isModifier(key)){modifiers.push(key)}}action=_pickBestAction(key,modifiers,action);return{key:key,modifiers:modifiers,action:action}}function _belongsTo(element,ancestor){if(element===null||element===document){return false}if(element===ancestor){return true}return _belongsTo(element.parentNode,ancestor)}function Mousetrap(targetElement){var self=this;targetElement=targetElement||document;if(!(self instanceof Mousetrap)){return new Mousetrap(targetElement)}self.target=targetElement;self._callbacks={};self._directMap={};var _sequenceLevels={};var _resetTimer;var _ignoreNextKeyup=false;var _ignoreNextKeypress=false;var _nextExpectedAction=false;function _resetSequences(doNotReset){doNotReset=doNotReset||{};var activeSequences=false,key;for(key in _sequenceLevels){if(doNotReset[key]){activeSequences=true;continue}_sequenceLevels[key]=0}if(!activeSequences){_nextExpectedAction=false}}function _getMatches(character,modifiers,e,sequenceName,combination,level){var i;var callback;var matches=[];var action=e.type;if(!self._callbacks[character]){return[]}if(action=="keyup"&&_isModifier(character)){modifiers=[character]}for(i=0;i<self._callbacks[character].length;++i){callback=self._callbacks[character][i];if(!sequenceName&&callback.seq&&_sequenceLevels[callback.seq]!=callback.level){continue}if(action!=callback.action){continue}if(action=="keypress"&&!e.metaKey&&!e.ctrlKey||_modifiersMatch(modifiers,callback.modifiers)){var deleteCombo=!sequenceName&&callback.combo==combination;var deleteSequence=sequenceName&&callback.seq==sequenceName&&callback.level==level;if(deleteCombo||deleteSequence){self._callbacks[character].splice(i,1)}matches.push(callback)}}return matches}function _fireCallback(callback,e,combo,sequence){if(self.stopCallback(e,e.target||e.srcElement,combo,sequence)){return}if(callback(e,combo)===false){_preventDefault(e);_stopPropagation(e)}}self._handleKey=function(character,modifiers,e){var callbacks=_getMatches(character,modifiers,e);var i;var doNotReset={};var maxLevel=0;var processedSequenceCallback=false;for(i=0;i<callbacks.length;++i){if(callbacks[i].seq){maxLevel=Math.max(maxLevel,callbacks[i].level)}}for(i=0;i<callbacks.length;++i){if(callbacks[i].seq){if(callbacks[i].level!=maxLevel){continue}processedSequenceCallback=true;doNotReset[callbacks[i].seq]=1;_fireCallback(callbacks[i].callback,e,callbacks[i].combo,callbacks[i].seq);continue}if(!processedSequenceCallback){_fireCallback(callbacks[i].callback,e,callbacks[i].combo)}}var ignoreThisKeypress=e.type=="keypress"&&_ignoreNextKeypress;if(e.type==_nextExpectedAction&&!_isModifier(character)&&!ignoreThisKeypress){_resetSequences(doNotReset)}_ignoreNextKeypress=processedSequenceCallback&&e.type=="keydown"};function _handleKeyEvent(e){if(typeof e.which!=="number"){e.which=e.keyCode}var character=_characterFromEvent(e);if(!character){return}if(e.type=="keyup"&&_ignoreNextKeyup===character){_ignoreNextKeyup=false;return}self.handleKey(character,_eventModifiers(e),e)}function _resetSequenceTimer(){clearTimeout(_resetTimer);_resetTimer=setTimeout(_resetSequences,1e3)}function _bindSequence(combo,keys,callback,action){_sequenceLevels[combo]=0;function _increaseSequence(nextAction){return function(){_nextExpectedAction=nextAction;++_sequenceLevels[combo];_resetSequenceTimer()}}function _callbackAndReset(e){_fireCallback(callback,e,combo);if(action!=="keyup"){_ignoreNextKeyup=_characterFromEvent(e)}setTimeout(_resetSequences,10)}for(var i=0;i<keys.length;++i){var isFinal=i+1===keys.length;var wrappedCallback=isFinal?_callbackAndReset:_increaseSequence(action||_getKeyInfo(keys[i+1]).action);_bindSingle(keys[i],wrappedCallback,action,combo,i)}}function _bindSingle(combination,callback,action,sequenceName,level){self._directMap[combination+":"+action]=callback;combination=combination.replace(/\s+/g," ");var sequence=combination.split(" ");var info;if(sequence.length>1){_bindSequence(combination,sequence,callback,action);return}info=_getKeyInfo(combination,action);self._callbacks[info.key]=self._callbacks[info.key]||[];_getMatches(info.key,info.modifiers,{type:info.action},sequenceName,combination,level);self._callbacks[info.key][sequenceName?"unshift":"push"]({callback:callback,modifiers:info.modifiers,action:info.action,seq:sequenceName,level:level,combo:combination})}self._bindMultiple=function(combinations,callback,action){for(var i=0;i<combinations.length;++i){_bindSingle(combinations[i],callback,action)}};_addEvent(targetElement,"keypress",_handleKeyEvent);_addEvent(targetElement,"keydown",_handleKeyEvent);_addEvent(targetElement,"keyup",_handleKeyEvent)}Mousetrap.prototype.bind=function(keys,callback,action){var self=this;keys=keys instanceof Array?keys:[keys];self._bindMultiple.call(self,keys,callback,action);return self};Mousetrap.prototype.unbind=function(keys,action){var self=this;return self.bind.call(self,keys,function(){},action)};Mousetrap.prototype.trigger=function(keys,action){var self=this;if(self._directMap[keys+":"+action]){self._directMap[keys+":"+action]({},keys)}return self};Mousetrap.prototype.reset=function(){var self=this;self._callbacks={};self._directMap={};return self};Mousetrap.prototype.stopCallback=function(e,element){var self=this;if((" "+element.className+" ").indexOf(" mousetrap ")>-1){return false}if(_belongsTo(element,self.target)){return false}return element.tagName=="INPUT"||element.tagName=="SELECT"||element.tagName=="TEXTAREA"||element.isContentEditable};Mousetrap.prototype.handleKey=function(){var self=this;return self._handleKey.apply(self,arguments)};Mousetrap.init=function(){var documentMousetrap=Mousetrap(document);for(var method in documentMousetrap){if(method.charAt(0)!=="_"){Mousetrap[method]=function(method){return function(){return documentMousetrap[method].apply(documentMousetrap,arguments)}}(method)}}};Mousetrap.init();window.Mousetrap=Mousetrap;if(typeof module!=="undefined"&&module.exports){module.exports=Mousetrap}if(typeof define==="function"&&define.amd){define(function(){return Mousetrap})}})(window,document)},{}],4:[function(require,module,exports){(function(process){function normalizeArray(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last==="."){parts.splice(i,1)}else if(last===".."){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up--;up){parts.unshift("..")}}return parts}var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;var splitPath=function(filename){return splitPathRe.exec(filename).slice(1)};exports.resolve=function(){var resolvedPath="",resolvedAbsolute=false;for(var i=arguments.length-1;i>=-1&&!resolvedAbsolute;i--){var path=i>=0?arguments[i]:process.cwd();if(typeof path!=="string"){throw new TypeError("Arguments to path.resolve must be strings")}else if(!path){continue}resolvedPath=path+"/"+resolvedPath;resolvedAbsolute=path.charAt(0)==="/"}resolvedPath=normalizeArray(filter(resolvedPath.split("/"),function(p){return!!p}),!resolvedAbsolute).join("/");return(resolvedAbsolute?"/":"")+resolvedPath||"."};exports.normalize=function(path){var isAbsolute=exports.isAbsolute(path),trailingSlash=substr(path,-1)==="/";path=normalizeArray(filter(path.split("/"),function(p){return!!p}),!isAbsolute).join("/");if(!path&&!isAbsolute){path="."}if(path&&trailingSlash){path+="/"}return(isAbsolute?"/":"")+path};exports.isAbsolute=function(path){return path.charAt(0)==="/"};exports.join=function(){var paths=Array.prototype.slice.call(arguments,0);return exports.normalize(filter(paths,function(p,index){if(typeof p!=="string"){throw new TypeError("Arguments to path.join must be strings")}return p}).join("/"))};exports.relative=function(from,to){from=exports.resolve(from).substr(1);to=exports.resolve(to).substr(1);function trim(arr){var start=0;for(;start<arr.length;start++){if(arr[start]!=="")break}var end=arr.length-1;for(;end>=0;end--){if(arr[end]!=="")break}if(start>end)return[];return arr.slice(start,end-start+1)}var fromParts=trim(from.split("/"));var toParts=trim(to.split("/"));var length=Math.min(fromParts.length,toParts.length);var samePartsLength=length;for(var i=0;i<length;i++){if(fromParts[i]!==toParts[i]){samePartsLength=i;break}}var outputParts=[];for(var i=samePartsLength;i<fromParts.length;i++){outputParts.push("..")}outputParts=outputParts.concat(toParts.slice(samePartsLength));return outputParts.join("/")};exports.sep="/";exports.delimiter=":";exports.dirname=function(path){var result=splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return"."}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir};exports.basename=function(path,ext){var f=splitPath(path)[2];if(ext&&f.substr(-1*ext.length)===ext){f=f.substr(0,f.length-ext.length)}return f};exports.extname=function(path){return splitPath(path)[3]};function filter(xs,f){if(xs.filter)return xs.filter(f);var res=[];for(var i=0;i<xs.length;i++){if(f(xs[i],i,xs))res.push(xs[i])}return res}var substr="ab".substr(-1)==="b"?function(str,start,len){return str.substr(start,len)}:function(str,start,len){if(start<0)start=str.length+start;return str.substr(start,len)}}).call(this,require("_process"))},{_process:5}],5:[function(require,module,exports){var process=module.exports={};var queue=[];var draining=false;var currentQueue;var queueIndex=-1;function cleanUpNextTick(){draining=false;if(currentQueue.length){queue=currentQueue.concat(queue)}else{queueIndex=-1}if(queue.length){drainQueue()}}function drainQueue(){if(draining){return}var timeout=setTimeout(cleanUpNextTick);draining=true;var len=queue.length;while(len){currentQueue=queue;queue=[];while(++queueIndex<len){if(currentQueue){currentQueue[queueIndex].run()}}queueIndex=-1;len=queue.length}currentQueue=null;draining=false;clearTimeout(timeout)}process.nextTick=function(fun){var args=new Array(arguments.length-1);if(arguments.length>1){for(var i=1;i<arguments.length;i++){args[i-1]=arguments[i]}}queue.push(new Item(fun,args));if(queue.length===1&&!draining){setTimeout(drainQueue,0)}};function Item(fun,array){this.fun=fun;this.array=array}Item.prototype.run=function(){this.fun.apply(null,this.array)};process.title="browser";process.browser=true;process.env={};process.argv=[];process.version="";process.versions={};function noop(){}process.on=noop;process.addListener=noop;process.once=noop;process.off=noop;process.removeListener=noop;process.removeAllListeners=noop;process.emit=noop;process.binding=function(name){throw new Error("process.binding is not supported")};process.cwd=function(){return"/"};process.chdir=function(dir){throw new Error("process.chdir is not supported")};process.umask=function(){return 0}},{}],6:[function(require,module,exports){(function(global){(function(root){var freeExports=typeof exports=="object"&&exports&&!exports.nodeType&&exports;var freeModule=typeof module=="object"&&module&&!module.nodeType&&module;var freeGlobal=typeof global=="object"&&global;if(freeGlobal.global===freeGlobal||freeGlobal.window===freeGlobal||freeGlobal.self===freeGlobal){root=freeGlobal}var punycode,maxInt=2147483647,base=36,tMin=1,tMax=26,skew=38,damp=700,initialBias=72,initialN=128,delimiter="-",regexPunycode=/^xn--/,regexNonASCII=/[^\x20-\x7E]/,regexSeparators=/[\x2E\u3002\uFF0E\uFF61]/g,errors={overflow:"Overflow: input needs wider integers to process","not-basic":"Illegal input >= 0x80 (not a basic code point)","invalid-input":"Invalid input"},baseMinusTMin=base-tMin,floor=Math.floor,stringFromCharCode=String.fromCharCode,key;function error(type){throw RangeError(errors[type])}function map(array,fn){var length=array.length;var result=[];while(length--){result[length]=fn(array[length])}return result}function mapDomain(string,fn){var parts=string.split("@");var result="";if(parts.length>1){result=parts[0]+"@";string=parts[1]}string=string.replace(regexSeparators,".");var labels=string.split(".");var encoded=map(labels,fn).join(".");return result+encoded}function ucs2decode(string){var output=[],counter=0,length=string.length,value,extra;while(counter<length){value=string.charCodeAt(counter++);if(value>=55296&&value<=56319&&counter<length){extra=string.charCodeAt(counter++);if((extra&64512)==56320){output.push(((value&1023)<<10)+(extra&1023)+65536)}else{output.push(value);counter--}}else{output.push(value)}}return output}function ucs2encode(array){return map(array,function(value){var output="";if(value>65535){value-=65536;output+=stringFromCharCode(value>>>10&1023|55296);value=56320|value&1023}output+=stringFromCharCode(value);return output}).join("")}function basicToDigit(codePoint){if(codePoint-48<10){return codePoint-22}if(codePoint-65<26){return codePoint-65}if(codePoint-97<26){return codePoint-97}return base}function digitToBasic(digit,flag){return digit+22+75*(digit<26)-((flag!=0)<<5)}function adapt(delta,numPoints,firstTime){var k=0;delta=firstTime?floor(delta/damp):delta>>1;delta+=floor(delta/numPoints);for(;delta>baseMinusTMin*tMax>>1;k+=base){delta=floor(delta/baseMinusTMin)}return floor(k+(baseMinusTMin+1)*delta/(delta+skew))}function decode(input){var output=[],inputLength=input.length,out,i=0,n=initialN,bias=initialBias,basic,j,index,oldi,w,k,digit,t,baseMinusT;basic=input.lastIndexOf(delimiter);if(basic<0){basic=0}for(j=0;j<basic;++j){if(input.charCodeAt(j)>=128){error("not-basic")}output.push(input.charCodeAt(j))}for(index=basic>0?basic+1:0;index<inputLength;){for(oldi=i,w=1,k=base;;k+=base){if(index>=inputLength){error("invalid-input")}digit=basicToDigit(input.charCodeAt(index++));if(digit>=base||digit>floor((maxInt-i)/w)){error("overflow")}i+=digit*w;t=k<=bias?tMin:k>=bias+tMax?tMax:k-bias;if(digit<t){break}baseMinusT=base-t;if(w>floor(maxInt/baseMinusT)){error("overflow")}w*=baseMinusT}out=output.length+1;bias=adapt(i-oldi,out,oldi==0);if(floor(i/out)>maxInt-n){error("overflow")}n+=floor(i/out);i%=out;output.splice(i++,0,n)}return ucs2encode(output)}function encode(input){var n,delta,handledCPCount,basicLength,bias,j,m,q,k,t,currentValue,output=[],inputLength,handledCPCountPlusOne,baseMinusT,qMinusT;input=ucs2decode(input);inputLength=input.length;n=initialN;delta=0;bias=initialBias;for(j=0;j<inputLength;++j){currentValue=input[j];if(currentValue<128){output.push(stringFromCharCode(currentValue))}}handledCPCount=basicLength=output.length;if(basicLength){output.push(delimiter)}while(handledCPCount<inputLength){for(m=maxInt,j=0;j<inputLength;++j){currentValue=input[j];if(currentValue>=n&&currentValue<m){m=currentValue}}handledCPCountPlusOne=handledCPCount+1;if(m-n>floor((maxInt-delta)/handledCPCountPlusOne)){error("overflow")}delta+=(m-n)*handledCPCountPlusOne;n=m;for(j=0;j<inputLength;++j){currentValue=input[j];if(currentValue<n&&++delta>maxInt){error("overflow")}if(currentValue==n){for(q=delta,k=base;;k+=base){t=k<=bias?tMin:k>=bias+tMax?tMax:k-bias;if(q<t){break}qMinusT=q-t;baseMinusT=base-t;output.push(stringFromCharCode(digitToBasic(t+qMinusT%baseMinusT,0)));q=floor(qMinusT/baseMinusT)}output.push(stringFromCharCode(digitToBasic(q,0)));bias=adapt(delta,handledCPCountPlusOne,handledCPCount==basicLength);delta=0;++handledCPCount}}++delta;++n}return output.join("")}function toUnicode(input){return mapDomain(input,function(string){return regexPunycode.test(string)?decode(string.slice(4).toLowerCase()):string})}function toASCII(input){return mapDomain(input,function(string){return regexNonASCII.test(string)?"xn--"+encode(string):string})}punycode={version:"1.3.2",ucs2:{decode:ucs2decode,encode:ucs2encode},decode:decode,encode:encode,toASCII:toASCII,toUnicode:toUnicode};if(typeof define=="function"&&typeof define.amd=="object"&&define.amd){define("punycode",function(){return punycode})}else if(freeExports&&freeModule){if(module.exports==freeExports){freeModule.exports=punycode}else{for(key in punycode){punycode.hasOwnProperty(key)&&(freeExports[key]=punycode[key])}}}else{root.punycode=punycode}})(this)}).call(this,typeof global!=="undefined"?global:typeof self!=="undefined"?self:typeof window!=="undefined"?window:{})},{}],7:[function(require,module,exports){"use strict";function hasOwnProperty(obj,prop){return Object.prototype.hasOwnProperty.call(obj,prop)}module.exports=function(qs,sep,eq,options){sep=sep||"&";eq=eq||"=";var obj={};if(typeof qs!=="string"||qs.length===0){return obj}var regexp=/\+/g;qs=qs.split(sep);var maxKeys=1e3;if(options&&typeof options.maxKeys==="number"){maxKeys=options.maxKeys}var len=qs.length;if(maxKeys>0&&len>maxKeys){len=maxKeys}for(var i=0;i<len;++i){var x=qs[i].replace(regexp,"%20"),idx=x.indexOf(eq),kstr,vstr,k,v;if(idx>=0){kstr=x.substr(0,idx);vstr=x.substr(idx+1)}else{kstr=x;vstr=""}k=decodeURIComponent(kstr);v=decodeURIComponent(vstr);if(!hasOwnProperty(obj,k)){obj[k]=v}else if(isArray(obj[k])){obj[k].push(v)}else{obj[k]=[obj[k],v]}}return obj};var isArray=Array.isArray||function(xs){return Object.prototype.toString.call(xs)==="[object Array]"}},{}],8:[function(require,module,exports){"use strict";var stringifyPrimitive=function(v){switch(typeof v){case"string":return v;case"boolean":return v?"true":"false";case"number":return isFinite(v)?v:"";default:return""}};module.exports=function(obj,sep,eq,name){sep=sep||"&";eq=eq||"=";if(obj===null){obj=undefined}if(typeof obj==="object"){return map(objectKeys(obj),function(k){var ks=encodeURIComponent(stringifyPrimitive(k))+eq;if(isArray(obj[k])){return map(obj[k],function(v){return ks+encodeURIComponent(stringifyPrimitive(v))}).join(sep)}else{return ks+encodeURIComponent(stringifyPrimitive(obj[k]))}}).join(sep)}if(!name)return"";return encodeURIComponent(stringifyPrimitive(name))+eq+encodeURIComponent(stringifyPrimitive(obj))};var isArray=Array.isArray||function(xs){return Object.prototype.toString.call(xs)==="[object Array]"};function map(xs,f){if(xs.map)return xs.map(f);var res=[];for(var i=0;i<xs.length;i++){res.push(f(xs[i],i))}return res}var objectKeys=Object.keys||function(obj){var res=[];for(var key in obj){if(Object.prototype.hasOwnProperty.call(obj,key))res.push(key)}return res}},{}],9:[function(require,module,exports){"use strict";exports.decode=exports.parse=require("./decode");exports.encode=exports.stringify=require("./encode")},{"./decode":7,"./encode":8}],10:[function(require,module,exports){var punycode=require("punycode");exports.parse=urlParse;exports.resolve=urlResolve;exports.resolveObject=urlResolveObject;exports.format=urlFormat;exports.Url=Url;function Url(){this.protocol=null;this.slashes=null;this.auth=null;this.host=null;this.port=null;this.hostname=null;this.hash=null;this.search=null;this.query=null;this.pathname=null;this.path=null;this.href=null}var protocolPattern=/^([a-z0-9.+-]+:)/i,portPattern=/:[0-9]*$/,delims=["<",">",'"',"`"," ","\r","\n","	"],unwise=["{","}","|","\\","^","`"].concat(delims),autoEscape=["'"].concat(unwise),nonHostChars=["%","/","?",";","#"].concat(autoEscape),hostEndingChars=["/","?","#"],hostnameMaxLen=255,hostnamePartPattern=/^[a-z0-9A-Z_-]{0,63}$/,hostnamePartStart=/^([a-z0-9A-Z_-]{0,63})(.*)$/,unsafeProtocol={javascript:true,"javascript:":true},hostlessProtocol={javascript:true,"javascript:":true},slashedProtocol={http:true,https:true,ftp:true,gopher:true,file:true,"http:":true,"https:":true,"ftp:":true,"gopher:":true,"file:":true},querystring=require("querystring");function urlParse(url,parseQueryString,slashesDenoteHost){if(url&&isObject(url)&&url instanceof Url)return url;var u=new Url;u.parse(url,parseQueryString,slashesDenoteHost);return u}Url.prototype.parse=function(url,parseQueryString,slashesDenoteHost){if(!isString(url)){throw new TypeError("Parameter 'url' must be a string, not "+typeof url)}var rest=url;rest=rest.trim();var proto=protocolPattern.exec(rest);if(proto){proto=proto[0];var lowerProto=proto.toLowerCase();this.protocol=lowerProto;rest=rest.substr(proto.length)}if(slashesDenoteHost||proto||rest.match(/^\/\/[^@\/]+@[^@\/]+/)){var slashes=rest.substr(0,2)==="//";if(slashes&&!(proto&&hostlessProtocol[proto])){rest=rest.substr(2);this.slashes=true}}if(!hostlessProtocol[proto]&&(slashes||proto&&!slashedProtocol[proto])){var hostEnd=-1;for(var i=0;i<hostEndingChars.length;i++){var hec=rest.indexOf(hostEndingChars[i]);if(hec!==-1&&(hostEnd===-1||hec<hostEnd))hostEnd=hec}var auth,atSign;if(hostEnd===-1){atSign=rest.lastIndexOf("@")}else{atSign=rest.lastIndexOf("@",hostEnd)}if(atSign!==-1){auth=rest.slice(0,atSign);rest=rest.slice(atSign+1);this.auth=decodeURIComponent(auth)}hostEnd=-1;for(var i=0;i<nonHostChars.length;i++){var hec=rest.indexOf(nonHostChars[i]);if(hec!==-1&&(hostEnd===-1||hec<hostEnd))hostEnd=hec}if(hostEnd===-1)hostEnd=rest.length;this.host=rest.slice(0,hostEnd);rest=rest.slice(hostEnd);this.parseHost();this.hostname=this.hostname||"";var ipv6Hostname=this.hostname[0]==="["&&this.hostname[this.hostname.length-1]==="]";if(!ipv6Hostname){var hostparts=this.hostname.split(/\./);
-for(var i=0,l=hostparts.length;i<l;i++){var part=hostparts[i];if(!part)continue;if(!part.match(hostnamePartPattern)){var newpart="";for(var j=0,k=part.length;j<k;j++){if(part.charCodeAt(j)>127){newpart+="x"}else{newpart+=part[j]}}if(!newpart.match(hostnamePartPattern)){var validParts=hostparts.slice(0,i);var notHost=hostparts.slice(i+1);var bit=part.match(hostnamePartStart);if(bit){validParts.push(bit[1]);notHost.unshift(bit[2])}if(notHost.length){rest="/"+notHost.join(".")+rest}this.hostname=validParts.join(".");break}}}}if(this.hostname.length>hostnameMaxLen){this.hostname=""}else{this.hostname=this.hostname.toLowerCase()}if(!ipv6Hostname){var domainArray=this.hostname.split(".");var newOut=[];for(var i=0;i<domainArray.length;++i){var s=domainArray[i];newOut.push(s.match(/[^A-Za-z0-9_-]/)?"xn--"+punycode.encode(s):s)}this.hostname=newOut.join(".")}var p=this.port?":"+this.port:"";var h=this.hostname||"";this.host=h+p;this.href+=this.host;if(ipv6Hostname){this.hostname=this.hostname.substr(1,this.hostname.length-2);if(rest[0]!=="/"){rest="/"+rest}}}if(!unsafeProtocol[lowerProto]){for(var i=0,l=autoEscape.length;i<l;i++){var ae=autoEscape[i];var esc=encodeURIComponent(ae);if(esc===ae){esc=escape(ae)}rest=rest.split(ae).join(esc)}}var hash=rest.indexOf("#");if(hash!==-1){this.hash=rest.substr(hash);rest=rest.slice(0,hash)}var qm=rest.indexOf("?");if(qm!==-1){this.search=rest.substr(qm);this.query=rest.substr(qm+1);if(parseQueryString){this.query=querystring.parse(this.query)}rest=rest.slice(0,qm)}else if(parseQueryString){this.search="";this.query={}}if(rest)this.pathname=rest;if(slashedProtocol[lowerProto]&&this.hostname&&!this.pathname){this.pathname="/"}if(this.pathname||this.search){var p=this.pathname||"";var s=this.search||"";this.path=p+s}this.href=this.format();return this};function urlFormat(obj){if(isString(obj))obj=urlParse(obj);if(!(obj instanceof 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sidebar=require("./sidebar");var toolbar=require("./toolbar");function start(config){sidebar.init();keyboard.init();dropdown.init();navigation.init();toolbar.createButton({index:0,icon:"fa fa-align-justify",onClick:function(e){e.preventDefault();sidebar.toggle()}});events.trigger("start",config);navigation.notify()}var gitbook={start:start,events:events,state:state,toolbar:toolbar,sidebar:sidebar,storage:storage,keyboard:keyboard};var MODULES={gitbook:gitbook,jquery:$,lodash:_};window.gitbook=gitbook;window.$=$;window.jQuery=$;gitbook.require=function(mods,fn){mods=_.map(mods,function(mod){mod=mod.toLowerCase();if(!MODULES[mod]){throw new Error("GitBook module "+mod+" doesn't exist")}return MODULES[mod]});fn.apply(null,mods)};module.exports={}},{"./dropdown":11,"./events":12,"./keyboard":14,"./navigation":16,"./sidebar":18,"./state":19,"./storage":20,"./toolbar":21,jquery:1,lodash:2}],14:[function(require,module,exports){var Mousetrap=require("mousetrap");var 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uri=url.resolve(window.location.pathname,relativeUrl);notifyPageChange();location.href=relativeUrl;return;return loading.show($.get(uri).done(function(html){if(push)history.pushState({path:uri},null,uri);html=html.replace(/<(\/?)(html|head|body)([^>]*)>/gi,function(a,b,c,d){return"<"+b+"div"+(b?"":' data-element="'+c+'"')+d+">"});var $page=$(html);var $pageHead=$page.find("[data-element=head]");var $pageBody=$page.find(".book");document.title=$pageHead.find("title").text();var $head=$("head");$head.find("link[rel=prev]").remove();$head.find("link[rel=next]").remove();$head.append($pageHead.find("link[rel=prev]"));$head.append($pageHead.find("link[rel=next]"));var bodyClass=$(".book").attr("class");var scrollPosition=$(".book-summary .summary").scrollTop();$pageBody.toggleClass("with-summary",$(".book").hasClass("with-summary"));$(".book").replaceWith($pageBody);$(".book").attr("class",bodyClass);$(".book-summary .summary").scrollTop(scrollPosition);state.update($("html"));preparePage()}).fail(function(e){location.href=relativeUrl}))}function updateNavigationPosition(){var bodyInnerWidth,pageWrapperWidth;bodyInnerWidth=parseInt($(".body-inner").css("width"),10);pageWrapperWidth=parseInt($(".page-wrapper").css("width"),10);$(".navigation-next").css("margin-right",bodyInnerWidth-pageWrapperWidth+"px")}function notifyPageChange(){events.trigger("page.change")}function preparePage(notify){var $bookBody=$(".book-body");var $bookInner=$bookBody.find(".body-inner");var $pageWrapper=$bookInner.find(".page-wrapper");updateNavigationPosition();$bookInner.scrollTop(0);$bookBody.scrollTop(0);if(notify!==false)notifyPageChange()}function isLeftClickEvent(e){return e.button===0}function isModifiedEvent(e){return!!(e.metaKey||e.altKey||e.ctrlKey||e.shiftKey)}function handlePagination(e){if(isModifiedEvent(e)||!isLeftClickEvent(e)){return}e.stopPropagation();e.preventDefault();var 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value=object[key],result=customizer?customizer(value,srcValue,key,object,source):undefined,isCommon=result===undefined;if(isCommon){result=srcValue}if((result!==undefined||isSrcArr&&!(key in object))&&(isCommon||(result===result?result!==value:value===value))){object[key]=result}}});return object}function baseMergeDeep(object,source,key,mergeFunc,customizer,stackA,stackB){var length=stackA.length,srcValue=source[key];while(length--){if(stackA[length]==srcValue){object[key]=stackB[length];return}}var value=object[key],result=customizer?customizer(value,srcValue,key,object,source):undefined,isCommon=result===undefined;if(isCommon){result=srcValue;if(isArrayLike(srcValue)&&(isArray(srcValue)||isTypedArray(srcValue))){result=isArray(value)?value:isArrayLike(value)?arrayCopy(value):[]}else 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baseReduce(collection,iteratee,accumulator,initFromCollection,eachFunc){eachFunc(collection,function(value,index,collection){accumulator=initFromCollection?(initFromCollection=false,value):iteratee(accumulator,value,index,collection)});return accumulator}var baseSetData=!metaMap?identity:function(func,data){metaMap.set(func,data);return func};function baseSlice(array,start,end){var index=-1,length=array.length;start=start==null?0:+start||0;if(start<0){start=-start>length?0:length+start}end=end===undefined||end>length?length:+end||0;if(end<0){end+=length}length=start>end?0:end-start>>>0;start>>>=0;var result=Array(length);while(++index<length){result[index]=array[index+start]}return result}function baseSome(collection,predicate){var result;baseEach(collection,function(value,index,collection){result=predicate(value,index,collection);return!result});return!!result}function baseSortBy(array,comparer){var length=array.length;array.sort(comparer);while(length--){array[length]=array[length].value}return array}function baseSortByOrder(collection,iteratees,orders){var callback=getCallback(),index=-1;iteratees=arrayMap(iteratees,function(iteratee){return callback(iteratee)});var result=baseMap(collection,function(value){var criteria=arrayMap(iteratees,function(iteratee){return iteratee(value)});return{criteria:criteria,index:++index,value:value}});return baseSortBy(result,function(object,other){return compareMultiple(object,other,orders)})}function baseSum(collection,iteratee){var result=0;baseEach(collection,function(value,index,collection){result+=+iteratee(value,index,collection)||0});return result}function baseUniq(array,iteratee){var index=-1,indexOf=getIndexOf(),length=array.length,isCommon=indexOf==baseIndexOf,isLarge=isCommon&&length>=LARGE_ARRAY_SIZE,seen=isLarge?createCache():null,result=[];if(seen){indexOf=cacheIndexOf;isCommon=false}else{isLarge=false;seen=iteratee?[]:result}outer:while(++index<length){var value=array[index],computed=iteratee?iteratee(value,index,array):value;if(isCommon&&value===value){var seenIndex=seen.length;while(seenIndex--){if(seen[seenIndex]===computed){continue outer}}if(iteratee){seen.push(computed)}result.push(value)}else if(indexOf(seen,computed,0)<0){if(iteratee||isLarge){seen.push(computed)}result.push(value)}}return result}function baseValues(object,props){var index=-1,length=props.length,result=Array(length);while(++index<length){result[index]=object[props[index]]}return result}function baseWhile(array,predicate,isDrop,fromRight){var length=array.length,index=fromRight?length:-1;while((fromRight?index--:++index<length)&&predicate(array[index],index,array)){}return isDrop?baseSlice(array,fromRight?0:index,fromRight?index+1:length):baseSlice(array,fromRight?index+1:0,fromRight?length:index)}function baseWrapperValue(value,actions){var result=value;if(result instanceof LazyWrapper){result=result.value()}var index=-1,length=actions.length;while(++index<length){var action=actions[index];result=action.func.apply(action.thisArg,arrayPush([result],action.args))}return result}function binaryIndex(array,value,retHighest){var low=0,high=array?array.length:low;if(typeof value=="number"&&value===value&&high<=HALF_MAX_ARRAY_LENGTH){while(low<high){var mid=low+high>>>1,computed=array[mid];if((retHighest?computed<=value:computed<value)&&computed!==null){low=mid+1}else{high=mid}}return high}return binaryIndexBy(array,value,identity,retHighest)}function binaryIndexBy(array,value,iteratee,retHighest){value=iteratee(value);var low=0,high=array?array.length:0,valIsNaN=value!==value,valIsNull=value===null,valIsUndef=value===undefined;while(low<high){var mid=nativeFloor((low+high)/2),computed=iteratee(array[mid]),isDef=computed!==undefined,isReflexive=computed===computed;if(valIsNaN){var setLow=isReflexive||retHighest}else if(valIsNull){setLow=isReflexive&&isDef&&(retHighest||computed!=null)}else if(valIsUndef){setLow=isReflexive&&(retHighest||isDef)}else if(computed==null){setLow=false}else{setLow=retHighest?computed<=value:computed<value}if(setLow){low=mid+1}else{high=mid}}return nativeMin(high,MAX_ARRAY_INDEX)}function bindCallback(func,thisArg,argCount){if(typeof func!="function"){return identity}if(thisArg===undefined){return func}switch(argCount){case 1:return function(value){return func.call(thisArg,value)};case 3:return function(value,index,collection){return func.call(thisArg,value,index,collection)};case 4:return function(accumulator,value,index,collection){return func.call(thisArg,accumulator,value,index,collection)};case 5:return function(value,other,key,object,source){return func.call(thisArg,value,other,key,object,source)}}return function(){return func.apply(thisArg,arguments)}}function bufferClone(buffer){var result=new ArrayBuffer(buffer.byteLength),view=new Uint8Array(result);view.set(new Uint8Array(buffer));return result}function composeArgs(args,partials,holders){var holdersLength=holders.length,argsIndex=-1,argsLength=nativeMax(args.length-holdersLength,0),leftIndex=-1,leftLength=partials.length,result=Array(leftLength+argsLength);while(++leftIndex<leftLength){result[leftIndex]=partials[leftIndex]}while(++argsIndex<holdersLength){result[holders[argsIndex]]=args[argsIndex]}while(argsLength--){result[leftIndex++]=args[argsIndex++]}return result}function composeArgsRight(args,partials,holders){var holdersIndex=-1,holdersLength=holders.length,argsIndex=-1,argsLength=nativeMax(args.length-holdersLength,0),rightIndex=-1,rightLength=partials.length,result=Array(argsLength+rightLength);while(++argsIndex<argsLength){result[argsIndex]=args[argsIndex]}var offset=argsIndex;while(++rightIndex<rightLength){result[offset+rightIndex]=partials[rightIndex]}while(++holdersIndex<holdersLength){result[offset+holders[holdersIndex]]=args[argsIndex++]}return result}function createAggregator(setter,initializer){return function(collection,iteratee,thisArg){var result=initializer?initializer():{};iteratee=getCallback(iteratee,thisArg,3);if(isArray(collection)){var index=-1,length=collection.length;while(++index<length){var value=collection[index];setter(result,value,iteratee(value,index,collection),collection)}}else{baseEach(collection,function(value,key,collection){setter(result,value,iteratee(value,key,collection),collection)})}return result}}function createAssigner(assigner){return restParam(function(object,sources){var index=-1,length=object==null?0:sources.length,customizer=length>2?sources[length-2]:undefined,guard=length>2?sources[2]:undefined,thisArg=length>1?sources[length-1]:undefined;if(typeof 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key=props[index];if(iteratee(iterable[key],key,iterable)===false){break}}return object}}function createBindWrapper(func,thisArg){var Ctor=createCtorWrapper(func);function wrapper(){var fn=this&&this!==root&&this instanceof wrapper?Ctor:func;return fn.apply(thisArg,arguments)}return wrapper}function createCache(values){return nativeCreate&&Set?new SetCache(values):null}function createCompounder(callback){return function(string){var index=-1,array=words(deburr(string)),length=array.length,result="";while(++index<length){result=callback(result,array[index],index)}return result}}function createCtorWrapper(Ctor){return function(){var args=arguments;switch(args.length){case 0:return new Ctor;case 1:return new Ctor(args[0]);case 2:return new Ctor(args[0],args[1]);case 3:return new Ctor(args[0],args[1],args[2]);case 4:return new Ctor(args[0],args[1],args[2],args[3]);case 5:return new Ctor(args[0],args[1],args[2],args[3],args[4]);case 6:return new Ctor(args[0],args[1],args[2],args[3],args[4],args[5]);case 7:return new Ctor(args[0],args[1],args[2],args[3],args[4],args[5],args[6])}var thisBinding=baseCreate(Ctor.prototype),result=Ctor.apply(thisBinding,args);return isObject(result)?result:thisBinding}}function createCurry(flag){function curryFunc(func,arity,guard){if(guard&&isIterateeCall(func,arity,guard)){arity=undefined}var result=createWrapper(func,flag,undefined,undefined,undefined,undefined,undefined,arity);result.placeholder=curryFunc.placeholder;return result}return curryFunc}function createDefaults(assigner,customizer){return restParam(function(args){var object=args[0];if(object==null){return object}args.push(customizer);return assigner.apply(undefined,args)})}function createExtremum(comparator,exValue){return function(collection,iteratee,thisArg){if(thisArg&&isIterateeCall(collection,iteratee,thisArg)){iteratee=undefined}iteratee=getCallback(iteratee,thisArg,3);if(iteratee.length==1){collection=isArray(collection)?collection:toIterable(collection);var result=arrayExtremum(collection,iteratee,comparator,exValue);if(!(collection.length&&result===exValue)){return result}}return baseExtremum(collection,iteratee,comparator,exValue)}}function createFind(eachFunc,fromRight){return function(collection,predicate,thisArg){predicate=getCallback(predicate,thisArg,3);if(isArray(collection)){var index=baseFindIndex(collection,predicate,fromRight);return index>-1?collection[index]:undefined}return baseFind(collection,predicate,eachFunc)}}function createFindIndex(fromRight){return function(array,predicate,thisArg){if(!(array&&array.length)){return-1}predicate=getCallback(predicate,thisArg,3);return baseFindIndex(array,predicate,fromRight)}}function createFindKey(objectFunc){return function(object,predicate,thisArg){predicate=getCallback(predicate,thisArg,3);return baseFind(object,predicate,objectFunc,true)}}function createFlow(fromRight){return function(){var wrapper,length=arguments.length,index=fromRight?length:-1,leftIndex=0,funcs=Array(length);while(fromRight?index--:++index<length){var func=funcs[leftIndex++]=arguments[index];if(typeof func!="function"){throw new TypeError(FUNC_ERROR_TEXT)}if(!wrapper&&LodashWrapper.prototype.thru&&getFuncName(func)=="wrapper"){wrapper=new LodashWrapper([],true)}}index=wrapper?-1:length;while(++index<length){func=funcs[index];var funcName=getFuncName(func),data=funcName=="wrapper"?getData(func):undefined;if(data&&isLaziable(data[0])&&data[1]==(ARY_FLAG|CURRY_FLAG|PARTIAL_FLAG|REARG_FLAG)&&!data[4].length&&data[9]==1){wrapper=wrapper[getFuncName(data[0])].apply(wrapper,data[3])}else{wrapper=func.length==1&&isLaziable(func)?wrapper[funcName]():wrapper.thru(func)}}return function(){var args=arguments,value=args[0];if(wrapper&&args.length==1&&isArray(value)&&value.length>=LARGE_ARRAY_SIZE){return wrapper.plant(value).value()}var index=0,result=length?funcs[index].apply(this,args):value;while(++index<length){result=funcs[index].call(this,result)}return result}}}function createForEach(arrayFunc,eachFunc){return function(collection,iteratee,thisArg){return typeof iteratee=="function"&&thisArg===undefined&&isArray(collection)?arrayFunc(collection,iteratee):eachFunc(collection,bindCallback(iteratee,thisArg,3))}}function createForIn(objectFunc){return function(object,iteratee,thisArg){if(typeof iteratee!="function"||thisArg!==undefined){iteratee=bindCallback(iteratee,thisArg,3)}return objectFunc(object,iteratee,keysIn)}}function createForOwn(objectFunc){return function(object,iteratee,thisArg){if(typeof iteratee!="function"||thisArg!==undefined){iteratee=bindCallback(iteratee,thisArg,3)}return objectFunc(object,iteratee)}}function createObjectMapper(isMapKeys){return function(object,iteratee,thisArg){var result={};iteratee=getCallback(iteratee,thisArg,3);baseForOwn(object,function(value,key,object){var mapped=iteratee(value,key,object);key=isMapKeys?mapped:key;value=isMapKeys?value:mapped;result[key]=value});return result}}function createPadDir(fromRight){return function(string,length,chars){string=baseToString(string);return(fromRight?string:"")+createPadding(string,length,chars)+(fromRight?"":string)}}function createPartial(flag){var partialFunc=restParam(function(func,partials){var holders=replaceHolders(partials,partialFunc.placeholder);return createWrapper(func,flag,undefined,partials,holders)});return partialFunc}function createReduce(arrayFunc,eachFunc){return function(collection,iteratee,accumulator,thisArg){var initFromArray=arguments.length<3;return typeof 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newArgPos=argPos?arrayCopy(argPos):undefined,newArity=nativeMax(arity-length,0),newsHolders=isCurry?argsHolders:undefined,newHoldersRight=isCurry?undefined:argsHolders,newPartials=isCurry?args:undefined,newPartialsRight=isCurry?undefined:args;bitmask|=isCurry?PARTIAL_FLAG:PARTIAL_RIGHT_FLAG;bitmask&=~(isCurry?PARTIAL_RIGHT_FLAG:PARTIAL_FLAG);if(!isCurryBound){bitmask&=~(BIND_FLAG|BIND_KEY_FLAG)}var newData=[func,bitmask,thisArg,newPartials,newsHolders,newPartialsRight,newHoldersRight,newArgPos,ary,newArity],result=createHybridWrapper.apply(undefined,newData);if(isLaziable(func)){setData(result,newData)}result.placeholder=placeholder;return result}}var thisBinding=isBind?thisArg:this,fn=isBindKey?thisBinding[func]:func;if(argPos){args=reorder(args,argPos)}if(isAry&&ary<args.length){args.length=ary}if(this&&this!==root&&this instanceof wrapper){fn=Ctor||createCtorWrapper(func)}return fn.apply(thisBinding,args)}return wrapper}function createPadding(string,length,chars){var strLength=string.length;length=+length;if(strLength>=length||!nativeIsFinite(length)){return""}var padLength=length-strLength;chars=chars==null?" ":chars+"";return repeat(chars,nativeCeil(padLength/chars.length)).slice(0,padLength)}function createPartialWrapper(func,bitmask,thisArg,partials){var isBind=bitmask&BIND_FLAG,Ctor=createCtorWrapper(func);function wrapper(){var argsIndex=-1,argsLength=arguments.length,leftIndex=-1,leftLength=partials.length,args=Array(leftLength+argsLength);while(++leftIndex<leftLength){args[leftIndex]=partials[leftIndex]}while(argsLength--){args[leftIndex++]=arguments[++argsIndex]}var fn=this&&this!==root&&this instanceof wrapper?Ctor:func;return fn.apply(isBind?thisArg:this,args)}return wrapper}function createRound(methodName){var func=Math[methodName];return function(number,precision){precision=precision===undefined?0:+precision||0;if(precision){precision=pow(10,precision);return func(number*precision)/precision}return func(number)}}function createSortedIndex(retHighest){return function(array,value,iteratee,thisArg){var callback=getCallback(iteratee);return iteratee==null&&callback===baseCallback?binaryIndex(array,value,retHighest):binaryIndexBy(array,value,callback(iteratee,thisArg,1),retHighest)}}function createWrapper(func,bitmask,thisArg,partials,holders,argPos,ary,arity){var isBindKey=bitmask&BIND_KEY_FLAG;if(!isBindKey&&typeof func!="function"){throw new TypeError(FUNC_ERROR_TEXT)}var length=partials?partials.length:0;if(!length){bitmask&=~(PARTIAL_FLAG|PARTIAL_RIGHT_FLAG);partials=holders=undefined}length-=holders?holders.length:0;if(bitmask&PARTIAL_RIGHT_FLAG){var partialsRight=partials,holdersRight=holders;partials=holders=undefined}var data=isBindKey?undefined:getData(func),newData=[func,bitmask,thisArg,partials,holders,partialsRight,holdersRight,argPos,ary,arity];if(data){mergeData(newData,data);bitmask=newData[1];arity=newData[9]}newData[9]=arity==null?isBindKey?0:func.length:nativeMax(arity-length,0)||0;if(bitmask==BIND_FLAG){var result=createBindWrapper(newData[0],newData[2])}else if((bitmask==PARTIAL_FLAG||bitmask==(BIND_FLAG|PARTIAL_FLAG))&&!newData[4].length){result=createPartialWrapper.apply(undefined,newData)}else{result=createHybridWrapper.apply(undefined,newData)}var setter=data?baseSetData:setData;return setter(result,newData)}function equalArrays(array,other,equalFunc,customizer,isLoose,stackA,stackB){var index=-1,arrLength=array.length,othLength=other.length;if(arrLength!=othLength&&!(isLoose&&othLength>arrLength)){return false}while(++index<arrLength){var arrValue=array[index],othValue=other[index],result=customizer?customizer(isLoose?othValue:arrValue,isLoose?arrValue:othValue,index):undefined;if(result!==undefined){if(result){continue}return false}if(isLoose){if(!arraySome(other,function(othValue){return arrValue===othValue||equalFunc(arrValue,othValue,customizer,isLoose,stackA,stackB)})){return false}}else if(!(arrValue===othValue||equalFunc(arrValue,othValue,customizer,isLoose,stackA,stackB))){return false}}return true}function equalByTag(object,other,tag){switch(tag){case boolTag:case dateTag:return+object==+other;case errorTag:return object.name==other.name&&object.message==other.message;case numberTag:return object!=+object?other!=+other:object==+other;case regexpTag:case stringTag:return object==other+""}return false}function equalObjects(object,other,equalFunc,customizer,isLoose,stackA,stackB){var objProps=keys(object),objLength=objProps.length,othProps=keys(other),othLength=othProps.length;if(objLength!=othLength&&!isLoose){return false}var index=objLength;while(index--){var key=objProps[index];if(!(isLoose?key in other:hasOwnProperty.call(other,key))){return false}}var skipCtor=isLoose;while(++index<objLength){key=objProps[index];var objValue=object[key],othValue=other[key],result=customizer?customizer(isLoose?othValue:objValue,isLoose?objValue:othValue,key):undefined;if(!(result===undefined?equalFunc(objValue,othValue,customizer,isLoose,stackA,stackB):result)){return false}skipCtor||(skipCtor=key=="constructor")}if(!skipCtor){var objCtor=object.constructor,othCtor=other.constructor;if(objCtor!=othCtor&&("constructor"in object&&"constructor"in other)&&!(typeof objCtor=="function"&&objCtor instanceof objCtor&&typeof othCtor=="function"&&othCtor instanceof othCtor)){return false}}return true}function getCallback(func,thisArg,argCount){var result=lodash.callback||callback;result=result===callback?baseCallback:result;return argCount?result(func,thisArg,argCount):result}var getData=!metaMap?noop:function(func){return metaMap.get(func)};function getFuncName(func){var result=func.name,array=realNames[result],length=array?array.length:0;while(length--){var data=array[length],otherFunc=data.func;if(otherFunc==null||otherFunc==func){return data.name}}return result}function getIndexOf(collection,target,fromIndex){var result=lodash.indexOf||indexOf;result=result===indexOf?baseIndexOf:result;return collection?result(collection,target,fromIndex):result}var getLength=baseProperty("length");function getMatchData(object){var result=pairs(object),length=result.length;while(length--){result[length][2]=isStrictComparable(result[length][1])}return result}function getNative(object,key){var value=object==null?undefined:object[key];return isNative(value)?value:undefined}function getView(start,end,transforms){var index=-1,length=transforms.length;while(++index<length){var data=transforms[index],size=data.size;switch(data.type){case"drop":start+=size;break;case"dropRight":end-=size;break;case"take":end=nativeMin(end,start+size);break;case"takeRight":start=nativeMax(start,end-size);break}}return{start:start,end:end}}function initCloneArray(array){var length=array.length,result=new array.constructor(length);if(length&&typeof array[0]=="string"&&hasOwnProperty.call(array,"index")){result.index=array.index;result.input=array.input}return result}function initCloneObject(object){var Ctor=object.constructor;if(!(typeof Ctor=="function"&&Ctor instanceof Ctor)){Ctor=Object}return new Ctor}function initCloneByTag(object,tag,isDeep){var Ctor=object.constructor;switch(tag){case arrayBufferTag:return bufferClone(object);case boolTag:case dateTag:return new Ctor(+object);case float32Tag:case float64Tag:case int8Tag:case int16Tag:case int32Tag:case uint8Tag:case uint8ClampedTag:case uint16Tag:case uint32Tag:var buffer=object.buffer;return new Ctor(isDeep?bufferClone(buffer):buffer,object.byteOffset,object.length);case numberTag:case stringTag:return new Ctor(object);case regexpTag:var result=new Ctor(object.source,reFlags.exec(object));result.lastIndex=object.lastIndex}return result}function invokePath(object,path,args){if(object!=null&&!isKey(path,object)){path=toPath(path);object=path.length==1?object:baseGet(object,baseSlice(path,0,-1));path=last(path)}var func=object==null?object:object[path];return func==null?undefined:func.apply(object,args)}function isArrayLike(value){return value!=null&&isLength(getLength(value))}function isIndex(value,length){value=typeof value=="number"||reIsUint.test(value)?+value:-1;length=length==null?MAX_SAFE_INTEGER:length;return value>-1&&value%1==0&&value<length}function isIterateeCall(value,index,object){if(!isObject(object)){return false}var type=typeof index;if(type=="number"?isArrayLike(object)&&isIndex(index,object.length):type=="string"&&index in object){var other=object[index];return value===value?value===other:other!==other}return false}function isKey(value,object){var type=typeof value;if(type=="string"&&reIsPlainProp.test(value)||type=="number"){return true}if(isArray(value)){return false}var result=!reIsDeepProp.test(value);return result||object!=null&&value in toObject(object)}function isLaziable(func){var funcName=getFuncName(func);if(!(funcName in LazyWrapper.prototype)){return false}var other=lodash[funcName];if(func===other){return true}var data=getData(other);return!!data&&func===data[0]}function isLength(value){return typeof value=="number"&&value>-1&&value%1==0&&value<=MAX_SAFE_INTEGER}function isStrictComparable(value){return value===value&&!isObject(value)}function mergeData(data,source){var bitmask=data[1],srcBitmask=source[1],newBitmask=bitmask|srcBitmask,isCommon=newBitmask<ARY_FLAG;var isCombo=srcBitmask==ARY_FLAG&&bitmask==CURRY_FLAG||srcBitmask==ARY_FLAG&&bitmask==REARG_FLAG&&data[7].length<=source[8]||srcBitmask==(ARY_FLAG|REARG_FLAG)&&bitmask==CURRY_FLAG;if(!(isCommon||isCombo)){return data}if(srcBitmask&BIND_FLAG){data[2]=source[2];newBitmask|=bitmask&BIND_FLAG?0:CURRY_BOUND_FLAG}var value=source[3];if(value){var 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toIterable(value){if(value==null){return[]}if(!isArrayLike(value)){return values(value)}return isObject(value)?value:Object(value)}function toObject(value){return isObject(value)?value:Object(value)}function toPath(value){if(isArray(value)){return value}var result=[];baseToString(value).replace(rePropName,function(match,number,quote,string){result.push(quote?string.replace(reEscapeChar,"$1"):number||match)});return result}function wrapperClone(wrapper){return wrapper instanceof LazyWrapper?wrapper.clone():new LodashWrapper(wrapper.__wrapped__,wrapper.__chain__,arrayCopy(wrapper.__actions__))}function chunk(array,size,guard){if(guard?isIterateeCall(array,size,guard):size==null){size=1}else{size=nativeMax(nativeFloor(size)||1,1)}var index=0,length=array?array.length:0,resIndex=-1,result=Array(nativeCeil(length/size));while(index<length){result[++resIndex]=baseSlice(array,index,index+=size)}return result}function compact(array){var index=-1,length=array?array.length:0,resIndex=-1,result=[];while(++index<length){var value=array[index];if(value){result[++resIndex]=value}}return result}var difference=restParam(function(array,values){return isObjectLike(array)&&isArrayLike(array)?baseDifference(array,baseFlatten(values,false,true)):[]});function drop(array,n,guard){var length=array?array.length:0;if(!length){return[]}if(guard?isIterateeCall(array,n,guard):n==null){n=1}return baseSlice(array,n<0?0:n)}function dropRight(array,n,guard){var length=array?array.length:0;if(!length){return[]}if(guard?isIterateeCall(array,n,guard):n==null){n=1}n=length-(+n||0);return baseSlice(array,0,n<0?0:n)}function dropRightWhile(array,predicate,thisArg){return array&&array.length?baseWhile(array,getCallback(predicate,thisArg,3),true,true):[]}function dropWhile(array,predicate,thisArg){return array&&array.length?baseWhile(array,getCallback(predicate,thisArg,3),true):[]}function fill(array,value,start,end){var 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fromIndex=="number"){index=(fromIndex<0?nativeMax(length+fromIndex,0):nativeMin(fromIndex||0,length-1))+1}else if(fromIndex){index=binaryIndex(array,value,true)-1;var other=array[index];if(value===value?value===other:other!==other){return index}return-1}if(value!==value){return indexOfNaN(array,index,true)}while(index--){if(array[index]===value){return index}}return-1}function pull(){var args=arguments,array=args[0];if(!(array&&array.length)){return array}var index=0,indexOf=getIndexOf(),length=args.length;while(++index<length){var fromIndex=0,value=args[index];while((fromIndex=indexOf(array,value,fromIndex))>-1){splice.call(array,fromIndex,1)}}return array}var pullAt=restParam(function(array,indexes){indexes=baseFlatten(indexes);var result=baseAt(array,indexes);basePullAt(array,indexes.sort(baseCompareAscending));return result});function remove(array,predicate,thisArg){var result=[];if(!(array&&array.length)){return result}var index=-1,indexes=[],length=array.length;predicate=getCallback(predicate,thisArg,3);while(++index<length){var value=array[index];if(predicate(value,index,array)){result.push(value);indexes.push(index)}}basePullAt(array,indexes);return result}function rest(array){return drop(array,1)}function slice(array,start,end){var length=array?array.length:0;if(!length){return[]}if(end&&typeof end!="number"&&isIterateeCall(array,start,end)){start=0;end=length}return baseSlice(array,start,end)}var sortedIndex=createSortedIndex();var sortedLastIndex=createSortedIndex(true);function take(array,n,guard){var length=array?array.length:0;if(!length){return[]}if(guard?isIterateeCall(array,n,guard):n==null){n=1}return baseSlice(array,0,n<0?0:n)}function takeRight(array,n,guard){var length=array?array.length:0;if(!length){return[]}if(guard?isIterateeCall(array,n,guard):n==null){n=1}n=length-(+n||0);return baseSlice(array,n<0?0:n)}function takeRightWhile(array,predicate,thisArg){return array&&array.length?baseWhile(array,getCallback(predicate,thisArg,3),false,true):[]}function takeWhile(array,predicate,thisArg){return array&&array.length?baseWhile(array,getCallback(predicate,thisArg,3)):[]}var union=restParam(function(arrays){return baseUniq(baseFlatten(arrays,false,true))});function uniq(array,isSorted,iteratee,thisArg){var length=array?array.length:0;if(!length){return[]}if(isSorted!=null&&typeof isSorted!="boolean"){thisArg=iteratee;iteratee=isIterateeCall(array,isSorted,thisArg)?undefined:isSorted;isSorted=false}var callback=getCallback();if(!(iteratee==null&&callback===baseCallback)){iteratee=callback(iteratee,thisArg,3)}return isSorted&&getIndexOf()==baseIndexOf?sortedUniq(array,iteratee):baseUniq(array,iteratee)}function unzip(array){if(!(array&&array.length)){return[]}var index=-1,length=0;array=arrayFilter(array,function(group){if(isArrayLike(group)){length=nativeMax(group.length,length);return true}});var result=Array(length);while(++index<length){result[index]=arrayMap(array,baseProperty(index))}return result}function unzipWith(array,iteratee,thisArg){var length=array?array.length:0;if(!length){return[]}var result=unzip(array);if(iteratee==null){return result}iteratee=bindCallback(iteratee,thisArg,4);return arrayMap(result,function(group){return arrayReduce(group,iteratee,undefined,true)})}var without=restParam(function(array,values){return isArrayLike(array)?baseDifference(array,values):[]});function xor(){var index=-1,length=arguments.length;while(++index<length){var array=arguments[index];if(isArrayLike(array)){var result=result?arrayPush(baseDifference(result,array),baseDifference(array,result)):array}}return result?baseUniq(result):[]}var zip=restParam(unzip);function zipObject(props,values){var index=-1,length=props?props.length:0,result={};if(length&&!values&&!isArray(props[0])){values=[]}while(++index<length){var key=props[index];if(values){result[key]=values[index]}else 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result,parent=this;while(parent instanceof baseLodash){var clone=wrapperClone(parent);if(result){previous.__wrapped__=clone}else{result=clone}var previous=clone;parent=parent.__wrapped__}previous.__wrapped__=value;return result}function wrapperReverse(){var value=this.__wrapped__;var interceptor=function(value){return wrapped&&wrapped.__dir__<0?value:value.reverse()};if(value instanceof LazyWrapper){var wrapped=value;if(this.__actions__.length){wrapped=new LazyWrapper(this)}wrapped=wrapped.reverse();wrapped.__actions__.push({func:thru,args:[interceptor],thisArg:undefined});return new LodashWrapper(wrapped,this.__chain__)}return this.thru(interceptor)}function wrapperToString(){return this.value()+""}function wrapperValue(){return baseWrapperValue(this.__wrapped__,this.__actions__)}var at=restParam(function(collection,props){return baseAt(collection,baseFlatten(props))});var countBy=createAggregator(function(result,value,key){hasOwnProperty.call(result,key)?++result[key]:result[key]=1});function every(collection,predicate,thisArg){var func=isArray(collection)?arrayEvery:baseEvery;if(thisArg&&isIterateeCall(collection,predicate,thisArg)){predicate=undefined}if(typeof predicate!="function"||thisArg!==undefined){predicate=getCallback(predicate,thisArg,3)}return func(collection,predicate)}function filter(collection,predicate,thisArg){var func=isArray(collection)?arrayFilter:baseFilter;predicate=getCallback(predicate,thisArg,3);return func(collection,predicate)}var find=createFind(baseEach);var findLast=createFind(baseEachRight,true);function findWhere(collection,source){return find(collection,baseMatches(source))}var forEach=createForEach(arrayEach,baseEach);var forEachRight=createForEach(arrayEachRight,baseEachRight);var groupBy=createAggregator(function(result,value,key){if(hasOwnProperty.call(result,key)){result[key].push(value)}else{result[key]=[value]}});function includes(collection,target,fromIndex,guard){var length=collection?getLength(collection):0;if(!isLength(length)){collection=values(collection);length=collection.length}if(typeof fromIndex!="number"||guard&&isIterateeCall(target,fromIndex,guard)){fromIndex=0}else{fromIndex=fromIndex<0?nativeMax(length+fromIndex,0):fromIndex||0}return typeof collection=="string"||!isArray(collection)&&isString(collection)?fromIndex<=length&&collection.indexOf(target,fromIndex)>-1:!!length&&getIndexOf(collection,target,fromIndex)>-1}var indexBy=createAggregator(function(result,value,key){result[key]=value});var invoke=restParam(function(collection,path,args){var index=-1,isFunc=typeof path=="function",isProp=isKey(path),result=isArrayLike(collection)?Array(collection.length):[];baseEach(collection,function(value){var func=isFunc?path:isProp&&value!=null?value[path]:undefined;result[++index]=func?func.apply(value,args):invokePath(value,path,args)});return result});function map(collection,iteratee,thisArg){var func=isArray(collection)?arrayMap:baseMap;iteratee=getCallback(iteratee,thisArg,3);return func(collection,iteratee)}var partition=createAggregator(function(result,value,key){result[key?0:1].push(value)},function(){return[[],[]]});function pluck(collection,path){return map(collection,property(path))}var reduce=createReduce(arrayReduce,baseEach);var reduceRight=createReduce(arrayReduceRight,baseEachRight);function reject(collection,predicate,thisArg){var func=isArray(collection)?arrayFilter:baseFilter;predicate=getCallback(predicate,thisArg,3);return func(collection,function(value,index,collection){return!predicate(value,index,collection)})}function sample(collection,n,guard){if(guard?isIterateeCall(collection,n,guard):n==null){collection=toIterable(collection);var length=collection.length;return length>0?collection[baseRandom(0,length-1)]:undefined}var index=-1,result=toArray(collection),length=result.length,lastIndex=length-1;n=nativeMin(n<0?0:+n||0,length);while(++index<n){var rand=baseRandom(index,lastIndex),value=result[rand];result[rand]=result[index];result[index]=value}result.length=n;return result}function shuffle(collection){return sample(collection,POSITIVE_INFINITY)}function size(collection){var length=collection?getLength(collection):0;return isLength(length)?length:keys(collection).length}function some(collection,predicate,thisArg){var func=isArray(collection)?arraySome:baseSome;if(thisArg&&isIterateeCall(collection,predicate,thisArg)){predicate=undefined}if(typeof predicate!="function"||thisArg!==undefined){predicate=getCallback(predicate,thisArg,3)}return func(collection,predicate)}function sortBy(collection,iteratee,thisArg){if(collection==null){return[]}if(thisArg&&isIterateeCall(collection,iteratee,thisArg)){iteratee=undefined}var index=-1;iteratee=getCallback(iteratee,thisArg,3);var result=baseMap(collection,function(value,key,collection){return{criteria:iteratee(value,key,collection),index:++index,value:value}});return baseSortBy(result,compareAscending)}var sortByAll=restParam(function(collection,iteratees){if(collection==null){return[]}var guard=iteratees[2];if(guard&&isIterateeCall(iteratees[0],iteratees[1],guard)){iteratees.length=1}return baseSortByOrder(collection,baseFlatten(iteratees),[])});function sortByOrder(collection,iteratees,orders,guard){if(collection==null){return[]}if(guard&&isIterateeCall(iteratees,orders,guard)){orders=undefined}if(!isArray(iteratees)){iteratees=iteratees==null?[]:[iteratees]}if(!isArray(orders)){orders=orders==null?[]:[orders]}return baseSortByOrder(collection,iteratees,orders)}function where(collection,source){return filter(collection,baseMatches(source))}var now=nativeNow||function(){return(new Date).getTime()};function after(n,func){if(typeof func!="function"){if(typeof n=="function"){var temp=n;n=func;func=temp}else{throw new TypeError(FUNC_ERROR_TEXT)}}n=nativeIsFinite(n=+n)?n:0;return function(){if(--n<1){return func.apply(this,arguments)}}}function ary(func,n,guard){if(guard&&isIterateeCall(func,n,guard)){n=undefined}n=func&&n==null?func.length:nativeMax(+n||0,0);return createWrapper(func,ARY_FLAG,undefined,undefined,undefined,undefined,n)}function before(n,func){var result;if(typeof func!="function"){if(typeof n=="function"){var temp=n;n=func;func=temp}else{throw new TypeError(FUNC_ERROR_TEXT)}}return function(){if(--n>0){result=func.apply(this,arguments)}if(n<=1){func=undefined}return result}}var bind=restParam(function(func,thisArg,partials){var bitmask=BIND_FLAG;if(partials.length){var holders=replaceHolders(partials,bind.placeholder);bitmask|=PARTIAL_FLAG}return createWrapper(func,bitmask,thisArg,partials,holders)});var bindAll=restParam(function(object,methodNames){methodNames=methodNames.length?baseFlatten(methodNames):functions(object);var index=-1,length=methodNames.length;while(++index<length){var key=methodNames[index];object[key]=createWrapper(object[key],BIND_FLAG,object)}return object});var bindKey=restParam(function(object,key,partials){var bitmask=BIND_FLAG|BIND_KEY_FLAG;if(partials.length){var holders=replaceHolders(partials,bindKey.placeholder);bitmask|=PARTIAL_FLAG}return createWrapper(key,bitmask,object,partials,holders)});var curry=createCurry(CURRY_FLAG);var curryRight=createCurry(CURRY_RIGHT_FLAG);function debounce(func,wait,options){var args,maxTimeoutId,result,stamp,thisArg,timeoutId,trailingCall,lastCalled=0,maxWait=false,trailing=true;if(typeof func!="function"){throw new TypeError(FUNC_ERROR_TEXT)}wait=wait<0?0:+wait||0;if(options===true){var leading=true;trailing=false}else if(isObject(options)){leading=!!options.leading;maxWait="maxWait"in options&&nativeMax(+options.maxWait||0,wait);trailing="trailing"in options?!!options.trailing:trailing}function cancel(){if(timeoutId){clearTimeout(timeoutId)}if(maxTimeoutId){clearTimeout(maxTimeoutId)}lastCalled=0;maxTimeoutId=timeoutId=trailingCall=undefined}function complete(isCalled,id){if(id){clearTimeout(id)}maxTimeoutId=timeoutId=trailingCall=undefined;if(isCalled){lastCalled=now();result=func.apply(thisArg,args);if(!timeoutId&&!maxTimeoutId){args=thisArg=undefined}}}function delayed(){var remaining=wait-(now()-stamp);if(remaining<=0||remaining>wait){complete(trailingCall,maxTimeoutId)}else{timeoutId=setTimeout(delayed,remaining)}}function maxDelayed(){complete(trailing,timeoutId)}function debounced(){args=arguments;stamp=now();thisArg=this;trailingCall=trailing&&(timeoutId||!leading);if(maxWait===false){var leadingCall=leading&&!timeoutId}else{if(!maxTimeoutId&&!leading){lastCalled=stamp}var remaining=maxWait-(stamp-lastCalled),isCalled=remaining<=0||remaining>maxWait;if(isCalled){if(maxTimeoutId){maxTimeoutId=clearTimeout(maxTimeoutId)}lastCalled=stamp;result=func.apply(thisArg,args)}else if(!maxTimeoutId){maxTimeoutId=setTimeout(maxDelayed,remaining)}}if(isCalled&&timeoutId){timeoutId=clearTimeout(timeoutId)}else if(!timeoutId&&wait!==maxWait){timeoutId=setTimeout(delayed,wait)}if(leadingCall){isCalled=true;result=func.apply(thisArg,args)}if(isCalled&&!timeoutId&&!maxTimeoutId){args=thisArg=undefined}return result}debounced.cancel=cancel;return debounced}var defer=restParam(function(func,args){return baseDelay(func,1,args)});var delay=restParam(function(func,wait,args){return baseDelay(func,wait,args)});var flow=createFlow();var flowRight=createFlow(true);function memoize(func,resolver){if(typeof func!="function"||resolver&&typeof resolver!="function"){throw new TypeError(FUNC_ERROR_TEXT)}var memoized=function(){var 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checkIteratee=/^(?:filter|map|reject)|While$/.test(methodName),retUnwrapped=/^(?:first|last)$/.test(methodName),lodashFunc=lodash[retUnwrapped?"take"+(methodName=="last"?"Right":""):methodName];if(!lodashFunc){return}lodash.prototype[methodName]=function(){var args=retUnwrapped?[1]:arguments,chainAll=this.__chain__,value=this.__wrapped__,isHybrid=!!this.__actions__.length,isLazy=value instanceof LazyWrapper,iteratee=args[0],useLazy=isLazy||isArray(value);if(useLazy&&checkIteratee&&typeof iteratee=="function"&&iteratee.length!=1){isLazy=useLazy=false}var interceptor=function(value){return retUnwrapped&&chainAll?lodashFunc(value,1)[0]:lodashFunc.apply(undefined,arrayPush([value],args))};var action={func:thru,args:[interceptor],thisArg:undefined},onlyLazy=isLazy&&!isHybrid;if(retUnwrapped&&!chainAll){if(onlyLazy){value=value.clone();value.__actions__.push(action);return func.call(value)}return lodashFunc.call(undefined,this.value())[0]}if(!retUnwrapped&&useLazy){value=onlyLazy?value:new LazyWrapper(this);var result=func.apply(value,args);result.__actions__.push(action);return new LodashWrapper(result,chainAll)}return this.thru(interceptor)}});arrayEach(["join","pop","push","replace","shift","sort","splice","split","unshift"],function(methodName){var func=(/^(?:replace|split)$/.test(methodName)?stringProto:arrayProto)[methodName],chainName=/^(?:push|sort|unshift)$/.test(methodName)?"tap":"thru",retUnwrapped=/^(?:join|pop|replace|shift)$/.test(methodName);lodash.prototype[methodName]=function(){var args=arguments;if(retUnwrapped&&!this.__chain__){return func.apply(this.value(),args)}return this[chainName](function(value){return func.apply(value,args)})}});baseForOwn(LazyWrapper.prototype,function(func,methodName){var lodashFunc=lodash[methodName];if(lodashFunc){var key=lodashFunc.name,names=realNames[key]||(realNames[key]=[]);names.push({name:methodName,func:lodashFunc})}});realNames[createHybridWrapper(undefined,BIND_KEY_FLAG).name]=[{name:"wrapper",func:undefined}];LazyWrapper.prototype.clone=lazyClone;LazyWrapper.prototype.reverse=lazyReverse;LazyWrapper.prototype.value=lazyValue;lodash.prototype.chain=wrapperChain;lodash.prototype.commit=wrapperCommit;lodash.prototype.concat=wrapperConcat;lodash.prototype.plant=wrapperPlant;lodash.prototype.reverse=wrapperReverse;lodash.prototype.toString=wrapperToString;lodash.prototype.run=lodash.prototype.toJSON=lodash.prototype.valueOf=lodash.prototype.value=wrapperValue;lodash.prototype.collect=lodash.prototype.map;lodash.prototype.head=lodash.prototype.first;lodash.prototype.select=lodash.prototype.filter;lodash.prototype.tail=lodash.prototype.rest;return lodash}var _=runInContext();if(typeof define=="function"&&typeof define.amd=="object"&&define.amd){root._=_;define(function(){return _})}else if(freeExports&&freeModule){if(moduleExports){(freeModule.exports=_)._=_}else{freeExports._=_}}else{root._=_}}).call(this)}).call(this,typeof global!=="undefined"?global:typeof self!=="undefined"?self:typeof window!=="undefined"?window:{})},{}],3:[function(require,module,exports){(function(window,document,undefined){var _MAP={8:"backspace",9:"tab",13:"enter",16:"shift",17:"ctrl",18:"alt",20:"capslock",27:"esc",32:"space",33:"pageup",34:"pagedown",35:"end",36:"home",37:"left",38:"up",39:"right",40:"down",45:"ins",46:"del",91:"meta",93:"meta",224:"meta"};var _KEYCODE_MAP={106:"*",107:"+",109:"-",110:".",111:"/",186:";",187:"=",188:",",189:"-",190:".",191:"/",192:"`",219:"[",220:"\\",221:"]",222:"'"};var _SHIFT_MAP={"~":"`","!":"1","@":"2","#":"3",$:"4","%":"5","^":"6","&":"7","*":"8","(":"9",")":"0",_:"-","+":"=",":":";",'"':"'","<":",",">":".","?":"/","|":"\\"};var _SPECIAL_ALIASES={option:"alt",command:"meta",return:"enter",escape:"esc",plus:"+",mod:/Mac|iPod|iPhone|iPad/.test(navigator.platform)?"meta":"ctrl"};var _REVERSE_MAP;for(var i=1;i<20;++i){_MAP[111+i]="f"+i}for(i=0;i<=9;++i){_MAP[i+96]=i}function _addEvent(object,type,callback){if(object.addEventListener){object.addEventListener(type,callback,false);return}object.attachEvent("on"+type,callback)}function _characterFromEvent(e){if(e.type=="keypress"){var character=String.fromCharCode(e.which);if(!e.shiftKey){character=character.toLowerCase()}return character}if(_MAP[e.which]){return _MAP[e.which]}if(_KEYCODE_MAP[e.which]){return _KEYCODE_MAP[e.which]}return String.fromCharCode(e.which).toLowerCase()}function _modifiersMatch(modifiers1,modifiers2){return modifiers1.sort().join(",")===modifiers2.sort().join(",")}function _eventModifiers(e){var modifiers=[];if(e.shiftKey){modifiers.push("shift")}if(e.altKey){modifiers.push("alt")}if(e.ctrlKey){modifiers.push("ctrl")}if(e.metaKey){modifiers.push("meta")}return modifiers}function _preventDefault(e){if(e.preventDefault){e.preventDefault();return}e.returnValue=false}function _stopPropagation(e){if(e.stopPropagation){e.stopPropagation();return}e.cancelBubble=true}function _isModifier(key){return key=="shift"||key=="ctrl"||key=="alt"||key=="meta"}function _getReverseMap(){if(!_REVERSE_MAP){_REVERSE_MAP={};for(var key in _MAP){if(key>95&&key<112){continue}if(_MAP.hasOwnProperty(key)){_REVERSE_MAP[_MAP[key]]=key}}}return _REVERSE_MAP}function _pickBestAction(key,modifiers,action){if(!action){action=_getReverseMap()[key]?"keydown":"keypress"}if(action=="keypress"&&modifiers.length){action="keydown"}return action}function _keysFromString(combination){if(combination==="+"){return["+"]}combination=combination.replace(/\+{2}/g,"+plus");return combination.split("+")}function _getKeyInfo(combination,action){var keys;var key;var i;var modifiers=[];keys=_keysFromString(combination);for(i=0;i<keys.length;++i){key=keys[i];if(_SPECIAL_ALIASES[key]){key=_SPECIAL_ALIASES[key]}if(action&&action!="keypress"&&_SHIFT_MAP[key]){key=_SHIFT_MAP[key];modifiers.push("shift")}if(_isModifier(key)){modifiers.push(key)}}action=_pickBestAction(key,modifiers,action);return{key:key,modifiers:modifiers,action:action}}function _belongsTo(element,ancestor){if(element===null||element===document){return false}if(element===ancestor){return true}return _belongsTo(element.parentNode,ancestor)}function Mousetrap(targetElement){var self=this;targetElement=targetElement||document;if(!(self instanceof Mousetrap)){return new Mousetrap(targetElement)}self.target=targetElement;self._callbacks={};self._directMap={};var _sequenceLevels={};var _resetTimer;var _ignoreNextKeyup=false;var _ignoreNextKeypress=false;var _nextExpectedAction=false;function _resetSequences(doNotReset){doNotReset=doNotReset||{};var activeSequences=false,key;for(key in _sequenceLevels){if(doNotReset[key]){activeSequences=true;continue}_sequenceLevels[key]=0}if(!activeSequences){_nextExpectedAction=false}}function _getMatches(character,modifiers,e,sequenceName,combination,level){var i;var callback;var matches=[];var action=e.type;if(!self._callbacks[character]){return[]}if(action=="keyup"&&_isModifier(character)){modifiers=[character]}for(i=0;i<self._callbacks[character].length;++i){callback=self._callbacks[character][i];if(!sequenceName&&callback.seq&&_sequenceLevels[callback.seq]!=callback.level){continue}if(action!=callback.action){continue}if(action=="keypress"&&!e.metaKey&&!e.ctrlKey||_modifiersMatch(modifiers,callback.modifiers)){var deleteCombo=!sequenceName&&callback.combo==combination;var deleteSequence=sequenceName&&callback.seq==sequenceName&&callback.level==level;if(deleteCombo||deleteSequence){self._callbacks[character].splice(i,1)}matches.push(callback)}}return matches}function _fireCallback(callback,e,combo,sequence){if(self.stopCallback(e,e.target||e.srcElement,combo,sequence)){return}if(callback(e,combo)===false){_preventDefault(e);_stopPropagation(e)}}self._handleKey=function(character,modifiers,e){var callbacks=_getMatches(character,modifiers,e);var i;var doNotReset={};var maxLevel=0;var processedSequenceCallback=false;for(i=0;i<callbacks.length;++i){if(callbacks[i].seq){maxLevel=Math.max(maxLevel,callbacks[i].level)}}for(i=0;i<callbacks.length;++i){if(callbacks[i].seq){if(callbacks[i].level!=maxLevel){continue}processedSequenceCallback=true;doNotReset[callbacks[i].seq]=1;_fireCallback(callbacks[i].callback,e,callbacks[i].combo,callbacks[i].seq);continue}if(!processedSequenceCallback){_fireCallback(callbacks[i].callback,e,callbacks[i].combo)}}var ignoreThisKeypress=e.type=="keypress"&&_ignoreNextKeypress;if(e.type==_nextExpectedAction&&!_isModifier(character)&&!ignoreThisKeypress){_resetSequences(doNotReset)}_ignoreNextKeypress=processedSequenceCallback&&e.type=="keydown"};function _handleKeyEvent(e){if(typeof e.which!=="number"){e.which=e.keyCode}var character=_characterFromEvent(e);if(!character){return}if(e.type=="keyup"&&_ignoreNextKeyup===character){_ignoreNextKeyup=false;return}self.handleKey(character,_eventModifiers(e),e)}function _resetSequenceTimer(){clearTimeout(_resetTimer);_resetTimer=setTimeout(_resetSequences,1e3)}function _bindSequence(combo,keys,callback,action){_sequenceLevels[combo]=0;function _increaseSequence(nextAction){return function(){_nextExpectedAction=nextAction;++_sequenceLevels[combo];_resetSequenceTimer()}}function _callbackAndReset(e){_fireCallback(callback,e,combo);if(action!=="keyup"){_ignoreNextKeyup=_characterFromEvent(e)}setTimeout(_resetSequences,10)}for(var i=0;i<keys.length;++i){var isFinal=i+1===keys.length;var wrappedCallback=isFinal?_callbackAndReset:_increaseSequence(action||_getKeyInfo(keys[i+1]).action);_bindSingle(keys[i],wrappedCallback,action,combo,i)}}function _bindSingle(combination,callback,action,sequenceName,level){self._directMap[combination+":"+action]=callback;combination=combination.replace(/\s+/g," ");var sequence=combination.split(" ");var info;if(sequence.length>1){_bindSequence(combination,sequence,callback,action);return}info=_getKeyInfo(combination,action);self._callbacks[info.key]=self._callbacks[info.key]||[];_getMatches(info.key,info.modifiers,{type:info.action},sequenceName,combination,level);self._callbacks[info.key][sequenceName?"unshift":"push"]({callback:callback,modifiers:info.modifiers,action:info.action,seq:sequenceName,level:level,combo:combination})}self._bindMultiple=function(combinations,callback,action){for(var i=0;i<combinations.length;++i){_bindSingle(combinations[i],callback,action)}};_addEvent(targetElement,"keypress",_handleKeyEvent);_addEvent(targetElement,"keydown",_handleKeyEvent);_addEvent(targetElement,"keyup",_handleKeyEvent)}Mousetrap.prototype.bind=function(keys,callback,action){var self=this;keys=keys instanceof Array?keys:[keys];self._bindMultiple.call(self,keys,callback,action);return self};Mousetrap.prototype.unbind=function(keys,action){var self=this;return self.bind.call(self,keys,function(){},action)};Mousetrap.prototype.trigger=function(keys,action){var self=this;if(self._directMap[keys+":"+action]){self._directMap[keys+":"+action]({},keys)}return self};Mousetrap.prototype.reset=function(){var self=this;self._callbacks={};self._directMap={};return self};Mousetrap.prototype.stopCallback=function(e,element){var self=this;if((" "+element.className+" ").indexOf(" mousetrap ")>-1){return false}if(_belongsTo(element,self.target)){return false}return element.tagName=="INPUT"||element.tagName=="SELECT"||element.tagName=="TEXTAREA"||element.isContentEditable};Mousetrap.prototype.handleKey=function(){var self=this;return self._handleKey.apply(self,arguments)};Mousetrap.init=function(){var documentMousetrap=Mousetrap(document);for(var method in documentMousetrap){if(method.charAt(0)!=="_"){Mousetrap[method]=function(method){return function(){return documentMousetrap[method].apply(documentMousetrap,arguments)}}(method)}}};Mousetrap.init();window.Mousetrap=Mousetrap;if(typeof module!=="undefined"&&module.exports){module.exports=Mousetrap}if(typeof define==="function"&&define.amd){define(function(){return Mousetrap})}})(window,document)},{}],4:[function(require,module,exports){(function(process){function normalizeArray(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last==="."){parts.splice(i,1)}else if(last===".."){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up--;up){parts.unshift("..")}}return parts}var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;var splitPath=function(filename){return splitPathRe.exec(filename).slice(1)};exports.resolve=function(){var resolvedPath="",resolvedAbsolute=false;for(var i=arguments.length-1;i>=-1&&!resolvedAbsolute;i--){var path=i>=0?arguments[i]:process.cwd();if(typeof path!=="string"){throw new TypeError("Arguments to path.resolve must be strings")}else if(!path){continue}resolvedPath=path+"/"+resolvedPath;resolvedAbsolute=path.charAt(0)==="/"}resolvedPath=normalizeArray(filter(resolvedPath.split("/"),function(p){return!!p}),!resolvedAbsolute).join("/");return(resolvedAbsolute?"/":"")+resolvedPath||"."};exports.normalize=function(path){var isAbsolute=exports.isAbsolute(path),trailingSlash=substr(path,-1)==="/";path=normalizeArray(filter(path.split("/"),function(p){return!!p}),!isAbsolute).join("/");if(!path&&!isAbsolute){path="."}if(path&&trailingSlash){path+="/"}return(isAbsolute?"/":"")+path};exports.isAbsolute=function(path){return path.charAt(0)==="/"};exports.join=function(){var paths=Array.prototype.slice.call(arguments,0);return exports.normalize(filter(paths,function(p,index){if(typeof p!=="string"){throw new TypeError("Arguments to path.join must be strings")}return p}).join("/"))};exports.relative=function(from,to){from=exports.resolve(from).substr(1);to=exports.resolve(to).substr(1);function trim(arr){var start=0;for(;start<arr.length;start++){if(arr[start]!=="")break}var end=arr.length-1;for(;end>=0;end--){if(arr[end]!=="")break}if(start>end)return[];return arr.slice(start,end-start+1)}var fromParts=trim(from.split("/"));var toParts=trim(to.split("/"));var length=Math.min(fromParts.length,toParts.length);var samePartsLength=length;for(var i=0;i<length;i++){if(fromParts[i]!==toParts[i]){samePartsLength=i;break}}var outputParts=[];for(var i=samePartsLength;i<fromParts.length;i++){outputParts.push("..")}outputParts=outputParts.concat(toParts.slice(samePartsLength));return outputParts.join("/")};exports.sep="/";exports.delimiter=":";exports.dirname=function(path){var result=splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return"."}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir};exports.basename=function(path,ext){var f=splitPath(path)[2];if(ext&&f.substr(-1*ext.length)===ext){f=f.substr(0,f.length-ext.length)}return f};exports.extname=function(path){return splitPath(path)[3]};function filter(xs,f){if(xs.filter)return xs.filter(f);var res=[];for(var i=0;i<xs.length;i++){if(f(xs[i],i,xs))res.push(xs[i])}return res}var substr="ab".substr(-1)==="b"?function(str,start,len){return str.substr(start,len)}:function(str,start,len){if(start<0)start=str.length+start;return str.substr(start,len)}}).call(this,require("_process"))},{_process:5}],5:[function(require,module,exports){var process=module.exports={};var queue=[];var draining=false;var currentQueue;var queueIndex=-1;function cleanUpNextTick(){draining=false;if(currentQueue.length){queue=currentQueue.concat(queue)}else{queueIndex=-1}if(queue.length){drainQueue()}}function drainQueue(){if(draining){return}var timeout=setTimeout(cleanUpNextTick);draining=true;var len=queue.length;while(len){currentQueue=queue;queue=[];while(++queueIndex<len){if(currentQueue){currentQueue[queueIndex].run()}}queueIndex=-1;len=queue.length}currentQueue=null;draining=false;clearTimeout(timeout)}process.nextTick=function(fun){var args=new Array(arguments.length-1);if(arguments.length>1){for(var i=1;i<arguments.length;i++){args[i-1]=arguments[i]}}queue.push(new Item(fun,args));if(queue.length===1&&!draining){setTimeout(drainQueue,0)}};function Item(fun,array){this.fun=fun;this.array=array}Item.prototype.run=function(){this.fun.apply(null,this.array)};process.title="browser";process.browser=true;process.env={};process.argv=[];process.version="";process.versions={};function noop(){}process.on=noop;process.addListener=noop;process.once=noop;process.off=noop;process.removeListener=noop;process.removeAllListeners=noop;process.emit=noop;process.binding=function(name){throw new Error("process.binding is not supported")};process.cwd=function(){return"/"};process.chdir=function(dir){throw new Error("process.chdir is not supported")};process.umask=function(){return 0}},{}],6:[function(require,module,exports){(function(global){(function(root){var freeExports=typeof exports=="object"&&exports&&!exports.nodeType&&exports;var freeModule=typeof module=="object"&&module&&!module.nodeType&&module;var freeGlobal=typeof global=="object"&&global;if(freeGlobal.global===freeGlobal||freeGlobal.window===freeGlobal||freeGlobal.self===freeGlobal){root=freeGlobal}var punycode,maxInt=2147483647,base=36,tMin=1,tMax=26,skew=38,damp=700,initialBias=72,initialN=128,delimiter="-",regexPunycode=/^xn--/,regexNonASCII=/[^\x20-\x7E]/,regexSeparators=/[\x2E\u3002\uFF0E\uFF61]/g,errors={overflow:"Overflow: input needs wider integers to process","not-basic":"Illegal input >= 0x80 (not a basic code point)","invalid-input":"Invalid input"},baseMinusTMin=base-tMin,floor=Math.floor,stringFromCharCode=String.fromCharCode,key;function error(type){throw RangeError(errors[type])}function map(array,fn){var length=array.length;var result=[];while(length--){result[length]=fn(array[length])}return result}function mapDomain(string,fn){var parts=string.split("@");var result="";if(parts.length>1){result=parts[0]+"@";string=parts[1]}string=string.replace(regexSeparators,".");var labels=string.split(".");var encoded=map(labels,fn).join(".");return result+encoded}function ucs2decode(string){var output=[],counter=0,length=string.length,value,extra;while(counter<length){value=string.charCodeAt(counter++);if(value>=55296&&value<=56319&&counter<length){extra=string.charCodeAt(counter++);if((extra&64512)==56320){output.push(((value&1023)<<10)+(extra&1023)+65536)}else{output.push(value);counter--}}else{output.push(value)}}return output}function ucs2encode(array){return map(array,function(value){var output="";if(value>65535){value-=65536;output+=stringFromCharCode(value>>>10&1023|55296);value=56320|value&1023}output+=stringFromCharCode(value);return output}).join("")}function basicToDigit(codePoint){if(codePoint-48<10){return codePoint-22}if(codePoint-65<26){return codePoint-65}if(codePoint-97<26){return codePoint-97}return base}function digitToBasic(digit,flag){return digit+22+75*(digit<26)-((flag!=0)<<5)}function adapt(delta,numPoints,firstTime){var k=0;delta=firstTime?floor(delta/damp):delta>>1;delta+=floor(delta/numPoints);for(;delta>baseMinusTMin*tMax>>1;k+=base){delta=floor(delta/baseMinusTMin)}return floor(k+(baseMinusTMin+1)*delta/(delta+skew))}function decode(input){var output=[],inputLength=input.length,out,i=0,n=initialN,bias=initialBias,basic,j,index,oldi,w,k,digit,t,baseMinusT;basic=input.lastIndexOf(delimiter);if(basic<0){basic=0}for(j=0;j<basic;++j){if(input.charCodeAt(j)>=128){error("not-basic")}output.push(input.charCodeAt(j))}for(index=basic>0?basic+1:0;index<inputLength;){for(oldi=i,w=1,k=base;;k+=base){if(index>=inputLength){error("invalid-input")}digit=basicToDigit(input.charCodeAt(index++));if(digit>=base||digit>floor((maxInt-i)/w)){error("overflow")}i+=digit*w;t=k<=bias?tMin:k>=bias+tMax?tMax:k-bias;if(digit<t){break}baseMinusT=base-t;if(w>floor(maxInt/baseMinusT)){error("overflow")}w*=baseMinusT}out=output.length+1;bias=adapt(i-oldi,out,oldi==0);if(floor(i/out)>maxInt-n){error("overflow")}n+=floor(i/out);i%=out;output.splice(i++,0,n)}return ucs2encode(output)}function encode(input){var n,delta,handledCPCount,basicLength,bias,j,m,q,k,t,currentValue,output=[],inputLength,handledCPCountPlusOne,baseMinusT,qMinusT;input=ucs2decode(input);inputLength=input.length;n=initialN;delta=0;bias=initialBias;for(j=0;j<inputLength;++j){currentValue=input[j];if(currentValue<128){output.push(stringFromCharCode(currentValue))}}handledCPCount=basicLength=output.length;if(basicLength){output.push(delimiter)}while(handledCPCount<inputLength){for(m=maxInt,j=0;j<inputLength;++j){currentValue=input[j];if(currentValue>=n&&currentValue<m){m=currentValue}}handledCPCountPlusOne=handledCPCount+1;if(m-n>floor((maxInt-delta)/handledCPCountPlusOne)){error("overflow")}delta+=(m-n)*handledCPCountPlusOne;n=m;for(j=0;j<inputLength;++j){currentValue=input[j];if(currentValue<n&&++delta>maxInt){error("overflow")}if(currentValue==n){for(q=delta,k=base;;k+=base){t=k<=bias?tMin:k>=bias+tMax?tMax:k-bias;if(q<t){break}qMinusT=q-t;baseMinusT=base-t;output.push(stringFromCharCode(digitToBasic(t+qMinusT%baseMinusT,0)));q=floor(qMinusT/baseMinusT)}output.push(stringFromCharCode(digitToBasic(q,0)));bias=adapt(delta,handledCPCountPlusOne,handledCPCount==basicLength);delta=0;++handledCPCount}}++delta;++n}return output.join("")}function toUnicode(input){return mapDomain(input,function(string){return regexPunycode.test(string)?decode(string.slice(4).toLowerCase()):string})}function toASCII(input){return mapDomain(input,function(string){return regexNonASCII.test(string)?"xn--"+encode(string):string})}punycode={version:"1.3.2",ucs2:{decode:ucs2decode,encode:ucs2encode},decode:decode,encode:encode,toASCII:toASCII,toUnicode:toUnicode};if(typeof define=="function"&&typeof define.amd=="object"&&define.amd){define("punycode",function(){return punycode})}else if(freeExports&&freeModule){if(module.exports==freeExports){freeModule.exports=punycode}else{for(key in punycode){punycode.hasOwnProperty(key)&&(freeExports[key]=punycode[key])}}}else{root.punycode=punycode}})(this)}).call(this,typeof global!=="undefined"?global:typeof self!=="undefined"?self:typeof window!=="undefined"?window:{})},{}],7:[function(require,module,exports){"use strict";function hasOwnProperty(obj,prop){return Object.prototype.hasOwnProperty.call(obj,prop)}module.exports=function(qs,sep,eq,options){sep=sep||"&";eq=eq||"=";var obj={};if(typeof qs!=="string"||qs.length===0){return obj}var regexp=/\+/g;qs=qs.split(sep);var maxKeys=1e3;if(options&&typeof options.maxKeys==="number"){maxKeys=options.maxKeys}var len=qs.length;if(maxKeys>0&&len>maxKeys){len=maxKeys}for(var i=0;i<len;++i){var x=qs[i].replace(regexp,"%20"),idx=x.indexOf(eq),kstr,vstr,k,v;if(idx>=0){kstr=x.substr(0,idx);vstr=x.substr(idx+1)}else{kstr=x;vstr=""}k=decodeURIComponent(kstr);v=decodeURIComponent(vstr);if(!hasOwnProperty(obj,k)){obj[k]=v}else if(isArray(obj[k])){obj[k].push(v)}else{obj[k]=[obj[k],v]}}return obj};var isArray=Array.isArray||function(xs){return Object.prototype.toString.call(xs)==="[object Array]"}},{}],8:[function(require,module,exports){"use strict";var stringifyPrimitive=function(v){switch(typeof v){case"string":return v;case"boolean":return v?"true":"false";case"number":return isFinite(v)?v:"";default:return""}};module.exports=function(obj,sep,eq,name){sep=sep||"&";eq=eq||"=";if(obj===null){obj=undefined}if(typeof obj==="object"){return map(objectKeys(obj),function(k){var ks=encodeURIComponent(stringifyPrimitive(k))+eq;if(isArray(obj[k])){return map(obj[k],function(v){return ks+encodeURIComponent(stringifyPrimitive(v))}).join(sep)}else{return ks+encodeURIComponent(stringifyPrimitive(obj[k]))}}).join(sep)}if(!name)return"";return encodeURIComponent(stringifyPrimitive(name))+eq+encodeURIComponent(stringifyPrimitive(obj))};var isArray=Array.isArray||function(xs){return Object.prototype.toString.call(xs)==="[object Array]"};function map(xs,f){if(xs.map)return xs.map(f);var res=[];for(var i=0;i<xs.length;i++){res.push(f(xs[i],i))}return res}var objectKeys=Object.keys||function(obj){var res=[];for(var key in obj){if(Object.prototype.hasOwnProperty.call(obj,key))res.push(key)}return res}},{}],9:[function(require,module,exports){"use strict";exports.decode=exports.parse=require("./decode");exports.encode=exports.stringify=require("./encode")},{"./decode":7,"./encode":8}],10:[function(require,module,exports){var punycode=require("punycode");exports.parse=urlParse;exports.resolve=urlResolve;exports.resolveObject=urlResolveObject;exports.format=urlFormat;exports.Url=Url;function Url(){this.protocol=null;this.slashes=null;this.auth=null;this.host=null;this.port=null;this.hostname=null;this.hash=null;this.search=null;this.query=null;this.pathname=null;this.path=null;this.href=null}var protocolPattern=/^([a-z0-9.+-]+:)/i,portPattern=/:[0-9]*$/,delims=["<",">",'"',"`"," ","\r","\n","\t"],unwise=["{","}","|","\\","^","`"].concat(delims),autoEscape=["'"].concat(unwise),nonHostChars=["%","/","?",";","#"].concat(autoEscape),hostEndingChars=["/","?","#"],hostnameMaxLen=255,hostnamePartPattern=/^[a-z0-9A-Z_-]{0,63}$/,hostnamePartStart=/^([a-z0-9A-Z_-]{0,63})(.*)$/,unsafeProtocol={javascript:true,"javascript:":true},hostlessProtocol={javascript:true,"javascript:":true},slashedProtocol={http:true,https:true,ftp:true,gopher:true,file:true,"http:":true,"https:":true,"ftp:":true,"gopher:":true,"file:":true},querystring=require("querystring");function urlParse(url,parseQueryString,slashesDenoteHost){if(url&&isObject(url)&&url instanceof Url)return url;var u=new Url;u.parse(url,parseQueryString,slashesDenoteHost);return u}Url.prototype.parse=function(url,parseQueryString,slashesDenoteHost){if(!isString(url)){throw new TypeError("Parameter 'url' must be a string, not "+typeof url)}var rest=url;rest=rest.trim();var proto=protocolPattern.exec(rest);if(proto){proto=proto[0];var lowerProto=proto.toLowerCase();this.protocol=lowerProto;rest=rest.substr(proto.length)}if(slashesDenoteHost||proto||rest.match(/^\/\/[^@\/]+@[^@\/]+/)){var slashes=rest.substr(0,2)==="//";if(slashes&&!(proto&&hostlessProtocol[proto])){rest=rest.substr(2);this.slashes=true}}if(!hostlessProtocol[proto]&&(slashes||proto&&!slashedProtocol[proto])){var hostEnd=-1;for(var i=0;i<hostEndingChars.length;i++){var hec=rest.indexOf(hostEndingChars[i]);if(hec!==-1&&(hostEnd===-1||hec<hostEnd))hostEnd=hec}var auth,atSign;if(hostEnd===-1){atSign=rest.lastIndexOf("@")}else{atSign=rest.lastIndexOf("@",hostEnd)}if(atSign!==-1){auth=rest.slice(0,atSign);rest=rest.slice(atSign+1);this.auth=decodeURIComponent(auth)}hostEnd=-1;for(var i=0;i<nonHostChars.length;i++){var hec=rest.indexOf(nonHostChars[i]);if(hec!==-1&&(hostEnd===-1||hec<hostEnd))hostEnd=hec}if(hostEnd===-1)hostEnd=rest.length;this.host=rest.slice(0,hostEnd);rest=rest.slice(hostEnd);this.parseHost();this.hostname=this.hostname||"";var ipv6Hostname=this.hostname[0]==="["&&this.hostname[this.hostname.length-1]==="]";if(!ipv6Hostname){var hostparts=this.hostname.split(/\./);for(var i=0,l=hostparts.length;i<l;i++){var part=hostparts[i];if(!part)continue;if(!part.match(hostnamePartPattern)){var newpart="";for(var j=0,k=part.length;j<k;j++){if(part.charCodeAt(j)>127){newpart+="x"}else{newpart+=part[j]}}if(!newpart.match(hostnamePartPattern)){var validParts=hostparts.slice(0,i);var notHost=hostparts.slice(i+1);var bit=part.match(hostnamePartStart);if(bit){validParts.push(bit[1]);notHost.unshift(bit[2])}if(notHost.length){rest="/"+notHost.join(".")+rest}this.hostname=validParts.join(".");break}}}}if(this.hostname.length>hostnameMaxLen){this.hostname=""}else{this.hostname=this.hostname.toLowerCase()}if(!ipv6Hostname){var domainArray=this.hostname.split(".");var newOut=[];for(var i=0;i<domainArray.length;++i){var s=domainArray[i];newOut.push(s.match(/[^A-Za-z0-9_-]/)?"xn--"+punycode.encode(s):s)}this.hostname=newOut.join(".")}var p=this.port?":"+this.port:"";var h=this.hostname||"";this.host=h+p;this.href+=this.host;if(ipv6Hostname){this.hostname=this.hostname.substr(1,this.hostname.length-2);if(rest[0]!=="/"){rest="/"+rest}}}if(!unsafeProtocol[lowerProto]){for(var i=0,l=autoEscape.length;i<l;i++){var ae=autoEscape[i];var esc=encodeURIComponent(ae);if(esc===ae){esc=escape(ae)}rest=rest.split(ae).join(esc)}}var hash=rest.indexOf("#");if(hash!==-1){this.hash=rest.substr(hash);rest=rest.slice(0,hash)}var qm=rest.indexOf("?");if(qm!==-1){this.search=rest.substr(qm);this.query=rest.substr(qm+1);if(parseQueryString){this.query=querystring.parse(this.query)}rest=rest.slice(0,qm)}else if(parseQueryString){this.search="";this.query={}}if(rest)this.pathname=rest;if(slashedProtocol[lowerProto]&&this.hostname&&!this.pathname){this.pathname="/"}if(this.pathname||this.search){var p=this.pathname||"";var s=this.search||"";this.path=p+s}this.href=this.format();return this};function urlFormat(obj){if(isString(obj))obj=urlParse(obj);if(!(obj instanceof Url))return Url.prototype.format.call(obj);return obj.format()}Url.prototype.format=function(){var auth=this.auth||"";if(auth){auth=encodeURIComponent(auth);auth=auth.replace(/%3A/i,":");auth+="@"}var protocol=this.protocol||"",pathname=this.pathname||"",hash=this.hash||"",host=false,query="";if(this.host){host=auth+this.host}else if(this.hostname){host=auth+(this.hostname.indexOf(":")===-1?this.hostname:"["+this.hostname+"]");if(this.port){host+=":"+this.port}}if(this.query&&isObject(this.query)&&Object.keys(this.query).length){query=querystring.stringify(this.query)}var search=this.search||query&&"?"+query||"";if(protocol&&protocol.substr(-1)!==":")protocol+=":";if(this.slashes||(!protocol||slashedProtocol[protocol])&&host!==false){host="//"+(host||"");if(pathname&&pathname.charAt(0)!=="/")pathname="/"+pathname}else if(!host){host=""}if(hash&&hash.charAt(0)!=="#")hash="#"+hash;if(search&&search.charAt(0)!=="?")search="?"+search;pathname=pathname.replace(/[?#]/g,function(match){return encodeURIComponent(match)});search=search.replace("#","%23");return protocol+host+pathname+search+hash};function urlResolve(source,relative){return urlParse(source,false,true).resolve(relative)}Url.prototype.resolve=function(relative){return this.resolveObject(urlParse(relative,false,true)).format()};function urlResolveObject(source,relative){if(!source)return relative;return urlParse(source,false,true).resolveObject(relative)}Url.prototype.resolveObject=function(relative){if(isString(relative)){var rel=new Url;rel.parse(relative,false,true);relative=rel}var result=new Url;Object.keys(this).forEach(function(k){result[k]=this[k]},this);result.hash=relative.hash;if(relative.href===""){result.href=result.format();return result}if(relative.slashes&&!relative.protocol){Object.keys(relative).forEach(function(k){if(k!=="protocol")result[k]=relative[k]});if(slashedProtocol[result.protocol]&&result.hostname&&!result.pathname){result.path=result.pathname="/"}result.href=result.format();return result}if(relative.protocol&&relative.protocol!==result.protocol){if(!slashedProtocol[relative.protocol]){Object.keys(relative).forEach(function(k){result[k]=relative[k]});result.href=result.format();return result}result.protocol=relative.protocol;if(!relative.host&&!hostlessProtocol[relative.protocol]){var relPath=(relative.pathname||"").split("/");while(relPath.length&&!(relative.host=relPath.shift()));if(!relative.host)relative.host="";if(!relative.hostname)relative.hostname="";if(relPath[0]!=="")relPath.unshift("");if(relPath.length<2)relPath.unshift("");result.pathname=relPath.join("/")}else{result.pathname=relative.pathname}result.search=relative.search;result.query=relative.query;result.host=relative.host||"";result.auth=relative.auth;result.hostname=relative.hostname||relative.host;result.port=relative.port;if(result.pathname||result.search){var p=result.pathname||"";var s=result.search||"";result.path=p+s}result.slashes=result.slashes||relative.slashes;result.href=result.format();return result}var isSourceAbs=result.pathname&&result.pathname.charAt(0)==="/",isRelAbs=relative.host||relative.pathname&&relative.pathname.charAt(0)==="/",mustEndAbs=isRelAbs||isSourceAbs||result.host&&relative.pathname,removeAllDots=mustEndAbs,srcPath=result.pathname&&result.pathname.split("/")||[],relPath=relative.pathname&&relative.pathname.split("/")||[],psychotic=result.protocol&&!slashedProtocol[result.protocol];if(psychotic){result.hostname="";result.port=null;if(result.host){if(srcPath[0]==="")srcPath[0]=result.host;else srcPath.unshift(result.host)}result.host="";if(relative.protocol){relative.hostname=null;relative.port=null;if(relative.host){if(relPath[0]==="")relPath[0]=relative.host;else relPath.unshift(relative.host)}relative.host=null}mustEndAbs=mustEndAbs&&(relPath[0]===""||srcPath[0]==="")}if(isRelAbs){result.host=relative.host||relative.host===""?relative.host:result.host;result.hostname=relative.hostname||relative.hostname===""?relative.hostname:result.hostname;result.search=relative.search;result.query=relative.query;srcPath=relPath}else if(relPath.length){if(!srcPath)srcPath=[];srcPath.pop();srcPath=srcPath.concat(relPath);result.search=relative.search;result.query=relative.query}else if(!isNullOrUndefined(relative.search)){if(psychotic){result.hostname=result.host=srcPath.shift();var authInHost=result.host&&result.host.indexOf("@")>0?result.host.split("@"):false;if(authInHost){result.auth=authInHost.shift();result.host=result.hostname=authInHost.shift()}}result.search=relative.search;result.query=relative.query;if(!isNull(result.pathname)||!isNull(result.search)){result.path=(result.pathname?result.pathname:"")+(result.search?result.search:"")}result.href=result.format();return result}if(!srcPath.length){result.pathname=null;if(result.search){result.path="/"+result.search}else{result.path=null}result.href=result.format();return result}var last=srcPath.slice(-1)[0];var hasTrailingSlash=(result.host||relative.host)&&(last==="."||last==="..")||last==="";var up=0;for(var i=srcPath.length;i>=0;i--){last=srcPath[i];if(last=="."){srcPath.splice(i,1)}else if(last===".."){srcPath.splice(i,1);up++}else if(up){srcPath.splice(i,1);up--}}if(!mustEndAbs&&!removeAllDots){for(;up--;up){srcPath.unshift("..")}}if(mustEndAbs&&srcPath[0]!==""&&(!srcPath[0]||srcPath[0].charAt(0)!=="/")){srcPath.unshift("")}if(hasTrailingSlash&&srcPath.join("/").substr(-1)!=="/"){srcPath.push("")}var isAbsolute=srcPath[0]===""||srcPath[0]&&srcPath[0].charAt(0)==="/";if(psychotic){result.hostname=result.host=isAbsolute?"":srcPath.length?srcPath.shift():"";var authInHost=result.host&&result.host.indexOf("@")>0?result.host.split("@"):false;if(authInHost){result.auth=authInHost.shift();result.host=result.hostname=authInHost.shift()}}mustEndAbs=mustEndAbs||result.host&&srcPath.length;if(mustEndAbs&&!isAbsolute){srcPath.unshift("")}if(!srcPath.length){result.pathname=null;result.path=null}else{result.pathname=srcPath.join("/")}if(!isNull(result.pathname)||!isNull(result.search)){result.path=(result.pathname?result.pathname:"")+(result.search?result.search:"")}result.auth=relative.auth||result.auth;result.slashes=result.slashes||relative.slashes;result.href=result.format();return result};Url.prototype.parseHost=function(){var host=this.host;var port=portPattern.exec(host);if(port){port=port[0];if(port!==":"){this.port=port.substr(1)}host=host.substr(0,host.length-port.length)}if(host)this.hostname=host};function isString(arg){return typeof arg==="string"}function isObject(arg){return typeof arg==="object"&&arg!==null}function isNull(arg){return arg===null}function isNullOrUndefined(arg){return arg==null}},{punycode:6,querystring:9}],11:[function(require,module,exports){var $=require("jquery");function toggleDropdown(e){var $dropdown=$(e.currentTarget).parent().find(".dropdown-menu");$dropdown.toggleClass("open");e.stopPropagation();e.preventDefault()}function closeDropdown(e){$(".dropdown-menu").removeClass("open")}function init(){$(document).on("click",".toggle-dropdown",toggleDropdown);$(document).on("click",".dropdown-menu",function(e){e.stopPropagation()});$(document).on("click",closeDropdown)}module.exports={init:init}},{jquery:1}],12:[function(require,module,exports){var $=require("jquery");module.exports=$({})},{jquery:1}],13:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var storage=require("./storage");var dropdown=require("./dropdown");var events=require("./events");var state=require("./state");var keyboard=require("./keyboard");var navigation=require("./navigation");var sidebar=require("./sidebar");var toolbar=require("./toolbar");function start(config){sidebar.init();keyboard.init();dropdown.init();navigation.init();toolbar.createButton({index:0,icon:"fa fa-align-justify",label:"Toggle Sidebar",onClick:function(e){e.preventDefault();sidebar.toggle()}});events.trigger("start",config);navigation.notify()}var gitbook={start:start,events:events,state:state,toolbar:toolbar,sidebar:sidebar,storage:storage,keyboard:keyboard};var MODULES={gitbook:gitbook,jquery:$,lodash:_};window.gitbook=gitbook;window.$=$;window.jQuery=$;gitbook.require=function(mods,fn){mods=_.map(mods,function(mod){mod=mod.toLowerCase();if(!MODULES[mod]){throw new Error("GitBook module "+mod+" doesn't exist")}return MODULES[mod]});fn.apply(null,mods)};module.exports={}},{"./dropdown":11,"./events":12,"./keyboard":14,"./navigation":16,"./sidebar":18,"./state":19,"./storage":20,"./toolbar":21,jquery:1,lodash:2}],14:[function(require,module,exports){var Mousetrap=require("mousetrap");var navigation=require("./navigation");var sidebar=require("./sidebar");function bindShortcut(keys,fn){Mousetrap.bind(keys,function(e){fn();return false})}function init(){bindShortcut(["right"],function(e){navigation.goNext()});bindShortcut(["left"],function(e){navigation.goPrev()});bindShortcut(["s"],function(e){sidebar.toggle()})}module.exports={init:init,bind:bindShortcut}},{"./navigation":16,"./sidebar":18,mousetrap:3}],15:[function(require,module,exports){var state=require("./state");function showLoading(p){state.$book.addClass("is-loading");p.always(function(){state.$book.removeClass("is-loading")});return p}module.exports={show:showLoading}},{"./state":19}],16:[function(require,module,exports){var $=require("jquery");var url=require("url");var events=require("./events");var state=require("./state");var loading=require("./loading");var usePushState=typeof history.pushState!=="undefined";function handleNavigation(relativeUrl,push){var uri=url.resolve(window.location.pathname,relativeUrl);notifyPageChange();location.href=relativeUrl;return}function updateNavigationPosition(){var bodyInnerWidth,pageWrapperWidth;bodyInnerWidth=parseInt($(".body-inner").css("width"),10);pageWrapperWidth=parseInt($(".page-wrapper").css("width"),10);$(".navigation-next").css("margin-right",bodyInnerWidth-pageWrapperWidth+"px")}function notifyPageChange(){events.trigger("page.change")}function preparePage(notify){var $bookBody=$(".book-body");var $bookInner=$bookBody.find(".body-inner");var $pageWrapper=$bookInner.find(".page-wrapper");updateNavigationPosition();$bookInner.scrollTop(0);$bookBody.scrollTop(0);if(notify!==false)notifyPageChange()}function isLeftClickEvent(e){return e.button===0}function isModifiedEvent(e){return!!(e.metaKey||e.altKey||e.ctrlKey||e.shiftKey)}function handlePagination(e){if(isModifiedEvent(e)||!isLeftClickEvent(e)){return}e.stopPropagation();e.preventDefault();var url=$(this).attr("href");if(url)handleNavigation(url,true)}function goNext(){var url=$(".navigation-next").attr("href");if(url)handleNavigation(url,true)}function goPrev(){var url=$(".navigation-prev").attr("href");if(url)handleNavigation(url,true)}function init(){$.ajaxSetup({});if(location.protocol!=="file:"){history.replaceState({path:window.location.href},"")}window.onpopstate=function(event){if(event.state===null){return}return handleNavigation(event.state.path,false)};$(document).on("click",".navigation-prev",handlePagination);$(document).on("click",".navigation-next",handlePagination);$(document).on("click",".summary [data-path] a",handlePagination);$(window).resize(updateNavigationPosition);preparePage(false)}module.exports={init:init,goNext:goNext,goPrev:goPrev,notify:notifyPageChange}},{"./events":12,"./loading":15,"./state":19,jquery:1,url:10}],17:[function(require,module,exports){module.exports={isMobile:function(){return document.body.clientWidth<=600}}},{}],18:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var storage=require("./storage");var platform=require("./platform");var state=require("./state");function toggleSidebar(_state,animation){if(state!=null&&isOpen()==_state)return;if(animation==null)animation=true;state.$book.toggleClass("without-animation",!animation);state.$book.toggleClass("with-summary",_state);storage.set("sidebar",isOpen())}function isOpen(){return state.$book.hasClass("with-summary")}function init(){if(platform.isMobile()){toggleSidebar(false,false)}else{toggleSidebar(storage.get("sidebar",true),false)}$(document).on("click",".book-summary li.chapter a",function(e){if(platform.isMobile())toggleSidebar(false,false)})}function filterSummary(paths){var $summary=$(".book-summary");$summary.find("li").each(function(){var path=$(this).data("path");var st=paths==null||_.contains(paths,path);$(this).toggle(st);if(st)$(this).parents("li").show()})}module.exports={init:init,isOpen:isOpen,toggle:toggleSidebar,filter:filterSummary}},{"./platform":17,"./state":19,"./storage":20,jquery:1,lodash:2}],19:[function(require,module,exports){var $=require("jquery");var url=require("url");var path=require("path");var state={};state.update=function(dom){var $book=$(dom.find(".book"));state.$book=$book;state.level=$book.data("level");state.basePath=$book.data("basepath");state.innerLanguage=$book.data("innerlanguage");state.revision=$book.data("revision");state.filepath=$book.data("filepath");state.chapterTitle=$book.data("chapter-title");state.root=url.resolve(location.protocol+"//"+location.host,path.dirname(path.resolve(location.pathname.replace(/\/$/,"/index.html"),state.basePath))).replace(/\/?$/,"/");state.bookRoot=state.innerLanguage?url.resolve(state.root,".."):state.root};state.update($);module.exports=state},{jquery:1,path:4,url:10}],20:[function(require,module,exports){var baseKey="";module.exports={setBaseKey:function(key){baseKey=key},set:function(key,value){key=baseKey+":"+key;try{sessionStorage[key]=JSON.stringify(value)}catch(e){}},get:function(key,def){key=baseKey+":"+key;if(sessionStorage[key]===undefined)return def;try{var v=JSON.parse(sessionStorage[key]);return v==null?def:v}catch(err){return sessionStorage[key]||def}},remove:function(key){key=baseKey+":"+key;sessionStorage.removeItem(key)}}},{}],21:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var events=require("./events");var buttons=[];function insertAt(parent,selector,index,element){var lastIndex=parent.children(selector).size();if(index<0){index=Math.max(0,lastIndex+1+index)}parent.append(element);if(index<lastIndex){parent.children(selector).eq(index).before(parent.children(selector).last())}}function defaultOnClick(e){e.preventDefault()}function createDropdownMenu(dropdown){var $menu=$("<div>",{class:"dropdown-menu",html:'<div class="dropdown-caret"><span class="caret-outer"></span><span class="caret-inner"></span></div>'});if(_.isString(dropdown)){$menu.append(dropdown)}else{var groups=_.map(dropdown,function(group){if(_.isArray(group))return group;else return[group]});_.each(groups,function(group){var $group=$("<div>",{class:"buttons"});var sizeClass="size-"+group.length;_.each(group,function(btn){btn=_.defaults(btn||{},{text:"",className:"",onClick:defaultOnClick});var $btn=$("<button>",{class:"button "+sizeClass+" "+btn.className,text:btn.text});$btn.click(btn.onClick);$group.append($btn)});$menu.append($group)})}return $menu}function createButton(opts){opts=_.defaults(opts||{},{label:"",icon:"",text:"",position:"left",className:"",onClick:defaultOnClick,dropdown:null,index:null});buttons.push(opts);updateButton(opts)}function updateButton(opts){var $result;var $toolbar=$(".book-header");var $title=$toolbar.find("h1");var positionClass="pull-"+opts.position;var $btn=$("<a>",{class:"btn",text:opts.text?" "+opts.text:"","aria-label":opts.label,title:opts.label,href:"#"});$btn.click(opts.onClick);if(opts.icon){$("<i>",{class:opts.icon}).prependTo($btn)}if(opts.dropdown){var $container=$("<div>",{class:"dropdown "+positionClass+" "+opts.className});$btn.addClass("toggle-dropdown");$container.append($btn);var $menu=createDropdownMenu(opts.dropdown);$menu.addClass("dropdown-"+(opts.position=="right"?"left":"right"));$container.append($menu);$result=$container}else{$btn.addClass(positionClass);$btn.addClass(opts.className);$result=$btn}$result.addClass("js-toolbar-action");if(_.isNumber(opts.index)&&opts.index>=0){insertAt($toolbar,".btn, .dropdown, h1",opts.index,$result)}else{$result.insertBefore($title)}}module.exports={createButton:createButton}},{"./events":12,jquery:1,lodash:2}]},{},[13]);
\ No newline at end of file
diff --git a/docs/libs/gitbook-2.6.7/js/jquery.highlight.js b/docs/libs/gitbook-2.6.7/js/jquery.highlight.js
index a0b69fc..8ff7a0b 100644
--- a/docs/libs/gitbook-2.6.7/js/jquery.highlight.js
+++ b/docs/libs/gitbook-2.6.7/js/jquery.highlight.js
@@ -60,6 +60,8 @@ jQuery.fn.highlight = function (words, options) {
 
     if (words.constructor === String) {
         words = [words];
+        // also match 'foo-bar' if search for 'foo bar'
+        if (/\s/.test(words[0])) words.push(words[0].replace(/\s+/, '-'));
     }
     words = jQuery.grep(words, function(word, i){
       return word !== '';
diff --git a/docs/libs/gitbook-2.6.7/js/plugin-bookdown.js b/docs/libs/gitbook-2.6.7/js/plugin-bookdown.js
index bd90080..04d56dd 100644
--- a/docs/libs/gitbook-2.6.7/js/plugin-bookdown.js
+++ b/docs/libs/gitbook-2.6.7/js/plugin-bookdown.js
@@ -16,6 +16,19 @@ gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) {
       }
     });
 
+    // add the History button (file history on Github)
+    var history = config.history;
+    if (history && history.link) gitbook.toolbar.createButton({
+      icon: 'fa fa-history',
+      label: history.text || 'History',
+      position: 'left',
+      onClick: function(e) {
+        e.preventDefault();
+        window.open(history.link);
+      }
+    });
+
+    // add the Download button
     var down = config.download;
     var normalizeDownload = function() {
       if (!down || !(down instanceof Array) || down.length === 0) return;
@@ -52,6 +65,23 @@ gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) {
       });
     }
 
+    // add the Information button
+    var info = ['Keyboard shortcuts (<> indicates arrow keys):',
+      '<left>/<right>: navigate to previous/next page',
+      's: Toggle sidebar'];
+    if (config.search !== false) info.push('f: Toggle search input ' +
+      '(use <up>/<down>/Enter in the search input to navigate through search matches; ' +
+      'press Esc to cancel search)');
+    gitbook.toolbar.createButton({
+      icon: 'fa fa-info',
+      label: 'Information about the toolbar',
+      position: 'left',
+      onClick: function(e) {
+        e.preventDefault();
+        window.alert(info.join('\n\n'));
+      }
+    });
+
     // highlight the current section in TOC
     var href = window.location.pathname;
     href = href.substr(href.lastIndexOf('/') + 1);
@@ -178,9 +208,6 @@ gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) {
   var chapterTitle = function() {
     return bookInner.find('.page-inner').find('h1,h2').first().text();
   };
-  var bookTitle = function() {
-    return bookInner.find('.book-header > h1').first().text();
-  };
   var saveScrollPos = function(e) {
     // save scroll position before page is reloaded
     gs.set('bodyScrollTop', {
@@ -198,21 +225,19 @@ gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) {
     try { inIframe = window.self !== window.top; } catch (e) {}
     return inIframe;
   };
-  $(window).on('blur unload', function(e) {
-    if (inIFrame()) saveScrollPos(e);
-    gs.set('bookTitle', bookTitle());
-  });
+  if (inIFrame()) {
+    $(window).on('blur unload', saveScrollPos);
+  }
 
   $(function(e) {
-    if (gs.get('bookTitle', '') !== bookTitle()) localStorage.clear();
     var pos = gs.get('bodyScrollTop');
     if (pos) {
       if (pos.title === chapterTitle()) {
         if (pos.body !== 0) bookBody.scrollTop(pos.body);
         if (pos.inner !== 0) bookInner.scrollTop(pos.inner);
       }
-      if (pos.focused) bookInner.find('.page-wrapper').focus();
     }
+    if ((pos && pos.focused) || !inIFrame()) bookInner.find('.page-wrapper').focus();
     // clear book body scroll position
     gs.remove('bodyScrollTop');
   });
diff --git a/docs/libs/gitbook-2.6.7/js/plugin-search.js b/docs/libs/gitbook-2.6.7/js/plugin-search.js
index f8bd5af..31b5786 100644
--- a/docs/libs/gitbook-2.6.7/js/plugin-search.js
+++ b/docs/libs/gitbook-2.6.7/js/plugin-search.js
@@ -1,8 +1,8 @@
 gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) {
     var index = null;
-    var $searchInput, $searchForm;
-    var $highlighted, hi = 0, hiOpts = { className: 'search-highlight' };
-    var collapse = false;
+    var $searchInput, $searchLabel, $searchForm;
+    var $highlighted = [], hi, hiOpts = { className: 'search-highlight' };
+    var collapse = false, toc_visible = [];
 
     // Use a specific index
     function loadIndex(data) {
@@ -49,25 +49,35 @@ gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) {
         .value();
 
         // [Yihui] Highlight the search keyword on current page
-        hi = 0;
-        $highlighted = results.length === 0 ? undefined : $('.page-inner')
+        $highlighted = results.length === 0 ? [] : $('.page-inner')
           .unhighlight(hiOpts).highlight(q, hiOpts).find('span.search-highlight');
-        scrollToHighlighted();
-        toggleTOC(results.length > 0);
+        scrollToHighlighted(0);
 
         return results;
     }
 
     // [Yihui] Scroll the chapter body to the i-th highlighted string
-    function scrollToHighlighted() {
-      if (!$highlighted) return;
+    function scrollToHighlighted(d) {
       var n = $highlighted.length;
-      if (n === 0) return;
-      var $p = $highlighted.eq(hi), p = $p[0], rect = p.getBoundingClientRect();
-      if (rect.top < 0 || rect.bottom > $(window).height()) {
-        ($(window).width() >= 1240 ? $('.body-inner') : $('.book-body'))
-          .scrollTop(p.offsetTop - 100);
+      hi = hi === undefined ? 0 : hi + d;
+      // navignate to the previous/next page in the search results if reached the top/bottom
+      var b = hi < 0;
+      if (d !== 0 && (b || hi >= n)) {
+        var path = currentPath(), n2 = toc_visible.length;
+        if (n2 === 0) return;
+        for (var i = b ? 0 : n2; (b && i < n2) || (!b && i >= 0); i += b ? 1 : -1) {
+          if (toc_visible.eq(i).data('path') === path) break;
+        }
+        i += b ? -1 : 1;
+        if (i < 0) i = n2 - 1;
+        if (i >= n2) i = 0;
+        var lnk = toc_visible.eq(i).find('a[href$=".html"]');
+        if (lnk.length) lnk[0].click();
+        return;
       }
+      if (n === 0) return;
+      var $p = $highlighted.eq(hi);
+      $p[0].scrollIntoView();
       $highlighted.css('background-color', '');
       // an orange background color on the current item and removed later
       $p.css('background-color', 'orange');
@@ -76,22 +86,16 @@ gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) {
       }, 2000);
     }
 
-    // [Yihui] Expand/collapse TOC
-    function toggleTOC(show) {
-      if (!collapse) return;
-      var toc_sub = $('ul.summary').children('li[data-level]').children('ul');
-      if (show) return toc_sub.show();
+    function currentPath() {
       var href = window.location.pathname;
       href = href.substr(href.lastIndexOf('/') + 1);
-      if (href === '') href = 'index.html';
-      var li = $('a[href^="' + href + location.hash + '"]').parent('li.chapter').first();
-      toc_sub.hide().parent().has(li).children('ul').show();
-      li.children('ul').show();
+      return href === '' ? 'index.html' : href;
     }
 
     // Create search form
     function createForm(value) {
         if ($searchForm) $searchForm.remove();
+        if ($searchLabel) $searchLabel.remove();
         if ($searchInput) $searchInput.remove();
 
         $searchForm = $('<div>', {
@@ -99,13 +103,23 @@ gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) {
             'role': 'search'
         });
 
+        $searchLabel = $('<label>', {
+            'for': 'search-box',
+            'aria-hidden': 'false',
+            'hidden': ''
+        });
+
         $searchInput = $('<input>', {
+            'id': 'search-box',
             'type': 'search',
             'class': 'form-control',
             'val': value,
-            'placeholder': 'Type to search'
+            'placeholder': 'Type to search (Enter for navigation)',
+            'title': 'Use Enter or the <Down> key to navigate to the next match, or the <Up> key to the previous match'
         });
 
+        $searchLabel.append("Type to search");
+        $searchLabel.appendTo($searchForm);
         $searchInput.appendTo($searchForm);
         $searchForm.prependTo(gitbook.state.$book.find('.book-summary'));
     }
@@ -132,10 +146,14 @@ gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) {
             gitbook.storage.remove("keyword");
             gitbook.sidebar.filter(null);
             $('.page-inner').unhighlight(hiOpts);
-            toggleTOC(false);
         }
     }
 
+    function sidebarFilter(results) {
+        gitbook.sidebar.filter(_.pluck(results, "path"));
+        toc_visible = $('ul.summary').find('li:visible');
+    }
+
     // Recover current search when page changed
     function recoverSearch() {
         var keyword = gitbook.storage.get("keyword", "");
@@ -146,7 +164,7 @@ gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) {
             if(!isSearchOpen()) {
                 toggleSearch(true); // [Yihui] open the search box
             }
-            gitbook.sidebar.filter(_.pluck(search(keyword), "path"));
+            sidebarFilter(search(keyword));
         }
     }
 
@@ -166,18 +184,14 @@ gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) {
         // Type in search bar
         $(document).on("keyup", ".book-search input", function(e) {
             var key = (e.keyCode ? e.keyCode : e.which);
-            // [Yihui] Escape -> close search box; Up/Down: previous/next highlighted
+            // [Yihui] Escape -> close search box; Up/Down/Enter: previous/next highlighted
             if (key == 27) {
                 e.preventDefault();
                 toggleSearch(false);
             } else if (key == 38) {
-              if (hi <= 0 && $highlighted) hi = $highlighted.length;
-              hi--;
-              scrollToHighlighted();
-            } else if (key == 40) {
-              hi++;
-              if ($highlighted && hi >= $highlighted.length) hi = 0;
-              scrollToHighlighted();
+              scrollToHighlighted(-1);
+            } else if (key == 40 || key == 13) {
+              scrollToHighlighted(1);
             }
         }).on("input", ".book-search input", function(e) {
             var q = $(this).val().trim();
@@ -185,12 +199,9 @@ gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) {
                 gitbook.sidebar.filter(null);
                 gitbook.storage.remove("keyword");
                 $('.page-inner').unhighlight(hiOpts);
-                toggleTOC(false);
             } else {
                 var results = search(q);
-                gitbook.sidebar.filter(
-                    _.pluck(results, "path")
-                );
+                sidebarFilter(results);
                 gitbook.storage.set("keyword", q);
             }
         });
diff --git a/docs/libs/gitbook-2.6.7/js/plugin-sharing.js b/docs/libs/gitbook-2.6.7/js/plugin-sharing.js
index afa2148..bc27114 100644
--- a/docs/libs/gitbook-2.6.7/js/plugin-sharing.js
+++ b/docs/libs/gitbook-2.6.7/js/plugin-sharing.js
@@ -103,7 +103,8 @@ gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) {
 
             gitbook.toolbar.createButton({
                 icon: site.icon,
-                label: site.text,
+                label: site.label,
+                title: site.label,
                 position: 'right',
                 onClick: site.onClick
             });
diff --git a/docs/practical.html b/docs/practical.html
index 4c3adc3..c422361 100644
--- a/docs/practical.html
+++ b/docs/practical.html
@@ -1,40 +1,40 @@
 <!DOCTYPE html>
-<html >
-
+<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
 <head>
 
-  <meta charset="UTF-8">
-  <meta http-equiv="X-UA-Compatible" content="IE=edge">
-  <title>Rapid Assessment Method for Older People (RAM-OP): The Manual</title>
-  <meta name="description" content="Rapid assessment method for older people (RAM-OP) manual">
-  <meta name="generator" content="bookdown 0.7 and GitBook 2.6.7">
+  <meta charset="utf-8" />
+  <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+  <title>5 Practical Fieldwork | Rapid Assessment Method for Older People (RAM-OP): The Manual</title>
+  <meta name="description" content="Rapid assessment method for older people (RAM-OP) manual" />
+  <meta name="generator" content="bookdown 0.11 and GitBook 2.6.7" />
 
-  <meta property="og:title" content="Rapid Assessment Method for Older People (RAM-OP): The Manual" />
+  <meta property="og:title" content="5 Practical Fieldwork | Rapid Assessment Method for Older People (RAM-OP): The Manual" />
   <meta property="og:type" content="book" />
-  <meta property="og:url" content="https://validmeasures.org/ramOPmanual" />
-  <meta property="og:image" content="https://validmeasures.org/ramOPmanualfigures/coverImage.jpg" />
+  <meta property="og:url" content="https://rapidsurveys.io/ramOPmanual" />
+  <meta property="og:image" content="https://rapidsurveys.io/ramOPmanualfigures/coverImage.jpg" />
   <meta property="og:description" content="Rapid assessment method for older people (RAM-OP) manual" />
-  <meta name="github-repo" content="validmeasures/ramOPmanual" />
+  <meta name="github-repo" content="rapidsurveys/ramOPmanual" />
 
   <meta name="twitter:card" content="summary" />
-  <meta name="twitter:title" content="Rapid Assessment Method for Older People (RAM-OP): The Manual" />
+  <meta name="twitter:title" content="5 Practical Fieldwork | Rapid Assessment Method for Older People (RAM-OP): The Manual" />
   
   <meta name="twitter:description" content="Rapid assessment method for older people (RAM-OP) manual" />
-  <meta name="twitter:image" content="https://validmeasures.org/ramOPmanualfigures/coverImage.jpg" />
+  <meta name="twitter:image" content="https://rapidsurveys.io/ramOPmanualfigures/coverImage.jpg" />
 
-<meta name="author" content="Pascale Fritsch, Ernest Guevarra, Katja Siling, Mark Myatt">
+<meta name="author" content="Pascale Fritsch, Ernest Guevarra, Katja Siling, Mark Myatt" />
 
 
 
-  <meta name="viewport" content="width=device-width, initial-scale=1">
-  <meta name="apple-mobile-web-app-capable" content="yes">
-  <meta name="apple-mobile-web-app-status-bar-style" content="black">
+  <meta name="viewport" content="width=device-width, initial-scale=1" />
+  <meta name="apple-mobile-web-app-capable" content="yes" />
+  <meta name="apple-mobile-web-app-status-bar-style" content="black" />
   
   
 <link rel="prev" href="datasets.html">
 <link rel="next" href="software.html">
 <script src="libs/jquery-2.2.3/jquery.min.js"></script>
 <link href="libs/gitbook-2.6.7/css/style.css" rel="stylesheet" />
+<link href="libs/gitbook-2.6.7/css/plugin-table.css" rel="stylesheet" />
 <link href="libs/gitbook-2.6.7/css/plugin-bookdown.css" rel="stylesheet" />
 <link href="libs/gitbook-2.6.7/css/plugin-highlight.css" rel="stylesheet" />
 <link href="libs/gitbook-2.6.7/css/plugin-search.css" rel="stylesheet" />
@@ -49,20 +49,26 @@
 
 
 <style type="text/css">
-a.sourceLine { display: inline-block; min-height: 1.25em; }
+a.sourceLine { display: inline-block; line-height: 1.25; }
 a.sourceLine { pointer-events: none; color: inherit; text-decoration: inherit; }
+a.sourceLine:empty { height: 1.2em; }
 .sourceCode { overflow: visible; }
-code.sourceCode { white-space: pre; }
+code.sourceCode { white-space: pre; position: relative; }
+div.sourceCode { margin: 1em 0; }
+pre.sourceCode { margin: 0; }
+@media screen {
+div.sourceCode { overflow: auto; }
+}
 @media print {
 code.sourceCode { white-space: pre-wrap; }
 a.sourceLine { text-indent: -1em; padding-left: 1em; }
 }
 pre.numberSource a.sourceLine
-  { position: relative; }
+  { position: relative; left: -4em; }
 pre.numberSource a.sourceLine::before
   { content: attr(data-line-number);
-    position: absolute; left: -5em; text-align: right; vertical-align: baseline;
-    border: none; pointer-events: all;
+    position: relative; left: -1em; text-align: right; vertical-align: baseline;
+    border: none; pointer-events: all; display: inline-block;
     -webkit-touch-callout: none; -webkit-user-select: none;
     -khtml-user-select: none; -moz-user-select: none;
     -ms-user-select: none; user-select: none;
@@ -70,38 +76,39 @@
     color: #aaaaaa;
   }
 pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa;  padding-left: 4px; }
-pre, code { background-color: #f8f8f8; }
+div.sourceCode
+  { background-color: #f8f8f8; }
 @media screen {
-a.sourceLine::before { text-decoration: underline; color: initial; }
+a.sourceLine::before { text-decoration: underline; }
 }
-code span.kw { color: #204a87; font-weight: bold; } /* Keyword */
-code span.dt { color: #204a87; } /* DataType */
-code span.dv { color: #0000cf; } /* DecVal */
+code span.al { color: #ef2929; } /* Alert */
+code span.an { color: #8f5902; font-weight: bold; font-style: italic; } /* Annotation */
+code span.at { color: #c4a000; } /* Attribute */
 code span.bn { color: #0000cf; } /* BaseN */
-code span.fl { color: #0000cf; } /* Float */
+code span.cf { color: #204a87; font-weight: bold; } /* ControlFlow */
 code span.ch { color: #4e9a06; } /* Char */
-code span.st { color: #4e9a06; } /* String */
+code span.cn { color: #000000; } /* Constant */
 code span.co { color: #8f5902; font-style: italic; } /* Comment */
-code span.ot { color: #8f5902; } /* Other */
-code span.al { color: #ef2929; } /* Alert */
-code span.fu { color: #000000; } /* Function */
+code span.cv { color: #8f5902; font-weight: bold; font-style: italic; } /* CommentVar */
+code span.do { color: #8f5902; font-weight: bold; font-style: italic; } /* Documentation */
+code span.dt { color: #204a87; } /* DataType */
+code span.dv { color: #0000cf; } /* DecVal */
 code span.er { color: #a40000; font-weight: bold; } /* Error */
-code span.wa { color: #8f5902; font-weight: bold; font-style: italic; } /* Warning */
-code span.cn { color: #000000; } /* Constant */
-code span.sc { color: #000000; } /* SpecialChar */
-code span.vs { color: #4e9a06; } /* VerbatimString */
-code span.ss { color: #4e9a06; } /* SpecialString */
+code span.ex { } /* Extension */
+code span.fl { color: #0000cf; } /* Float */
+code span.fu { color: #000000; } /* Function */
 code span.im { } /* Import */
-code span.va { color: #000000; } /* Variable */
-code span.cf { color: #204a87; font-weight: bold; } /* ControlFlow */
+code span.in { color: #8f5902; font-weight: bold; font-style: italic; } /* Information */
+code span.kw { color: #204a87; font-weight: bold; } /* Keyword */
 code span.op { color: #ce5c00; font-weight: bold; } /* Operator */
+code span.ot { color: #8f5902; } /* Other */
 code span.pp { color: #8f5902; font-style: italic; } /* Preprocessor */
-code span.ex { } /* Extension */
-code span.at { color: #c4a000; } /* Attribute */
-code span.do { color: #8f5902; font-weight: bold; font-style: italic; } /* Documentation */
-code span.an { color: #8f5902; font-weight: bold; font-style: italic; } /* Annotation */
-code span.cv { color: #8f5902; font-weight: bold; font-style: italic; } /* CommentVar */
-code span.in { color: #8f5902; font-weight: bold; font-style: italic; } /* Information */
+code span.sc { color: #000000; } /* SpecialChar */
+code span.ss { color: #4e9a06; } /* SpecialString */
+code span.st { color: #4e9a06; } /* String */
+code span.va { color: #000000; } /* Variable */
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@@ -208,7 +215,7 @@ <h1>
 
             <section class="normal" id="section-">
 <div id="practical" class="section level1">
-<h1><span class="header-section-number">Chapter 5</span> Practical Fieldwork</h1>
+<h1><span class="header-section-number">5</span> Practical Fieldwork</h1>
 <p>This section is intended to guide you through the different steps leading up to the fieldwork once the survey location has been identified, and gives some tips on how the fieldwork might be organised.</p>
 <div id="authorisations-and-clearances" class="section level2">
 <h2><span class="header-section-number">5.1</span> Authorisations and clearances</h2>
@@ -434,7 +441,7 @@ <h2><span class="header-section-number">5.8</span> Survey planning</h2>
 <li>Arrange the logistics for the field test (vehicles, drivers, equipment)</li>
 <li>Review logistics arrangements with the trainees</li>
 </ul>
-<p><strong>At the end of the trainin</strong></p>
+<p><strong>At the end of the training</strong></p>
 <ul>
 <li>Provide each trainee with a certificate of attendance</li>
 <li>Pay the training incentives</li>
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-  <title>Rapid Assessment Method for Older People (RAM-OP): The Manual</title>
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+  <title>3 The RAM-OP questionnaire | Rapid Assessment Method for Older People (RAM-OP): The Manual</title>
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+  <meta name="twitter:image" content="https://rapidsurveys.io/ramOPmanualfigures/coverImage.jpg" />
 
-<meta name="author" content="Pascale Fritsch, Ernest Guevarra, Katja Siling, Mark Myatt">
+<meta name="author" content="Pascale Fritsch, Ernest Guevarra, Katja Siling, Mark Myatt" />
 
 
 
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@@ -208,7 +215,7 @@ <h1>
 
             <section class="normal" id="section-">
 <div id="questionnaire" class="section level1">
-<h1><span class="header-section-number">Chapter 3</span> The RAM-OP questionnaire</h1>
+<h1><span class="header-section-number">3</span> The RAM-OP questionnaire</h1>
 <p>Modules of the RAM-OP questionnaire are presented in the <a href="indicators.html#indicators"><strong>RAM-OP indicators</strong></a> section of this manual.
 The entire RAM-OP questionnaire is presented in the following pages. This questionnaire is composed of many tested and validated components. The order of the questions and the format of the questionnaire have been tested in several settings (Chad, Dadaab Camps, South Sudan, Ethiopia, and Tanzania) over a period of three years. It is strongly recommended that you do <strong>not</strong> change the questionnaire, other than translating it into a language other than English and necessary localisation (i.e. adapting the questions to meet the language, cultural, and other requirements of a specific target population in order to ensure that the words, names, terms, and concepts used are culturally appropriate and understandable to them), unless you are very sure of what you are doing. Modifying the questionnaire may have one or more of the following consequences:</p>
 <ul>
@@ -246,13 +253,13 @@ <h1><span class="header-section-number">Chapter 3</span> The RAM-OP questionnair
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+  {\end{rmdblock}}
+\newenvironment{rmdcaution}
+  {\begin{rmdblock}{caution}}
+  {\end{rmdblock}}
+\newenvironment{rmdimportant}
+  {\begin{rmdblock}{important}}
+  {\end{rmdblock}}
+\newenvironment{rmdtip}
+  {\begin{rmdblock}{tip}}
+  {\end{rmdblock}}
 \newenvironment{rmdwarning}
-  {\begin{tcolorbox}[width=\textwidth, 
-                     colback = {white}, 
-                     title = {\textbf{Warning}}, 
-                     colbacktitle = lightgray,
-                     coltitle = black]
-  \begin{includegraphics}[scale = 1]{warning.png}}
-  {\end{includegraphics}
-  \end{tcolorbox}}
-
-\newenvironment{rmddownload}
-  {\begin{tcolorbox}[width=\textwidth, 
-                     colback = {white}, 
-                     title = {\textbf{Download}}, 
-                     colbacktitle = lightgray,
-                     coltitle = black]
-  \begin{includegraphics}[scale = 1]{download.png}}
-  {\end{includegraphics}
-  \end{tcolorbox}}
-
-\usepackage{amsthm}
-\newtheorem{theorem}{Theorem}[chapter]
-\newtheorem{lemma}{Lemma}[chapter]
-\theoremstyle{definition}
-\newtheorem{definition}{Definition}[chapter]
-\newtheorem{corollary}{Corollary}[chapter]
-\newtheorem{proposition}{Proposition}[chapter]
-\theoremstyle{definition}
-\newtheorem{example}{Example}[chapter]
-\theoremstyle{definition}
-\newtheorem{exercise}{Exercise}[chapter]
-\theoremstyle{remark}
-\newtheorem*{remark}{Remark}
-\newtheorem*{solution}{Solution}
+  {\begin{rmdblock}{warning}}
+  {\end{rmdblock}}
+
 \let\BeginKnitrBlock\begin \let\EndKnitrBlock\end
 \begin{document}
 \maketitle
 
 {
 \hypersetup{linkcolor=black}
-\setcounter{tocdepth}{1}
+\setcounter{tocdepth}{2}
 \tableofcontents
 }
 \hypertarget{the-ram-op-manual}{%
 \chapter*{The RAM-OP Manual}\label{the-ram-op-manual}}
-\addcontentsline{toc}{chapter}{The RAM-OP Manual}
+
 
 \includegraphics{figures/coverImage.jpg}
 
 \hypertarget{introduction}{%
 \chapter*{Introduction}\label{introduction}}
-\addcontentsline{toc}{chapter}{Introduction}
-
-Older people (generally defined as people aged sixty years and older)
-are a vulnerable group for malnutrition in humanitarian and
-developmental contexts. Due to their age they have specific nutritional
-needs, such as easily digestible and palatable food adapted to those
-with chewing problems, which is dense in nutrients. In famine and
-displacement situations where populations are dependent on food
-distributions, older people often find the general ration inappropriate
-to their tastes and needs, have difficulties accessing the
-distributions, or have difficulties transporting rations home. As a
-result, older people can become malnourished and in need of specifically
-targeted food interventions. In times of drought or food scarcity, older
-people tend to reduce their food intake in order to share or give up
-their ration to younger members of their families. They are then at risk
-of malnutrition.
-
-Despite these potential vulnerabilities in humanitarian situations,
-older people are rarely identified as a group in need of specific
-nutritional or food assistance. Surveys and assessments almost always
-focus on children, and sometimes on pregnant and lactating women.
-Humanitarian workers argue that assessing the nutritional status and
-needs of older people is both costly and complicated. As a consequence,
-the nutritional status and needs of older people in crisis go
-unidentified and unaddressed.
-
-HelpAge International, VALID International, and Brixton Health, with
-financial assistance from the Humanitarian Innovation Fund (HIF), have
-developed a Rapid Assessment Method for Older People (RAM-OP) that
-provides accurate and reliable estimates of the needs of older people.
-The method uses simple procedures, in a short time frame (i.e.~about two
-weeks including training, data collection, data entry, and data
-analysis), and at considerably lower cost than other methods. The RAM-OP
-method is based on the following principles:
+
+
+Older people (generally defined as people aged sixty years and older) are a vulnerable group for malnutrition in humanitarian and developmental contexts. Due to their age they have specific nutritional needs, such as easily digestible and palatable food adapted to those with chewing problems, which is dense in nutrients. In famine and displacement situations where populations are dependent on food distributions, older people often find the general ration inappropriate to their tastes and needs, have difficulties accessing the distributions, or have difficulties transporting rations home. As a result, older people can become malnourished and in need of specifically targeted food interventions. In times of drought or food scarcity, older people tend to reduce their food intake in order to share or give up their ration to younger members of their families. They are then at risk of malnutrition.
+
+Despite these potential vulnerabilities in humanitarian situations, older people are rarely identified as a group in need of specific nutritional or food assistance. Surveys and assessments almost always focus on children, and sometimes on pregnant and lactating women. Humanitarian workers argue that assessing the nutritional status and needs of older people is both costly and complicated. As a consequence, the nutritional status and needs of older people in crisis go unidentified and unaddressed.
+
+HelpAge International, VALID International, and Brixton Health, with financial assistance from the Humanitarian Innovation Fund (HIF), have developed a Rapid Assessment Method for Older People (RAM-OP) that provides accurate and reliable estimates of the needs of older people. The method uses simple procedures, in a short time frame (i.e.~about two weeks including training, data collection, data entry, and data analysis), and at considerably lower cost than other methods. The RAM-OP method is based on the following principles:
 
 \begin{itemize}
 \item
-  Use of a familiar ``household survey'' design employing a two-stage
-  cluster sample design optimised to allow the use of a small primary
-  sample ( m ≥ 16 clusters) and a small overall ( n ≥ 192) sample.
+  Use of a familiar ``household survey'' design employing a two-stage cluster sample design optimised to allow the use of a small primary sample (\(m ≥ 16\) clusters) and a small overall (\(n ≥ 192\)) sample.
 \item
-  Assessment of multiple dimensions of need in older people (including
-  prevalence of global, moderate and severe acute malnutrition) using,
-  whenever possible, standard and well-tested indicators and question
-  sets.
+  Assessment of multiple dimensions of need in older people (including prevalence of global, moderate and severe acute malnutrition) using, whenever possible, standard and well-tested indicators and question sets.
 \item
-  Data analysis performed using modern computer-intensive methods to
-  allow estimates of indicator levels to be made with useful precision
-  using a small sample size.
+  Data analysis performed using modern computer-intensive methods to allow estimates of indicator levels to be made with useful precision using a small sample size.
 \end{itemize}
 
-The following tools are currently available under the General Public
-Licence / Free Documentation License, meaning that you are free to copy
-and adapt these tools:
+The following tools are currently available under the General Public Licence / Free Documentation License, meaning that you are free to copy and adapt these tools:
 
 \begin{itemize}
 \item
@@ -305,18 +264,12 @@ \chapter*{Introduction}\label{introduction}}
 \item
   a questionnaire (available in English and French)
 \item
-  data entry and data checking software (available in English and
-  French)
+  data entry and data checking software (available in English and French)
 \item
   data analysis software.
 \end{itemize}
 
-We believe that the availability of a rapid, low-cost, and user-friendly
-method will encourage governments, UN agencies, as well as international
-and local non-governmental organisations to actively assess the
-situation of older people in humanitarian contexts, and implement,
-monitor, and evaluate relevant and timely responses to address their
-needs.
+We believe that the availability of a rapid, low-cost, and user-friendly method will encourage governments, UN agencies, as well as international and local non-governmental organisations to actively assess the situation of older people in humanitarian contexts, and implement, monitor, and evaluate relevant and timely responses to address their needs.
 
 \hypertarget{sampling}{%
 \chapter{Sampling}\label{sampling}}
@@ -326,28 +279,21 @@ \section{The RAM-OP sample}\label{the-ram-op-sample}}
 
 RAM-OP uses a two-stage sample:
 
-\textbf{First stage sample:} A sample of communities (e.g.~villages or
-city-blocks) in the survey area is taken. A sampled community is also
-called a primary sampling unit (PSU).
+\textbf{First stage sample:} A sample of communities (e.g.~villages or city-blocks) in the survey area is
+taken. A sampled community is also called a primary sampling unit (PSU).
 
-\textbf{Second stage sample:} Domestic dwellings are sampled from within
-the communities selected in the first stage sample. All eligible
-individuals in the sampled dwelling are included in the sample.
+\textbf{Second stage sample:} Domestic dwellings are sampled from within the communities selected in the
+first stage sample. All eligible individuals in the sampled dwelling are included in the sample.
 
 \hypertarget{the-first-stage-sample}{%
 \subsection{The first-stage sample}\label{the-first-stage-sample}}
 
-The first stage sample is a systematic spatial sample. Two methods can
-be used and both methods take the sample from all parts of the survey
-area:
+The first stage sample is a systematic spatial sample. Two methods can be used and both methods take the sample from all parts of the survey area:
 
 \begin{itemize}
 \tightlist
 \item
-  \textbf{List-based method:} Communities to be sampled are selected
-  systematically from a complete list of communities in the survey area.
-  This list of communities is sorted by one or more non-overlapping
-  spatial factors such as district and subdistricts within districts:
+  \textbf{List-based method:} Communities to be sampled are selected systematically from a complete list of communities in the survey area. This list of communities is sorted by one or more non-overlapping spatial factors such as district and subdistricts within districts:
 \end{itemize}
 
 \begin{figure}[H]
@@ -362,73 +308,43 @@ \subsection{The first-stage sample}\label{the-first-stage-sample}}
 \begin{itemize}
 \tightlist
 \item
-  \textbf{Map-based method:} Communities to be sampled are selected from
-  the centres of the squares of a grid drawn over a map. The map must be
-  sufficiently well made and of sufficiently large scale to show the
-  position of every community in the survey area. This type of sample is
-  known as a centric systematic area sample and is often referred to as
-  a CSAS sample.
+  \textbf{Map-based method:} Communities to be sampled are selected from the centres of the squares of a grid drawn over a map. The map must be sufficiently well made and of sufficiently large scale to show the position of every community in the survey area. This type of sample is known as a centric systematic area sample and is often referred to as a CSAS sample.
 \end{itemize}
 
-\textbf{Note:} \emph{Population proportional sampling} (PPS) is
-\textbf{not} used in RAM-OP surveys. Population estimates for all
-communities are \textbf{not} required for sampling purposes. Population
-estimates are required only for the selected communities. These are used
-during data analysis in order to weight results by population size. If
-this information is not available before the survey, it can be collected
-during the survey.
+\textbf{Note:} \emph{Population proportional sampling} (PPS) is \textbf{not} used in RAM-OP surveys. Population estimates for all communities are \textbf{not} required for sampling purposes. Population estimates are required only for the selected communities. These are used during data analysis in order to weight results by population size. If this information is not available before the survey, it can be collected during the survey.
 
 \hypertarget{the-second-stage-sample}{%
 \subsection{The second stage sample}\label{the-second-stage-sample}}
 
-The second stage within-community sample uses a method called
-map-segment-sample. This method takes the within-community sample from
-all parts of a sampled community.
+The second stage within-community sample uses a method called map-segment-sample. This method takes the within-community sample from all parts of a sampled community.
 
 \hypertarget{implicit-stratification}{%
 \section{Implicit stratification}\label{implicit-stratification}}
 
-Both the first and second stage samples use a form of spatial
-stratification:
+Both the first and second stage samples use a form of spatial stratification:
 
 \begin{itemize}
 \item
-  The list-based method's first stage systematic spatial sample
-  stratifies the sample by non-overlapping spatial factor such as
-  districts and subdistricts within districts.
+  The list-based method's first stage systematic spatial sample stratifies the sample by non-overlapping
+  spatial factor such as districts and subdistricts within districts.
 \item
-  The map-based (CSAS) method's first stage sample stratifies the sample
-  by grid square.
+  The map-based (CSAS) method's first stage sample stratifies the sample by grid square.
 \item
-  The map-segment-sample second stage within-community sample stratifies
-  the sample by parts of the community being sampled.
+  The map-segment-sample second stage within-community sample stratifies the sample by parts of the community being sampled.
 \item
-  The first and second stage samples also ensure that a reasonably even
-  spatial sample is taken from the entire survey area and from each of
-  the sampled communities.
+  The first and second stage samples also ensure that a reasonably even spatial sample is taken from the entire survey area and from each of the sampled communities.
 \end{itemize}
 
-These sampling procedures provide \emph{implicit stratification} and
-tend to spread the sample properly among important sub-groups of the
-population such as rural / urban / peri-urban populations,
-administrative areas, ethnic sub-populations, religious sub-populations,
-and socio-economic groups. This often improves the precision of
-estimates made from survey data.
+These sampling procedures provide \emph{implicit stratification} and tend to spread the sample properly among important sub-groups of the population such as rural / urban / peri-urban populations, administrative areas, ethnic sub-populations, religious sub-populations, and socio-economic groups. This often improves the precision of estimates made from survey data.
 
-The use of implicit stratification improves the efficiency of a
-two-stage cluster sample and allows RAM-OP to use relatively small
-sample sizes compared to other methods, such as SMART surveys. The use
-of modern computer-intensive data analysis techniques also allows RAM-OP
-to make better use of the available sample than is done in other
-methods.
+The use of implicit stratification improves the efficiency of a two-stage cluster sample and allows RAM-OP to use relatively small sample sizes compared to other methods, such as SMART surveys. The use of modern computer-intensive data analysis techniques also allows RAM-OP to make better use of the available sample than is done in other methods.
 
 \newpage
 
 \hypertarget{ram-op-survey-sample-size}{%
 \section{RAM-OP survey sample size}\label{ram-op-survey-sample-size}}
 
-The following shorthand symbols will be used when describing sample
-designs:
+The following shorthand symbols will be used when describing sample designs:
 
 ~
 
@@ -441,24 +357,20 @@ \section{RAM-OP survey sample size}\label{ram-op-survey-sample-size}}
 
 ~
 
-The overall sample size for a RAM-OP survey is about \(n = 192\)
-individual subjects. You should aim to collect an overall sample of at
-least \(n = 192\) individuals.
+The overall sample size for a RAM-OP survey is about \(n = 192\) individual subjects. You should aim to collect
+an overall sample of at least \(n = 192\) individuals.
 
 The RAM-OP sample is collected in two stages:
 
 \begin{itemize}
 \item
-  The first stage sample uses a sample size of about \(m = 16\)
-  communities (or PSUs).
+  The first stage sample uses a sample size of about \(m = 16\) communities (or PSUs).
 \item
-  The second stage sample uses a sample size of about \(n = 12\)
-  eligible subjects sampled from each of the communities selected for
-  inclusion in the first stage sample.
+  The second stage sample uses a sample size of about \(n = 12\) eligible subjects sampled from each of
+  the communities selected for inclusion in the first stage sample.
 \end{itemize}
 
-The overall sample size from \(m = 16\) communities and \(n = 12\)
-eligible subjects is about:
+The overall sample size from \(m = 16\) communities and \(n = 12\) eligible subjects is about:
 
 ~
 
@@ -466,24 +378,13 @@ \section{RAM-OP survey sample size}\label{ram-op-survey-sample-size}}
 
 ~
 
-It is not recommended that fewer than \(m = 16\) communities are
-sampled.
+It is not recommended that fewer than \(m = 16\) communities are sampled.
 
-Sampling fewer than \(m = 16\) communities will tend to reduce the
-precision with which estimates can be made. If you have the resources to
-sample more than \(m = 16\) communities then you should do so. A sample
-of \(m = 24\) communities and \(n = 8\) eligible subjects, for example,
-will tend to yield estimates with better precision than a sample with
-\(m = 16\) communities and \(n = 12\) eligible subjects.
+Sampling fewer than \(m = 16\) communities will tend to reduce the precision with which estimates can be made. If you have the resources to sample more than \(m = 16\) communities then you should do so. A sample of \(m = 24\) communities and \(n = 8\) eligible subjects, for example, will tend to yield estimates with better precision than a sample with \(m = 16\) communities and \(n = 12\) eligible subjects.
 
-Do not be tempted to increase the size of the within-community sample in
-order to achieve an overall sample size of \(n = 192\) from fewer than
-\(m = 16\) communities. Doing so will tend to reduce the precision with
-which estimates are made. It may also be impossible to do this in many
-settings.
+Do not be tempted to increase the size of the within-community sample in order to achieve an overall sample size of \(n = 192\) from fewer than \(m = 16\) communities. Doing so will tend to reduce the precision with which estimates are made. It may also be impossible to do this in many settings.
 
-Here, for example, is a \emph{population pyramid} for a typical
-developing country:
+Here, for example, is a \emph{population pyramid} for a typical developing country:
 
 \begin{figure}[H]
 
@@ -494,30 +395,18 @@ \section{RAM-OP survey sample size}\label{ram-op-survey-sample-size}}
 \caption{Population pyramid for a typical developing country}\label{fig:sample2}
 \end{figure}
 
-If the average community population is \(N = 300\) then there will be
-fewer than 15 people aged 60 years and older in about half of the
-selected communities. This is because about half of the selected
-communities are likely to have a population below the average
-population.
+If the average community population is \(N = 300\) then there will be fewer than 15 people aged 60 years and older in about half of the selected communities. This is because about half of the selected communities are likely to have a population below the average population.
 
 \hypertarget{eligibility}{%
 \section{Eligibility}\label{eligibility}}
 
-Older people are usual defined as persons aged 60 years and older (UN
-definition). This means your sample will usually be restricted to people
-aged 60 years and older.
+Older people are usual defined as persons aged 60 years and older (UN definition). This means your sample will usually be restricted to people aged 60 years and older.
 
-In some settings different eligibility criteria may apply. This will
-likely be the case in settings with very high life-expectancies (usually
-middle and high income countries) or very low life-expectancies (usually
-low income countries and in emergencies).
+In some settings different eligibility criteria may apply. This will likely be the case in settings with very high life-expectancies (usually middle and high income countries) or very low life-expectancies (usually low income countries and in emergencies).
 
-In a setting of very high life-expectancy you may want to restrict
-eligibility - to persons aged 65 years or older, for example. A local
-definition of older people is likely to be available.
+In a setting of very high life-expectancy you may want to restrict eligibility - to persons aged 65 years or older, for example. A local definition of older people is likely to be available.
 
-In a setting with very low life-expectancy, very few people are aged 60
-years or older. For example:
+In a setting with very low life-expectancy, very few people are aged 60 years or older. For example:
 
 \begin{figure}[H]
 
@@ -528,31 +417,17 @@ \section{Eligibility}\label{eligibility}}
 \caption{Population pyramid for a setting with low life-expectancy}\label{fig:sample3}
 \end{figure}
 
-It is common in such setting for there to be a local definition of older
-people. This will usually be ``persons aged 50 years or older'' or
-``persons aged 55 years or older''.
+It is common in such setting for there to be a local definition of older people. This will usually be ``persons
+aged 50 years or older'' or ``persons aged 55 years or older''.
 
 \hypertarget{age-distribution-eligibility-criteria-and-sample-design}{%
-\section{Age distribution, eligibility criteria, and sample
-design}\label{age-distribution-eligibility-criteria-and-sample-design}}
-
-The age distribution of the population and the survey eligibility
-criteria will affect the sample design in terms of the number of
-communities that you will need to sample (\(m\)) and the number of older
-persons (\(n\)) that can be sampled from each community.
-
-The overall sample size for a RAM-OP sample should be at least
-\(n = 192\) usually collected as \(n = 12\) eligible subjects sampled
-from \(m = 16\) communities. If older people make up a very small
-proportion (i.e.~much less than 5\%) of the total population and / or
-the average population of communities is small then you will usually
-need to sample more than m = 16 communities in order to get about
-\(n = 192\) older people in the overall sample. This is likely to occur
-when there are fewer than 20 to 25 older people in a community of
-average size.
-
-You can calculate the number of older people that you would expect to be
-living in a community of average size using the following formula:
+\section{Age distribution, eligibility criteria, and sample design}\label{age-distribution-eligibility-criteria-and-sample-design}}
+
+The age distribution of the population and the survey eligibility criteria will affect the sample design in terms of the number of communities that you will need to sample (\(m\)) and the number of older persons (\(n\)) that can be sampled from each community.
+
+The overall sample size for a RAM-OP sample should be at least \(n = 192\) usually collected as \(n = 12\) eligible subjects sampled from \(m = 16\) communities. If older people make up a very small proportion (i.e.~much less than 5\%) of the total population and / or the average population of communities is small then you will usually need to sample more than \(m = 16\) communities in order to get about \(n = 192\) older people in the overall sample. This is likely to occur when there are fewer than 20 to 25 older people in a community of average size.
+
+You can calculate the number of older people that you would expect to be living in a community of average size using the following formula:
 
 ~
 
@@ -560,63 +435,30 @@ \section{Age distribution, eligibility criteria, and sample
 
 ~
 
-If this is below about 20 people then you should consider how you will
-collect the required overall sample size. Three approaches may be used:
+If this is below about 20 people then you should consider how you will collect the required overall sample size. Three approaches may be used:
 
 \begin{itemize}
 \item
-  \textbf{Relax the eligibility criteria:} You may decide to define
-  older people as ``persons aged 50 years or older'' or ``persons aged
-  55 years or older''. This may double the size of the eligible
-  population and make the sample easier to collect. This approach is
-  only reasonable if life-expectancy is low.
-\item
-  \textbf{Increase the number of communities that you plan to sample:}
-  You may choose to collect your sample as \(n = 7\) eligible subjects
-  sampled from \(m = 30\) communities giving an expected overall sample
-  size of \(n = 210\). This would be a very good sample. The
-  disadvantage of this approach is that survey costs increase with the
-  number of communities that are sampled, because a lot of survey time
-  and vehicle costs are spent on travelling to and from the selected
-  communities.
-\item
-  \textbf{Take a ``top-up'' sample only when you need to:} The basic
-  procedure when a selected community is small and likely to contain
-  fewer than \(n = 12\) older people is to collect data on all older
-  people in the selected community using a door-to-door census. If the
-  within-community sample size is much smaller than the required one
-  then a ``top-up'' sample is taken from the nearest neighbouring
-  community using the map-segment-sample method (or a door-to-door
-  census if this community is also small). The advantage of this
-  approach is that travelling time and survey costs are better
-  controlled.
+  \textbf{Relax the eligibility criteria:} You may decide to define older people as ``persons aged 50 years or older'' or ``persons aged 55 years or older''. This may double the size of the eligible population and make the sample easier to collect. This approach is only reasonable if life-expectancy is low.
+\item
+  \textbf{Increase the number of communities that you plan to sample:} You may choose to collect your sample as \(n = 7\) eligible subjects sampled from \(m = 30\) communities giving an expected overall sample size of \(n = 210\). This would be a very good sample. The disadvantage of this approach is that survey costs increase with the number of communities that are sampled, because a lot of survey time and vehicle costs are spent on travelling to and from the selected communities.
+\item
+  \textbf{Take a ``top-up'' sample only when you need to:} The basic procedure when a selected community is small and likely to contain fewer than \(n = 12\) older people is to collect data on all older people in the selected community using a door-to-door census. If the within-community sample size is much smaller than the required one then a ``top-up'' sample is taken from the nearest neighbouring community using the map-segment-sample method (or a door-to-door census if this community is also small). The advantage of this approach is that travelling time and survey costs are better controlled.
 \end{itemize}
 
-If the proportion of older people is not very small and / or communities
-are large then you should have no problems achieving the overall sample
-size.
+If the proportion of older people is not very small and / or communities are large then you should have no problems achieving the overall sample size.
 
 \hypertarget{practical-sampling}{%
 \section{Practical sampling}\label{practical-sampling}}
 
 \hypertarget{the-first-stage-sample---list-based-sampling}{%
-\subsection{The first stage sample - list-based
-sampling}\label{the-first-stage-sample---list-based-sampling}}
+\subsection{The first stage sample - list-based sampling}\label{the-first-stage-sample---list-based-sampling}}
 
-The first stage sample can be drawn from a list of all communities. The
-list-based sample is a simple systematic sample taken from a complete
-list of communities in the survey area sorted by one or more non-
-overlapping spatial factors (such as administrative units or electoral
-wards) in the survey area. \emph{Population proportional sampling} (PPS)
-is not used since this would concentrate the sample in the larger
-communities.
+The first stage sample can be drawn from a list of all communities. The list-based sample is a simple systematic sample taken from a complete list of communities in the survey area sorted by one or more non- overlapping spatial factors (such as administrative units or electoral wards) in the survey area. \emph{Population proportional sampling} (PPS) is not used since this would concentrate the sample in the larger communities.
 
-Below is a worked example of how a RAM-OP first stage, list-based sample
-can be drawn from a survey area composed of 67 villages.
+Below is a worked example of how a RAM-OP first stage, list-based sample can be drawn from a survey area composed of 67 villages.
 
-\textbf{Step 1:} Calculate the \emph{sampling interval} by dividing the
-total villages in the survey area (67 villages) with the number of
-villages to be drawn from the sample (16 villages).
+\textbf{Step 1:} Calculate the \emph{sampling interval} by dividing the total villages in the survey area (67 villages) with the number of villages to be drawn from the sample (16 villages).
 
 ~
 
@@ -624,18 +466,11 @@ \subsection{The first stage sample - list-based
 
 ~
 
-The \emph{sampling interval} needs to be a whole number. Remember to
-\textbf{always} \emph{round down} when calculating the \emph{sampling
-interval} to the nearest whole number.
+The \emph{sampling interval} needs to be a whole number. Remember to \textbf{always} \emph{round down} when calculating the \emph{sampling interval} to the nearest whole number.
 
-\textbf{Step 2:} Choose a \emph{random starting point} between 1 and
-\emph{sampling interval}. In this example, this would be a random number
-\textbf{between 1 and 4}.
+\textbf{Step 2:} Choose a \emph{random starting point} between 1 and \emph{sampling interval}. In this example, this would be a random number \textbf{between 1 and 4}.
 
-A random number can be selected through simple lottery (i.e., draw from
-a lot of 4 numbered from 1 to 4). A standard spreadsheet software can
-also be used to draw the random number using the \texttt{RANDBETWEEN}
-function as follows:
+A random number can be selected through simple lottery (i.e., draw from a lot of 4 numbered from 1 to 4). A standard spreadsheet software can also be used to draw the random number using the \texttt{RANDBETWEEN} function as follows:
 
 ~
 
@@ -647,10 +482,7 @@ \subsection{The first stage sample - list-based
 
 ~
 
-\textbf{Step 3:} Using the \emph{random starting point} and the
-\emph{sampling interval}, select the sampling villages from a list of
-all villages organised/sorted by a \textbf{non-overalapping} spatial
-factor such as district or sub-district.
+\textbf{Step 3:} Using the \emph{random starting point} and the \emph{sampling interval}, select the sampling villages from a list of all villages organised/sorted by a \textbf{non-overlapping} spatial factor such as district or sub-district.
 
 \newpage
 
@@ -666,24 +498,13 @@ \subsection{The first stage sample - list-based
 \newpage
 
 \hypertarget{the-first-stage-sample---map-based-sampling}{%
-\subsection{The first stage sample - map-based
-sampling}\label{the-first-stage-sample---map-based-sampling}}
+\subsection{The first stage sample - map-based sampling}\label{the-first-stage-sample---map-based-sampling}}
 
-An alternative approach to list-based sampling is to use map-based
-sampling. The map-based (CSAS) sample selects communities from the
-centre of squares of a grid drawn over a map. The map must be
-sufficiently well made and of sufficiently large scale to show the
-position of \textbf{all} communities in the survey area.
+An alternative approach to list-based sampling is to use map-based sampling. The map-based (CSAS) sample selects communities from the centre of squares of a grid drawn over a map. The map must be sufficiently well made and of sufficiently large scale to show the position of \textbf{all} communities in the survey area.
 
-A square grid is drawn over the map. The size of the grid squares should
-be small enough so that the number of squares covering the survey area
-is the same as (or very similar to) the number of communities that you
-plan to sample. You may need to experiment with different grid sizes to
-achieve this. Figure \ref{fig:sample6} shows an example map and grid
-with \(m = 16\) grid squares.
+A square grid is drawn over the map. The size of the grid squares should be small enough so that the number of squares covering the survey area is the same as (or very similar to) the number of communities that you plan to sample. You may need to experiment with different grid sizes to achieve this. Figure \ref{fig:sample6} shows an example map and grid with \(m = 16\) grid squares.
 
-The sample is drawn by selecting the community that is located closest
-to the centre of each grid square:
+The sample is drawn by selecting the community that is located closest to the centre of each grid square:
 
 \begin{figure}[H]
 
@@ -694,12 +515,9 @@ \subsection{The first stage sample - map-based
 \caption{Selection of sampling villages using maps}\label{fig:sample5}
 \end{figure}
 
-If two or more villages are located the same distance from the centre of
-a grid square then a single village is picked at random, by tossing a
-coin for example.
+If two or more villages are located the same distance from the centre of a grid square then a single village is picked at random, by tossing a coin for example.
 
-Figure \ref{fig:sample7} shows the sample selected by this process for
-the area shown in Figure \ref{fig:sample6}.
+Figure \ref{fig:sample7} shows the sample selected by this process for the area shown in Figure \ref{fig:sample6}.
 
 \begin{figure}[H]
 
@@ -719,17 +537,9 @@ \subsection{The first stage sample - map-based
 \caption{Drawing the first-stage CSAS sample}\label{fig:sample7}
 \end{figure}
 
-Both the list-based and the map-based (CSAS) sampling methods spread the
-sample of communities evenly across the entire survey area. Each
-community has an equal chance of being included in the sample.
-Population proportional sampling (PPS) is not used since this would
-concentrate the sample in the larger communities.
+Both the list-based and the map-based (CSAS) sampling methods spread the sample of communities evenly across the entire survey area. Each community has an equal chance of being included in the sample. Population proportional sampling (PPS) is not used since this would concentrate the sample in the larger communities.
 
-The same method can be used when sampling in urban contexts. Figure
-\ref{fig:sample8} shows a sample drawn from a list of census enumeration
-areas sorted by administrative district. Figure \ref{fig:sample9} shows
-a sample drawn using the map- based (CSAS) method. In both cases the
-primary sampling units (PSUs) are census enumeration areas.
+The same method can be used when sampling in urban contexts. Figure \ref{fig:sample8} shows a sample drawn from a list of census enumeration areas sorted by administrative district. Figure \ref{fig:sample9} shows a sample drawn using the map- based (CSAS) method. In both cases the primary sampling units (PSUs) are census enumeration areas.
 
 \begin{figure}[H]
 
@@ -749,23 +559,15 @@ \subsection{The first stage sample - map-based
 \caption{Example of an urban sample (map-based)}\label{fig:sample9}
 \end{figure}
 
-\textbf{Note:} In this example twenty-one (21) blocks have been
-selected. It can be difficult to achieve exactly the number of blocks
-that you need when using this type of sample. It is best to select more
-rather than fewer blocks than you need Here we would take our sample as
-\(n = 10\) individuals from \(m = 21\) blocks (overall \(n = 210\)).
+\textbf{Note:} In this example twenty-one (21) blocks have been selected. It can be difficult to achieve exactly the number of blocks that you need when using this type of sample. It is best to select more rather than fewer blocks than you need
+Here we would take our sample as \(n = 10\) individuals from \(m = 21\) blocks (overall \(n = 210\)).
 
 \hypertarget{the-second-stage-within-community-sample}{%
-\subsection{The second stage (within-community)
-sample}\label{the-second-stage-within-community-sample}}
+\subsection{The second stage (within-community) sample}\label{the-second-stage-within-community-sample}}
 
-The second stage (within-community) sample uses a map-segment-sample
-approach:
+The second stage (within-community) sample uses a map-segment-sample approach:
 
-\textbf{Map:} Make a rough map of the community to be sampled. It is
-helpful to think of communities as being made of ribbons (i.e.~lines of
-dwellings located along roads, tracks, or rivers) and clusters of
-dwellings.
+\textbf{Map:} Make a rough map of the community to be sampled. It is helpful to think of communities as being made of ribbons (i.e.~lines of dwellings located along roads, tracks, or rivers) and clusters of dwellings.
 
 Here is an example of a ribbon of dwellings:
 
@@ -789,8 +591,7 @@ \subsection{The second stage (within-community)
 \caption{Example of a cluster of dwellings}\label{fig:sample11}
 \end{figure}
 
-\textbf{Segment:} Divide the community into ribbon and cluster segments
-defined by the physical layout of the community being sampled.
+\textbf{Segment:} Divide the community into ribbon and cluster segments defined by the physical layout of the community being sampled.
 
 \textbf{Sample:} Ribbons and clusters are sampled in different ways:
 
@@ -802,13 +603,10 @@ \subsection{The second stage (within-community)
   \textbf{Clusters} are sampled using a \textbf{random walk} method.
 \end{itemize}
 
-\textbf{Note:} If a small community is selected that is likely to have
-fewer than the required number of eligible persons then \textbf{all}
-eligible persons in that community are sampled by moving door-to-door.
+\textbf{Note:} If a small community is selected that is likely to have fewer than the required number of eligible persons then \textbf{all} eligible persons in that community are sampled by moving door-to-door.
 
 \hypertarget{mapping-the-community---single-and-multiple-clusters}{%
-\subsection{Mapping the community - single and multiple
-clusters}\label{mapping-the-community---single-and-multiple-clusters}}
+\subsection{Mapping the community - single and multiple clusters}\label{mapping-the-community---single-and-multiple-clusters}}
 
 Some communities consist of a single cluster of dwellings:
 
@@ -832,12 +630,10 @@ \subsection{Mapping the community - single and multiple
 \caption{Example of a set of clusters of dwellings}\label{fig:sample13}
 \end{figure}
 
-For communities (or parts of communities) structured in this way we use
-a sampling method called the \textbf{random walk}.
+For communities (or parts of communities) structured in this way we use a sampling method called the \textbf{random walk}.
 
 \hypertarget{mapping-the-community---ribbon-communities}{%
-\subsection{Mapping the community - ribbon
-communities}\label{mapping-the-community---ribbon-communities}}
+\subsection{Mapping the community - ribbon communities}\label{mapping-the-community---ribbon-communities}}
 
 Ribbon communities have dwellings arranged in a line:
 
@@ -861,12 +657,10 @@ \subsection{Mapping the community - ribbon
 \caption{Dwellings arranged in several lines}\label{fig:sample15}
 \end{figure}
 
-For communities (or parts of communities) structured in this way we use
-a sampling method called \textbf{systematic sampling}.
+For communities (or parts of communities) structured in this way we use a sampling method called \textbf{systematic sampling}.
 
 \hypertarget{mapping-the-community---mixed-communities}{%
-\subsection{Mapping the community - mixed
-communities}\label{mapping-the-community---mixed-communities}}
+\subsection{Mapping the community - mixed communities}\label{mapping-the-community---mixed-communities}}
 
 Some communities are a mixture of clusters and ribbons:
 
@@ -879,21 +673,14 @@ \subsection{Mapping the community - mixed
 \caption{Mixture of clusters and ribbons}\label{fig:sample16}
 \end{figure}
 
-For mixed communities we use a mixture of the \textbf{random walk}
-method (in the clusters) and \textbf{systematic sampling} (along the
-ribbons).
+For mixed communities we use a mixture of the \textbf{random walk} method (in the clusters) and \textbf{systematic sampling} (along the ribbons).
 
-\textbf{Segmentation} involves dividing a community into several parts
-and taking part of the within-community sample from each
-\textbf{segment}. With simple communities, segmentation is not required
-and we take a single sample from the entire community using the
-appropriate sampling method.
+\textbf{Segmentation} involves dividing a community into several parts and taking part of the within-community sample from each \textbf{segment}. With simple communities, segmentation is not required and we take a single sample from the entire community using the appropriate sampling method.
 
 \hypertarget{segmentation}{%
 \subsection{Segmentation}\label{segmentation}}
 
-For more complicated communities we divide the community into several
-parts or segments, such as a community made up of several clusters:
+For more complicated communities we divide the community into several parts or segments, such as a community made up of several clusters:
 
 \begin{figure}[H]
 
@@ -926,8 +713,7 @@ \subsection{Segmentation}\label{segmentation}}
 \caption{Mixture of clusters and ribbons}\label{fig:sample19}
 \end{figure}
 
-We take a small sample from each segment using the appropriate sampling
-method.
+We take a small sample from each segment using the appropriate sampling method.
 
 For example, with a community made up of three segments:
 
@@ -942,23 +728,15 @@ \subsection{Segmentation}\label{segmentation}}
 
 we would take one third of the overall sample from each segment.
 
-If the within-community sample size is twelve eligible subjects. we
-would sample four eligible subjects from each segment (i.e.
-\(12 / 3 = 4\)).
+If the within-community sample size is twelve eligible subjects. we would sample four eligible subjects from each segment (i.e. \(12 / 3 = 4\)).
 
-Dividing the sample up in this way means that we will sample from every
-part of the community rather than just one part of the community.
+Dividing the sample up in this way means that we will sample from every part of the community rather than just one part of the community.
 
-When taking the sample we use the random walk method to take part of the
-sample from clusters and the systematic sampling method to take part of
-the sample from ribbons.
+When taking the sample we use the random walk method to take part of the sample from clusters and the systematic sampling method to take part of the sample from ribbons.
 
-Segments should be either ribbons or clusters but should \textbf{never}
-contain both a ribbon and a cluster. This is because clusters and
-ribbons are sampled in different ways.
+Segments should be either ribbons or clusters but should \textbf{never} contain both a ribbon and a cluster. This is because clusters and ribbons are sampled in different ways.
 
-A dwelling can only belong to one segment. Segments should \textbf{not}
-overlap.
+A dwelling can only belong to one segment. Segments should \textbf{not} overlap.
 
 \hypertarget{sample-dwellings}{%
 \subsection{Sample dwellings}\label{sample-dwellings}}
@@ -976,25 +754,17 @@ \subsection{Sample dwellings}\label{sample-dwellings}}
 \caption{Community made up of five segments}\label{fig:sample21}
 \end{figure}
 
-and the within-community sample size is twelve eligible subjects, then
-you would plan to sample two eligible subjects from each segment (i.e.
-\(12 / 5 = 2.4\) \textbf{rounded down} to two) and, if necessary, return
-to the \textbf{largest} segment to complete the sample.
+and the within-community sample size is twelve eligible subjects, then you would plan to sample two eligible subjects from each segment (i.e. \(12 / 5 = 2.4\) \textbf{rounded down} to two) and, if necessary, return to the \textbf{largest} segment to complete the sample.
 
-\textbf{All} segments should be sampled, even if this means that you
-take a larger sample than you expected to.
+\textbf{All} segments should be sampled, even if this means that you take a larger sample than you expected to.
 
 Remember that different types of segment are sampled in different ways:
 
 \begin{itemize}
 \item
-  Dwellings in \textbf{cluster segments} are sampled using a method
-  called the \textbf{random walk}. This involves sampling houses by
-  walking in random directions within the cluster.
+  Dwellings in \textbf{cluster segments} are sampled using a method called the \textbf{random walk}. This involves sampling houses by walking in random directions within the cluster.
 \item
-  Dwellings in \textbf{ribbon segments} are sampled using a method
-  called \textbf{systematic sampling}. This involves sampling houses at
-  regular intervals along the ribbon.
+  Dwellings in \textbf{ribbon segments} are sampled using a method called \textbf{systematic sampling}. This involves sampling houses at regular intervals along the ribbon.
 \end{itemize}
 
 We will look at each of these sampling methods in turn.
@@ -1002,45 +772,23 @@ \subsection{Sample dwellings}\label{sample-dwellings}}
 \hypertarget{random-walk-sampling}{%
 \subsection{Random walk sampling}\label{random-walk-sampling}}
 
-The \textbf{random walk} method is used to sample dwellings in
-\textbf{cluster segments}. Sampling proceeds as follows:
+The \textbf{random walk} method is used to sample dwellings in \textbf{cluster segments}. Sampling proceeds as follows:
 
 \begin{enumerate}
 \def\labelenumi{\arabic{enumi}.}
 \item
   Move to the approximate centre of the cluster.
 \item
-  Select a \textbf{random direction} by spinning a bottle on the ground.
-  The neck indicates the \textbf{sampling direction}. This is the
-  direction you should walk in order to sample a dwelling. Walk in the
-  sampling direction counting the dwellings that you pass. Sample the
-  third \textbf{dwelling}. If there are no eligible persons in the
-  selected dwelling then sample the \textbf{nearest} dwelling with an
-  eligible person. Sample \textbf{all} eligible persons in the selected
-  dwelling.
-\item
-  Apply the survey questionnaire for \textbf{all} eligible persons in
-  the selected dwelling.
-\item
-  Select the next dwelling to sample by spinning a bottle and walking in
-  the indicated direction. Count the dwellings you pass. Sample the
-  \textbf{third} dwelling. If there are no eligible persons in the
-  selected dwelling then sample the \textbf{nearest} dwelling with an
-  eligible person. Sample all eligible persons in the selected dwelling.
-  If you reach the edge of the cluster segment then return to the centre
-  of the cluster and repeat step (2) above. Remember to keep count of
-  the number of eligible persons sampled from the segment.
-\item
-  Stop sampling in the segment when you have sampled the required number
-  of eligible persons from the segment. Since you sample \textbf{all}
-  eligible persons in a selected dwelling, you may sample a few more
-  eligible persons than expected. This is OK. Always sample \textbf{all}
-  eligible persons in a selected dwelling.
+  Select a \textbf{random direction} by spinning a bottle on the ground. The neck indicates the \textbf{sampling direction}. This is the direction you should walk in order to sample a dwelling. Walk in the sampling direction counting the dwellings that you pass. Sample the third \textbf{dwelling}. If there are no eligible persons in the selected dwelling then sample the \textbf{nearest} dwelling with an eligible person. Sample \textbf{all} eligible persons in the selected dwelling.
+\item
+  Apply the survey questionnaire for \textbf{all} eligible persons in the selected dwelling.
+\item
+  Select the next dwelling to sample by spinning a bottle and walking in the indicated direction. Count the dwellings you pass. Sample the \textbf{third} dwelling. If there are no eligible persons in the selected dwelling then sample the \textbf{nearest} dwelling with an eligible person. Sample all eligible persons in the selected dwelling. If you reach the edge of the cluster segment then return to the centre of the cluster and repeat step (2) above. Remember to keep count of the number of eligible persons sampled from the segment.
+\item
+  Stop sampling in the segment when you have sampled the required number of eligible persons from the segment. Since you sample \textbf{all} eligible persons in a selected dwelling, you may sample a few more eligible persons than expected. This is OK. Always sample \textbf{all} eligible persons in a selected dwelling.
 \end{enumerate}
 
-If, when you have sampled all segments, you have not sampled twelve
-eligible persons, you should return to the \textbf{largest} segment to
-finish sampling using the appropriate sampling method.
+If, when you have sampled all segments, you have not sampled twelve eligible persons, you should return to the \textbf{largest} segment to finish sampling using the appropriate sampling method.
 
 The random walk method is illustrated in Figure \ref{fig:sample22}.
 
@@ -1056,8 +804,7 @@ \subsection{Random walk sampling}\label{random-walk-sampling}}
 \hypertarget{systematic-sampling}{%
 \subsection{Systematic sampling}\label{systematic-sampling}}
 
-The \textbf{systematic sampling} method is used to sample houses in
-\textbf{ribbon segments}.
+The \textbf{systematic sampling} method is used to sample houses in \textbf{ribbon segments}.
 
 Sampling proceeds as follows:
 
@@ -1066,41 +813,20 @@ \subsection{Systematic sampling}\label{systematic-sampling}}
 \item
   Move to one end of the ribbon segment.
 \item
-  Walk to the other end of the segment counting the houses that you
-  pass.
-\item
-  Calculate the \textbf{step size} by dividing the number of dwellings
-  in the segment by the required sample size for the segment. Use the
-  \textbf{whole number} part of the result only. Do \textbf{not} round
-  up.
-\item
-  Pick a random number between one and the step size. This is your
-  \textbf{starting point}. Select the first dwelling to sample by
-  walking along the segment counting the dwellings that you pass and
-  sample the dwelling indicated by the \textbf{starting point}. If there
-  are no eligible persons in the selected dwelling then sample the
-  \textbf{nearest} dwelling in any direction with an eligible person.
-  Sample \textbf{all} eligible persons in the selected dwelling.
-\item
-  Select the next dwelling to sample by walking along the segment. Count
-  the dwellings that you pass. Sample the dwelling indicated by the
-  \textbf{step size}. If there are no eligible persons in the selected
-  dwelling then sample the \textbf{nearest} dwelling in any direction
-  with an eligible person. Sample \textbf{all} eligible persons in the
-  selected dwelling.
-\item
-  Stop sampling in the segment when you reach the end of the ribbon
-  segment. This may mean that you sample extra eligible persons. This is
-  OK. Do \textbf{not} stop sampling from a ribbon until you reach the
-  end of the ribbon.
+  Walk to the other end of the segment counting the houses that you pass.
+\item
+  Calculate the \textbf{step size} by dividing the number of dwellings in the segment by the required sample size for the segment. Use the \textbf{whole number} part of the result only. Do \textbf{not} round up.
+\item
+  Pick a random number between one and the step size. This is your \textbf{starting point}. Select the first dwelling to sample by walking along the segment counting the dwellings that you pass and sample the dwelling indicated by the \textbf{starting point}. If there are no eligible persons in the selected dwelling then sample the \textbf{nearest} dwelling in any direction with an eligible person. Sample \textbf{all} eligible persons in the selected dwelling.
+\item
+  Select the next dwelling to sample by walking along the segment. Count the dwellings that you pass. Sample the dwelling indicated by the \textbf{step size}. If there are no eligible persons in the selected dwelling then sample the \textbf{nearest} dwelling in any direction with an eligible person. Sample \textbf{all} eligible persons in the selected dwelling.
+\item
+  Stop sampling in the segment when you reach the end of the ribbon segment. This may mean that you sample extra eligible persons. This is OK. Do \textbf{not} stop sampling from a ribbon until you reach the end of the ribbon.
 \end{enumerate}
 
-If, when you have sampled all segments, you have not sampled twelve
-eligible persons, you should return to the \textbf{largest} segment to
-finish sampling using the appropriate sampling method.
+If, when you have sampled all segments, you have not sampled twelve eligible persons, you should return to the \textbf{largest} segment to finish sampling using the appropriate sampling method.
 
-The systematic sampling method is illustrated in Figure
-\ref{fig:sample23}.
+The systematic sampling method is illustrated in Figure \ref{fig:sample23}.
 
 \begin{figure}[H]
 
@@ -1112,13 +838,9 @@ \subsection{Systematic sampling}\label{systematic-sampling}}
 \end{figure}
 
 \hypertarget{sampling-in-urban-settings}{%
-\subsection{Sampling in urban
-settings}\label{sampling-in-urban-settings}}
+\subsection{Sampling in urban settings}\label{sampling-in-urban-settings}}
 
-In urban areas the first stage sample is taken by replacing
-sub-districts with ``sections'' and communities with city blocks.
-Examples of sections may be administrative districts/sub-districts or
-electoral wards.
+In urban areas the first stage sample is taken by replacing sub-districts with ``sections'' and communities with city blocks. Examples of sections may be administrative districts/sub-districts or electoral wards.
 
 \begin{figure}[H]
 
@@ -1129,35 +851,13 @@ \subsection{Sampling in urban
 \caption{Administrative divisions in an urban setting}\label{fig:sample24}
 \end{figure}
 
-Census enumeration areas (EAs) are usually city blocks. Central
-statistics offices can usually provide lists of EAs by ``section'' and
-large-scale maps of EAs selected for sampling (See Figure
-\ref{fig:sample25} and Figure \ref{fig:sample26}). These maps make it
-easy to locate EAs and their boundaries. The sample of EAs can be
-decided using list-based or map-based (CSAS) sampling.
-
-In these settings, eligible persons may be sampled by moving from
-door-to-door. All dwellings in the selected block are sampled and all
-eligible persons in the selected dwellings are sampled. This means that
-all eligible persons in a selected block are sampled.
-
-If city blocks are large then a type of systematic sampling may be used.
-With this method a rough map of the streets in the block is made and the
-number of doorways on each street is counted and copied onto the rough
-street map (as shown in Figure \ref{fig:sample27}). The total number of
-doorways on all streets is calculated. A step size is calculated by
-dividing the total number of doorways on all streets by the number of
-dwellings to be sampled. A systematic sample along a route around the
-block that includes all streets in the block is taken. Streets can be
-sampled in any order. If you find that you have sampled all streets but
-have not yet sampled the required number of eligible persons then you
-should return to the street with the largest number of houses to collect
-the remainder of the sample.
-
-The number of blocks to be sampled will depend on the expected number of
-eligible persons in each block. You should aim for an overall sample
-size of about \(n = 192\). You should not sample fewer than \(m = 16\)
-blocks.
+Census enumeration areas (EAs) are usually city blocks. Central statistics offices can usually provide lists of EAs by ``section'' and large-scale maps of EAs selected for sampling (See Figure \ref{fig:sample25} and Figure \ref{fig:sample26}). These maps make it easy to locate EAs and their boundaries. The sample of EAs can be decided using list-based or map-based (CSAS) sampling.
+
+In these settings, eligible persons may be sampled by moving from door-to-door. All dwellings in the selected block are sampled and all eligible persons in the selected dwellings are sampled. This means that all eligible persons in a selected block are sampled.
+
+If city blocks are large then a type of systematic sampling may be used. With this method a rough map of the streets in the block is made and the number of doorways on each street is counted and copied onto the rough street map (as shown in Figure \ref{fig:sample27}). The total number of doorways on all streets is calculated. A step size is calculated by dividing the total number of doorways on all streets by the number of dwellings to be sampled. A systematic sample along a route around the block that includes all streets in the block is taken. Streets can be sampled in any order. If you find that you have sampled all streets but have not yet sampled the required number of eligible persons then you should return to the street with the largest number of houses to collect the remainder of the sample.
+
+The number of blocks to be sampled will depend on the expected number of eligible persons in each block. You should aim for an overall sample size of about \(n = 192\). You should not sample fewer than \(m = 16\) blocks.
 
 \begin{figure}[H]
 
@@ -1188,14 +888,9 @@ \subsection{Sampling in urban
 \caption{Systematic sampling in a city block}\label{fig:sample27}
 \end{figure}
 
-When useful lists and maps are not available then satellite imagery
-available though free services such as Google Earth
-(\url{http://earth.google.com}) may be used.
+When useful lists and maps are not available then satellite imagery available though free services such as Google Earth (\url{http://earth.google.com}) may be used.
 
-The quality (resolution) of the images available from these services is
-variable but is usually good enough to allow you to segment the town
-into small areas of approximately equal volume (approximately the same
-number of dwellings) in each:
+The quality (resolution) of the images available from these services is variable but is usually good enough to allow you to segment the town into small areas of approximately equal volume (approximately the same number of dwellings) in each:
 
 \begin{figure}[H]
 
@@ -1206,18 +901,11 @@ \subsection{Sampling in urban
 \caption{Segmenting a town into smaller sampling areas}\label{fig:sample28}
 \end{figure}
 
-When creating segments using maps or satellite images it is a good idea
-to use main roads, rivers, canals, railway lines, public parks, etc as
-boundaries. This simplifies the segmentation process and also simplifies
-fieldwork by making areas and their boundaries easier to locate and
-sample.
+When creating segments using maps or satellite images it is a good idea to use main roads, rivers, canals, railway lines, public parks, etc as boundaries. This simplifies the segmentation process and also simplifies fieldwork by making areas and their boundaries easier to locate and sample.
 
-The first stage sample can be list-based (such as where each area is
-numbered in a systematic north to south and east to west order and a
-systematic sample taken) or map-based (CSAS).
+The first stage sample can be list-based (such as where each area is numbered in a systematic north to south and east to west order and a systematic sample taken) or map-based (CSAS).
 
-Larger scale ``maps'' of blocks to be sampled can also me made using
-satellite imagery (see Figure \ref{fig:sample29}).
+Larger scale ``maps'' of blocks to be sampled can also me made using satellite imagery (see Figure \ref{fig:sample29}).
 
 \newpage
 
@@ -1236,8 +924,7 @@ \chapter{Indicators}\label{indicators}}
 \hypertarget{the-ram-op-indicator-set}{%
 \section{The RAM-OP indicator set}\label{the-ram-op-indicator-set}}
 
-RAM-OP surveys collect and report on data for a broad range of
-indicators relevant to older people.
+RAM-OP surveys collect and report on data for a broad range of indicators relevant to older people.
 
 These indicators cover the following dimensions:
 
@@ -1269,38 +956,24 @@ \section{The RAM-OP indicator set}\label{the-ram-op-indicator-set}}
   Visual impairment
 \end{itemize}
 
-Data for a small group of miscellaneous indicators are also collected
-and reported.
+Data for a small group of miscellaneous indicators are also collected and reported.
 
-The RAM-OP indicator set has been designed on a modular basis. Each
-module is a set of indicators relating to a single dimension from the
-list given above and is collected using a dedicated set of questions and
-measurements. This means that the RAM-OP questionnaire also consists of
-a set of modules.
+The RAM-OP indicator set has been designed on a modular basis. Each module is a set of indicators relating to a single dimension from the list given above and is collected using a dedicated set of questions and measurements. This means that the RAM-OP questionnaire also consists of a set of modules.
 
-Whenever possible, RAM-OP uses standard and validated indicators and
-question sets.
+Whenever possible, RAM-OP uses standard and validated indicators and question sets.
 
-Indicators are described below, showing the questionnaire components
-that are used to collect and record the data required, and flowcharts of
-the process used to derive indicators from the collected data. Standard
-symbols are used. For example:
+Indicators are described below, showing the questionnaire components that are used to collect and record the data required, and flowcharts of the process used to derive indicators from the collected data. Standard symbols are used. For example:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators01} \end{center}
 
-A non-standard symbol is used to show \textbf{recode operations}. A
-recode operation shows changes that are made to data so that it can be
-used to derive indicators without having to show many decision nodes in
-the flowchart. They are also used to specify what should be done with
-missing or out-of-range values. For example:
+A non-standard symbol is used to show \textbf{recode operations}. A recode operation shows changes that are made to data so that it can be used to derive indicators without having to show many decision nodes in the flowchart. They are also used to specify what should be done with missing or out-of-range values. For example:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators02} \end{center}
 
 \hypertarget{demography-and-situation}{%
 \subsection{Demography and situation}\label{demography-and-situation}}
 
-The demography and situation indicators are used to describe the survey
-sample and are derived from this questionnaire component:
+The demography and situation indicators are used to describe the survey sample and are derived from this questionnaire component:
 
 \begin{center}\includegraphics[width=23.96in]{figures/questionnaire01} \end{center}
 
@@ -1313,8 +986,7 @@ \subsection{Demography and situation}\label{demography-and-situation}}
 \hypertarget{food-intake}{%
 \subsection{Food intake}\label{food-intake}}
 
-Food-intake indicators are derived from this questionnaire component.
-This data can be queried to yield a large number of useful indicators.
+Food-intake indicators are derived from this questionnaire component. This data can be queried to yield a large number of useful indicators.
 
 \newpage
 
@@ -1338,109 +1010,75 @@ \subsection{Food intake}\label{food-intake}}
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators04} \end{center}
 
-The data on the number of meals taken in the previous twenty-four hours
-forms a \emph{meal frequency score}.
+The data on the number of meals taken in the previous twenty-four hours forms a \emph{meal frequency score}.
 
-Food intake data from each subject is combined into a \emph{dietary
-diversity score}. The dietary diversity score is a crude measure of food
-security. The dietary diversity score ranges between zero (i.e.~no food
-groups) and eleven (i.e.~eleven food groups). Higher values of the
-dietary diversity sore are associated with better food security.
+Food intake data from each subject is combined into a \emph{dietary diversity score}. The dietary diversity score is a crude measure of food security. The dietary diversity score ranges between zero (i.e.~no food groups) and eleven (i.e.~eleven food groups). Higher values of the dietary diversity sore are associated with better food security.
 
 The meal frequency score and the dietary diversity score follow:
 
 \begin{itemize}
 \item
-  Swindale A, Bilinsky P, \emph{Household Dietary Diversity Score (HDDS)
-  for measurement of household food access: Indicator guide}.,Washington
-  DC, Food and Nutrition Technical Assistance (FANTA) Project, 2006
+  Swindale A, Bilinsky P, \emph{Household Dietary Diversity Score (HDDS) for measurement of household food access: Indicator guide}.,Washington DC, Food and Nutrition Technical Assistance (FANTA) Project, 2006
 \item
-  Kennedy G, Ballard T, Dop MC, \emph{Guidelines for Measuring Household
-  and Individual Dietary Diversity}, Rome, Food and Agricultural
-  Organization, 2010
+  Kennedy G, Ballard T, Dop MC, \emph{Guidelines for Measuring Household and Individual Dietary Diversity}, Rome, Food and Agricultural Organization, 2010
 \end{itemize}
 
-The data on the types of food consumed in the previous twenty-four hours
-are analysed in order to determine the diet's content of specific
-micronutrients that are important for older people. This also follows
-Swindale \& Bilinsky (2006) and Kennedy et al (2010), and:
+The data on the types of food consumed in the previous twenty-four hours are analysed in order to determine the diet's content of specific micronutrients that are important for older people. This also follows Swindale \& Bilinsky (2006) and Kennedy et al (2010), and:
 
 \begin{itemize}
 \tightlist
 \item
-  World Health Organisation, \emph{The management of nutrition in major
-  emergencies}, Geneva, WHO, 2000
+  World Health Organisation, \emph{The management of nutrition in major emergencies}, Geneva, WHO, 2000
 \end{itemize}
 
 \hypertarget{meal-frequency}{%
 \subsection{Meal frequency}\label{meal-frequency}}
 
-The meal frequency score indicator is the answer given to the first food
-intake question:
+The meal frequency score indicator is the answer given to the first food intake question:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators05} \end{center}
 
 Meal frequency is a crude measure of food security.
 
-Higher values of meal frequency are associated with better food
-security.
+Higher values of meal frequency are associated with better food security.
 
 \hypertarget{food-groups-and-dietary-diversity}{%
-\subsection{Food groups and dietary
-diversity}\label{food-groups-and-dietary-diversity}}
+\subsection{Food groups and dietary diversity}\label{food-groups-and-dietary-diversity}}
 
-Questions relating to the consumption of individual food items / food
-types are combined to create food groups and the number of food groups
-consumed are counted to create a dietary diversity score:
+Questions relating to the consumption of individual food items / food types are combined to create food groups and the number of food groups consumed are counted to create a dietary diversity score:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators06} \end{center}
 
-The consumption of the eleven individual food groups and the dietary
-diversity score are reported separately.
+The consumption of the eleven individual food groups and the dietary diversity score are reported separately.
 
-The dietary diversity score is a crude measure of food security. The
-dietary diversity score ranges between zero (no food groups) and eleven
-(eleven food groups). Higher values of the dietary diversity score are
-associated with better food security.
+The dietary diversity score is a crude measure of food security. The dietary diversity score ranges between zero (no food groups) and eleven (eleven food groups). Higher values of the dietary diversity score are associated with better food security.
 
 \hypertarget{indicators-of-nutrient-consumption}{%
-\subsection{Indicators of nutrient
-consumption}\label{indicators-of-nutrient-consumption}}
+\subsection{Indicators of nutrient consumption}\label{indicators-of-nutrient-consumption}}
 
 \textbf{Overview}
 
-Questions and combinations of questions relating to the consumption of
-individual food items and food types can be used to determine whether
-the reported diet is likely to be provide sufficient nutrients of
-various types:
+Questions and combinations of questions relating to the consumption of individual food items and food types can be used to determine whether the reported diet is likely to be provide sufficient nutrients of various types:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators07} \end{center}
 
-Each indicator is formed using logical ``or'' operations (i.e.~the
-indicator is true if \textbf{any} of the constituent foods are
-consumed). For example, the indicator for the consumption of iron rich
-foods:
+Each indicator is formed using logical ``or'' operations (i.e.~the indicator is true if \textbf{any} of the constituent foods are consumed). For example, the indicator for the consumption of iron rich foods:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators08} \end{center}
 
-requires the consumption of one or more of green leafy vegetables, organ
-meats, meat, or fish and shellfish. Consumption of \textbf{any} of these
-foods is sufficient to indicate that the survey subject consumes iron
-rich food.
+requires the consumption of one or more of green leafy vegetables, organ meats, meat, or fish and shellfish. Consumption of \textbf{any} of these foods is sufficient to indicate that the survey subject consumes iron rich food.
 
 \hypertarget{protein-rich-foods}{%
 \subsubsection{Protein rich foods}\label{protein-rich-foods}}
 
-Indicators of consumption of protein rich foods from animal sources,
-plant source, and any / all sources are calculated as:
+Indicators of consumption of protein rich foods from animal sources, plant source, and any / all sources are calculated as:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators09} \end{center}
 
 \hypertarget{vitamin-a-rich-foods}{%
 \subsubsection{Vitamin A rich foods}\label{vitamin-a-rich-foods}}
 
-Indicators of consumption of vitamin A rich foods from animal sources,
-plant source, and any / all sources are calculated as:
+Indicators of consumption of vitamin A rich foods from animal sources, plant source, and any / all sources are calculated as:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators10} \end{center}
 
@@ -1449,24 +1087,21 @@ \subsubsection{Vitamin A rich foods}\label{vitamin-a-rich-foods}}
 \hypertarget{iron-rich-foods}{%
 \subsubsection{Iron rich foods}\label{iron-rich-foods}}
 
-An indicator of consumption of iron rich foods from any / all sources is
-calculated as:
+An indicator of consumption of iron rich foods from any / all sources is calculated as:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators11} \end{center}
 
 \hypertarget{calcium-rich-foods}{%
 \subsubsection{Calcium rich foods}\label{calcium-rich-foods}}
 
-An indicator of consumption of calcium rich foods from any / all sources
-is calculated as:
+An indicator of consumption of calcium rich foods from any / all sources is calculated as:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators12} \end{center}
 
 \hypertarget{zinc-rich-foods}{%
 \subsubsection{Zinc rich foods}\label{zinc-rich-foods}}
 
-An indicator of consumption of zinc rich foods from any / all sources is
-calculated as:
+An indicator of consumption of zinc rich foods from any / all sources is calculated as:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators13} \end{center}
 
@@ -1475,20 +1110,16 @@ \subsubsection{Zinc rich foods}\label{zinc-rich-foods}}
 \hypertarget{vitamin-b-rich-foods}{%
 \subsubsection{Vitamin B rich foods}\label{vitamin-b-rich-foods}}
 
-Indicators of consumption of vitamin B rich foods from any / all sources
-are calculated as:
+Indicators of consumption of vitamin B rich foods from any / all sources are calculated as:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators14} \end{center}
 
-Note that the vitamin B complex indicator requires that at least one
-food from each of the B1, B2, B3, B6, and B12 rich food combinations is
-consumed.
+Note that the vitamin B complex indicator requires that at least one food from each of the B1, B2, B3, B6, and B12 rich food combinations is consumed.
 
 \hypertarget{severe-food-insecurity}{%
 \subsection{Severe food insecurity}\label{severe-food-insecurity}}
 
-An indicator of severe food insecurity (hunger) is derived from this
-questionnaire component:
+An indicator of severe food insecurity (hunger) is derived from this questionnaire component:
 
 \begin{center}\includegraphics[width=23.88in]{figures/questionnaire03} \end{center}
 
@@ -1498,38 +1129,25 @@ \subsection{Severe food insecurity}\label{severe-food-insecurity}}
 
 \newpage
 
-This indicator is the \emph{Household Hunger Scale (HHS)} and is a
-simple, well-validated, and widely used indicator of severe food
-insecurity:
+This indicator is the \emph{Household Hunger Scale (HHS)} and is a simple, well-validated, and widely used indicator of severe food insecurity:
 
 \begin{itemize}
 \item
-  Ballard T, Coates J, Swindale A, Deitchler M, \emph{Household Hunger
-  Scale: Indicator Definition and Measurement Guide}, Washington DC,
-  FANTA-2 Bridge, FHI 360, 2011
+  Ballard T, Coates J, Swindale A, Deitchler M, \emph{Household Hunger Scale: Indicator Definition and Measurement Guide}, Washington DC, FANTA-2 Bridge, FHI 360, 2011
 \item
-  Ruel MT, Ballard TJ, Deitchler M, \emph{Measuring and Tracking the
-  Access Dimension of Food Security: Available Indicators and
-  Recommendations for Future Investments}, Global Nutrition Report 2014:
-  Technical Note 6, Washington DC, International Food Policy Research
-  Institute, 2014
+  Ruel MT, Ballard TJ, Deitchler M, \emph{Measuring and Tracking the Access Dimension of Food Security: Available Indicators and Recommendations for Future Investments}, Global Nutrition Report 2014: Technical Note 6, Washington DC, International Food Policy Research Institute, 2014
 \end{itemize}
 
 \hypertarget{disability}{%
 \subsection{Disability}\label{disability}}
 
-Indicators of disability across six different domains are derived from
-this questionnaire component:
+Indicators of disability across six different domains are derived from this questionnaire component:
 
 \begin{center}\includegraphics[width=23.94in]{figures/questionnaire04} \end{center}
 
 \newpage
 
-Individual disability indicators are reported for each domain
-(i.e.~vision, hearing, mobility, remembering, self-care, and
-communication) of disability in the Washington Group's short set of
-question designed to identify people with a disability in a census or
-survey format:
+Individual disability indicators are reported for each domain (i.e.~vision, hearing, mobility, remembering, self-care, and communication) of disability in the Washington Group's short set of question designed to identify people with a disability in a census or survey format:
 
 \begin{itemize}
 \item
@@ -1548,11 +1166,11 @@ \subsection{Disability}\label{disability}}
 
 \begin{longtable}[]{@{}ll@{}}
 \toprule
+\endhead
 \begin{minipage}[t]{0.11\columnwidth}\raggedright
 \texttt{P0} = 1\strut
 \end{minipage} & \begin{minipage}[t]{0.83\columnwidth}\raggedright
-if no domain has \texttt{D1} = 1, else = 0 (no disability in any
-domain)\strut
+if no domain has \texttt{D1} = 1, else = 0 (no disability in any domain)\strut
 \end{minipage}\tabularnewline
 \begin{minipage}[t]{0.11\columnwidth}\raggedright
 \texttt{P1} = 1\strut
@@ -1572,57 +1190,40 @@ \subsection{Disability}\label{disability}}
 \begin{minipage}[t]{0.11\columnwidth}\raggedright
 \texttt{PM} = 1\strut
 \end{minipage} & \begin{minipage}[t]{0.83\columnwidth}\raggedright
-if at more than one domain has \texttt{D1} = 1, else = 0 (M stands for
-``Multiple'')\strut
+if at more than one domain has \texttt{D1} = 1, else = 0 (M stands for ``Multiple'')\strut
 \end{minipage}\tabularnewline
 \bottomrule
 \end{longtable}
 
 \hypertarget{activities-of-daily-living}{%
-\subsection{Activities of daily
-living}\label{activities-of-daily-living}}
+\subsection{Activities of daily living}\label{activities-of-daily-living}}
 
-Indicators of how well the subject copes with activities of daily living
-are derived from this questionnaire component:
+Indicators of how well the subject copes with activities of daily living are derived from this questionnaire component:
 
 \begin{center}\includegraphics[width=23.89in]{figures/questionnaire05} \end{center}
 
-Individual \emph{independence} indicators are reported for each
-dimension (i.e.~bathing, dressing, toilet, mobility, continence, and
-eating) of daily living activities.
+Individual \emph{independence} indicators are reported for each dimension (i.e.~bathing, dressing, toilet, mobility, continence, and eating) of daily living activities.
 
-A composite indicator of the degree of \emph{independence} (i.e.~how
-well the subject can cope with activities of daily living) is also
-reported. This indicator is the \emph{Katz Index of Independence in
-Activities of Daily Living} (or the \emph{Katz Index of ADL} for short)
-and is a simple, well-validated, and widely used indicator of how well
-the subject can cope with activities of daily living:
+A composite indicator of the degree of \emph{independence} (i.e.~how well the subject can cope with activities of daily living) is also reported. This indicator is the \emph{Katz Index of Independence in Activities of Daily Living} (or the \emph{Katz Index of ADL} for short) and is a simple, well-validated, and widely used indicator of how well the subject can cope with activities of daily living:
 
 \begin{itemize}
 \item
-  Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW, \emph{Studies of
-  illness in the aged. The Index of ADL: A standardized measure of
-  biological and psychosocial function}, JAMA, 185(12), 1963, pp.~914-9
+  Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW, \emph{Studies of illness in the aged. The Index of ADL: A standardized measure of biological and psychosocial function}, JAMA, 185(12), 1963, pp.~914-9
 \item
-  Katz S, Down TD, Cash HR, Grotz, RC, \emph{Progress in the development
-  of the index of ADL}, The Gerontologist, 10(1), 1970, pp.~20-30
+  Katz S, Down TD, Cash HR, Grotz, RC, \emph{Progress in the development of the index of ADL}, The Gerontologist, 10(1), 1970, pp.~20-30
 \item
-  Katz S, \emph{Assessing self-maintenance: Activities of daily living,
-  mobility and instrumental activities of daily living}, JAGS, 31(12),
-  1983, pp.~721-726
+  Katz S, \emph{Assessing self-maintenance: Activities of daily living, mobility and instrumental activities of daily living}, JAGS, 31(12), 1983, pp.~721-726
 \end{itemize}
 
-The Katz Index of ADL ranges between zero (complete dependence) and six
-(independence).
+The Katz Index of ADL ranges between zero (complete dependence) and six (independence).
 
 \newpage
 
-The seventh question of this module, which is not part of the Katz Index
-of ADL, is reported separately and indicates whether the subject has
-someone to help them with activities of daily living:
+The seventh question of this module, which is not part of the Katz Index of ADL, is reported separately and indicates whether the subject has someone to help them with activities of daily living:
 
 \begin{longtable}[]{@{}c@{}}
 \toprule
+\endhead
 \begin{minipage}[t]{0.97\columnwidth}\centering
 \textbf{Activities of Daily Living}\strut
 \end{minipage}\tabularnewline
@@ -1631,11 +1232,13 @@ \subsection{Activities of daily
 
 \begin{longtable}[]{@{}llll@{}}
 \toprule
+\endhead
 \begin{minipage}[t]{0.09\columnwidth}\raggedright
 a7\strut
 \end{minipage} & \begin{minipage}[t]{0.41\columnwidth}\raggedright
-Is someone taking care of you or helping you with everyday activities
-such as shopping, cooking, bathing and dressing?\strut
+Is someone taking care of you or helping you with
+everyday activities such as shopping, cooking,
+bathing and dressing?\strut
 \end{minipage} & \begin{minipage}[t]{0.25\columnwidth}\raggedright
 1 = Yes; 2 = No\strut
 \end{minipage} & \begin{minipage}[t]{0.13\columnwidth}\raggedright
@@ -1644,10 +1247,7 @@ \subsection{Activities of daily
 \bottomrule
 \end{longtable}
 
-It is not possible to know if the help available completely meets a
-subject's needs, but we can identify the proportion of subjects needing
-help with one or more activities of daily living who also report not
-having someone to help them:
+It is not possible to know if the help available completely meets a subject's needs, but we can identify the proportion of subjects needing help with one or more activities of daily living who also report not having someone to help them:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators17} \end{center}
 
@@ -1655,37 +1255,24 @@ \subsection{Activities of daily
 
 \newpage
 
-Indicators of how well the subject can cope with activities of daily
-living and probable unmet need are calculated as:
+Indicators of how well the subject can cope with activities of daily living and probable unmet need are calculated as:
 
 \begin{center}\includegraphics[width=7.68in]{figures/indicators18} \end{center}
 
 \hypertarget{mental-health-and-well-being}{%
-\subsection{Mental health and
-well-being}\label{mental-health-and-well-being}}
+\subsection{Mental health and well-being}\label{mental-health-and-well-being}}
 
-Indicators of mental health and well being are derived from this
-questionnaire component:
+Indicators of mental health and well being are derived from this questionnaire component:
 
 \begin{center}\includegraphics[width=23.86in]{figures/questionnaire06} \end{center}
 
-A score is calculated. This is the \emph{Kessler K6 Psychological
-Distress Scale}. The score ranges from zero (indicating no psychological
-distress) to twenty-four (indicating severe psychological distress). A
-score of thirteen or more indicates serious psychological distress. The
-Kessler K6 Psychological Distress Scale is a widely recommended, widely
-used, accurate, reliable, and simple measure of psychological distress:
+A score is calculated. This is the \emph{Kessler K6 Psychological Distress Scale}. The score ranges from zero (indicating no psychological distress) to twenty-four (indicating severe psychological distress). A score of thirteen or more indicates serious psychological distress. The Kessler K6 Psychological Distress Scale is a widely recommended, widely used, accurate, reliable, and simple measure of psychological distress:
 
 \begin{itemize}
 \item
-  Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek, DK, Normand SLT,
-  et al, ``Short screening scales to monitor population prevalences and
-  trends in non-specific psychological distress'', \emph{Psychological
-  Medicine}, 32(6), 2002, pp.~959--976
+  Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek, DK, Normand SLT, et al, ``Short screening scales to monitor population prevalences and trends in non-specific psychological distress'', \emph{Psychological Medicine}, 32(6), 2002, pp.~959--976
 \item
-  Kessler RC, Barker PR, Colpe LJ, Epstein JF, Gfroerer JC, Hiripi E,
-  ``Screening for Serious Mental Illness in the General Population'',
-  \emph{Archives of General Psychiatry}, 60(2), 2003, pp.~184-189
+  Kessler RC, Barker PR, Colpe LJ, Epstein JF, Gfroerer JC, Hiripi E, ``Screening for Serious Mental Illness in the General Population'', \emph{Archives of General Psychiatry}, 60(2), 2003, pp.~184-189
 \end{itemize}
 
 Indicators of mental health and well-being are calculated as:
@@ -1697,8 +1284,7 @@ \subsection{Mental health and
 \hypertarget{dementia}{%
 \subsection{Dementia}\label{dementia}}
 
-An indicator of probable dementia is derived from this questionnaire
-component:
+An indicator of probable dementia is derived from this questionnaire component:
 
 \begin{center}\includegraphics[width=23.94in]{figures/questionnaire07} \end{center}
 
@@ -1708,50 +1294,39 @@ \subsection{Dementia}\label{dementia}}
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators20} \end{center}
 
-This indicator is derived from the Community Screening Instrument for
-Dementia (CSID) developed by the 10/66 Dementia Research Group. This is
-a simple, validated, and widely used indicator of probable dementia:
+This indicator is derived from the Community Screening Instrument for Dementia (CSID) developed by the 10/66 Dementia Research Group. This is a simple, validated, and widely used indicator of probable dementia:
 
 \begin{itemize}
 \tightlist
 \item
-  Prince M, et al, ``A brief dementia screener suitable for use by
-  non-specialists in resource poor settings - The cross-cultural
-  derivation and validation of the brief Community Screening Instrument
-  for Dementia'', \emph{International Journal of Geriatric Psychiatry},
-  26(9), 2011, pp.~899--907
+  Prince M, et al, ``A brief dementia screener suitable for use by non-specialists in resource poor settings - The cross-cultural derivation and validation of the brief Community Screening Instrument for Dementia'', \emph{International Journal of Geriatric Psychiatry}, 26(9), 2011, pp.~899--907
 \end{itemize}
 
 \newpage
 
 \hypertarget{health-and-health-seeking-behaviour}{%
-\subsection{Health and health-seeking
-behaviour}\label{health-and-health-seeking-behaviour}}
+\subsection{Health and health-seeking behaviour}\label{health-and-health-seeking-behaviour}}
 
-Indicators of health and health-seeking behaviour for chronic and acute
-conditions are derived from this questionnaire component:
+Indicators of health and health-seeking behaviour for chronic and acute conditions are derived from this questionnaire component:
 
 \begin{center}\includegraphics[width=23.97in]{figures/questionnaire08} \end{center}
 
 \newpage
 
-Indicators of health and health-seeking behaviour for chronic conditions
-are calculated as:
+Indicators of health and health-seeking behaviour for chronic conditions are calculated as:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators21} \end{center}
 
 \newpage
 
-Indicators of health and health-seeking behaviour for acute conditions
-are calculated as:
+Indicators of health and health-seeking behaviour for acute conditions are calculated as:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators22} \end{center}
 
 \hypertarget{sources-of-income}{%
 \subsection{Sources of income}\label{sources-of-income}}
 
-Indicators related to sources of income are derived from this
-questionnaire component:
+Indicators related to sources of income are derived from this questionnaire component:
 
 \begin{center}\includegraphics[width=23.93in]{figures/questionnaire09} \end{center}
 
@@ -1759,19 +1334,14 @@ \subsection{Sources of income}\label{sources-of-income}}
 
 \begin{center}\includegraphics[width=7.68in]{figures/indicators23} \end{center}
 
-The grouped income sources (i.e. \texttt{m2a}, \texttt{m2b}, etc.) and
-individual income sources may vary between settings. The questionnaire
-component shown above has proved suitable for use in Ethiopia, South
-Sudan, and Tanzania.
+The grouped income sources (i.e. \texttt{m2a}, \texttt{m2b}, etc.) and individual income sources may vary between settings. The questionnaire component shown above has proved suitable for use in Ethiopia, South Sudan, and Tanzania.
 
 \newpage
 
 \hypertarget{water-sanitation-and-hygiene}{%
-\subsection{Water, sanitation, and
-hygiene}\label{water-sanitation-and-hygiene}}
+\subsection{Water, sanitation, and hygiene}\label{water-sanitation-and-hygiene}}
 
-Indicators relating to water, sanitation, and hygiene (WASH) are derived
-from this questionnaire component:
+Indicators relating to water, sanitation, and hygiene (WASH) are derived from this questionnaire component:
 
 \begin{center}\includegraphics[width=23.85in]{figures/questionnaire10} \end{center}
 
@@ -1782,23 +1352,19 @@ \subsection{Water, sanitation, and
 \begin{itemize}
 \tightlist
 \item
-  WHO / UNICEF, \emph{Core Questions on Drinking-water and Sanitation
-  for Household Surveys}, Geneva, WHO / UNICEF, 2006
+  WHO / UNICEF, \emph{Core Questions on Drinking-water and Sanitation for Household Surveys}, Geneva, WHO / UNICEF, 2006
 \end{itemize}
 
-Indicators relating to water, sanitation, and hygiene (WASH) are
-calculated as:
+Indicators relating to water, sanitation, and hygiene (WASH) are calculated as:
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators24} \end{center}
 
 \newpage
 
 \hypertarget{anthropometry-and-screening-coverage}{%
-\subsection{Anthropometry and screening
-coverage}\label{anthropometry-and-screening-coverage}}
+\subsection{Anthropometry and screening coverage}\label{anthropometry-and-screening-coverage}}
 
-Indicators relating to anthropometry and screening coverage are derived
-from this questionnaire component:
+Indicators relating to anthropometry and screening coverage are derived from this questionnaire component:
 
 \begin{center}\includegraphics[width=23.86in]{figures/questionnaire11} \end{center}
 
@@ -1806,36 +1372,19 @@ \subsection{Anthropometry and screening
 
 \begin{center}\includegraphics[width=8.38in]{figures/indicators25} \end{center}
 
-Raw MUAC data (i.e.~not MUAC class) is collected, entered, and analysed.
-This requires that an adult MUAC tape (i.e.~capable of measuring MUAC to
-450 mm) is used.
+Raw MUAC data (i.e.~not MUAC class) is collected, entered, and analysed. This requires that an adult MUAC tape (i.e.~capable of measuring MUAC to 450 mm) is used.
 
-The presence of bilateral oedema is assessed by pressing with your
-thumbs \textbf{both} feet of the older person for three seconds and
-checking whether this creates a lasting depression or ``pit'' on both
-feet. Bilateral pitting oedema in older people may not be
-``nutritional'' oedema (as is almost always the case with children).
-Older people with bilateral pitting oedema should be advised to consult
-a doctor.
+The presence of bilateral oedema is assessed by pressing with your thumbs \textbf{both} feet of the older person for three seconds and checking whether this creates a lasting depression or ``pit'' on both feet. Bilateral pitting oedema in older people may not be ``nutritional'' oedema (as is almost always the case with children). Older people with bilateral pitting oedema should be advised to consult a doctor.
 
-The prevalence of GAM, MAM, and SAM are estimated using a PROBIT
-estimator. This type of estimator provides better precision than a
-classic estimator at small sample sizes:
+The prevalence of GAM, MAM, and SAM are estimated using a PROBIT estimator. This type of estimator provides better precision than a classic estimator at small sample sizes:
 
 \begin{itemize}
 \item
-  World Health Organisation, \emph{Physical Status: The use and
-  interpretation of anthropometry. Report of a WHO expert committee},
-  WHO Technical Report Series 854, WHO, Geneva, 1995
+  World Health Organisation, \emph{Physical Status: The use and interpretation of anthropometry. Report of a WHO expert committee}, WHO Technical Report Series 854, WHO, Geneva, 1995
 \item
-  Dale NM, Myatt M, Prudhon C, Briend, A, ``Assessment of the PROBIT
-  approach for estimating the prevalence of global, moderate and severe
-  acute malnutrition from population surveys'', \emph{Public Health
-  Nutrition}, 1--6. \url{doi:10.1017/S1368980012003345}, 2012
+  Dale NM, Myatt M, Prudhon C, Briend, A, ``Assessment of the PROBIT approach for estimating the prevalence of global, moderate and severe acute malnutrition from population surveys'', \emph{Public Health Nutrition}, 1--6. \url{doi:10.1017/S1368980012003345}, 2012
 \item
-  Blanton CJ, Bilukha, OO, ``The PROBIT approach in estimating the
-  prevalence of wasting: revisiting bias and precision'', \emph{Emerging
-  Themes in Epidemiology}, 10(1), 2013, p.~8
+  Blanton CJ, Bilukha, OO, ``The PROBIT approach in estimating the prevalence of wasting: revisiting bias and precision'', \emph{Emerging Themes in Epidemiology}, 10(1), 2013, p.~8
 \end{itemize}
 
 The PROBIT estimator is described in Box 1.
@@ -1844,60 +1393,41 @@ \subsection{Anthropometry and screening
 
 \begin{longtable}[]{@{}cl@{}}
 \toprule
-\begin{minipage}[t]{0.48\columnwidth}\centering
+\endhead
+\begin{minipage}[t]{0.14\columnwidth}\centering
 \textbf{GAM}\strut
-\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright
+\end{minipage} & \begin{minipage}[t]{0.31\columnwidth}\raggedright
 MUAC \textless{} 210 mm\strut
 \end{minipage}\tabularnewline
-\begin{minipage}[t]{0.48\columnwidth}\centering
+\begin{minipage}[t]{0.14\columnwidth}\centering
 \textbf{MAM}\strut
-\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright
+\end{minipage} & \begin{minipage}[t]{0.31\columnwidth}\raggedright
 185 mm ≤ MUAC \textless{} 210mm\strut
 \end{minipage}\tabularnewline
-\begin{minipage}[t]{0.48\columnwidth}\centering
+\begin{minipage}[t]{0.14\columnwidth}\centering
 \textbf{SAM}\strut
-\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright
+\end{minipage} & \begin{minipage}[t]{0.31\columnwidth}\raggedright
 MUAC \textless{} 185mm\strut
 \end{minipage}\tabularnewline
 \bottomrule
 \end{longtable}
 
-These are standard case definitions for acute malnutrition in adults and
-recommended by HelpAge International for use in older people in
-humanitarian contexts.
+These are standard case definitions for acute malnutrition in adults and recommended by HelpAge International for use in older people in humanitarian contexts.
 
-\textbf{Note} : MUAC in adults should be measured on the non-dominant
-arm. This is usually the left arm. The importance of high levels of
-accuracy and precision at the individual level is of lesser importance
-in survey work compared to case-finding or diagnosis in clinical
-contexts, for example. This means that a simple rule such as ``Always
-measure MUAC on the left arm'' may be used.
+\textbf{Note} : MUAC in adults should be measured on the non-dominant arm. This is usually the left arm. The importance of high levels of accuracy and precision at the individual level is of lesser importance in survey work compared to case-finding or diagnosis in clinical contexts, for example. This means that a simple rule such as ``Always measure MUAC on the left arm'' may be used.
 
 \newpage
 
-\BeginKnitrBlock{rmdbox}
+\BeginKnitrBlock{rmdnote}
 An estimate of GAM prevalence can be made using a classic estimator:
 
 \[\text{prevalence} = \frac{\text{number of respondents with MUAC < 210 mm}}{\text{total number of respondents}}\]
 
-The estimate of GAM prevalence made from the RAM-OP survey data is made
-using a PROBIT estimator. The PROBIT function is also known as the
-\emph{inverse cumulative distribution} function. This function converts
-parameters of the distribution of an indicator (e.g.~the mean and
-standard deviation of a \emph{normally} distributed variable) into
-cumulative percentiles. This means that it is possible to use the normal
-PROBIT function with estimates of the mean and standard deviation of
-indicator values in a survey sample to predict (or estimate) the
-proportion of the population falling below a given threshold. For
-example, for data with a mean MUAC of 256 mm and a standard deviation of
-28 mm the output of the normal PROBIT function for a threshold of 210 mm
-is 0.0502 meaning that 5.02\% of the population are \emph{predicted} (or
-\emph{estimated}) to fall below the 210 mm threshold.
+The estimate of GAM prevalence made from the RAM-OP survey data is made using a PROBIT estimator. The PROBIT function is also known as the \emph{inverse cumulative distribution} function. This function converts parameters of the distribution of an indicator (e.g.~the mean and standard deviation of a \emph{normally} distributed variable) into cumulative percentiles. This means that it is possible to use the normal PROBIT function with estimates of the mean and standard deviation of indicator values in a survey sample to predict (or estimate) the proportion of the population falling below a given threshold. For example, for data with a mean MUAC of 256 mm and a standard deviation of 28 mm the output of the normal PROBIT function for a threshold of 210 mm is 0.0502 meaning that 5.02\% of the population are \emph{predicted} (or \emph{estimated}) to fall below the 210 mm threshold.
 
 ~
 
-Both the classic and the PROBIT methods can be thought of as estimating
-area:
+Both the classic and the PROBIT methods can be thought of as estimating area:
 
 ~
 
@@ -1905,32 +1435,25 @@ \subsection{Anthropometry and screening
 
 ~
 
-The principal advantage of the PROBIT approach is that the required
-sample size is usually smaller than that required to estimate prevalence
-with a given precision using the classic method.
+The principal advantage of the PROBIT approach is that the required sample size is usually smaller than that required to estimate prevalence with a given precision using the classic method.
 
 ~
 
-The PROBIT method assumes that MUAC is a normally distributed variable.
-If this is not the case then the distribution of MUAC is transformed
-towards normality.
+The PROBIT method assumes that MUAC is a normally distributed variable. If this is not the case then the distribution of MUAC is transformed towards normality.
 
 ~
 
-The prevalence of SAM is estimated in a similar way to GAM. The
-prevalence of MAM is estimated as the difference between the GAM and SAM
-prevalence estimates:
+The prevalence of SAM is estimated in a similar way to GAM. The prevalence of MAM is estimated as the difference between the GAM and SAM prevalence estimates:
 
 \[\widehat{MAM prevalence} = \widehat{GAM prevalence} - \widehat{SAM prevalence}\]
-\EndKnitrBlock{rmdbox}
+\EndKnitrBlock{rmdnote}
 
 \newpage
 
 \hypertarget{visual-impairment}{%
 \subsection{Visual impairment}\label{visual-impairment}}
 
-An indicator of visual impairment is derived from this questionnaire
-component:
+An indicator of visual impairment is derived from this questionnaire component:
 
 \begin{center}\includegraphics[width=23.81in]{figures/questionnaire12} \end{center}
 
@@ -1940,19 +1463,13 @@ \subsection{Visual impairment}\label{visual-impairment}}
 
 \begin{center}\includegraphics[width=9.76in]{figures/indicators27} \end{center}
 
-The ``illiterate E'' or ``tumbling E'' (the preferred term) is a
-validated and widely used method for measuring visual acuity:
+The ``illiterate E'' or ``tumbling E'' (the preferred term) is a validated and widely used method for measuring visual acuity:
 
 \begin{itemize}
 \item
-  Taylor HR, ``Applying new design principles to the construction of an
-  illiterate E chart'', \emph{American Journal of Optometry \&
-  Physiological Optics}, 55:348, 1978
+  Taylor HR, ``Applying new design principles to the construction of an illiterate E chart'', \emph{American Journal of Optometry \& Physiological Optics}, 55:348, 1978
 \item
-  Kaiser PK, ``Prospective Evaluation of Visual Acuity Assessment: A
-  Comparison of Snellen Versus ETDRS Charts in Clinical Practice (An AOS
-  Thesis)'', \emph{Transactions of the American Ophthalmological
-  Society}, 107: 311--324, 2009
+  Kaiser PK, ``Prospective Evaluation of Visual Acuity Assessment: A Comparison of Snellen Versus ETDRS Charts in Clinical Practice (An AOS Thesis)'', \emph{Transactions of the American Ophthalmological Society}, 107: 311--324, 2009
 \end{itemize}
 
 \newpage
@@ -1961,31 +1478,13 @@ \subsection{Visual impairment}\label{visual-impairment}}
 
 \begin{center}\includegraphics[width=8.33in]{figures/indicators28} \end{center}
 
-as well as the distance used for the test (two metres) and the indicator
-calculation apply the WHO case definition of visual impairment
-(i.e.~visual acuity \textless{} 6 / 18).
-
-The tumbling E card should be laminated (i.e.~plastic coated and have a
-two metre cord attached which helps to ensure that the visual acuity
-test is performed at the correct distance (See Figure
-\ref{fig:indicators29}).
-
-After demonstrating to the respondent what the test is about (i.e.~the
-subject should indicate which direction the branches of the `E' are
-pointing), the test is administered at a distance of two meters, turning
-the card in four different directions, and asking the person to indicate
-which direction the branches of the ``E'' is pointing. If the subject
-wears glasses, they are allowed to use them during the test if they want
-to.
-
-\textbf{Note} : If the person is unable to correctly answer at least
-three times out of four, they have a visual impairment. A simple visual
-acuity test such as the `tumbling E' test also does not indicate
-anything about an underlying disease such as glaucoma or the need for
-reading spectacles (presbyopia). These conditions are common in people
-aged 60 years or older. Subjects failing the visual acuity test should
-be counselled to visit an ophthalmologist for a detailed eye
-examination.
+as well as the distance used for the test (two metres) and the indicator calculation apply the WHO case definition of visual impairment (i.e.~visual acuity \textless{} 6 / 18).
+
+The tumbling E card should be laminated (i.e.~plastic coated and have a two metre cord attached which helps to ensure that the visual acuity test is performed at the correct distance (See Figure \ref{fig:indicators29}).
+
+After demonstrating to the respondent what the test is about (i.e.~the subject should indicate which direction the branches of the `E' are pointing), the test is administered at a distance of two meters, turning the card in four different directions, and asking the person to indicate which direction the branches of the ``E'' is pointing. If the subject wears glasses, they are allowed to use them during the test if they want to.
+
+\textbf{Note} : If the person is unable to correctly answer at least three times out of four, they have a visual impairment. A simple visual acuity test such as the `tumbling E' test also does not indicate anything about an underlying disease such as glaucoma or the need for reading spectacles (presbyopia). These conditions are common in people aged 60 years or older. Subjects failing the visual acuity test should be counselled to visit an ophthalmologist for a detailed eye examination.
 
 \newpage
 
@@ -1996,23 +1495,24 @@ \subsection{Visual impairment}\label{visual-impairment}}
 }
 
 \caption{Equipment used to measure visual acuity}\label{fig:indicators29}
-\end{figure}\newpage
+\end{figure}
+\newpage
 
 \hypertarget{miscellaneous-indicators}{%
 \subsection{Miscellaneous indicators}\label{miscellaneous-indicators}}
 
-Data for a small group of miscellaneous indicators are also collected
-and reported. These are derived from these questions:
+Data for a small group of miscellaneous indicators are also collected and reported. These are derived from these questions:
 
 \textbf{Hunger -- Ration - Relief}
 
 \begin{longtable}[]{@{}llll@{}}
 \toprule
+\endhead
 \begin{minipage}[t]{0.09\columnwidth}\raggedright
 f6\strut
 \end{minipage} & \begin{minipage}[t]{0.41\columnwidth}\raggedright
-Are you or anyone in your household receiving a food ration on a regular
-basis?\strut
+Are you or anyone in your household receiving a
+food ration on a regular basis?\strut
 \end{minipage} & \begin{minipage}[t]{0.25\columnwidth}\raggedright
 1 = Yes; 2 = No\strut
 \end{minipage} & \begin{minipage}[t]{0.13\columnwidth}\raggedright
@@ -2021,9 +1521,11 @@ \subsection{Miscellaneous indicators}\label{miscellaneous-indicators}}
 \begin{minipage}[t]{0.09\columnwidth}\raggedright
 f7\strut
 \end{minipage} & \begin{minipage}[t]{0.41\columnwidth}\raggedright
-Have you or another member of your household received non-food relief
-items such as soap, bucket, water container, bedding, mosquito net,
-clothes, or plastic sheet in the previous four weeks?\strut
+Have you or another member of your household
+received non-food relief items such as soap,
+bucket, water container, bedding, mosquito net,
+clothes, or plastic sheet in the previous four
+weeks?\strut
 \end{minipage} & \begin{minipage}[t]{0.25\columnwidth}\raggedright
 1 = Yes; 2 = No\strut
 \end{minipage} & \begin{minipage}[t]{0.13\columnwidth}\raggedright
@@ -2038,6 +1540,7 @@ \subsection{Miscellaneous indicators}\label{miscellaneous-indicators}}
 
 \begin{longtable}[]{@{}llll@{}}
 \toprule
+\endhead
 \begin{minipage}[t]{0.09\columnwidth}\raggedright
 a8\strut
 \end{minipage} & \begin{minipage}[t]{0.41\columnwidth}\raggedright
@@ -2057,73 +1560,33 @@ \subsection{Miscellaneous indicators}\label{miscellaneous-indicators}}
 \begin{center}\includegraphics[width=9.76in]{figures/indicators30} \end{center}
 
 \hypertarget{a-note-on-data-management-and-data-analysis}{%
-\section{A note on data management and data
-analysis}\label{a-note-on-data-management-and-data-analysis}}
-
-This section has described how RAM-OP data is used to create a broad set
-of indicators. If you do not want to use the standard RAM-OP software to
-do this then you can use this information to create data entry systems
-and data management scripts for your favoured database or statistical
-analysis software. See the sections on
-\protect\hyperlink{datasets}{\textbf{RAM-OP datasets}} and
-\protect\hyperlink{questionnaire}{\textbf{RAM-OP questionnaire}} for
-more compact information on variable names and codes that you may find
-helpful.
-
-It is important to note that data analysis procedures need to account
-for the sample design. All major statistical analysis software can do
-this (details vary). There are two things to note:
+\section{A note on data management and data analysis}\label{a-note-on-data-management-and-data-analysis}}
+
+This section has described how RAM-OP data is used to create a broad set of indicators. If you do not want to use the standard RAM-OP software to do this then you can use this information to create data entry systems and data management scripts for your favoured database or statistical analysis software. See the sections on \protect\hyperlink{datasets}{\textbf{RAM-OP datasets}} and \protect\hyperlink{questionnaire}{\textbf{RAM-OP questionnaire}} for more compact information on variable names and codes that you may find helpful.
+
+It is important to note that data analysis procedures need to account for the sample design. All major statistical analysis software can do this (details vary). There are two things to note:
 
 \begin{itemize}
 \tightlist
 \item
-  The RAM-OP sample is a two-stage sample. Subjects are sampled from a
-  small number of primary sampling units (PSUs).
+  The RAM-OP sample is a two-stage sample. Subjects are sampled from a small number of primary sampling units (PSUs).
 \item
-  The RAM-OP sample is \textbf{not} prior weighted. This means that you
-  will need to provide per-PSU sampling weights. These are usually the
-  populations of the PSU.
+  The RAM-OP sample is \textbf{not} prior weighted. This means that you will need to provide per-PSU sampling weights. These are usually the populations of the PSU.
 \end{itemize}
 
-You will need to specify this sample design to your statistical analysis
-software. If you fail to do this then your analysis may produce
-estimates that place undue weight to observations from smaller
-communities with confidence intervals with lower than nominal coverage
-(i.e.~they will be too narrow).
+You will need to specify this sample design to your statistical analysis software. If you fail to do this then your analysis may produce estimates that place undue weight to observations from smaller communities with confidence intervals with lower than nominal coverage (i.e.~they will be too narrow).
 
-The standard RAM-OP software uses \emph{blocked weighted bootstrap}
-estimation approach:
+The standard RAM-OP software uses \emph{blocked weighted bootstrap} estimation approach:
 
 \begin{itemize}
 \tightlist
 \item
   \textbf{Blocked} : The block corresponds to the PSU or cluster.
 \item
-  \textbf{Weighted} : The RAM-OP sampling procedure does not use
-  population proportional sampling to weight the sample prior to data
-  collection as is done with SMART type surveys. This means that a
-  posterior weighting procedure is required. The standard RAM-OP
-  software uses a ``roulette wheel'' algorithm to weight (i.e.~by
-  population) the selection probability of PSUs in bootstrap replicates.
+  \textbf{Weighted} : The RAM-OP sampling procedure does not use population proportional sampling to weight the sample prior to data collection as is done with SMART type surveys. This means that a posterior weighting procedure is required. The standard RAM-OP software uses a ``roulette wheel'' algorithm to weight (i.e.~by population) the selection probability of PSUs in bootstrap replicates.
 \end{itemize}
 
-A total of \texttt{m\textquotesingle{}} PSUs are sampled
-\emph{with-replacement} from the survey dataset where
-\texttt{m\textquotesingle{}} is the number of PSUs in the survey sample.
-Individual records within each PSU are then sampled
-\emph{with-replacement}. A total of n' records are sampled
-\emph{with-replacement} from each of the selected PSUs where
-\texttt{n\textquotesingle{}} is the number of individual records in a
-selected PSU. The resulting collection of records replicates the
-original survey in terms of both sample design and sample size. A large
-number of replicate surveys are taken (the standard RAM-OP software uses
-\(r = 399\) replicate surveys but this can be changed). The required
-statistic (e.g.~the mean of an indicator value) is applied to each
-replicate survey. The reported estimate consists of the 50th (point
-estimate), 2.5th (lower 95\% confidence limit), and the 97.5th (upper
-95\% confidence limit) percentiles of the distribution of the statistic
-observed across all replicate surveys. The blocked weighted bootstrap
-procedure is outlined in Figure \ref{fig:indicators31}.
+A total of \texttt{m\textquotesingle{}} PSUs are sampled \emph{with-replacement} from the survey dataset where \texttt{m\textquotesingle{}} is the number of PSUs in the survey sample. Individual records within each PSU are then sampled \emph{with-replacement}. A total of n' records are sampled \emph{with-replacement} from each of the selected PSUs where \texttt{n\textquotesingle{}} is the number of individual records in a selected PSU. The resulting collection of records replicates the original survey in terms of both sample design and sample size. A large number of replicate surveys are taken (the standard RAM-OP software uses \(r = 399\) replicate surveys but this can be changed). The required statistic (e.g.~the mean of an indicator value) is applied to each replicate survey. The reported estimate consists of the 50th (point estimate), 2.5th (lower 95\% confidence limit), and the 97.5th (upper 95\% confidence limit) percentiles of the distribution of the statistic observed across all replicate surveys. The blocked weighted bootstrap procedure is outlined in Figure \ref{fig:indicators31}.
 
 The principal advantages of using a bootstrap estimator are:
 
@@ -2132,16 +1595,12 @@ \section{A note on data management and data
 \item
   Bootstrap estimators work well with small sample sizes.
 \item
-  The method is \emph{non-parametric} and uses empirical rather than
-  theoretical distributions. There are no assumptions of things like
-  normality to worry about.
+  The method is \emph{non-parametric} and uses empirical rather than theoretical distributions. There are no assumptions of things like normality to worry about.
 \item
-  The method allows estimation of the sampling distribution of almost
-  any statistic using only simple computational methods.
+  The method allows estimation of the sampling distribution of almost any statistic using only simple computational methods.
 \end{itemize}
 
-The standard RAM-OP data analysis software is described in the section
-\protect\hyperlink{software}{\textbf{Standard RAM-OP software}}.
+The standard RAM-OP data analysis software is described in the section \protect\hyperlink{software}{\textbf{Standard RAM-OP software}}.
 
 \newpage
 
@@ -2157,101 +1616,59 @@ \section{A note on data management and data
 \hypertarget{questionnaire}{%
 \chapter{The RAM-OP questionnaire}\label{questionnaire}}
 
-Modules of the RAM-OP questionnaire are presented in the
-\protect\hyperlink{indicators}{\textbf{RAM-OP indicators}} section of
-this manual. The entire RAM-OP questionnaire is presented in the
-following pages. This questionnaire is composed of many tested and
-validated components. The order of the questions and the format of the
-questionnaire have been tested in several settings (Chad, Dadaab Camps,
-South Sudan, Ethiopia, and Tanzania) over a period of three years. It is
-strongly recommended that you do \textbf{not} change the questionnaire,
-other than translating it into a language other than English and
-necessary localisation (i.e.~adapting the questions to meet the
-language, cultural, and other requirements of a specific target
-population in order to ensure that the words, names, terms, and concepts
-used are culturally appropriate and understandable to them), unless you
-are very sure of what you are doing. Modifying the questionnaire may
-have one or more of the following consequences:
+Modules of the RAM-OP questionnaire are presented in the \protect\hyperlink{indicators}{\textbf{RAM-OP indicators}} section of this manual.
+The entire RAM-OP questionnaire is presented in the following pages. This questionnaire is composed of many tested and validated components. The order of the questions and the format of the questionnaire have been tested in several settings (Chad, Dadaab Camps, South Sudan, Ethiopia, and Tanzania) over a period of three years. It is strongly recommended that you do \textbf{not} change the questionnaire, other than translating it into a language other than English and necessary localisation (i.e.~adapting the questions to meet the language, cultural, and other requirements of a specific target population in order to ensure that the words, names, terms, and concepts used are culturally appropriate and understandable to them), unless you are very sure of what you are doing. Modifying the questionnaire may have one or more of the following consequences:
 
 \begin{itemize}
 \item
-  \textbf{Modifying the order of the questions or adding questions} :
-  The links with the data entry, data checking, and data analysis
-  software will be broken. You will have to modify the software to
-  accommodate your changes.
+  \textbf{Modifying the order of the questions or adding questions} : The links with the data entry, data checking, and data analysis software will be broken. You will have to modify the software to accommodate your changes.
 \item
-  \textbf{Modifying the variable names} : The links with the data entry,
-  data checking, and data analysis software will be broken. You will
-  have to modify the software to accommodate your changes.
+  \textbf{Modifying the variable names} : The links with the data entry, data checking, and data analysis software will be broken. You will have to modify the software to accommodate your changes.
 \item
-  \textbf{Modifying the content or the phrasing of questions} : All
-  questions have been tested and are formulated for accuracy and
-  reliability (precision). Modifying them may lead to loss of accuracy
-  (bias) and precision
+  \textbf{Modifying the content or the phrasing of questions} : All questions have been tested and are formulated for accuracy and reliability (precision). Modifying them may lead to loss of accuracy (bias) and precision
 \end{itemize}
 
-When translating the questionnaire you should check if validated
-question sets for each indicator module are already available in your
-local language. This is likely to be the case for the food intake,
-severe food insecurity, activities of daily living, mental health and
-well-being, dementia, water / sanitation / hygiene, and visual
-impairment indicator modules. There may also be local language training
-modules and guidelines available for these modules.
+When translating the questionnaire you should check if validated question sets for each indicator module are already available in your local language. This is likely to be the case for the food intake, severe food insecurity, activities of daily living, mental health and well-being, dementia, water / sanitation / hygiene, and visual impairment indicator modules. There may also be local language training modules and guidelines available for these modules.
 
 Localisation is recommended for:
 
 \begin{itemize}
 \tightlist
 \item
-  \textbf{Food groups} : Remove inappropriate foodstuffs and give
-  examples of local foodstuffs.
+  \textbf{Food groups} : Remove inappropriate foodstuffs and give examples of local foodstuffs.
 \item
   \textbf{Income sources} : Review income types and income categories.
 \end{itemize}
 
-The question numbers used on the questionnaire are the names of
-variables used in the RAM-OP data entry, data checking, and data
-analysis software. Leaving these as they are will be helpful if you
-intend to use the RAM-OP data-entry and data-analysis software.
+The question numbers used on the questionnaire are the names of variables used in the RAM-OP data entry, data checking, and data analysis software. Leaving these as they are will be helpful if you intend to use the RAM-OP data-entry and data-analysis software.
 
-The questionnaire can be downloaded (in ODT and PDF format) from
-\url{http://www.brixtonhealth.com/qesRAMOP.zip}
+The questionnaire can be downloaded (in ODT and PDF format) from \url{http://www.brixtonhealth.com/qesRAMOP.zip}
 
 \hypertarget{datasets}{%
 \chapter{Datasets}\label{datasets}}
 
-This section details the RAM-OP datasets. The information presented here
-is of most use if you decide not to use the RAM-OP data entry and data
-checking software. You might, for example, decide to enter survey data
-using spreadsheet software such as Microsoft Excel. If you do this and
-want to use the RAM-OP data analysis software then you will need to
-export the data as a comma-separated-value (CSV) file with the same
-variable names, variable types and lengths, and using the same codes as
-shown in the tables in this section. For the main RAM-OP survey dataset
-these are the same variable names, variable types, variable lengths,
-codes, and in the same order as shown on the standard RAM-OP
-questionnaire.
+This section details the RAM-OP datasets. The information presented here is of most use if you decide not to use the RAM-OP data entry and data checking software. You might, for example, decide to enter survey data using spreadsheet software such as Microsoft Excel. If you do this and want to use the RAM-OP data analysis software then you will need to export the data as a comma-separated-value (CSV) file with the same variable names, variable types and lengths, and using the same codes as shown in the tables in this section. For the main RAM-OP survey dataset these are the same variable names, variable types, variable lengths, codes, and in the same order as shown on the standard RAM-OP questionnaire.
 
 There are \textbf{two} RAM-OP datasets:
 
 \begin{enumerate}
 \def\labelenumi{\arabic{enumi}.}
 \item
-  \textbf{The main RAM-OP survey dataset} : This is the data collected
-  by the survey questionnaire. The dataset definition for the main
-  RAM-OP dataset is shown in Figure \ref{fig:dataset01}.
+  \textbf{The main RAM-OP survey dataset} :
+  This is the data collected by the survey questionnaire. The dataset definition for the main RAM-OP dataset is shown in Figure \ref{fig:dataset01}.
 \item
-  \textbf{The PSU dataset} : This a short and narrow file with one
-  record per PSU and just two variables:
+  \textbf{The PSU dataset} :
+  This a short and narrow file with one record per PSU and just two variables:
 \end{enumerate}
 
 \begin{longtable}[]{@{}ll@{}}
 \toprule
+\endhead
 \begin{minipage}[t]{0.11\columnwidth}\raggedright
 \textbf{psu}\strut
 \end{minipage} & \begin{minipage}[t]{0.83\columnwidth}\raggedright
-The PSU identifier. This \textbf{must} use the same coding system used
-to identify PSUs that is used in the main RAM-OP dataset.\strut
+The PSU identifier. This \textbf{must} use the same coding system used to identify
+PSUs that is used in the main RAM-OP dataset.\strut
 \end{minipage}\tabularnewline
 \begin{minipage}[t]{0.11\columnwidth}\raggedright
 \textbf{pop}\strut
@@ -2261,25 +1678,20 @@ \chapter{Datasets}\label{datasets}}
 \bottomrule
 \end{longtable}
 
-The PSU dataset is used during data-analysis to weight data by PSU
-population.
+The PSU dataset is used during data-analysis to weight data by PSU population.
 
-If you do not know population sizes (as might be the case in
-emergencies) then you can collect this data:
+If you do not know population sizes (as might be the case in emergencies) then you can collect this data:
 
 \begin{itemize}
 \item
-  When you visit the PSU (i.e.~from community leaders or health
-  centres).
+  When you visit the PSU (i.e.~from community leaders or health centres).
 \item
   When you visit the PSU as a doorway count or roof count.
 \item
   Using recent satellite imagery as a roof count.
 \end{itemize}
 
-Relative population sizes can be used. If no better data is available
-then it is reasonable to use a simple semi-quantitative assessment such
-as:
+Relative population sizes can be used. If no better data is available then it is reasonable to use a simple semi-quantitative assessment such as:
 
 \begin{longtable}[]{@{}lclc@{}}
 \toprule
@@ -2290,7 +1702,8 @@ \chapter{Datasets}\label{datasets}}
 \end{minipage} & \begin{minipage}[b]{0.32\columnwidth}\raggedright
 \textbf{Features}\strut
 \end{minipage} & \begin{minipage}[b]{0.21\columnwidth}\centering
-\textbf{Record population as \ldots{}}\strut
+\textbf{Record population
+as \ldots{}}\strut
 \end{minipage}\tabularnewline
 \midrule
 \endhead
@@ -2317,8 +1730,8 @@ \chapter{Datasets}\label{datasets}}
 \end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\centering
 1,000 -- 4,000\strut
 \end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright
-Market and small shops serving the village and the surrounding
-hamlets\strut
+Market and small shops serving the
+village and the surrounding hamlets\strut
 \end{minipage} & \begin{minipage}[t]{0.21\columnwidth}\centering
 2\strut
 \end{minipage}\tabularnewline
@@ -2336,8 +1749,9 @@ \chapter{Datasets}\label{datasets}}
 \end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\centering
 \(>\) 4,000\strut
 \end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright
-Large market, many shops (some specialised), guest houses, bus station,
-government offices\strut
+Large market, many shops (some
+specialised), guest houses, bus
+station, government offices\strut
 \end{minipage} & \begin{minipage}[t]{0.21\columnwidth}\centering
 4\strut
 \end{minipage}\tabularnewline
@@ -2346,9 +1760,7 @@ \chapter{Datasets}\label{datasets}}
 
 *These ranges may need to be adjusted to match local circumstances.
 
-The PSU dataset must be in comma-separated-value (CSV) format (see
-Figure \ref{fig:dataset02}) for use with the RAM-OP data analysis
-software.
+The PSU dataset must be in comma-separated-value (CSV) format (see Figure \ref{fig:dataset02}) for use with the RAM-OP data analysis software.
 
 \newpage
 
@@ -2361,12 +1773,7 @@ \chapter{Datasets}\label{datasets}}
 \caption{Main RAM-OP dataset definition}\label{fig:dataset01}
 \end{figure}
 
-The RAM-OP data analysis requires that the main RAM-OP survey dataset is
-supplied in either an \textbf{EpiInfo v6.xx} or \textbf{EpiData (REC)}
-format or in a comma-separated-value (CSV) format file The RAM-OP data
-analysis requires that the PSU dataset is supplied in a
-comma-separated-value (.CSV) format file. Figure \ref{fig:dataset02}
-shows an example of a PSU dataset in comma-separate-value (CSV) format.
+The RAM-OP data analysis requires that the main RAM-OP survey dataset is supplied in either an \textbf{EpiInfo v6.xx} or \textbf{EpiData (REC)} format or in a comma-separated-value (CSV) format file The RAM-OP data analysis requires that the PSU dataset is supplied in a comma-separated-value (.CSV) format file. Figure \ref{fig:dataset02} shows an example of a PSU dataset in comma-separate-value (CSV) format.
 
 \begin{figure}[H]
 
@@ -2377,253 +1784,119 @@ \chapter{Datasets}\label{datasets}}
 \caption{An example comma-separated-value (CSV) format file (the example is for a RAM-OP PSU dataset)}\label{fig:dataset02}
 \end{figure}
 
-Note that the first line of a CSV format file gives the names of the
-variables (e.g.~these are \texttt{psu} and \texttt{pop} for the PSU
-dataset) separated by commas. Subsequent lines contain data with items
-separated by commas and with one record per line. CSV format files can
-be created using a plain text editor (e.g. \textbf{Notepad}) or with a
-spreadsheet application such as \textbf{Microsoft Excel™}. If you use a
-spreadsheet application then you will have to be careful:
+Note that the first line of a CSV format file gives the names of the variables (e.g.~these are \texttt{psu} and \texttt{pop} for the PSU dataset) separated by commas. Subsequent lines contain data with items separated by commas and with one record per line. CSV format files can be created using a plain text editor (e.g. \textbf{Notepad}) or with a spreadsheet application such as \textbf{Microsoft Excel™}. If you use a spreadsheet application then you will have to be careful:
 
 \begin{itemize}
 \item
-  Variable names and data items must be separated by commas (not tab
-  characters or semi-colon characters).
+  Variable names and data items must be separated by commas (not tab characters or semi-colon characters).
 \item
-  Numbers with decimal places must use the full-stop character as the
-  decimal separator. In some settings a spreadsheet application may want
-  to use the comma character as the decimal separator.
+  Numbers with decimal places must use the full-stop character as the decimal separator. In some settings a spreadsheet application may want to use the comma character as the decimal separator.
 \item
-  Avoid using accented characters in the names of and in the data
-  entered into text variables. These characters can sometimes confuse
-  the RAM-OP data analysis software. A CSV file should contain only
-  plain text, number, and commas without formatting. Do \textbf{not} use
-  a word processor application such as \textbf{Microsoft Word™} to
-  create or edit a CSV file.
+  Avoid using accented characters in the names of and in the data entered into text variables. These characters can sometimes confuse the RAM-OP data analysis software. A CSV file should contain only plain text, number, and commas without formatting. Do \textbf{not} use a word processor application such as \textbf{Microsoft Word™} to create or edit a CSV file.
 \end{itemize}
 
-If you have problems using a CSV file then you should check and edit the
-file using a plain text-editor such as \textbf{Notepad} or a dedicated
-CSV editor such as Ron's Editor
-(\url{http://www.ronsplace.eu/Products/RonsEditor})
+If you have problems using a CSV file then you should check and edit the file using a plain text-editor such as \textbf{Notepad} or a dedicated CSV editor such as Ron's Editor (\url{http://www.ronsplace.eu/Products/RonsEditor})
 
 Remember to backup your data before editing it.
 
 \hypertarget{practical}{%
 \chapter{Practical Fieldwork}\label{practical}}
 
-This section is intended to guide you through the different steps
-leading up to the fieldwork once the survey location has been
-identified, and gives some tips on how the fieldwork might be organised.
+This section is intended to guide you through the different steps leading up to the fieldwork once the survey location has been identified, and gives some tips on how the fieldwork might be organised.
 
 \hypertarget{authorisations-and-clearances}{%
-\section{Authorisations and
-clearances}\label{authorisations-and-clearances}}
+\section{Authorisations and clearances}\label{authorisations-and-clearances}}
 
-Before implementing the survey, you will need to get all the
-authorisations relevant to the country in which you plan to work. These
-could include:
+Before implementing the survey, you will need to get all the authorisations relevant to the country in which you plan to work. These could include:
 
 \begin{itemize}
 \item
-  \textbf{Clearance from the national nutrition cluster} or the
-  equivalent structure co-ordinating national assessment activities. In
-  humanitarian contexts, this might be the only clearance that you will
-  need at the national level.
-\item
-  \textbf{Ethical approval} : This is obtained from the country's
-  national ethical committee (or equivalent). Some NGOs and UNOs also
-  have ethical committees and you may also need to submit your survey
-  plans to them for ethical approval. It may be necessary to work with
-  both national and local ethical committees. The process of gaining
-  ethical approval can take several months. It is important to note that
-  RAM-OP surveys are needs assessments rather than experiments upon
-  human subjects. This means that ethical clearance may not be required
-  for RAM-OP surveys, or that it can be given by the chair of the
-  appropriate ethical review committee without the need for a full
-  meeting of the ethical review committee. It is a good idea to check
-  this with the chair of the appropriate committees to see if
-  permissions can be expedited. Getting ethical clearance is often very
-  useful when applying for other permission as it shows that some
-  technical quality assurance has been done.
-\item
-  \textbf{Authorisation from the appropriate government departments} at
-  various levels (i.e.~national, regional, and at the level where you
-  are going to implement the survey). Authorisation of the authority
-  managing the survey site should be sought. For example, a survey in a
-  refugee camp will need the authorisation of UNHCR, the national
-  administrative authority in charge of refugees and displaced persons,
-  and the agency in charge of the camp management. In some settings you
-  may also need to obtain authorisation from other government
-  departments such as the Ministry of Health, the Department of Rural
-  Affairs, or the Department of Social Affairs.
-\item
-  \textbf{Authorisation from the administrative authorities at local
-  level} : Make sure that all levels of the local administration are
-  informed about what you intend to do (i.e.~what, where, and when). It
-  is essential to meet with the local administrative and health
-  authorities prior to the survey. This is done to avoid problems with
-  permissions and to involve them in the implementation of the survey.
-  Describe the survey and explain what might be expected from their
-  staff. You might, for example, need some help in identifying the exact
-  location and boundaries of villages and hamlets in rural areas, or
-  blocks and sections of towns in urban areas. You might need
-  translators or guides to travel with the enumerators, and you might
-  need facilitators to introduce you to village executives. Make sure
-  that you share the results of the survey with them once it is
-  available.
-\item
-  \textbf{Security clearance} : Be aware of the potential security
-  problems in the survey area. Inform all agencies with security
-  responsibilities in the area about the dates and locations of the
-  survey. The police or the army may have to be specifically informed.
-  You may also need to negotiate access with non-state actors. Field
-  staff should be provided with copies of official documents (in the
-  local language) proving that they are authorised to carry out survey
-  work in the specific area between specific dates. They will have to
-  carry this document with them at all times during the fieldwork and
-  present it on request to local authorities and study subjects. It can
-  also be useful to give a copy of this and other official documents to
-  village leaders on arrival at the survey location.
+  \textbf{Clearance from the national nutrition cluster} or the equivalent structure co-ordinating national assessment activities. In humanitarian contexts, this might be the only clearance that you will need at the national level.
+\item
+  \textbf{Ethical approval} : This is obtained from the country's national ethical committee (or equivalent). Some NGOs and UNOs also have ethical committees and you may also need to submit your survey plans to them for ethical approval. It may be necessary to work with both national and local ethical committees. The process of gaining ethical approval can take several months. It is important to note that RAM-OP surveys are needs assessments rather than experiments upon human subjects. This means that ethical clearance may not be required for RAM-OP surveys, or that it can be given by the chair of the appropriate ethical review committee without the need for a full meeting of the ethical review committee. It is a good idea to check this with the chair of the appropriate committees to see if permissions can be expedited. Getting ethical clearance is often very useful when applying for other permission as it shows that some technical quality assurance has been done.
+\item
+  \textbf{Authorisation from the appropriate government departments} at various levels (i.e.~national, regional, and at the level where you are going to implement the survey). Authorisation of the authority managing the survey site should be sought. For example, a survey in a refugee camp will need the authorisation of UNHCR, the national administrative authority in charge of refugees and displaced persons, and the agency in charge of the camp management. In some settings you may also need to obtain authorisation from other government departments such as the Ministry of Health, the Department of Rural Affairs, or the Department of Social Affairs.
+\item
+  \textbf{Authorisation from the administrative authorities at local level} : Make sure that all levels of the local administration are informed about what you intend to do (i.e.~what, where, and when). It is essential to meet with the local administrative and health authorities prior to the survey. This is done to avoid problems with permissions and to involve them in the implementation of the survey. Describe the survey and explain what might be expected from their staff. You might, for example, need some help in identifying the exact location and boundaries of villages and hamlets in rural areas, or blocks and sections of towns in urban areas. You might need translators or guides to travel with the enumerators, and you might need facilitators to introduce you to village executives. Make sure that you share the results of the survey with them once it is available.
+\item
+  \textbf{Security clearance} : Be aware of the potential security problems in the survey area. Inform all agencies with security responsibilities in the area about the dates and locations of the survey. The police or the army may have to be specifically informed. You may also need to negotiate access with non-state actors. Field staff should be provided with copies of official documents (in the local language) proving that they are authorised to carry out survey work in the specific area between specific dates. They will have to carry this document with them at all times during the fieldwork and present it on request to local authorities and study subjects. It can also be useful to give a copy of this and other official documents to village leaders on arrival at the survey location.
 \end{itemize}
 
 \hypertarget{working-with-a-local-partner}{%
-\section{Working with a local
-partner}\label{working-with-a-local-partner}}
+\section{Working with a local partner}\label{working-with-a-local-partner}}
 
-It is often very useful to prepare and carry out the survey in
-collaboration with one or more local partners, such as a local NGO, the
-local health authority, or the camp management agency in a refugee camp.
+It is often very useful to prepare and carry out the survey in collaboration with one or more local partners, such as a local NGO, the local health authority, or the camp management agency in a refugee camp.
 
-If feasible, you should recruit a representative of your local partner
-as a ``survey facilitator'' with responsibility for liaising with the
-national and local stakeholders.
+If feasible, you should recruit a representative of your local partner as a ``survey facilitator'' with responsibility for liaising with the national and local stakeholders.
 
 This person will support your survey preparation with the following:
 
 \begin{itemize}
 \item
-  At national or regional level, support the endorsement of the survey
-  objectives by the national authorities, and facilitation in obtaining
-  the relevant authorisations and clearances.
+  At national or regional level, support the endorsement of the survey objectives by the national authorities, and facilitation in obtaining the relevant authorisations and clearances.
 \item
-  At local level, be the link between you and the local communities
-  informing health staff and village leaders in the areas where the
-  enumerators are going to sample households. This information should be
-  disseminated before the survey starts and reiterated a day or two
-  before teams travel to survey locations either by telephone or by
-  personal visits.
+  At local level, be the link between you and the local communities informing health staff and village leaders in the areas where the enumerators are going to sample households. This information should be disseminated before the survey starts and reiterated a day or two before teams travel to survey locations either by telephone or by personal visits.
 \item
-  Provide you with a list of useful contacts (with telephone numbers)
-  for each of the areas covered by the survey. This list should be
-  shared with all program staff.
+  Provide you with a list of useful contacts (with telephone numbers) for each of the areas covered by the survey. This list should be shared with all program staff.
 \item
-  Identify local guides or translators to support the teams in the
-  field.
+  Identify local guides or translators to support the teams in the field.
 \item
-  In-depth knowledge of the survey area, useful for checking the
-  location of the villages to be surveyed on a map.
+  In-depth knowledge of the survey area, useful for checking the location of the villages to be surveyed on a map.
 \item
-  Information about travel and security constraints, travel distances
-  and times, and assist in formulating the survey travel plan.
+  Information about travel and security constraints, travel distances and times, and assist in formulating the survey travel plan.
 \item
-  Support with the survey logistics, such as renting vehicles, renting
-  accommodation and training venues, where to purchase food and drinks,
-  where to have forms and questionnaires printed / copied, etc.
+  Support with the survey logistics, such as renting vehicles, renting accommodation and training venues, where to purchase food and drinks, where to have forms and questionnaires printed / copied, etc.
 \item
-  Help with the referral of malnourished or sick older people identified
-  during the survey by liaising with community services, ambulance
-  services, and relevant health facilities as needed.
+  Help with the referral of malnourished or sick older people identified during the survey by liaising with community services, ambulance services, and relevant health facilities as needed.
 \end{itemize}
 
-The local partner will also help you disseminate the results of the
-survey to the various stakeholders, and might be involved in response
-plans following the assessment.
+The local partner will also help you disseminate the results of the survey to the various stakeholders, and might be involved in response plans following the assessment.
 
 \hypertarget{translating-the-questionnaire}{%
-\section{Translating the
-questionnaire}\label{translating-the-questionnaire}}
+\section{Translating the questionnaire}\label{translating-the-questionnaire}}
 
-Precision and accuracy are improved by translating the questionnaire in
-the local language appropriate to the survey area before data is
-collected. This allows enumerators to ask questions using the same
-language and terminology in every interview.
+Precision and accuracy are improved by translating the questionnaire in the local language appropriate to the survey area before data is collected. This allows enumerators to ask questions using the same language and terminology in every interview.
 
-Thorough training of the enumerators in applying the questionnaire will
-also improve the precision and accuracy of your survey results.
+Thorough training of the enumerators in applying the questionnaire will also improve the precision and accuracy of your survey results.
 
-A translated questionnaire may also be a requirement for getting the
-ethical clearance for the survey.
+A translated questionnaire may also be a requirement for getting the ethical clearance for the survey.
 
 We advise you to use an iterative translation process and use:
 
 \begin{itemize}
 \item
-  \textbf{Standard language if available} : Most indicators used in
-  RAM-OP have question sets available in different languages. You can
-  check for these online. You may need to alter some language to account
-  for local dialects and idioms but using standard language, when it is
-  available, can save you a lot of time and effort
-\item
-  \textbf{Knowledgeable lead translators} : You need to use people who
-  know the target language and culture but are also fluent in the
-  starting language of the questionnaire.
-\item
-  \textbf{Forward translation and back translation} : The questionnaire
-  is translated from English, for example, into the local language by
-  one person or team (this is \emph{forward translation}) and is then
-  translated back into the original language by another person or team
-  (this is \emph{back translation}). The back translated questionnaire
-  is then checked against the original questionnaire. Differences are
-  then analysed and a new translation produced. You may need to go
-  through this process several times until a satisfactory version of the
-  translated questionnaire is reached.
-\item
-  \textbf{Your survey staff} to provide language and to pilot
-  (i.e.~test) questionnaire components as they are translated. Piloting
-  can be done with community members and by role-playing between survey
-  staff. Test interviews and group discussions usually help to improve
-  the language used in the questionnaire.
-\item
-  \textbf{Your intended survey population} to help you make sure that
-  the language you are using is simple and to the point. Test interviews
-  and group discussions usually help to improve the language used in the
-  questionnaire.
+  \textbf{Standard language if available} : Most indicators used in RAM-OP have question sets available in different languages. You can check for these online. You may need to alter some language to account for local dialects and idioms but using standard language, when it is available, can save you a lot of time and effort
+\item
+  \textbf{Knowledgeable lead translators} : You need to use people who know the target language and culture but are also fluent in the starting language of the questionnaire.
+\item
+  \textbf{Forward translation and back translation} : The questionnaire is translated from English, for example, into the local language by one person or team (this is \emph{forward translation}) and is then translated back into the original language by another person or team (this is \emph{back translation}). The back translated questionnaire is then checked against the original questionnaire. Differences are then analysed and a new translation produced. You may need to go through this process several times until a satisfactory version of the translated questionnaire is reached.
+\item
+  \textbf{Your survey staff} to provide language and to pilot (i.e.~test) questionnaire components as they are translated. Piloting can be done with community members and by role-playing between survey staff. Test interviews and group discussions usually help to improve the language used in the questionnaire.
+\item
+  \textbf{Your intended survey population} to help you make sure that the language you are using is simple and to the point. Test interviews and group discussions usually help to improve the language used in the questionnaire.
 \end{itemize}
 
-Having enumerators translate the English language questionnaire (for
-example) each time they apply the questionnaire is \textbf{not} a good
-option and should be avoided.
+Having enumerators translate the English language questionnaire (for example) each time they apply the questionnaire is \textbf{not} a good option and should be avoided.
 
 \hypertarget{supervisors-enumerators-and-data-entry-staff}{%
-\section{Supervisors, enumerators, and data entry
-staff}\label{supervisors-enumerators-and-data-entry-staff}}
+\section{Supervisors, enumerators, and data entry staff}\label{supervisors-enumerators-and-data-entry-staff}}
 
-The more survey teams you recruit and use, the quicker the survey will
-be finished. However, the number of teams should be linked to your
-capacity for supervision. Also, having a large number of teams usually
-means that you will need a large number of vehicles and drivers. This
-can be hard to achieve and hard to manage.
+The more survey teams you recruit and use, the quicker the survey will be finished. However, the number of teams should be linked to your capacity for supervision. Also, having a large number of teams usually means that you will need a large number of vehicles and drivers. This can be hard to achieve and hard to manage.
 
-We recommend that you recruit three teams of two enumerators with one
-supervisor per team. The duties of supervisors and enumerators are:
+We recommend that you recruit three teams of two enumerators with one supervisor per team. The duties of supervisors and enumerators are:
 
-\textbf{Supervisors} have to take all necessary actions to ensure the
-accuracy of the collected data, particularly:
+\textbf{Supervisors} have to take all necessary actions to ensure the accuracy of the collected data, particularly:
 
 \begin{itemize}
 \tightlist
 \item
   Checking equipment before departure and when leaving the survey site.
 \item
-  Travelling with a team every day, to observe and correct the
-  enumerators' work.
+  Travelling with a team every day, to observe and correct the enumerators' work.
 \item
   Introducing teams to local leaders.
 \item
-  Ensuring households and subjects are selected properly, that the
-  interviews are conducted with respect and thoroughness, and that
-  measurements are taken and recorded accurately.
+  Ensuring households and subjects are selected properly, that the interviews are conducted with respect and thoroughness, and that measurements are taken and recorded accurately.
 \end{itemize}
 
 \textbf{Enumerators} are in charge of implementing the field procedures:
@@ -2638,35 +1911,18 @@ \section{Supervisors, enumerators, and data entry
   Measure MUAC, oedema, and visual acuity and complete questionnaires.
 \end{itemize}
 
-If each team can complete a single PSU per day (this is the minimum you
-can expect from a team) then the survey may be completed in six days
-(i.e.~three PSUs per day for five days plus one PSU on the last day).
-This will depend on context and on the teams' expertise. It is often
-possible for a team to reach more than one location per day, such as in
-cities or camps where sectors and blocks are close to each other and
-travelling time is not high. You will often find that survey data can be
-collected in just four or five days.
-
-It is important not to rush data collection. It is also important to
-supervise the teams from day one in order to ensure they follow the
-proper sampling procedures and applying the questionnaires correctly.
-
-It is advisable to enlist more enumerators to be trained than the
-minimum number needed. This will ensure that you have sufficient
-enumerators should you find, during training, that some recruits cannot
-perform their duties well enough. It will also provide additional
-trained staff should you need to cover for absences, due to illness for
-example. Make sure that you enlist both male and female trainees.
-
-You will also need to recruit data entry staff. The workload for the
-data entry staff is usually between about thirty-six and seventy-two
-questionnaires per day.
+If each team can complete a single PSU per day (this is the minimum you can expect from a team) then the survey may be completed in six days (i.e.~three PSUs per day for five days plus one PSU on the last day). This will depend on context and on the teams' expertise. It is often possible for a team to reach more than one location per day, such as in cities or camps where sectors and blocks are close to each other and travelling time is not high. You will often find that survey data can be collected in just four or five days.
+
+It is important not to rush data collection. It is also important to supervise the teams from day one in order to ensure they follow the proper sampling procedures and applying the questionnaires correctly.
+
+It is advisable to enlist more enumerators to be trained than the minimum number needed. This will ensure that you have sufficient enumerators should you find, during training, that some recruits cannot perform their duties well enough. It will also provide additional trained staff should you need to cover for absences, due to illness for example. Make sure that you enlist both male and female trainees.
+
+You will also need to recruit data entry staff. The workload for the data entry staff is usually between about thirty-six and seventy-two questionnaires per day.
 
 \hypertarget{training-of-enumerators}{%
 \section{Training of enumerators}\label{training-of-enumerators}}
 
-Training the enumerators is a crucial step to ensuring the quality of
-the data collection.
+Training the enumerators is a crucial step to ensuring the quality of the data collection.
 
 At the end of training each enumerator should be able to:
 
@@ -2675,23 +1931,17 @@ \section{Training of enumerators}\label{training-of-enumerators}}
 \item
   Explain the objectives of the survey.
 \item
-  Sample households and older people in the survey area following the
-  appropriate field procedures.
+  Sample households and older people in the survey area following the appropriate field procedures.
 \item
-  Introduce themselves to older people in a polite and respectful
-  manner.
+  Introduce themselves to older people in a polite and respectful manner.
 \item
   Apply the questionnaire smoothly and efficiently.
 \item
-  Properly measure MUAC, check for bilateral pitting oedema, and
-  properly measure visual acuity.
+  Properly measure MUAC, check for bilateral pitting oedema, and properly measure visual acuity.
 \item
-  Complete the questionnaire neatly and without making mistakes
-  (including the correct numbering of PSU, households and individual
-  subjects).
+  Complete the questionnaire neatly and without making mistakes (including the correct numbering of PSU, households and individual subjects).
 \item
-  Advise the subject or their family in case there is a need for
-  referral, such as to a health facility.
+  Advise the subject or their family in case there is a need for referral, such as to a health facility.
 \end{itemize}
 
 \newpage
@@ -2737,8 +1987,7 @@ \section{Training of enumerators}\label{training-of-enumerators}}
 \begin{itemize}
 \tightlist
 \item
-  Measurements: MUAC, oedema, visual acuity (practice on ten older
-  people)
+  Measurements: MUAC, oedema, visual acuity (practice on ten older people)
 \item
   Testing the questionnaire with ten older people
 \item
@@ -2775,99 +2024,53 @@ \section{Training of enumerators}\label{training-of-enumerators}}
 
 \begin{itemize}
 \item
-  \textbf{Practising the questionnaire} : This is very important. Each
-  training day should contain some work on the questionnaire. Particular
-  attention is paid to the content and function of each question set,
-  the numbering system for the PSU, household, and subject, the meaning
-  / intention of each question set, skip / jump patterns, coding,
-  ranges, and checking for completeness and consistency. Extensive use
-  should be made of role-playing (in pairs and in groups) and testing
-  with eligible subjects. Care needs to be taken to ensure that
-  \textbf{all} field staff have extensive practice in working with the
-  questionnaire.
-\item
-  \textbf{Practising measurements} : This is very important. Most
-  training days should contain some work on measurement. Care needs to
-  be taken to ensure that \textbf{all} field staff have extensive
-  practice in taking \textbf{all} measurements.
-\item
-  \textbf{Standardisation of measurement} : A formal standardisation
-  exercise for MUAC measurement is \textbf{not} required. The format of
-  such an exercise does, however, provide a useful framework for
-  training enumerators to measure MUAC with acceptable accuracy and
-  precision by:
+  \textbf{Practising the questionnaire} : This is very important. Each training day should contain some work on the questionnaire. Particular attention is paid to the content and function of each question set, the numbering system for the PSU, household, and subject, the meaning / intention of each question set, skip / jump patterns, coding, ranges, and checking for completeness and consistency. Extensive use should be made of role-playing (in pairs and in groups) and testing with eligible subjects. Care needs to be taken to ensure that \textbf{all} field staff have extensive practice in working with the questionnaire.
+\item
+  \textbf{Practising measurements} : This is very important. Most training days should contain some work on measurement. Care needs to be taken to ensure that \textbf{all} field staff have extensive practice in taking \textbf{all} measurements.
+\item
+  \textbf{Standardisation of measurement} : A formal standardisation exercise for MUAC measurement is \textbf{not} required. The format of such an exercise does, however, provide a useful framework for training enumerators to measure MUAC with acceptable accuracy and precision by:
 
   \begin{itemize}
   \item
-    Comparisons of measurements made by different enumerators on the
-    same person to explore accuracy (bias).
+    Comparisons of measurements made by different enumerators on the same person to explore accuracy (bias).
   \item
-    Comparisons of measurements made by different enumerators on the
-    same person with the measurements made by the training supervisor to
-    explore accuracy (bias).
+    Comparisons of measurements made by different enumerators on the same person with the measurements made by the training supervisor to explore accuracy (bias).
   \item
-    Repeated measurements on the same persons by the same enumerator to
-    explore repeatability (precision).
+    Repeated measurements on the same persons by the same enumerator to explore repeatability (precision).
   \end{itemize}
 \item
-  \textbf{Ten older people} : The survey facilitator should be able to
-  mobilise ten older people (women and men) to participate in
-  questionnaire and measurement exercises. Make sure to explain to these
-  volunteers what is going to happen to them. The exercises will last a
-  half day (maybe longer) and may be quite tiring for older persons.
-  Make sure they are comfortably accommodated, organise their transport
-  to and from the training venue (which should be easily accessible and
-  so avoiding stairs), indoors or under shelter, with chairs or benches.
-  Provide safe drinking water and possibly tea, coffee and snacks. It is
-  also advisable to provide them with a small \emph{ex gratia} payment.
-\item
-  \textbf{The field test} : This will put the trainees in to field
-  conditions but without the stress of having to take a full sample. It
-  is a ``dummy run'' of all survey activities. Divide the trainees into
-  teams assigning a supervisor to each team. The test area should not be
-  one of the areas to be sampled for the survey and can be close to the
-  training venue.
+  \textbf{Ten older people} : The survey facilitator should be able to mobilise ten older people (women and men) to participate in questionnaire and measurement exercises. Make sure to explain to these volunteers what is going to happen to them. The exercises will last a half day (maybe longer) and may be quite tiring for older persons. Make sure they are comfortably accommodated, organise their transport to and from the training venue (which should be easily accessible and so avoiding stairs), indoors or under shelter, with chairs or benches. Provide safe drinking water and possibly tea, coffee and snacks. It is also advisable to provide them with a small \emph{ex gratia} payment.
+\item
+  \textbf{The field test} : This will put the trainees in to field conditions but without the stress of having to take a full sample. It is a ``dummy run'' of all survey activities. Divide the trainees into teams assigning a supervisor to each team. The test area should not be one of the areas to be sampled for the survey and can be close to the training venue.
 \end{itemize}
 
-At the end of the training week, you should be able to select the best
-enumerators and divide them in to teams, balancing genders and
-personalities, as well as strengths and weaknesses.
+At the end of the training week, you should be able to select the best enumerators and divide them in to teams, balancing genders and personalities, as well as strengths and weaknesses.
 
 \hypertarget{survey-logistics}{%
 \section{Survey logistics}\label{survey-logistics}}
 
-Thorough logistics preparation is essential to the smooth implementation
-of a survey. Transportation is particularly important.
+Thorough logistics preparation is essential to the smooth implementation of a survey. Transportation is particularly important.
 
 \hypertarget{transportation}{%
 \subsection{Transportation}\label{transportation}}
 
-Ideally you will need one car and driver per survey team. Depending on
-the area, you may be able to reduce this number and organise the survey
-travel plan so that vehicles can be pooled between the teams. You will
-need strong cars (ones adapted to the terrain) and reliable and safe
-drivers ready to work flexible hours.
+Ideally you will need one car and driver per survey team. Depending on the area, you may be able to reduce this number and organise the survey travel plan so that vehicles can be pooled between the teams. You will need strong cars (ones adapted to the terrain) and reliable and safe drivers ready to work flexible hours.
 
-It is very useful to brief the teams and the drivers about the
-management of the cars:
+It is very useful to brief the teams and the drivers about the management of the cars:
 
 \begin{itemize}
 \tightlist
 \item
-  Who gives instructions to the drivers on a daily basis. One person per
-  car should be in charge.
+  Who gives instructions to the drivers on a daily basis. One person per car should be in charge.
 \item
   Who is checking the car log book every morning and every evening.
 \item
   What to do in case of an accident.
 \end{itemize}
 
-All staff should be given the contact telephone numbers of all the
-drivers
+All staff should be given the contact telephone numbers of all the drivers
 
-Safety and security procedures should be followed thoroughly, such as
-use of seat belts, speed limits, prohibitions on ``racing'', carrying of
-water, first aid kits, and spare wheels.
+Safety and security procedures should be followed thoroughly, such as use of seat belts, speed limits, prohibitions on ``racing'', carrying of water, first aid kits, and spare wheels.
 
 \hypertarget{tools-and-equipment}{%
 \subsection{Tools and equipment}\label{tools-and-equipment}}
@@ -2877,8 +2080,7 @@ \subsection{Tools and equipment}\label{tools-and-equipment}}
 \begin{itemize}
 \tightlist
 \item
-  MUAC tapes for adults (i.e.~minimum 450 mm long, graduated in
-  millimetres).
+  MUAC tapes for adults (i.e.~minimum 450 mm long, graduated in millimetres).
 \item
   A ``tumbling E chart'' to perform the visual acuity test.
 \item
@@ -2893,73 +2095,34 @@ \subsection{Tools and equipment}\label{tools-and-equipment}}
   Official letters of authorisation to carry out the survey.
 \end{itemize}
 
-Every morning during the survey, each team should receive a schedule of
-that day's activities detailing the team's objectives for the day giving
-PSU numbers, location, PSU maps, sample target sizes, local contact
-information, emergency contact list, etc.
+Every morning during the survey, each team should receive a schedule of that day's activities detailing the team's objectives for the day giving PSU numbers, location, PSU maps, sample target sizes, local contact information, emergency contact list, etc.
 
 \hypertarget{data-collection}{%
 \section{Data collection}\label{data-collection}}
 
 Each team should be able to survey at least one PSU per day.
 
-No community mobilisation is necessary but community officials should be
-informed of your arrival in advance.
-
-Avoid sampling at special or busy times, such as holy days and market
-days.
-
-When arriving in the community, the teams should introduce themselves to
-community leaders and explain the conduct of the survey.
-
-The supervisor should collect and record information about the PSU's
-total population.
-
-The team should then explore the boundaries of the community and perform
-mapping and segmentation as required.
-
-Eligible subjects are usually people aged 60 years and older (this may
-differ in some settings). We accept the respondents' statement of their
-own age, and we do not challenge them if they appear younger or older
-than the age they declare. We usually do not ask for a proof of age.
-However, it might be useful to have compiled a list of events related to
-the past hundred years of the country's history, as some older people do
-not remember their age, but remember living at the time of some
-remarkable events (independence, elections, wars, etc). Women often
-remember the age at which they had their children
-
-\textbf{All} eligible older people present in and belonging to the
-sampled household are interviewed and measured, even if the required
-quota of respondents has been reached. Older people visiting the
-household should only be included if they are residing there for more
-than two weeks. Person such as maids, houseboys, watchmen, and carers
-should be treated as part of the household (i.e.~should be interviewed)
-if the sampled dwelling / compound is their principal place of
-residence. Older people should be interviewed in their houses. It is not
-good practice to have the older people in the community gathered in one
-place.
-
-When entering older people's houses, the enumerators should always be
-polite, respectful, and attentive not to intrude on people's privacy.
-
-Older people should not be intimidated into answering the questionnaire
-or being measured. They always have a right to refuse to answer some or
-all of the questions and to refuse measurements. Refusals should be
-noted on the questionnaire.
-
-Some of the questions are of an intimate nature. Be careful not to
-administer the questionnaire in an open space where everybody present
-can hear the answers. This may embarrass the respondent or cause the
-respondent to answer wrongly. This is important as a ``wrong answer''
-will tend to hide need and will bias the survey results.
-
-Some older women may object to having their MUAC taken by a man (or vice
-versa). This is why it is advisable that the team has both male and
-female members. This not always possible, but older people are often
-more free from prejudices than the younger members of their community.
-It is rare for an older woman to refuse to expose her arms or feet
-before a male enumerator, and also rare that an older man objects to
-being measured by a female enumerator.
+No community mobilisation is necessary but community officials should be informed of your arrival in advance.
+
+Avoid sampling at special or busy times, such as holy days and market days.
+
+When arriving in the community, the teams should introduce themselves to community leaders and explain the conduct of the survey.
+
+The supervisor should collect and record information about the PSU's total population.
+
+The team should then explore the boundaries of the community and perform mapping and segmentation as required.
+
+Eligible subjects are usually people aged 60 years and older (this may differ in some settings). We accept the respondents' statement of their own age, and we do not challenge them if they appear younger or older than the age they declare. We usually do not ask for a proof of age. However, it might be useful to have compiled a list of events related to the past hundred years of the country's history, as some older people do not remember their age, but remember living at the time of some remarkable events (independence, elections, wars, etc). Women often remember the age at which they had their children
+
+\textbf{All} eligible older people present in and belonging to the sampled household are interviewed and measured, even if the required quota of respondents has been reached. Older people visiting the household should only be included if they are residing there for more than two weeks. Person such as maids, houseboys, watchmen, and carers should be treated as part of the household (i.e.~should be interviewed) if the sampled dwelling / compound is their principal place of residence. Older people should be interviewed in their houses. It is not good practice to have the older people in the community gathered in one place.
+
+When entering older people's houses, the enumerators should always be polite, respectful, and attentive not to intrude on people's privacy.
+
+Older people should not be intimidated into answering the questionnaire or being measured. They always have a right to refuse to answer some or all of the questions and to refuse measurements. Refusals should be noted on the questionnaire.
+
+Some of the questions are of an intimate nature. Be careful not to administer the questionnaire in an open space where everybody present can hear the answers. This may embarrass the respondent or cause the respondent to answer wrongly. This is important as a ``wrong answer'' will tend to hide need and will bias the survey results.
+
+Some older women may object to having their MUAC taken by a man (or vice versa). This is why it is advisable that the team has both male and female members. This not always possible, but older people are often more free from prejudices than the younger members of their community. It is rare for an older woman to refuse to expose her arms or feet before a male enumerator, and also rare that an older man objects to being measured by a female enumerator.
 
 \newpage
 
@@ -3035,8 +2198,7 @@ \section{Survey planning}\label{survey-planning}}
 \item
   Finalise survey travel plans
 \item
-  Disseminate travel plans to local authorities (with survey
-  facilitator)
+  Disseminate travel plans to local authorities (with survey facilitator)
 \item
   Arrange logistics for the training
 \end{itemize}
@@ -3050,16 +2212,14 @@ \section{Survey planning}\label{survey-planning}}
 \item
   Revise the questionnaire with the trainees
 \item
-  Print revised version of questionnaire for the field test (c. 50
-  copies)
+  Print revised version of questionnaire for the field test (c. 50 copies)
 \item
-  Arrange the logistics for the field test (vehicles, drivers,
-  equipment)
+  Arrange the logistics for the field test (vehicles, drivers, equipment)
 \item
   Review logistics arrangements with the trainees
 \end{itemize}
 
-\textbf{At the end of the trainin}
+\textbf{At the end of the training}
 
 \begin{itemize}
 \tightlist
@@ -3080,8 +2240,7 @@ \section{Survey planning}\label{survey-planning}}
 \begin{itemize}
 \tightlist
 \item
-  Train the data entry clerk (day one) using the results of the field
-  test
+  Train the data entry clerk (day one) using the results of the field test
 \item
   Manage survey activities (see below)
 \item
@@ -3120,8 +2279,7 @@ \section{Daily survey activities}\label{daily-survey-activities}}
 \item
   Brief the teams on the day's objectives
 \item
-  Provide feedback on the previous day (success and failures, correction
-  of mistakes made in field procedures or data collection).
+  Provide feedback on the previous day (success and failures, correction of mistakes made in field procedures or data collection).
 \item
   Discuss problems with supervisors
 \item
@@ -3143,14 +2301,11 @@ \section{Daily survey activities}\label{daily-survey-activities}}
 \item
   Plan the next day's programme with the facilitator and the supervisors
 \item
-  Discuss the problems met during the day and their resolution
-  (particularly mistakes that they have observed in the field procedures
-  or in the data collection).
+  Discuss the problems met during the day and their resolution (particularly mistakes that they have observed in the field procedures or in the data collection).
 \item
   Prepare the day programme forms for the next day
 \item
-  Check that there are enough forms, questionnaires, maps for the next
-  day
+  Check that there are enough forms, questionnaires, maps for the next day
 \end{itemize}
 
 \hypertarget{software}{%
@@ -3161,52 +2316,36 @@ \section{Data entry}\label{data-entry}}
 
 A data entry system for RAM-OP data using EpiData has been developed.
 
-This software can be downloaded from
-\url{http://www.brixtonhealth.com/enterRAMOP.zip}.
+This software can be downloaded from \url{http://www.brixtonhealth.com/enterRAMOP.zip}.
 
-This software runs on the Windows operating system. It does not need to
-be installed and can be run from a USB drive.
+This software runs on the Windows operating system. It does not need to be installed and can be run from a USB drive.
 
-The data entry system provides facilities for entering data, interactive
-checking of data as it is entered, batch checking of entered data, data
-summary, and data export. The system creates files that can be read by
-the standard RAM-OP data analysis software.
+The data entry system provides facilities for entering data, interactive checking of data as it is entered, batch checking of entered data, data summary, and data export. The system creates files that can be read by the standard RAM-OP data analysis software.
 
-Documentation of Epidata can be found here
-\url{http://www.epidata.dk/index.htm}.
+Documentation of Epidata can be found here \url{http://www.epidata.dk/index.htm}.
 
 \hypertarget{data-analysis}{%
 \section{Data analysis}\label{data-analysis}}
 
-This manual covers analysing your data using the \textbf{RAnalyticFlow}
-workflow. An \textbf{RAnalyticFlow} workflow may be thought of as an
-\emph{``app''} that makes it easy to analyse your survey data.
+This manual covers analysing your data using the \textbf{RAnalyticFlow} workflow. An \textbf{RAnalyticFlow} workflow may be thought of as an \emph{``app''} that makes it easy to analyse your survey data.
 
 To use the \textbf{RAnalyticFlow} workflow you must install:
 
 \begin{itemize}
 \item
-  \textbf{The \emph{R Language for Data-Analysis and Graphics} (R)} :
-  This is the \emph{``engine''} which does all the work of analysing
-  your data. You can get the R installation program from:
-  \url{http://cran.r-project.org}. Following are links to download
-  operating sofware-specific versions of R:
+  \textbf{The \emph{R Language for Data-Analysis and Graphics} (R)} : This is the \emph{``engine''} which does all the
+  work of analysing your data. You can get the R installation program from: \url{http://cran.r-project.org}. Following are links to download operating software-specific versions of R:
 
   \begin{itemize}
   \item
     \href{https://cran.r-project.org/bin/linux/}{Download R for Linux}
   \item
-    \href{https://cran.r-project.org/bin/macosx/}{Download R for (Mac)
-    OS X}
+    \href{https://cran.r-project.org/bin/macosx/}{Download R for (Mac) OS X}
   \item
-    \href{https://cran.r-project.org/bin/windows/}{Download R for
-    Windowx}
+    \href{https://cran.r-project.org/bin/windows/}{Download R for Windows}
   \end{itemize}
 \item
-  \textbf{R packages} (libraries of functions needed to work with the
-  \textbf{RAnalyticFlow} workflow) : You can install these from within
-  \textbf{R} using the Package Installer function within R. The
-  libraries needed are:
+  \textbf{R packages} (libraries of functions needed to work with the \textbf{RAnalyticFlow} workflow) : You can install these from within \textbf{R} using the Package Installer function within R. The libraries needed are:
 \end{itemize}
 
 \begin{longtable}[]{@{}ll@{}}
@@ -3256,8 +2395,7 @@ \section{Data analysis}\label{data-analysis}}
 \bottomrule
 \end{longtable}
 
-The Package Installer function can be called in R using the following
-command:
+The Package Installer function can be called in R using the following command:
 
 \begin{Shaded}
 \begin{Highlighting}[]
@@ -3267,68 +2405,38 @@ \section{Data analysis}\label{data-analysis}}
 \end{Highlighting}
 \end{Shaded}
 
-The \texttt{repos} argument in the R command above specifies the CRAN
-mirror from which you to download the package/s you want to install.
-Here we specify the cloud-based mirror for CRAN provided by RStudio. If
-unspecified, the installiation process will prompt you to select a
-mirror from which to download packages from. If you already know the URL
-of the CRAN mirror you want to use, specify this in the \texttt{repos}
-argument.
+The \texttt{repos} argument in the R command above specifies the CRAN mirror from which you to download the package/s you want to install. Here we specify the cloud-based mirror for CRAN provided by RStudio. If unspecified, the installation process will prompt you to select a mirror from which to download packages from. If you already know the URL of the CRAN mirror you want to use, specify this in the \texttt{repos} argument.
 
-Note that \textbf{RAnalyticFlow} may require you to have \textbf{Java}
-installed. Check the instructions on the \textbf{RAnalyticFlow}
-\href{http://r.analyticflow.com/en/download/}{download page} and on this
-\href{http://download.ef-prime.com/ranalyticflow/3.1.5/readme.html}{starter
-guide}.
+Note that \textbf{RAnalyticFlow} may require you to have \textbf{Java} installed. Check the instructions on the \textbf{RAnalyticFlow} \href{http://r.analyticflow.com/en/download/}{download page} and on this \href{http://download.ef-prime.com/ranalyticflow/3.1.5/readme.html}{starter guide}.
 
-All of this software is open source and free to download, copy, and use.
-It will run on Windows, Mac OS X, and Linux (and other UNIX-like)
-operating systems. Your ICT department should be able to help you with
-installing this software.
+All of this software is open source and free to download, copy, and use. It will run on Windows, Mac OS X, and Linux (and other UNIX-like) operating systems. Your ICT department should be able to help you with installing this software.
 
-In addition you will also need a copy of the \textbf{RAnalyticFlow}
-workflow and supporting files. These are available from:
+In addition you will also need a copy of the \textbf{RAnalyticFlow} workflow and supporting files. These are available from:
 
 \url{http://www.brixtonhealth.com/ramOP.rflow.zip}
 
-You may need to extract the file from the ZIP archive before use if this
-is not done automatically.
+You may need to extract the file from the ZIP archive before use if this is not done automatically.
 
 \begin{center}\includegraphics[width=9.76in]{figures/dirStructureRAF} \end{center}
 
-Before starting to analyse your data you should create a project
-directory or project folder. This is just a normal folder or directory
-that can be created using your usual file manager (e.g.~Windows
-ExplorerTM in WindowsTM or the FinderTM in Macintosh OS-XTM). The
-project directory or project folder should contain:
+Before starting to analyse your data you should create a project directory or project folder. This is just a normal folder or directory that can be created using your usual file manager (e.g.~Windows ExplorerTM in WindowsTM or the FinderTM in Macintosh OS-XTM). The project directory or project folder should contain:
 
 \begin{enumerate}
 \def\labelenumi{\arabic{enumi}.}
 \item
-  Your PSU file (here we assume this file is called testPSU.csv but it
-  could have any name). This file must be a comma-separated-value (CSV)
-  file.
+  Your PSU file (here we assume this file is called testPSU.csv but it could have any name). This file must be a comma-separated-value (CSV) file.
 \item
-  Your survey data file (here we assume this file is called testSVY.rec
-  but it could have any name). This file can be an EpiData (REC) file or
-  a comma-separated-value (CSV) file.
+  Your survey data file (here we assume this file is called testSVY.rec but it could have any name). This file can be an EpiData (REC) file or a comma-separated-value (CSV) file.
 \item
-  The language file (always called ramOP.language.csv). This file
-  provides text that is used in reports and graphics. The purpose of
-  this file is to make the data analysis software produce reports in any
-  language. This file must be a comma-separated-value (CSV) file.
+  The language file (always called ramOP.language.csv). This file provides text that is used in reports and graphics. The purpose of this file is to make the data analysis software produce reports in any language. This file must be a comma-separated-value (CSV) file.
 \item
   A copy of the file ramOP.rflow.
 \end{enumerate}
 
-When you have created the project directory or project folder with the
-required files you can start RAnalyticFlow.
+When you have created the project directory or project folder with the required files you can start
+RAnalyticFlow.
 
-Note: The \texttt{testSVY.rec} and \texttt{testPSU.csv} files are
-example data files and are distributed with the \textbf{RAnalyticFlow}
-workflow. You can use these files to practice analysing data using
-\textbf{RAnalyticFlow}, and as examples of RAM-OP survey data and PSU
-files.
+Note: The \texttt{testSVY.rec} and \texttt{testPSU.csv} files are example data files and are distributed with the \textbf{RAnalyticFlow} workflow. You can use these files to practice analysing data using \textbf{RAnalyticFlow}, and as examples of RAM-OP survey data and PSU files.
 
 \begin{center}\includegraphics[width=9.76in]{figures/openProjectRAF} \end{center}
 
@@ -3339,32 +2447,18 @@ \section{Data analysis}\label{data-analysis}}
 \item
   Click the \textbf{New Project\ldots{}} button
 \item
-  Give your project a useful (i.e.~descriptive and memorable) name. This
-  might be a name that describes the survey. For example, if the survey
-  was done in the Kereinik locality of West Darfur in December 2015 you
-  might use the name \textbf{WD.Kereinik.Dec2015.RAMOP}
+  Give your project a useful (i.e.~descriptive and memorable) name. This might be a name that describes the survey. For example, if the survey was done in the Kereinik locality of West Darfur in December 2015 you might use the name \textbf{WD.Kereinik.Dec2015.RAMOP}
 \item
-  Give the location of your project directory or project folder. This is
-  the directory or folder which contains your survey data file, your PSU
-  file, the RAM-OP language file, and a copy of \textbf{RAMOP.rflow}
-  (see previous page). The location of the project directory or project
-  folder (labeled ``Path'' by the software) that \textbf{RAnalyticFlow}
-  selects automatically will almost always be wrong. You need to specify
-  this manually.
+  Give the location of your project directory or project folder. This is the directory or folder which contains your survey data file, your PSU file, the RAM-OP language file, and a copy of \textbf{RAMOP.rflow} (see previous page). The location of the project directory or project folder (labelled ``Path'' by the software) that \textbf{RAnalyticFlow} selects automatically will almost always be wrong. You need to specify this manually.
 \item
   Click the \textbf{OK} button
 \end{enumerate}
 
 \begin{center}\includegraphics[width=9.76in]{figures/openWorkflowRAF} \end{center}
 
-Double click the item named \textbf{ramOP.rflow} shown in the file
-manager pane of the \textbf{RAnalyticFlow} window. This will open the
-data-analysis workflow which will be shown in the workflow viewer /
-editor window of the \textbf{RAnalyticFlow} window.
+Double click the item named \textbf{ramOP.rflow} shown in the file manager pane of the \textbf{RAnalyticFlow} window. This will open the data-analysis workflow which will be shown in the workflow viewer / editor window of the \textbf{RAnalyticFlow} window.
 
-Once you have opened the workflow you need to initialise it (i.e.~load
-libraries, useful analysis function, and initialise the workspace for a
-new analysis):
+Once you have opened the workflow you need to initialise it (i.e.~load libraries, useful analysis function, and initialise the workspace for a new analysis):
 
 \newpage
 
@@ -3375,83 +2469,48 @@ \section{Data analysis}\label{data-analysis}}
 \begin{enumerate}
 \def\labelenumi{\arabic{enumi}.}
 \item
-  Retrieve your survey data. This can be in EpiDat (REC) format or CSV
-  format. Select and run the appropriate \textbf{Survey Data} node and
-  select the survey data file.
+  Retrieve your survey data. This can be in EpiData (REC) format or CSV format. Select and run the
+  appropriate \textbf{Survey Data} node and select the survey data file.
 \item
-  Retrieve the PSU date data. Select and run the \textbf{PSU Data} node
-  and select your PSU file.
+  Retrieve the PSU date data. Select and run the \textbf{PSU Data} node and select your PSU file.
 \item
-  Produce the survey report and graphics. Select and run the
-  \textbf{Report} node. This will take some time to complete because the
-  analysis uses computer intensive techniques to make best use of the
-  available data. The Report node/icon will have black lines around it
-  has completed running the report.
+  Produce the survey report and graphics. Select and run the \textbf{Report} node. This will take some time to complete because the analysis uses computer intensive techniques to make best use of the available data. The Report node/icon will have black lines around it has completed running the report.
 \end{enumerate}
 
-When the analysis is complete your project directory or project folder
-should contain eight new files:
+When the analysis is complete your project directory or project folder should contain eight new files:
 
 \begin{center}\includegraphics[width=9.76in]{figures/workflowResults01} \end{center}
 
-The files ending in \texttt{.png} are graphics files. The names of these
-files are intended to be informative. For example:
+The files ending in \texttt{.png} are graphics files. The names of these files are intended to be informative. For example:
 
 \begin{center}\includegraphics[width=9.76in]{figures/workflowResults02} \end{center}
 
-The file ending in \texttt{.report.csv} is the survey report file (see
-below).
+The file ending in \texttt{.report.csv} is the survey report file (see below).
 
-There may also be a file ending in \texttt{.afp}. This is a project
-information file used by \textbf{RAnalyticFlow} and can be ignored.
+There may also be a file ending in \texttt{.afp}. This is a project information file used by \textbf{RAnalyticFlow} and can be ignored.
 
-The file ending in \texttt{.report.csv} is the survey report file. This
-file can be opened, formatted and edited in a spreadsheet program such
-as \textbf{Microsoft Excel} or \textbf{OpenOffice Calc}:
+The file ending in \texttt{.report.csv} is the survey report file. This file can be opened, formatted and edited in a spreadsheet program such as \textbf{Microsoft Excel} or \textbf{OpenOffice Calc}:
 
 \begin{center}\includegraphics[width=16.67in]{figures/reportFile} \end{center}
 
-In the illustration above, proportions have been formatted as
-percentages with one decimal place, means have been formatted as numbers
-with two decimal places, and column titles have been formatted as bold
-text.
+In the illustration above, proportions have been formatted as percentages with one decimal place, means have been formatted as numbers with two decimal places, and column titles have been formatted as bold text.
 
-You can edit the labels as you see fit. If you plan to do several RAM-OP
-surveys then you may want to edit the language file (always called
-\texttt{ramOP.language.csv}). This file provides text that is used in
-reports and graphics. The purpose of this file is to make the data
-analysis software produce reports in any language. This file
-\textbf{must} be a comma-separated-value (CSV) file.
+You can edit the labels as you see fit. If you plan to do several RAM-OP surveys then you may want to edit the language file (always called \texttt{ramOP.language.csv}). This file provides text that is used in reports and graphics. The purpose of this file is to make the data analysis software produce reports in any language. This file \textbf{must} be a comma-separated-value (CSV) file.
 
 \hypertarget{conclusion}{%
 \chapter{Conclusion}\label{conclusion}}
 
-We live in an ageing world, where people aged 60 or over will be 2
-billion or about 22\% of the world's population by 2050.
+We live in an ageing world, where people aged 60 or over will be 2 billion or about 22\% of the world's population by 2050.
 
-Currently, two in three people aged 60 years or older live in developing
-countries. By 2050, nearly four in five older people will be living in
-the developing world.
+Currently, two in three people aged 60 years or older live in developing countries. By 2050, nearly four in five older people will be living in the developing world.
 
-The changing demographics of ageing combined with the increasing number
-of disasters will exert a disproportionate impact on the world's oldest
-and poorest.
+The changing demographics of ageing combined with the increasing number of disasters will exert a disproportionate impact on the world's oldest and poorest.
 
-In this context, identifying the needs of older people as accurately as
-possible is a necessity. More and more donors and UN agencies are now
-willing to include older people in their programmes. Age markers, to
-complement gender markers, will be disseminated very soon
+In this context, identifying the needs of older people as accurately as possible is a necessity. More and more donors and UN agencies are now willing to include older people in their programmes. Age markers, to complement gender markers, will be disseminated very soon
 
-RAM-OP is offering a fast, robust, reliable, tested and user-friendly
-way of assessing the needs of older people. It can be used in
-humanitarian situations as well as in development contexts. The modular
-structure of RAM-OP allows for adaptations, making it exhaustive or
-limited to essential indicators according to the immediate needs.
+RAM-OP is offering a fast, robust, reliable, tested and user-friendly way of assessing the needs of older people. It can be used in humanitarian situations as well as in development contexts. The modular structure of RAM-OP allows for adaptations, making it exhaustive or limited to essential indicators according to the immediate needs.
 
-As more organisations start to use it, RAM-OP will evolve and improve.
-New versions of RAM-OP can be created (for example, RAM-OP for refugee
-or displaced people camps). We wish that a greater number of actors will
-start using RAM-OP and make it their own.
+As more organisations start to use it, RAM-OP will evolve and improve. New versions of RAM-OP can be created (for example, RAM-OP for refugee or displaced people camps). We wish that a greater number of actors will start using RAM-OP and make it their own.
 
 \bibliography{book.bib}
 
diff --git a/docs/sampling.html b/docs/sampling.html
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+  <meta charset="utf-8" />
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+  <meta name="generator" content="bookdown 0.11 and GitBook 2.6.7" />
 
-  <meta property="og:title" content="Rapid Assessment Method for Older People (RAM-OP): The Manual" />
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-  <meta property="og:url" content="https://validmeasures.org/ramOPmanual" />
-  <meta property="og:image" content="https://validmeasures.org/ramOPmanualfigures/coverImage.jpg" />
+  <meta property="og:url" content="https://rapidsurveys.io/ramOPmanual" />
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-<meta name="author" content="Pascale Fritsch, Ernest Guevarra, Katja Siling, Mark Myatt">
+<meta name="author" content="Pascale Fritsch, Ernest Guevarra, Katja Siling, Mark Myatt" />
 
 
 
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@@ -208,7 +215,7 @@ <h1>
 
             <section class="normal" id="section-">
 <div id="sampling" class="section level1">
-<h1><span class="header-section-number">Chapter 1</span> Sampling</h1>
+<h1><span class="header-section-number">1</span> Sampling</h1>
 <div id="the-ram-op-sample" class="section level2">
 <h2><span class="header-section-number">1.1</span> The RAM-OP sample</h2>
 <p>RAM-OP uses a two-stage sample:</p>
@@ -305,7 +312,7 @@ <h2><span class="header-section-number">1.4</span> Eligibility</h2>
 <div id="age-distribution-eligibility-criteria-and-sample-design" class="section level2">
 <h2><span class="header-section-number">1.5</span> Age distribution, eligibility criteria, and sample design</h2>
 <p>The age distribution of the population and the survey eligibility criteria will affect the sample design in terms of the number of communities that you will need to sample (<span class="math inline">\(m\)</span>) and the number of older persons (<span class="math inline">\(n\)</span>) that can be sampled from each community.</p>
-<p>The overall sample size for a RAM-OP sample should be at least <span class="math inline">\(n = 192\)</span> usually collected as <span class="math inline">\(n = 12\)</span> eligible subjects sampled from <span class="math inline">\(m = 16\)</span> communities. If older people make up a very small proportion (i.e. much less than 5%) of the total population and / or the average population of communities is small then you will usually need to sample more than m = 16 communities in order to get about <span class="math inline">\(n = 192\)</span> older people in the overall sample. This is likely to occur when there are fewer than 20 to 25 older people in a community of average size.</p>
+<p>The overall sample size for a RAM-OP sample should be at least <span class="math inline">\(n = 192\)</span> usually collected as <span class="math inline">\(n = 12\)</span> eligible subjects sampled from <span class="math inline">\(m = 16\)</span> communities. If older people make up a very small proportion (i.e. much less than 5%) of the total population and / or the average population of communities is small then you will usually need to sample more than <span class="math inline">\(m = 16\)</span> communities in order to get about <span class="math inline">\(n = 192\)</span> older people in the overall sample. This is likely to occur when there are fewer than 20 to 25 older people in a community of average size.</p>
 <p>You can calculate the number of older people that you would expect to be living in a community of average size using the following formula:</p>
 <p> </p>
 <p><span class="math display">\[n_{\text{aged 60+ in an average village}} ~ = ~ \text{average village population}_{\text{all ages}} ~ \times ~ \frac{\text{percentage of population}_{\text{aged 60+}}}{100}\]</span></p>
@@ -332,9 +339,9 @@ <h3><span class="header-section-number">1.6.1</span> The first stage sample - li
 <p><strong>Step 2:</strong> Choose a <em>random starting point</em> between 1 and <em>sampling interval</em>. In this example, this would be a random number <strong>between 1 and 4</strong>.</p>
 <p>A random number can be selected through simple lottery (i.e., draw from a lot of 4 numbered from 1 to 4). A standard spreadsheet software can also be used to draw the random number using the <code>RANDBETWEEN</code> function as follows:</p>
 <p> </p>
-<pre class="sourceCode r" id="cb1"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw">RANDBETWEEN</span>(<span class="dv">1</span>, <span class="dv">4</span>)</a></code></pre>
+<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">RANDBETWEEN</span>(<span class="dv">1</span>, <span class="dv">4</span>)</code></pre>
 <p> </p>
-<p><strong>Step 3:</strong> Using the <em>random starting point</em> and the <em>sampling interval</em>, select the sampling villages from a list of all villages organised/sorted by a <strong>non-overalapping</strong> spatial factor such as district or sub-district.</p>
+<p><strong>Step 3:</strong> Using the <em>random starting point</em> and the <em>sampling interval</em>, select the sampling villages from a list of all villages organised/sorted by a <strong>non-overlapping</strong> spatial factor such as district or sub-district.</p>
 
 <div class="figure" style="text-align: center"><span id="fig:sample4"></span>
 <img src="figures/listSample2.png" alt="Selection of sampling villages using lists" width="603" />
@@ -631,13 +638,13 @@ <h3><span class="header-section-number">1.6.11</span> Sampling in urban settings
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diff --git a/docs/search_index.json b/docs/search_index.json
index 0a3ab34..0ce7359 100644
--- a/docs/search_index.json
+++ b/docs/search_index.json
@@ -1,11 +1,11 @@
 [
 ["index.html", "Rapid Assessment Method for Older People (RAM-OP): The Manual The RAM-OP Manual", " Rapid Assessment Method for Older People (RAM-OP): The Manual Pascale Fritsch, Ernest Guevarra, Katja Siling, Mark Myatt 21/12/2015 The RAM-OP Manual "],
-["introduction.html", "Introduction", " Introduction Older people (generally defined as people aged sixty years and older) are a vulnerable group for malnutrition in humanitarian and developmental contexts. Due to their age they have specific nutritional needs, such as easily digestible and palatable food adapted to those with chewing problems, which is dense in nutrients. In famine and displacement situations where populations are dependent on food distributions, older people often find the general ration inappropriate to their tastes and needs, have difficulties accessing the distributions, or have difficulties transporting rations home. As a result, older people can become malnourished and in need of specifically targeted food interventions. In times of drought or food scarcity, older people tend to reduce their food intake in order to share or give up their ration to younger members of their families. They are then at risk of malnutrition. Despite these potential vulnerabilities in humanitarian situations, older people are rarely identified as a group in need of specific nutritional or food assistance. Surveys and assessments almost always focus on children, and sometimes on pregnant and lactating women. Humanitarian workers argue that assessing the nutritional status and needs of older people is both costly and complicated. As a consequence, the nutritional status and needs of older people in crisis go unidentified and unaddressed. HelpAge International, VALID International, and Brixton Health, with financial assistance from the Humanitarian Innovation Fund (HIF), have developed a Rapid Assessment Method for Older People (RAM-OP) that provides accurate and reliable estimates of the needs of older people. The method uses simple procedures, in a short time frame (i.e. about two weeks including training, data collection, data entry, and data analysis), and at considerably lower cost than other methods. The RAM-OP method is based on the following principles: Use of a familiar “household survey” design employing a two-stage cluster sample design optimised to allow the use of a small primary sample ( m ≥ 16 clusters) and a small overall ( n ≥ 192) sample. Assessment of multiple dimensions of need in older people (including prevalence of global, moderate and severe acute malnutrition) using, whenever possible, standard and well-tested indicators and question sets. Data analysis performed using modern computer-intensive methods to allow estimates of indicator levels to be made with useful precision using a small sample size. The following tools are currently available under the General Public Licence / Free Documentation License, meaning that you are free to copy and adapt these tools: an English language manual / guidebook a questionnaire (available in English and French) data entry and data checking software (available in English and French) data analysis software. We believe that the availability of a rapid, low-cost, and user-friendly method will encourage governments, UN agencies, as well as international and local non-governmental organisations to actively assess the situation of older people in humanitarian contexts, and implement, monitor, and evaluate relevant and timely responses to address their needs. "],
-["sampling.html", "Chapter 1 Sampling 1.1 The RAM-OP sample 1.2 Implicit stratification 1.3 RAM-OP survey sample size 1.4 Eligibility 1.5 Age distribution, eligibility criteria, and sample design 1.6 Practical sampling", " Chapter 1 Sampling 1.1 The RAM-OP sample RAM-OP uses a two-stage sample: First stage sample: A sample of communities (e.g. villages or city-blocks) in the survey area is taken. A sampled community is also called a primary sampling unit (PSU). Second stage sample: Domestic dwellings are sampled from within the communities selected in the first stage sample. All eligible individuals in the sampled dwelling are included in the sample. 1.1.1 The first-stage sample The first stage sample is a systematic spatial sample. Two methods can be used and both methods take the sample from all parts of the survey area: List-based method: Communities to be sampled are selected systematically from a complete list of communities in the survey area. This list of communities is sorted by one or more non-overlapping spatial factors such as district and subdistricts within districts: Figure 1.1: Communities listing by district and sub-district Map-based method: Communities to be sampled are selected from the centres of the squares of a grid drawn over a map. The map must be sufficiently well made and of sufficiently large scale to show the position of every community in the survey area. This type of sample is known as a centric systematic area sample and is often referred to as a CSAS sample. Note: Population proportional sampling (PPS) is not used in RAM-OP surveys. Population estimates for all communities are not required for sampling purposes. Population estimates are required only for the selected communities. These are used during data analysis in order to weight results by population size. If this information is not available before the survey, it can be collected during the survey. 1.1.2 The second stage sample The second stage within-community sample uses a method called map-segment-sample. This method takes the within-community sample from all parts of a sampled community. 1.2 Implicit stratification Both the first and second stage samples use a form of spatial stratification: The list-based method’s first stage systematic spatial sample stratifies the sample by non-overlapping spatial factor such as districts and subdistricts within districts. The map-based (CSAS) method’s first stage sample stratifies the sample by grid square. The map-segment-sample second stage within-community sample stratifies the sample by parts of the community being sampled. The first and second stage samples also ensure that a reasonably even spatial sample is taken from the entire survey area and from each of the sampled communities. These sampling procedures provide implicit stratification and tend to spread the sample properly among important sub-groups of the population such as rural / urban / peri-urban populations, administrative areas, ethnic sub-populations, religious sub-populations, and socio-economic groups. This often improves the precision of estimates made from survey data. The use of implicit stratification improves the efficiency of a two-stage cluster sample and allows RAM-OP to use relatively small sample sizes compared to other methods, such as SMART surveys. The use of modern computer-intensive data analysis techniques also allows RAM-OP to make better use of the available sample than is done in other methods. 1.3 RAM-OP survey sample size The following shorthand symbols will be used when describing sample designs: \\[\\begin{aligned} m &amp; ~ = ~ \\text{Number of primary sampling units (PSUs).} \\\\[5pt] n &amp; ~ = ~ \\text{Size of the sample of individuals or households from a PSU.} \\\\[5pt] n &amp; ~ = ~ \\text{May also mean the overall survey sample size (this meaning will be made clear in the text).} \\\\[5pt] N &amp; ~ = ~ \\text{Population} \\end{aligned}\\] The overall sample size for a RAM-OP survey is about \\(n = 192\\) individual subjects. You should aim to collect an overall sample of at least \\(n = 192\\) individuals. The RAM-OP sample is collected in two stages: The first stage sample uses a sample size of about \\(m = 16\\) communities (or PSUs). The second stage sample uses a sample size of about \\(n = 12\\) eligible subjects sampled from each of the communities selected for inclusion in the first stage sample. The overall sample size from \\(m = 16\\) communities and \\(n = 12\\) eligible subjects is about: \\[\\text{overall sample size} ~ \\approx ~ m ~ \\times ~ n ~ \\approx ~ 16 ~ \\times ~ 12 ~ \\approx ~ 192\\] It is not recommended that fewer than \\(m = 16\\) communities are sampled. Sampling fewer than \\(m = 16\\) communities will tend to reduce the precision with which estimates can be made. If you have the resources to sample more than \\(m = 16\\) communities then you should do so. A sample of \\(m = 24\\) communities and \\(n = 8\\) eligible subjects, for example, will tend to yield estimates with better precision than a sample with \\(m = 16\\) communities and \\(n = 12\\) eligible subjects. Do not be tempted to increase the size of the within-community sample in order to achieve an overall sample size of \\(n = 192\\) from fewer than \\(m = 16\\) communities. Doing so will tend to reduce the precision with which estimates are made. It may also be impossible to do this in many settings. Here, for example, is a population pyramid for a typical developing country: Figure 1.2: Population pyramid for a typical developing country If the average community population is \\(N = 300\\) then there will be fewer than 15 people aged 60 years and older in about half of the selected communities. This is because about half of the selected communities are likely to have a population below the average population. 1.4 Eligibility Older people are usual defined as persons aged 60 years and older (UN definition). This means your sample will usually be restricted to people aged 60 years and older. In some settings different eligibility criteria may apply. This will likely be the case in settings with very high life-expectancies (usually middle and high income countries) or very low life-expectancies (usually low income countries and in emergencies). In a setting of very high life-expectancy you may want to restrict eligibility - to persons aged 65 years or older, for example. A local definition of older people is likely to be available. In a setting with very low life-expectancy, very few people are aged 60 years or older. For example: Figure 1.3: Population pyramid for a setting with low life-expectancy It is common in such setting for there to be a local definition of older people. This will usually be “persons aged 50 years or older” or “persons aged 55 years or older”. 1.5 Age distribution, eligibility criteria, and sample design The age distribution of the population and the survey eligibility criteria will affect the sample design in terms of the number of communities that you will need to sample (\\(m\\)) and the number of older persons (\\(n\\)) that can be sampled from each community. The overall sample size for a RAM-OP sample should be at least \\(n = 192\\) usually collected as \\(n = 12\\) eligible subjects sampled from \\(m = 16\\) communities. If older people make up a very small proportion (i.e. much less than 5%) of the total population and / or the average population of communities is small then you will usually need to sample more than m = 16 communities in order to get about \\(n = 192\\) older people in the overall sample. This is likely to occur when there are fewer than 20 to 25 older people in a community of average size. You can calculate the number of older people that you would expect to be living in a community of average size using the following formula: \\[n_{\\text{aged 60+ in an average village}} ~ = ~ \\text{average village population}_{\\text{all ages}} ~ \\times ~ \\frac{\\text{percentage of population}_{\\text{aged 60+}}}{100}\\] If this is below about 20 people then you should consider how you will collect the required overall sample size. Three approaches may be used: Relax the eligibility criteria: You may decide to define older people as “persons aged 50 years or older” or “persons aged 55 years or older”. This may double the size of the eligible population and make the sample easier to collect. This approach is only reasonable if life-expectancy is low. Increase the number of communities that you plan to sample: You may choose to collect your sample as \\(n = 7\\) eligible subjects sampled from \\(m = 30\\) communities giving an expected overall sample size of \\(n = 210\\). This would be a very good sample. The disadvantage of this approach is that survey costs increase with the number of communities that are sampled, because a lot of survey time and vehicle costs are spent on travelling to and from the selected communities. Take a “top-up” sample only when you need to: The basic procedure when a selected community is small and likely to contain fewer than \\(n = 12\\) older people is to collect data on all older people in the selected community using a door-to-door census. If the within-community sample size is much smaller than the required one then a “top-up” sample is taken from the nearest neighbouring community using the map-segment-sample method (or a door-to-door census if this community is also small). The advantage of this approach is that travelling time and survey costs are better controlled. If the proportion of older people is not very small and / or communities are large then you should have no problems achieving the overall sample size. 1.6 Practical sampling 1.6.1 The first stage sample - list-based sampling The first stage sample can be drawn from a list of all communities. The list-based sample is a simple systematic sample taken from a complete list of communities in the survey area sorted by one or more non- overlapping spatial factors (such as administrative units or electoral wards) in the survey area. Population proportional sampling (PPS) is not used since this would concentrate the sample in the larger communities. Below is a worked example of how a RAM-OP first stage, list-based sample can be drawn from a survey area composed of 67 villages. Step 1: Calculate the sampling interval by dividing the total villages in the survey area (67 villages) with the number of villages to be drawn from the sample (16 villages). \\[\\text{Sampling Interval} ~ = ~ \\left \\lfloor ~ \\frac{N_{\\text{villages}}}{N_{\\text{sample}}} ~ \\right \\rfloor ~ = ~ \\left \\lfloor ~ \\frac{67}{16} ~ \\right \\rfloor ~ \\approx ~ \\left \\lfloor ~ 4.19 ~ \\right \\rfloor ~ \\approx ~ 4\\] The sampling interval needs to be a whole number. Remember to always round down when calculating the sampling interval to the nearest whole number. Step 2: Choose a random starting point between 1 and sampling interval. In this example, this would be a random number between 1 and 4. A random number can be selected through simple lottery (i.e., draw from a lot of 4 numbered from 1 to 4). A standard spreadsheet software can also be used to draw the random number using the RANDBETWEEN function as follows: RANDBETWEEN(1, 4) Step 3: Using the random starting point and the sampling interval, select the sampling villages from a list of all villages organised/sorted by a non-overalapping spatial factor such as district or sub-district. Figure 1.4: Selection of sampling villages using lists 1.6.2 The first stage sample - map-based sampling An alternative approach to list-based sampling is to use map-based sampling. The map-based (CSAS) sample selects communities from the centre of squares of a grid drawn over a map. The map must be sufficiently well made and of sufficiently large scale to show the position of all communities in the survey area. A square grid is drawn over the map. The size of the grid squares should be small enough so that the number of squares covering the survey area is the same as (or very similar to) the number of communities that you plan to sample. You may need to experiment with different grid sizes to achieve this. Figure 1.6 shows an example map and grid with \\(m = 16\\) grid squares. The sample is drawn by selecting the community that is located closest to the centre of each grid square: Figure 1.5: Selection of sampling villages using maps If two or more villages are located the same distance from the centre of a grid square then a single village is picked at random, by tossing a coin for example. Figure 1.7 shows the sample selected by this process for the area shown in Figure 1.6. Figure 1.6: Drawing a square grid over the map Figure 1.7: Drawing the first-stage CSAS sample Both the list-based and the map-based (CSAS) sampling methods spread the sample of communities evenly across the entire survey area. Each community has an equal chance of being included in the sample. Population proportional sampling (PPS) is not used since this would concentrate the sample in the larger communities. The same method can be used when sampling in urban contexts. Figure 1.8 shows a sample drawn from a list of census enumeration areas sorted by administrative district. Figure 1.9 shows a sample drawn using the map- based (CSAS) method. In both cases the primary sampling units (PSUs) are census enumeration areas. Figure 1.8: Example of an urban sample (list-based) Figure 1.9: Example of an urban sample (map-based) Note: In this example twenty-one (21) blocks have been selected. It can be difficult to achieve exactly the number of blocks that you need when using this type of sample. It is best to select more rather than fewer blocks than you need Here we would take our sample as \\(n = 10\\) individuals from \\(m = 21\\) blocks (overall \\(n = 210\\)). 1.6.3 The second stage (within-community) sample The second stage (within-community) sample uses a map-segment-sample approach: Map: Make a rough map of the community to be sampled. It is helpful to think of communities as being made of ribbons (i.e. lines of dwellings located along roads, tracks, or rivers) and clusters of dwellings. Here is an example of a ribbon of dwellings: Figure 1.10: Example of a ribbon of dwellings Here is an example of a cluster of dwellings: Figure 1.11: Example of a cluster of dwellings Segment: Divide the community into ribbon and cluster segments defined by the physical layout of the community being sampled. Sample: Ribbons and clusters are sampled in different ways: Ribbons are sampled using systematic sampling. Clusters are sampled using a random walk method. Note: If a small community is selected that is likely to have fewer than the required number of eligible persons then all eligible persons in that community are sampled by moving door-to-door. 1.6.4 Mapping the community - single and multiple clusters Some communities consist of a single cluster of dwellings: Figure 1.12: Example of a cluster of dwellings or a set of clusters of dwellings: Figure 1.13: Example of a set of clusters of dwellings For communities (or parts of communities) structured in this way we use a sampling method called the random walk. 1.6.5 Mapping the community - ribbon communities Ribbon communities have dwellings arranged in a line: Figure 1.14: Dwellings arranged in a line or in a several lines: Figure 1.15: Dwellings arranged in several lines For communities (or parts of communities) structured in this way we use a sampling method called systematic sampling. 1.6.6 Mapping the community - mixed communities Some communities are a mixture of clusters and ribbons: Figure 1.16: Mixture of clusters and ribbons For mixed communities we use a mixture of the random walk method (in the clusters) and systematic sampling (along the ribbons). Segmentation involves dividing a community into several parts and taking part of the within-community sample from each segment. With simple communities, segmentation is not required and we take a single sample from the entire community using the appropriate sampling method. 1.6.7 Segmentation For more complicated communities we divide the community into several parts or segments, such as a community made up of several clusters: Figure 1.17: Example of a set of clusters of dwellings or a community made up of several ribbons: Figure 1.18: Dwellings arranged in several lines or a mixed community: Figure 1.19: Mixture of clusters and ribbons We take a small sample from each segment using the appropriate sampling method. For example, with a community made up of three segments: Figure 1.20: Community made up of three segments we would take one third of the overall sample from each segment. If the within-community sample size is twelve eligible subjects. we would sample four eligible subjects from each segment (i.e. \\(12 / 3 = 4\\)). Dividing the sample up in this way means that we will sample from every part of the community rather than just one part of the community. When taking the sample we use the random walk method to take part of the sample from clusters and the systematic sampling method to take part of the sample from ribbons. Segments should be either ribbons or clusters but should never contain both a ribbon and a cluster. This is because clusters and ribbons are sampled in different ways. A dwelling can only belong to one segment. Segments should not overlap. 1.6.8 Sample dwellings All segments should be sampled. If, for example, there are five segments in a community: Figure 1.21: Community made up of five segments and the within-community sample size is twelve eligible subjects, then you would plan to sample two eligible subjects from each segment (i.e. \\(12 / 5 = 2.4\\) rounded down to two) and, if necessary, return to the largest segment to complete the sample. All segments should be sampled, even if this means that you take a larger sample than you expected to. Remember that different types of segment are sampled in different ways: Dwellings in cluster segments are sampled using a method called the random walk. This involves sampling houses by walking in random directions within the cluster. Dwellings in ribbon segments are sampled using a method called systematic sampling. This involves sampling houses at regular intervals along the ribbon. We will look at each of these sampling methods in turn. 1.6.9 Random walk sampling The random walk method is used to sample dwellings in cluster segments. Sampling proceeds as follows: Move to the approximate centre of the cluster. Select a random direction by spinning a bottle on the ground. The neck indicates the sampling direction. This is the direction you should walk in order to sample a dwelling. Walk in the sampling direction counting the dwellings that you pass. Sample the third dwelling. If there are no eligible persons in the selected dwelling then sample the nearest dwelling with an eligible person. Sample all eligible persons in the selected dwelling. Apply the survey questionnaire for all eligible persons in the selected dwelling. Select the next dwelling to sample by spinning a bottle and walking in the indicated direction. Count the dwellings you pass. Sample the third dwelling. If there are no eligible persons in the selected dwelling then sample the nearest dwelling with an eligible person. Sample all eligible persons in the selected dwelling. If you reach the edge of the cluster segment then return to the centre of the cluster and repeat step (2) above. Remember to keep count of the number of eligible persons sampled from the segment. Stop sampling in the segment when you have sampled the required number of eligible persons from the segment. Since you sample all eligible persons in a selected dwelling, you may sample a few more eligible persons than expected. This is OK. Always sample all eligible persons in a selected dwelling. If, when you have sampled all segments, you have not sampled twelve eligible persons, you should return to the largest segment to finish sampling using the appropriate sampling method. The random walk method is illustrated in Figure 1.22. Figure 1.22: Random walk sampling in a cluster segment 1.6.10 Systematic sampling The systematic sampling method is used to sample houses in ribbon segments. Sampling proceeds as follows: Move to one end of the ribbon segment. Walk to the other end of the segment counting the houses that you pass. Calculate the step size by dividing the number of dwellings in the segment by the required sample size for the segment. Use the whole number part of the result only. Do not round up. Pick a random number between one and the step size. This is your starting point. Select the first dwelling to sample by walking along the segment counting the dwellings that you pass and sample the dwelling indicated by the starting point. If there are no eligible persons in the selected dwelling then sample the nearest dwelling in any direction with an eligible person. Sample all eligible persons in the selected dwelling. Select the next dwelling to sample by walking along the segment. Count the dwellings that you pass. Sample the dwelling indicated by the step size. If there are no eligible persons in the selected dwelling then sample the nearest dwelling in any direction with an eligible person. Sample all eligible persons in the selected dwelling. Stop sampling in the segment when you reach the end of the ribbon segment. This may mean that you sample extra eligible persons. This is OK. Do not stop sampling from a ribbon until you reach the end of the ribbon. If, when you have sampled all segments, you have not sampled twelve eligible persons, you should return to the largest segment to finish sampling using the appropriate sampling method. The systematic sampling method is illustrated in Figure 1.23. Figure 1.23: Systematic sampling in a ribbon segment 1.6.11 Sampling in urban settings In urban areas the first stage sample is taken by replacing sub-districts with “sections” and communities with city blocks. Examples of sections may be administrative districts/sub-districts or electoral wards. Figure 1.24: Administrative divisions in an urban setting Census enumeration areas (EAs) are usually city blocks. Central statistics offices can usually provide lists of EAs by “section” and large-scale maps of EAs selected for sampling (See Figure 1.25 and Figure 1.26). These maps make it easy to locate EAs and their boundaries. The sample of EAs can be decided using list-based or map-based (CSAS) sampling. In these settings, eligible persons may be sampled by moving from door-to-door. All dwellings in the selected block are sampled and all eligible persons in the selected dwellings are sampled. This means that all eligible persons in a selected block are sampled. If city blocks are large then a type of systematic sampling may be used. With this method a rough map of the streets in the block is made and the number of doorways on each street is counted and copied onto the rough street map (as shown in Figure 1.27). The total number of doorways on all streets is calculated. A step size is calculated by dividing the total number of doorways on all streets by the number of dwellings to be sampled. A systematic sample along a route around the block that includes all streets in the block is taken. Streets can be sampled in any order. If you find that you have sampled all streets but have not yet sampled the required number of eligible persons then you should return to the street with the largest number of houses to collect the remainder of the sample. The number of blocks to be sampled will depend on the expected number of eligible persons in each block. You should aim for an overall sample size of about \\(n = 192\\). You should not sample fewer than \\(m = 16\\) blocks. Figure 1.25: Enumeration area map for a city block in Freetown, Sierra Leone Figure 1.26: Enumeration area map for a city block in Addis Ababa, Ethiopia Figure 1.27: Systematic sampling in a city block When useful lists and maps are not available then satellite imagery available though free services such as Google Earth (http://earth.google.com) may be used. The quality (resolution) of the images available from these services is variable but is usually good enough to allow you to segment the town into small areas of approximately equal volume (approximately the same number of dwellings) in each: Figure 1.28: Segmenting a town into smaller sampling areas When creating segments using maps or satellite images it is a good idea to use main roads, rivers, canals, railway lines, public parks, etc as boundaries. This simplifies the segmentation process and also simplifies fieldwork by making areas and their boundaries easier to locate and sample. The first stage sample can be list-based (such as where each area is numbered in a systematic north to south and east to west order and a systematic sample taken) or map-based (CSAS). Larger scale “maps” of blocks to be sampled can also me made using satellite imagery (see Figure 1.29). Figure 1.29: A large scale “map” of a city block made from satellite imagery "],
-["indicators.html", "Chapter 2 Indicators 2.1 The RAM-OP indicator set 2.2 A note on data management and data analysis", " Chapter 2 Indicators 2.1 The RAM-OP indicator set RAM-OP surveys collect and report on data for a broad range of indicators relevant to older people. These indicators cover the following dimensions: Demography and situation Food intake Severe food insecurity Disability Activities of daily living Mental health and well-being Dementia Health and health-seeking behaviour Sources of income Water, sanitation, and hygiene Anthropometry and screening coverage Visual impairment Data for a small group of miscellaneous indicators are also collected and reported. The RAM-OP indicator set has been designed on a modular basis. Each module is a set of indicators relating to a single dimension from the list given above and is collected using a dedicated set of questions and measurements. This means that the RAM-OP questionnaire also consists of a set of modules. Whenever possible, RAM-OP uses standard and validated indicators and question sets. Indicators are described below, showing the questionnaire components that are used to collect and record the data required, and flowcharts of the process used to derive indicators from the collected data. Standard symbols are used. For example: A non-standard symbol is used to show recode operations. A recode operation shows changes that are made to data so that it can be used to derive indicators without having to show many decision nodes in the flowchart. They are also used to specify what should be done with missing or out-of-range values. For example: 2.1.1 Demography and situation The demography and situation indicators are used to describe the survey sample and are derived from this questionnaire component: Each of the questions yields a separate indicator: 2.1.2 Food intake Food-intake indicators are derived from this questionnaire component. This data can be queried to yield a large number of useful indicators. There are three related sets of diet-related indicators: meal frequency food groups consumed / dietary diversity indicators of nutrient consumption. The indicator hierarchy is: The data on the number of meals taken in the previous twenty-four hours forms a meal frequency score. Food intake data from each subject is combined into a dietary diversity score. The dietary diversity score is a crude measure of food security. The dietary diversity score ranges between zero (i.e. no food groups) and eleven (i.e. eleven food groups). Higher values of the dietary diversity sore are associated with better food security. The meal frequency score and the dietary diversity score follow: Swindale A, Bilinsky P, Household Dietary Diversity Score (HDDS) for measurement of household food access: Indicator guide.,Washington DC, Food and Nutrition Technical Assistance (FANTA) Project, 2006 Kennedy G, Ballard T, Dop MC, Guidelines for Measuring Household and Individual Dietary Diversity, Rome, Food and Agricultural Organization, 2010 The data on the types of food consumed in the previous twenty-four hours are analysed in order to determine the diet’s content of specific micronutrients that are important for older people. This also follows Swindale &amp; Bilinsky (2006) and Kennedy et al (2010), and: World Health Organisation, The management of nutrition in major emergencies, Geneva, WHO, 2000 2.1.3 Meal frequency The meal frequency score indicator is the answer given to the first food intake question: Meal frequency is a crude measure of food security. Higher values of meal frequency are associated with better food security. 2.1.4 Food groups and dietary diversity Questions relating to the consumption of individual food items / food types are combined to create food groups and the number of food groups consumed are counted to create a dietary diversity score: The consumption of the eleven individual food groups and the dietary diversity score are reported separately. The dietary diversity score is a crude measure of food security. The dietary diversity score ranges between zero (no food groups) and eleven (eleven food groups). Higher values of the dietary diversity score are associated with better food security. 2.1.5 Indicators of nutrient consumption Overview Questions and combinations of questions relating to the consumption of individual food items and food types can be used to determine whether the reported diet is likely to be provide sufficient nutrients of various types: Each indicator is formed using logical “or” operations (i.e. the indicator is true if any of the constituent foods are consumed). For example, the indicator for the consumption of iron rich foods: requires the consumption of one or more of green leafy vegetables, organ meats, meat, or fish and shellfish. Consumption of any of these foods is sufficient to indicate that the survey subject consumes iron rich food. 2.1.5.1 Protein rich foods Indicators of consumption of protein rich foods from animal sources, plant source, and any / all sources are calculated as: 2.1.5.2 Vitamin A rich foods Indicators of consumption of vitamin A rich foods from animal sources, plant source, and any / all sources are calculated as: 2.1.5.3 Iron rich foods An indicator of consumption of iron rich foods from any / all sources is calculated as: 2.1.5.4 Calcium rich foods An indicator of consumption of calcium rich foods from any / all sources is calculated as: 2.1.5.5 Zinc rich foods An indicator of consumption of zinc rich foods from any / all sources is calculated as: 2.1.5.6 Vitamin B rich foods Indicators of consumption of vitamin B rich foods from any / all sources are calculated as: Note that the vitamin B complex indicator requires that at least one food from each of the B1, B2, B3, B6, and B12 rich food combinations is consumed. 2.1.6 Severe food insecurity An indicator of severe food insecurity (hunger) is derived from this questionnaire component: and is calculated as: This indicator is the Household Hunger Scale (HHS) and is a simple, well-validated, and widely used indicator of severe food insecurity: Ballard T, Coates J, Swindale A, Deitchler M, Household Hunger Scale: Indicator Definition and Measurement Guide, Washington DC, FANTA-2 Bridge, FHI 360, 2011 Ruel MT, Ballard TJ, Deitchler M, Measuring and Tracking the Access Dimension of Food Security: Available Indicators and Recommendations for Future Investments, Global Nutrition Report 2014: Technical Note 6, Washington DC, International Food Policy Research Institute, 2014 2.1.7 Disability Indicators of disability across six different domains are derived from this questionnaire component: Individual disability indicators are reported for each domain (i.e. vision, hearing, mobility, remembering, self-care, and communication) of disability in the Washington Group’s short set of question designed to identify people with a disability in a census or survey format: http://www.washingtongroup-disability.com https://www.cdc.gov/nchs/washington_group/wg_documents.htm Overall disability prevalence indicators are also reported. Indicators of disability in each domain are calculated as: Overall disability prevalence indicators are calculated as: P0 = 1 if no domain has D1 = 1, else = 0 (no disability in any domain) P1 = 1 if at least one domain has D1 = 1, else = 0 P2 = 1 if at least one domain has D2 = 1, else = 0 P3 = 1 if at least one domain has D3 = 1, else = 0 PM = 1 if at more than one domain has D1 = 1, else = 0 (M stands for “Multiple”) 2.1.8 Activities of daily living Indicators of how well the subject copes with activities of daily living are derived from this questionnaire component: Individual independence indicators are reported for each dimension (i.e. bathing, dressing, toilet, mobility, continence, and eating) of daily living activities. A composite indicator of the degree of independence (i.e. how well the subject can cope with activities of daily living) is also reported. This indicator is the Katz Index of Independence in Activities of Daily Living (or the Katz Index of ADL for short) and is a simple, well-validated, and widely used indicator of how well the subject can cope with activities of daily living: Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW, Studies of illness in the aged. The Index of ADL: A standardized measure of biological and psychosocial function, JAMA, 185(12), 1963, pp. 914-9 Katz S, Down TD, Cash HR, Grotz, RC, Progress in the development of the index of ADL, The Gerontologist, 10(1), 1970, pp. 20-30 Katz S, Assessing self-maintenance: Activities of daily living, mobility and instrumental activities of daily living, JAGS, 31(12), 1983, pp. 721-726 The Katz Index of ADL ranges between zero (complete dependence) and six (independence). The seventh question of this module, which is not part of the Katz Index of ADL, is reported separately and indicates whether the subject has someone to help them with activities of daily living: Activities of Daily Living a7 Is someone taking care of you or helping you with everyday activities such as shopping, cooking, bathing and dressing? 1 = Yes; 2 = No [__] It is not possible to know if the help available completely meets a subject’s needs, but we can identify the proportion of subjects needing help with one or more activities of daily living who also report not having someone to help them: This is an indicator of unmet need. Indicators of how well the subject can cope with activities of daily living and probable unmet need are calculated as: 2.1.9 Mental health and well-being Indicators of mental health and well being are derived from this questionnaire component: A score is calculated. This is the Kessler K6 Psychological Distress Scale. The score ranges from zero (indicating no psychological distress) to twenty-four (indicating severe psychological distress). A score of thirteen or more indicates serious psychological distress. The Kessler K6 Psychological Distress Scale is a widely recommended, widely used, accurate, reliable, and simple measure of psychological distress: Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek, DK, Normand SLT, et al, “Short screening scales to monitor population prevalences and trends in non-specific psychological distress”, Psychological Medicine, 32(6), 2002, pp. 959–976 Kessler RC, Barker PR, Colpe LJ, Epstein JF, Gfroerer JC, Hiripi E, “Screening for Serious Mental Illness in the General Population”, Archives of General Psychiatry, 60(2), 2003, pp. 184-189 Indicators of mental health and well-being are calculated as: 2.1.10 Dementia An indicator of probable dementia is derived from this questionnaire component: The indicator of probable dementia is calculated as: This indicator is derived from the Community Screening Instrument for Dementia (CSID) developed by the 10/66 Dementia Research Group. This is a simple, validated, and widely used indicator of probable dementia: Prince M, et al, “A brief dementia screener suitable for use by non-specialists in resource poor settings - The cross-cultural derivation and validation of the brief Community Screening Instrument for Dementia”, International Journal of Geriatric Psychiatry, 26(9), 2011, pp. 899–907 2.1.11 Health and health-seeking behaviour Indicators of health and health-seeking behaviour for chronic and acute conditions are derived from this questionnaire component: Indicators of health and health-seeking behaviour for chronic conditions are calculated as: Indicators of health and health-seeking behaviour for acute conditions are calculated as: 2.1.12 Sources of income Indicators related to sources of income are derived from this questionnaire component: and are calculated as: The grouped income sources (i.e. m2a, m2b, etc.) and individual income sources may vary between settings. The questionnaire component shown above has proved suitable for use in Ethiopia, South Sudan, and Tanzania. 2.1.13 Water, sanitation, and hygiene Indicators relating to water, sanitation, and hygiene (WASH) are derived from this questionnaire component: Indicators are calculated following: WHO / UNICEF, Core Questions on Drinking-water and Sanitation for Household Surveys, Geneva, WHO / UNICEF, 2006 Indicators relating to water, sanitation, and hygiene (WASH) are calculated as: 2.1.14 Anthropometry and screening coverage Indicators relating to anthropometry and screening coverage are derived from this questionnaire component: And are calculated as: Raw MUAC data (i.e. not MUAC class) is collected, entered, and analysed. This requires that an adult MUAC tape (i.e. capable of measuring MUAC to 450 mm) is used. The presence of bilateral oedema is assessed by pressing with your thumbs both feet of the older person for three seconds and checking whether this creates a lasting depression or “pit” on both feet. Bilateral pitting oedema in older people may not be “nutritional” oedema (as is almost always the case with children). Older people with bilateral pitting oedema should be advised to consult a doctor. The prevalence of GAM, MAM, and SAM are estimated using a PROBIT estimator. This type of estimator provides better precision than a classic estimator at small sample sizes: World Health Organisation, Physical Status: The use and interpretation of anthropometry. Report of a WHO expert committee, WHO Technical Report Series 854, WHO, Geneva, 1995 Dale NM, Myatt M, Prudhon C, Briend, A, “Assessment of the PROBIT approach for estimating the prevalence of global, moderate and severe acute malnutrition from population surveys”, Public Health Nutrition, 1–6. doi:10.1017/S1368980012003345, 2012 Blanton CJ, Bilukha, OO, “The PROBIT approach in estimating the prevalence of wasting: revisiting bias and precision”, Emerging Themes in Epidemiology, 10(1), 2013, p. 8 The PROBIT estimator is described in Box 1. MUAC-based case definitions for acute malnutrition are used: GAM MUAC &lt; 210 mm MAM 185 mm ≤ MUAC &lt; 210mm SAM MUAC &lt; 185mm These are standard case definitions for acute malnutrition in adults and recommended by HelpAge International for use in older people in humanitarian contexts. Note : MUAC in adults should be measured on the non-dominant arm. This is usually the left arm. The importance of high levels of accuracy and precision at the individual level is of lesser importance in survey work compared to case-finding or diagnosis in clinical contexts, for example. This means that a simple rule such as “Always measure MUAC on the left arm” may be used. An estimate of GAM prevalence can be made using a classic estimator: \\[\\text{prevalence} = \\frac{\\text{number of respondents with MUAC &lt; 210 mm}}{\\text{total number of respondents}}\\] The estimate of GAM prevalence made from the RAM-OP survey data is made using a PROBIT estimator. The PROBIT function is also known as the inverse cumulative distribution function. This function converts parameters of the distribution of an indicator (e.g. the mean and standard deviation of a normally distributed variable) into cumulative percentiles. This means that it is possible to use the normal PROBIT function with estimates of the mean and standard deviation of indicator values in a survey sample to predict (or estimate) the proportion of the population falling below a given threshold. For example, for data with a mean MUAC of 256 mm and a standard deviation of 28 mm the output of the normal PROBIT function for a threshold of 210 mm is 0.0502 meaning that 5.02% of the population are predicted (or estimated) to fall below the 210 mm threshold. Both the classic and the PROBIT methods can be thought of as estimating area: The principal advantage of the PROBIT approach is that the required sample size is usually smaller than that required to estimate prevalence with a given precision using the classic method. The PROBIT method assumes that MUAC is a normally distributed variable. If this is not the case then the distribution of MUAC is transformed towards normality. The prevalence of SAM is estimated in a similar way to GAM. The prevalence of MAM is estimated as the difference between the GAM and SAM prevalence estimates: \\[\\widehat{MAM prevalence} = \\widehat{GAM prevalence} - \\widehat{SAM prevalence}\\] 2.1.15 Visual impairment An indicator of visual impairment is derived from this questionnaire component: And is calculated as: The “illiterate E” or “tumbling E” (the preferred term) is a validated and widely used method for measuring visual acuity: Taylor HR, “Applying new design principles to the construction of an illiterate E chart”, American Journal of Optometry &amp; Physiological Optics, 55:348, 1978 Kaiser PK, “Prospective Evaluation of Visual Acuity Assessment: A Comparison of Snellen Versus ETDRS Charts in Clinical Practice (An AOS Thesis)”, Transactions of the American Ophthalmological Society, 107: 311–324, 2009 The size of the “E” used: as well as the distance used for the test (two metres) and the indicator calculation apply the WHO case definition of visual impairment (i.e. visual acuity &lt; 6 / 18). The tumbling E card should be laminated (i.e. plastic coated and have a two metre cord attached which helps to ensure that the visual acuity test is performed at the correct distance (See Figure 2.1). After demonstrating to the respondent what the test is about (i.e. the subject should indicate which direction the branches of the ‘E’ are pointing), the test is administered at a distance of two meters, turning the card in four different directions, and asking the person to indicate which direction the branches of the “E” is pointing. If the subject wears glasses, they are allowed to use them during the test if they want to. Note : If the person is unable to correctly answer at least three times out of four, they have a visual impairment. A simple visual acuity test such as the ‘tumbling E’ test also does not indicate anything about an underlying disease such as glaucoma or the need for reading spectacles (presbyopia). These conditions are common in people aged 60 years or older. Subjects failing the visual acuity test should be counselled to visit an ophthalmologist for a detailed eye examination. Figure 2.1: Equipment used to measure visual acuity 2.1.16 Miscellaneous indicators Data for a small group of miscellaneous indicators are also collected and reported. These are derived from these questions: Hunger – Ration - Relief f6 Are you or anyone in your household receiving a food ration on a regular basis? 1 = Yes; 2 = No [__] f7 Have you or another member of your household received non-food relief items such as soap, bucket, water container, bedding, mosquito net, clothes, or plastic sheet in the previous four weeks? 1 = Yes; 2 = No [__] Activities of Daily Living a8 Do you have problems chewing food? 1 = Yes; 2 = No [__] and are calculated as: 2.2 A note on data management and data analysis This section has described how RAM-OP data is used to create a broad set of indicators. If you do not want to use the standard RAM-OP software to do this then you can use this information to create data entry systems and data management scripts for your favoured database or statistical analysis software. See the sections on RAM-OP datasets and RAM-OP questionnaire for more compact information on variable names and codes that you may find helpful. It is important to note that data analysis procedures need to account for the sample design. All major statistical analysis software can do this (details vary). There are two things to note: The RAM-OP sample is a two-stage sample. Subjects are sampled from a small number of primary sampling units (PSUs). The RAM-OP sample is not prior weighted. This means that you will need to provide per-PSU sampling weights. These are usually the populations of the PSU. You will need to specify this sample design to your statistical analysis software. If you fail to do this then your analysis may produce estimates that place undue weight to observations from smaller communities with confidence intervals with lower than nominal coverage (i.e. they will be too narrow). The standard RAM-OP software uses blocked weighted bootstrap estimation approach: Blocked : The block corresponds to the PSU or cluster. Weighted : The RAM-OP sampling procedure does not use population proportional sampling to weight the sample prior to data collection as is done with SMART type surveys. This means that a posterior weighting procedure is required. The standard RAM-OP software uses a “roulette wheel” algorithm to weight (i.e. by population) the selection probability of PSUs in bootstrap replicates. A total of m' PSUs are sampled with-replacement from the survey dataset where m' is the number of PSUs in the survey sample. Individual records within each PSU are then sampled with-replacement. A total of n’ records are sampled with-replacement from each of the selected PSUs where n' is the number of individual records in a selected PSU. The resulting collection of records replicates the original survey in terms of both sample design and sample size. A large number of replicate surveys are taken (the standard RAM-OP software uses \\(r = 399\\) replicate surveys but this can be changed). The required statistic (e.g. the mean of an indicator value) is applied to each replicate survey. The reported estimate consists of the 50th (point estimate), 2.5th (lower 95% confidence limit), and the 97.5th (upper 95% confidence limit) percentiles of the distribution of the statistic observed across all replicate surveys. The blocked weighted bootstrap procedure is outlined in Figure 2.2. The principal advantages of using a bootstrap estimator are: Bootstrap estimators work well with small sample sizes. The method is non-parametric and uses empirical rather than theoretical distributions. There are no assumptions of things like normality to worry about. The method allows estimation of the sampling distribution of almost any statistic using only simple computational methods. The standard RAM-OP data analysis software is described in the section Standard RAM-OP software. Figure 2.2: The blocked weighted bootstrap used by the standard RAM-OP software "],
-["questionnaire.html", "Chapter 3 The RAM-OP questionnaire", " Chapter 3 The RAM-OP questionnaire Modules of the RAM-OP questionnaire are presented in the RAM-OP indicators section of this manual. The entire RAM-OP questionnaire is presented in the following pages. This questionnaire is composed of many tested and validated components. The order of the questions and the format of the questionnaire have been tested in several settings (Chad, Dadaab Camps, South Sudan, Ethiopia, and Tanzania) over a period of three years. It is strongly recommended that you do not change the questionnaire, other than translating it into a language other than English and necessary localisation (i.e. adapting the questions to meet the language, cultural, and other requirements of a specific target population in order to ensure that the words, names, terms, and concepts used are culturally appropriate and understandable to them), unless you are very sure of what you are doing. Modifying the questionnaire may have one or more of the following consequences: Modifying the order of the questions or adding questions : The links with the data entry, data checking, and data analysis software will be broken. You will have to modify the software to accommodate your changes. Modifying the variable names : The links with the data entry, data checking, and data analysis software will be broken. You will have to modify the software to accommodate your changes. Modifying the content or the phrasing of questions : All questions have been tested and are formulated for accuracy and reliability (precision). Modifying them may lead to loss of accuracy (bias) and precision When translating the questionnaire you should check if validated question sets for each indicator module are already available in your local language. This is likely to be the case for the food intake, severe food insecurity, activities of daily living, mental health and well-being, dementia, water / sanitation / hygiene, and visual impairment indicator modules. There may also be local language training modules and guidelines available for these modules. Localisation is recommended for: Food groups : Remove inappropriate foodstuffs and give examples of local foodstuffs. Income sources : Review income types and income categories. The question numbers used on the questionnaire are the names of variables used in the RAM-OP data entry, data checking, and data analysis software. Leaving these as they are will be helpful if you intend to use the RAM-OP data-entry and data-analysis software. The questionnaire can be downloaded (in ODT and PDF format) from http://www.brixtonhealth.com/qesRAMOP.zip "],
-["datasets.html", "Chapter 4 Datasets", " Chapter 4 Datasets This section details the RAM-OP datasets. The information presented here is of most use if you decide not to use the RAM-OP data entry and data checking software. You might, for example, decide to enter survey data using spreadsheet software such as Microsoft Excel. If you do this and want to use the RAM-OP data analysis software then you will need to export the data as a comma-separated-value (CSV) file with the same variable names, variable types and lengths, and using the same codes as shown in the tables in this section. For the main RAM-OP survey dataset these are the same variable names, variable types, variable lengths, codes, and in the same order as shown on the standard RAM-OP questionnaire. There are two RAM-OP datasets: The main RAM-OP survey dataset : This is the data collected by the survey questionnaire. The dataset definition for the main RAM-OP dataset is shown in Figure 4.1. The PSU dataset : This a short and narrow file with one record per PSU and just two variables: psu The PSU identifier. This must use the same coding system used to identify PSUs that is used in the main RAM-OP dataset. pop The population of the PSU. The PSU dataset is used during data-analysis to weight data by PSU population. If you do not know population sizes (as might be the case in emergencies) then you can collect this data: When you visit the PSU (i.e. from community leaders or health centres). When you visit the PSU as a doorway count or roof count. Using recent satellite imagery as a roof count. Relative population sizes can be used. If no better data is available then it is reasonable to use a simple semi-quantitative assessment such as: Type of place Population range* Features Record population as … Hamlet \\(&lt;\\) 1,000 Very small local market or no market 1 Village 1,000 – 4,000 Market and small shops serving the village and the surrounding hamlets 2 Town \\(&gt;\\) 4,000 Large market, many shops (some specialised), guest houses, bus station, government offices 4 *These ranges may need to be adjusted to match local circumstances. The PSU dataset must be in comma-separated-value (CSV) format (see Figure 4.2) for use with the RAM-OP data analysis software. Figure 4.1: Main RAM-OP dataset definition The RAM-OP data analysis requires that the main RAM-OP survey dataset is supplied in either an EpiInfo v6.xx or EpiData (REC) format or in a comma-separated-value (CSV) format file The RAM-OP data analysis requires that the PSU dataset is supplied in a comma-separated-value (.CSV) format file. Figure 4.2 shows an example of a PSU dataset in comma-separate-value (CSV) format. Figure 4.2: An example comma-separated-value (CSV) format file (the example is for a RAM-OP PSU dataset) Note that the first line of a CSV format file gives the names of the variables (e.g. these are psu and pop for the PSU dataset) separated by commas. Subsequent lines contain data with items separated by commas and with one record per line. CSV format files can be created using a plain text editor (e.g. Notepad) or with a spreadsheet application such as Microsoft Excel™. If you use a spreadsheet application then you will have to be careful: Variable names and data items must be separated by commas (not tab characters or semi-colon characters). Numbers with decimal places must use the full-stop character as the decimal separator. In some settings a spreadsheet application may want to use the comma character as the decimal separator. Avoid using accented characters in the names of and in the data entered into text variables. These characters can sometimes confuse the RAM-OP data analysis software. A CSV file should contain only plain text, number, and commas without formatting. Do not use a word processor application such as Microsoft Word™ to create or edit a CSV file. If you have problems using a CSV file then you should check and edit the file using a plain text-editor such as Notepad or a dedicated CSV editor such as Ron’s Editor (http://www.ronsplace.eu/Products/RonsEditor) Remember to backup your data before editing it. "],
-["practical.html", "Chapter 5 Practical Fieldwork 5.1 Authorisations and clearances 5.2 Working with a local partner 5.3 Translating the questionnaire 5.4 Supervisors, enumerators, and data entry staff 5.5 Training of enumerators 5.6 Survey logistics 5.7 Data collection 5.8 Survey planning 5.9 Daily survey activities", " Chapter 5 Practical Fieldwork This section is intended to guide you through the different steps leading up to the fieldwork once the survey location has been identified, and gives some tips on how the fieldwork might be organised. 5.1 Authorisations and clearances Before implementing the survey, you will need to get all the authorisations relevant to the country in which you plan to work. These could include: Clearance from the national nutrition cluster or the equivalent structure co-ordinating national assessment activities. In humanitarian contexts, this might be the only clearance that you will need at the national level. Ethical approval : This is obtained from the country’s national ethical committee (or equivalent). Some NGOs and UNOs also have ethical committees and you may also need to submit your survey plans to them for ethical approval. It may be necessary to work with both national and local ethical committees. The process of gaining ethical approval can take several months. It is important to note that RAM-OP surveys are needs assessments rather than experiments upon human subjects. This means that ethical clearance may not be required for RAM-OP surveys, or that it can be given by the chair of the appropriate ethical review committee without the need for a full meeting of the ethical review committee. It is a good idea to check this with the chair of the appropriate committees to see if permissions can be expedited. Getting ethical clearance is often very useful when applying for other permission as it shows that some technical quality assurance has been done. Authorisation from the appropriate government departments at various levels (i.e. national, regional, and at the level where you are going to implement the survey). Authorisation of the authority managing the survey site should be sought. For example, a survey in a refugee camp will need the authorisation of UNHCR, the national administrative authority in charge of refugees and displaced persons, and the agency in charge of the camp management. In some settings you may also need to obtain authorisation from other government departments such as the Ministry of Health, the Department of Rural Affairs, or the Department of Social Affairs. Authorisation from the administrative authorities at local level : Make sure that all levels of the local administration are informed about what you intend to do (i.e. what, where, and when). It is essential to meet with the local administrative and health authorities prior to the survey. This is done to avoid problems with permissions and to involve them in the implementation of the survey. Describe the survey and explain what might be expected from their staff. You might, for example, need some help in identifying the exact location and boundaries of villages and hamlets in rural areas, or blocks and sections of towns in urban areas. You might need translators or guides to travel with the enumerators, and you might need facilitators to introduce you to village executives. Make sure that you share the results of the survey with them once it is available. Security clearance : Be aware of the potential security problems in the survey area. Inform all agencies with security responsibilities in the area about the dates and locations of the survey. The police or the army may have to be specifically informed. You may also need to negotiate access with non-state actors. Field staff should be provided with copies of official documents (in the local language) proving that they are authorised to carry out survey work in the specific area between specific dates. They will have to carry this document with them at all times during the fieldwork and present it on request to local authorities and study subjects. It can also be useful to give a copy of this and other official documents to village leaders on arrival at the survey location. 5.2 Working with a local partner It is often very useful to prepare and carry out the survey in collaboration with one or more local partners, such as a local NGO, the local health authority, or the camp management agency in a refugee camp. If feasible, you should recruit a representative of your local partner as a “survey facilitator” with responsibility for liaising with the national and local stakeholders. This person will support your survey preparation with the following: At national or regional level, support the endorsement of the survey objectives by the national authorities, and facilitation in obtaining the relevant authorisations and clearances. At local level, be the link between you and the local communities informing health staff and village leaders in the areas where the enumerators are going to sample households. This information should be disseminated before the survey starts and reiterated a day or two before teams travel to survey locations either by telephone or by personal visits. Provide you with a list of useful contacts (with telephone numbers) for each of the areas covered by the survey. This list should be shared with all program staff. Identify local guides or translators to support the teams in the field. In-depth knowledge of the survey area, useful for checking the location of the villages to be surveyed on a map. Information about travel and security constraints, travel distances and times, and assist in formulating the survey travel plan. Support with the survey logistics, such as renting vehicles, renting accommodation and training venues, where to purchase food and drinks, where to have forms and questionnaires printed / copied, etc. Help with the referral of malnourished or sick older people identified during the survey by liaising with community services, ambulance services, and relevant health facilities as needed. The local partner will also help you disseminate the results of the survey to the various stakeholders, and might be involved in response plans following the assessment. 5.3 Translating the questionnaire Precision and accuracy are improved by translating the questionnaire in the local language appropriate to the survey area before data is collected. This allows enumerators to ask questions using the same language and terminology in every interview. Thorough training of the enumerators in applying the questionnaire will also improve the precision and accuracy of your survey results. A translated questionnaire may also be a requirement for getting the ethical clearance for the survey. We advise you to use an iterative translation process and use: Standard language if available : Most indicators used in RAM-OP have question sets available in different languages. You can check for these online. You may need to alter some language to account for local dialects and idioms but using standard language, when it is available, can save you a lot of time and effort Knowledgeable lead translators : You need to use people who know the target language and culture but are also fluent in the starting language of the questionnaire. Forward translation and back translation : The questionnaire is translated from English, for example, into the local language by one person or team (this is forward translation) and is then translated back into the original language by another person or team (this is back translation). The back translated questionnaire is then checked against the original questionnaire. Differences are then analysed and a new translation produced. You may need to go through this process several times until a satisfactory version of the translated questionnaire is reached. Your survey staff to provide language and to pilot (i.e. test) questionnaire components as they are translated. Piloting can be done with community members and by role-playing between survey staff. Test interviews and group discussions usually help to improve the language used in the questionnaire. Your intended survey population to help you make sure that the language you are using is simple and to the point. Test interviews and group discussions usually help to improve the language used in the questionnaire. Having enumerators translate the English language questionnaire (for example) each time they apply the questionnaire is not a good option and should be avoided. 5.4 Supervisors, enumerators, and data entry staff The more survey teams you recruit and use, the quicker the survey will be finished. However, the number of teams should be linked to your capacity for supervision. Also, having a large number of teams usually means that you will need a large number of vehicles and drivers. This can be hard to achieve and hard to manage. We recommend that you recruit three teams of two enumerators with one supervisor per team. The duties of supervisors and enumerators are: Supervisors have to take all necessary actions to ensure the accuracy of the collected data, particularly: Checking equipment before departure and when leaving the survey site. Travelling with a team every day, to observe and correct the enumerators’ work. Introducing teams to local leaders. Ensuring households and subjects are selected properly, that the interviews are conducted with respect and thoroughness, and that measurements are taken and recorded accurately. Enumerators are in charge of implementing the field procedures: Identifying the households to survey. Apply the questionnaires to older people. Measure MUAC, oedema, and visual acuity and complete questionnaires. If each team can complete a single PSU per day (this is the minimum you can expect from a team) then the survey may be completed in six days (i.e. three PSUs per day for five days plus one PSU on the last day). This will depend on context and on the teams’ expertise. It is often possible for a team to reach more than one location per day, such as in cities or camps where sectors and blocks are close to each other and travelling time is not high. You will often find that survey data can be collected in just four or five days. It is important not to rush data collection. It is also important to supervise the teams from day one in order to ensure they follow the proper sampling procedures and applying the questionnaires correctly. It is advisable to enlist more enumerators to be trained than the minimum number needed. This will ensure that you have sufficient enumerators should you find, during training, that some recruits cannot perform their duties well enough. It will also provide additional trained staff should you need to cover for absences, due to illness for example. Make sure that you enlist both male and female trainees. You will also need to recruit data entry staff. The workload for the data entry staff is usually between about thirty-six and seventy-two questionnaires per day. 5.5 Training of enumerators Training the enumerators is a crucial step to ensuring the quality of the data collection. At the end of training each enumerator should be able to: Explain the objectives of the survey. Sample households and older people in the survey area following the appropriate field procedures. Introduce themselves to older people in a polite and respectful manner. Apply the questionnaire smoothly and efficiently. Properly measure MUAC, check for bilateral pitting oedema, and properly measure visual acuity. Complete the questionnaire neatly and without making mistakes (including the correct numbering of PSU, households and individual subjects). Advise the subject or their family in case there is a need for referral, such as to a health facility. A typical first RAM-OP training course will last for five days: Day 1 Presentation of your organisation (mission, code of conduct, etc.) Objectives of the survey How are we going to do it? Questionnaire : First reading and explanations Recap Day 2 Field procedures Job descriptions Measurements: MUAC, oedema, visual acuity (practice on each other) Questionnaire : Role-playing Lessons learned Recap Day 3 Measurements: MUAC, oedema, visual acuity (practice on ten older people) Testing the questionnaire with ten older people Lessons learned Recap Day 4 Questionnaire : Role-playing Field procedures : Recap and group work Recap Day 5 Field test : Practical field procedures, etc. in one community Lessons learned from field test Recap Additional notes: Practising the questionnaire : This is very important. Each training day should contain some work on the questionnaire. Particular attention is paid to the content and function of each question set, the numbering system for the PSU, household, and subject, the meaning / intention of each question set, skip / jump patterns, coding, ranges, and checking for completeness and consistency. Extensive use should be made of role-playing (in pairs and in groups) and testing with eligible subjects. Care needs to be taken to ensure that all field staff have extensive practice in working with the questionnaire. Practising measurements : This is very important. Most training days should contain some work on measurement. Care needs to be taken to ensure that all field staff have extensive practice in taking all measurements. Standardisation of measurement : A formal standardisation exercise for MUAC measurement is not required. The format of such an exercise does, however, provide a useful framework for training enumerators to measure MUAC with acceptable accuracy and precision by: Comparisons of measurements made by different enumerators on the same person to explore accuracy (bias). Comparisons of measurements made by different enumerators on the same person with the measurements made by the training supervisor to explore accuracy (bias). Repeated measurements on the same persons by the same enumerator to explore repeatability (precision). Ten older people : The survey facilitator should be able to mobilise ten older people (women and men) to participate in questionnaire and measurement exercises. Make sure to explain to these volunteers what is going to happen to them. The exercises will last a half day (maybe longer) and may be quite tiring for older persons. Make sure they are comfortably accommodated, organise their transport to and from the training venue (which should be easily accessible and so avoiding stairs), indoors or under shelter, with chairs or benches. Provide safe drinking water and possibly tea, coffee and snacks. It is also advisable to provide them with a small ex gratia payment. The field test : This will put the trainees in to field conditions but without the stress of having to take a full sample. It is a “dummy run” of all survey activities. Divide the trainees into teams assigning a supervisor to each team. The test area should not be one of the areas to be sampled for the survey and can be close to the training venue. At the end of the training week, you should be able to select the best enumerators and divide them in to teams, balancing genders and personalities, as well as strengths and weaknesses. 5.6 Survey logistics Thorough logistics preparation is essential to the smooth implementation of a survey. Transportation is particularly important. 5.6.1 Transportation Ideally you will need one car and driver per survey team. Depending on the area, you may be able to reduce this number and organise the survey travel plan so that vehicles can be pooled between the teams. You will need strong cars (ones adapted to the terrain) and reliable and safe drivers ready to work flexible hours. It is very useful to brief the teams and the drivers about the management of the cars: Who gives instructions to the drivers on a daily basis. One person per car should be in charge. Who is checking the car log book every morning and every evening. What to do in case of an accident. All staff should be given the contact telephone numbers of all the drivers Safety and security procedures should be followed thoroughly, such as use of seat belts, speed limits, prohibitions on “racing”, carrying of water, first aid kits, and spare wheels. 5.6.2 Tools and equipment The equipment needed for a RAM-OP survey is minimal: MUAC tapes for adults (i.e. minimum 450 mm long, graduated in millimetres). A “tumbling E chart” to perform the visual acuity test. Pens, notebooks, clip-boards, etc. Questionnaires. Maps of the area showing PSU locations. PSU maps (if required). Official letters of authorisation to carry out the survey. Every morning during the survey, each team should receive a schedule of that day’s activities detailing the team’s objectives for the day giving PSU numbers, location, PSU maps, sample target sizes, local contact information, emergency contact list, etc. 5.7 Data collection Each team should be able to survey at least one PSU per day. No community mobilisation is necessary but community officials should be informed of your arrival in advance. Avoid sampling at special or busy times, such as holy days and market days. When arriving in the community, the teams should introduce themselves to community leaders and explain the conduct of the survey. The supervisor should collect and record information about the PSU’s total population. The team should then explore the boundaries of the community and perform mapping and segmentation as required. Eligible subjects are usually people aged 60 years and older (this may differ in some settings). We accept the respondents’ statement of their own age, and we do not challenge them if they appear younger or older than the age they declare. We usually do not ask for a proof of age. However, it might be useful to have compiled a list of events related to the past hundred years of the country’s history, as some older people do not remember their age, but remember living at the time of some remarkable events (independence, elections, wars, etc). Women often remember the age at which they had their children All eligible older people present in and belonging to the sampled household are interviewed and measured, even if the required quota of respondents has been reached. Older people visiting the household should only be included if they are residing there for more than two weeks. Person such as maids, houseboys, watchmen, and carers should be treated as part of the household (i.e. should be interviewed) if the sampled dwelling / compound is their principal place of residence. Older people should be interviewed in their houses. It is not good practice to have the older people in the community gathered in one place. When entering older people’s houses, the enumerators should always be polite, respectful, and attentive not to intrude on people’s privacy. Older people should not be intimidated into answering the questionnaire or being measured. They always have a right to refuse to answer some or all of the questions and to refuse measurements. Refusals should be noted on the questionnaire. Some of the questions are of an intimate nature. Be careful not to administer the questionnaire in an open space where everybody present can hear the answers. This may embarrass the respondent or cause the respondent to answer wrongly. This is important as a “wrong answer” will tend to hide need and will bias the survey results. Some older women may object to having their MUAC taken by a man (or vice versa). This is why it is advisable that the team has both male and female members. This not always possible, but older people are often more free from prejudices than the younger members of their community. It is rare for an older woman to refuse to expose her arms or feet before a male enumerator, and also rare that an older man objects to being measured by a female enumerator. 5.8 Survey planning Here is a typical timetable of survey activities: Several weeks in advance Identify survey area Recruit local partner Obtain ethical clearance Obtain technical approval from relevant bodies Obtain permissions and letters from appropriate authorities Start translation of questionnaire Obtain maps / lists as required Obtain map(s) of the survey area One month in advance Advertise staff positions Recruit and contract supervisors Define first stage sample from list / map Make (draft) survey travel plans Obtain permissions for travel Book training venue Arrange staff accommodation (if required) Continue translation of questionnaire Begin sourcing equipment Identify potential suppliers and contractors One week before the training Recruit enumerators Purchase equipment Book vehicles and drivers Review and print / copy training manual Print translated questionnaire for the training Print “daily program” forms Finalise survey travel plans Disseminate travel plans to local authorities (with survey facilitator) Arrange logistics for the training During the training Arrange for ten older people to participate in training Revise the questionnaire with the trainees Print revised version of questionnaire for the field test (c. 50 copies) Arrange the logistics for the field test (vehicles, drivers, equipment) Review logistics arrangements with the trainees At the end of the trainin Provide each trainee with a certificate of attendance Pay the training incentives Review and amend questionnaire from the feedback of the field test Print survey questionnaires (c. 250 copies) Recruit enumerators and data entry staff During the survey Train the data entry clerk (day one) using the results of the field test Manage survey activities (see below) Data entry and cleaning At the end of the survey Complete data entry Provide all staff with certificates of participation Pay incentives Thank the local authorities Party for all staff Data analysis and reporting Disseminate results 5.9 Daily survey activities Here is a list of typical survey activities: Morning Brief the teams on the day’s objectives Provide feedback on the previous day (success and failures, correction of mistakes made in field procedures or data collection). Discuss problems with supervisors Provide water and snacks (or food allowance) Provide forms, questionnaires, maps (as required) Evening Check the data entry with the data-entry staff Identify problematic questionnaires Identify common data collection problems Plan the next day’s programme with the facilitator and the supervisors Discuss the problems met during the day and their resolution (particularly mistakes that they have observed in the field procedures or in the data collection). Prepare the day programme forms for the next day Check that there are enough forms, questionnaires, maps for the next day "],
-["software.html", "Chapter 6 RAM-OP Software 6.1 Data entry 6.2 Data analysis", " Chapter 6 RAM-OP Software 6.1 Data entry A data entry system for RAM-OP data using EpiData has been developed. This software can be downloaded from http://www.brixtonhealth.com/enterRAMOP.zip. This software runs on the Windows operating system. It does not need to be installed and can be run from a USB drive. The data entry system provides facilities for entering data, interactive checking of data as it is entered, batch checking of entered data, data summary, and data export. The system creates files that can be read by the standard RAM-OP data analysis software. Documentation of Epidata can be found here http://www.epidata.dk/index.htm. 6.2 Data analysis This manual covers analysing your data using the RAnalyticFlow workflow. An RAnalyticFlow workflow may be thought of as an “app” that makes it easy to analyse your survey data. To use the RAnalyticFlow workflow you must install: The R Language for Data-Analysis and Graphics (R) : This is the “engine” which does all the work of analysing your data. You can get the R installation program from: http://cran.r-project.org. Following are links to download operating sofware-specific versions of R: Download R for Linux Download R for (Mac) OS X Download R for Windowx R packages (libraries of functions needed to work with the RAnalyticFlow workflow) : You can install these from within R using the Package Installer function within R. The libraries needed are: Package Comments rJava Required: Used by RAnalyticFlow JavaGD Required: Used by RAnalyticFlow codetools Required: Used by RAnalyticFlow foreign Required: Opens EpiData (REC) files car Required: Used for PROBIT estimator ggplot2 Desirable: Provides many plotting functions data.table Desirable: Speeds up working with large dataset The Package Installer function can be called in R using the following command: install.packages(c(&quot;rJava&quot;, &quot;JavaGD&quot;, &quot;codetools&quot;, &quot;foreign&quot;, &quot;car&quot;, &quot;ggplot2&quot;, &quot;data.table&quot;), repos = &quot;https://cloud.r-project.org/&quot;) The repos argument in the R command above specifies the CRAN mirror from which you to download the package/s you want to install. Here we specify the cloud-based mirror for CRAN provided by RStudio. If unspecified, the installiation process will prompt you to select a mirror from which to download packages from. If you already know the URL of the CRAN mirror you want to use, specify this in the repos argument. Note that RAnalyticFlow may require you to have Java installed. Check the instructions on the RAnalyticFlow download page and on this starter guide. All of this software is open source and free to download, copy, and use. It will run on Windows, Mac OS X, and Linux (and other UNIX-like) operating systems. Your ICT department should be able to help you with installing this software. In addition you will also need a copy of the RAnalyticFlow workflow and supporting files. These are available from: http://www.brixtonhealth.com/ramOP.rflow.zip You may need to extract the file from the ZIP archive before use if this is not done automatically. Before starting to analyse your data you should create a project directory or project folder. This is just a normal folder or directory that can be created using your usual file manager (e.g. Windows ExplorerTM in WindowsTM or the FinderTM in Macintosh OS-XTM). The project directory or project folder should contain: Your PSU file (here we assume this file is called testPSU.csv but it could have any name). This file must be a comma-separated-value (CSV) file. Your survey data file (here we assume this file is called testSVY.rec but it could have any name). This file can be an EpiData (REC) file or a comma-separated-value (CSV) file. The language file (always called ramOP.language.csv). This file provides text that is used in reports and graphics. The purpose of this file is to make the data analysis software produce reports in any language. This file must be a comma-separated-value (CSV) file. A copy of the file ramOP.rflow. When you have created the project directory or project folder with the required files you can start RAnalyticFlow. Note: The testSVY.rec and testPSU.csv files are example data files and are distributed with the RAnalyticFlow workflow. You can use these files to practice analysing data using RAnalyticFlow, and as examples of RAM-OP survey data and PSU files. Before you start work you will need to create a project for your survey: Click the New Project… button Give your project a useful (i.e. descriptive and memorable) name. This might be a name that describes the survey. For example, if the survey was done in the Kereinik locality of West Darfur in December 2015 you might use the name WD.Kereinik.Dec2015.RAMOP Give the location of your project directory or project folder. This is the directory or folder which contains your survey data file, your PSU file, the RAM-OP language file, and a copy of RAMOP.rflow (see previous page). The location of the project directory or project folder (labeled “Path” by the software) that RAnalyticFlow selects automatically will almost always be wrong. You need to specify this manually. Click the OK button Double click the item named ramOP.rflow shown in the file manager pane of the RAnalyticFlow window. This will open the data-analysis workflow which will be shown in the workflow viewer / editor window of the RAnalyticFlow window. Once you have opened the workflow you need to initialise it (i.e. load libraries, useful analysis function, and initialise the workspace for a new analysis): Once this is done, you should: Retrieve your survey data. This can be in EpiDat (REC) format or CSV format. Select and run the appropriate Survey Data node and select the survey data file. Retrieve the PSU date data. Select and run the PSU Data node and select your PSU file. Produce the survey report and graphics. Select and run the Report node. This will take some time to complete because the analysis uses computer intensive techniques to make best use of the available data. The Report node/icon will have black lines around it has completed running the report. When the analysis is complete your project directory or project folder should contain eight new files: The files ending in .png are graphics files. The names of these files are intended to be informative. For example: The file ending in .report.csv is the survey report file (see below). There may also be a file ending in .afp. This is a project information file used by RAnalyticFlow and can be ignored. The file ending in .report.csv is the survey report file. This file can be opened, formatted and edited in a spreadsheet program such as Microsoft Excel or OpenOffice Calc: In the illustration above, proportions have been formatted as percentages with one decimal place, means have been formatted as numbers with two decimal places, and column titles have been formatted as bold text. You can edit the labels as you see fit. If you plan to do several RAM-OP surveys then you may want to edit the language file (always called ramOP.language.csv). This file provides text that is used in reports and graphics. The purpose of this file is to make the data analysis software produce reports in any language. This file must be a comma-separated-value (CSV) file. "],
-["conclusion.html", "Chapter 7 Conclusion", " Chapter 7 Conclusion We live in an ageing world, where people aged 60 or over will be 2 billion or about 22% of the world’s population by 2050. Currently, two in three people aged 60 years or older live in developing countries. By 2050, nearly four in five older people will be living in the developing world. The changing demographics of ageing combined with the increasing number of disasters will exert a disproportionate impact on the world’s oldest and poorest. In this context, identifying the needs of older people as accurately as possible is a necessity. More and more donors and UN agencies are now willing to include older people in their programmes. Age markers, to complement gender markers, will be disseminated very soon RAM-OP is offering a fast, robust, reliable, tested and user-friendly way of assessing the needs of older people. It can be used in humanitarian situations as well as in development contexts. The modular structure of RAM-OP allows for adaptations, making it exhaustive or limited to essential indicators according to the immediate needs. As more organisations start to use it, RAM-OP will evolve and improve. New versions of RAM-OP can be created (for example, RAM-OP for refugee or displaced people camps). We wish that a greater number of actors will start using RAM-OP and make it their own. "]
+["introduction.html", "Introduction", " Introduction Older people (generally defined as people aged sixty years and older) are a vulnerable group for malnutrition in humanitarian and developmental contexts. Due to their age they have specific nutritional needs, such as easily digestible and palatable food adapted to those with chewing problems, which is dense in nutrients. In famine and displacement situations where populations are dependent on food distributions, older people often find the general ration inappropriate to their tastes and needs, have difficulties accessing the distributions, or have difficulties transporting rations home. As a result, older people can become malnourished and in need of specifically targeted food interventions. In times of drought or food scarcity, older people tend to reduce their food intake in order to share or give up their ration to younger members of their families. They are then at risk of malnutrition. Despite these potential vulnerabilities in humanitarian situations, older people are rarely identified as a group in need of specific nutritional or food assistance. Surveys and assessments almost always focus on children, and sometimes on pregnant and lactating women. Humanitarian workers argue that assessing the nutritional status and needs of older people is both costly and complicated. As a consequence, the nutritional status and needs of older people in crisis go unidentified and unaddressed. HelpAge International, VALID International, and Brixton Health, with financial assistance from the Humanitarian Innovation Fund (HIF), have developed a Rapid Assessment Method for Older People (RAM-OP) that provides accurate and reliable estimates of the needs of older people. The method uses simple procedures, in a short time frame (i.e. about two weeks including training, data collection, data entry, and data analysis), and at considerably lower cost than other methods. The RAM-OP method is based on the following principles: Use of a familiar “household survey” design employing a two-stage cluster sample design optimised to allow the use of a small primary sample (\\(m ≥ 16\\) clusters) and a small overall (\\(n ≥ 192\\)) sample. Assessment of multiple dimensions of need in older people (including prevalence of global, moderate and severe acute malnutrition) using, whenever possible, standard and well-tested indicators and question sets. Data analysis performed using modern computer-intensive methods to allow estimates of indicator levels to be made with useful precision using a small sample size. The following tools are currently available under the General Public Licence / Free Documentation License, meaning that you are free to copy and adapt these tools: an English language manual / guidebook a questionnaire (available in English and French) data entry and data checking software (available in English and French) data analysis software. We believe that the availability of a rapid, low-cost, and user-friendly method will encourage governments, UN agencies, as well as international and local non-governmental organisations to actively assess the situation of older people in humanitarian contexts, and implement, monitor, and evaluate relevant and timely responses to address their needs. "],
+["sampling.html", "1 Sampling 1.1 The RAM-OP sample 1.2 Implicit stratification 1.3 RAM-OP survey sample size 1.4 Eligibility 1.5 Age distribution, eligibility criteria, and sample design 1.6 Practical sampling", " 1 Sampling 1.1 The RAM-OP sample RAM-OP uses a two-stage sample: First stage sample: A sample of communities (e.g. villages or city-blocks) in the survey area is taken. A sampled community is also called a primary sampling unit (PSU). Second stage sample: Domestic dwellings are sampled from within the communities selected in the first stage sample. All eligible individuals in the sampled dwelling are included in the sample. 1.1.1 The first-stage sample The first stage sample is a systematic spatial sample. Two methods can be used and both methods take the sample from all parts of the survey area: List-based method: Communities to be sampled are selected systematically from a complete list of communities in the survey area. This list of communities is sorted by one or more non-overlapping spatial factors such as district and subdistricts within districts: Figure 1.1: Communities listing by district and sub-district Map-based method: Communities to be sampled are selected from the centres of the squares of a grid drawn over a map. The map must be sufficiently well made and of sufficiently large scale to show the position of every community in the survey area. This type of sample is known as a centric systematic area sample and is often referred to as a CSAS sample. Note: Population proportional sampling (PPS) is not used in RAM-OP surveys. Population estimates for all communities are not required for sampling purposes. Population estimates are required only for the selected communities. These are used during data analysis in order to weight results by population size. If this information is not available before the survey, it can be collected during the survey. 1.1.2 The second stage sample The second stage within-community sample uses a method called map-segment-sample. This method takes the within-community sample from all parts of a sampled community. 1.2 Implicit stratification Both the first and second stage samples use a form of spatial stratification: The list-based method’s first stage systematic spatial sample stratifies the sample by non-overlapping spatial factor such as districts and subdistricts within districts. The map-based (CSAS) method’s first stage sample stratifies the sample by grid square. The map-segment-sample second stage within-community sample stratifies the sample by parts of the community being sampled. The first and second stage samples also ensure that a reasonably even spatial sample is taken from the entire survey area and from each of the sampled communities. These sampling procedures provide implicit stratification and tend to spread the sample properly among important sub-groups of the population such as rural / urban / peri-urban populations, administrative areas, ethnic sub-populations, religious sub-populations, and socio-economic groups. This often improves the precision of estimates made from survey data. The use of implicit stratification improves the efficiency of a two-stage cluster sample and allows RAM-OP to use relatively small sample sizes compared to other methods, such as SMART surveys. The use of modern computer-intensive data analysis techniques also allows RAM-OP to make better use of the available sample than is done in other methods. 1.3 RAM-OP survey sample size The following shorthand symbols will be used when describing sample designs: \\[\\begin{aligned} m &amp; ~ = ~ \\text{Number of primary sampling units (PSUs).} \\\\[5pt] n &amp; ~ = ~ \\text{Size of the sample of individuals or households from a PSU.} \\\\[5pt] n &amp; ~ = ~ \\text{May also mean the overall survey sample size (this meaning will be made clear in the text).} \\\\[5pt] N &amp; ~ = ~ \\text{Population} \\end{aligned}\\] The overall sample size for a RAM-OP survey is about \\(n = 192\\) individual subjects. You should aim to collect an overall sample of at least \\(n = 192\\) individuals. The RAM-OP sample is collected in two stages: The first stage sample uses a sample size of about \\(m = 16\\) communities (or PSUs). The second stage sample uses a sample size of about \\(n = 12\\) eligible subjects sampled from each of the communities selected for inclusion in the first stage sample. The overall sample size from \\(m = 16\\) communities and \\(n = 12\\) eligible subjects is about: \\[\\text{overall sample size} ~ \\approx ~ m ~ \\times ~ n ~ \\approx ~ 16 ~ \\times ~ 12 ~ \\approx ~ 192\\] It is not recommended that fewer than \\(m = 16\\) communities are sampled. Sampling fewer than \\(m = 16\\) communities will tend to reduce the precision with which estimates can be made. If you have the resources to sample more than \\(m = 16\\) communities then you should do so. A sample of \\(m = 24\\) communities and \\(n = 8\\) eligible subjects, for example, will tend to yield estimates with better precision than a sample with \\(m = 16\\) communities and \\(n = 12\\) eligible subjects. Do not be tempted to increase the size of the within-community sample in order to achieve an overall sample size of \\(n = 192\\) from fewer than \\(m = 16\\) communities. Doing so will tend to reduce the precision with which estimates are made. It may also be impossible to do this in many settings. Here, for example, is a population pyramid for a typical developing country: Figure 1.2: Population pyramid for a typical developing country If the average community population is \\(N = 300\\) then there will be fewer than 15 people aged 60 years and older in about half of the selected communities. This is because about half of the selected communities are likely to have a population below the average population. 1.4 Eligibility Older people are usual defined as persons aged 60 years and older (UN definition). This means your sample will usually be restricted to people aged 60 years and older. In some settings different eligibility criteria may apply. This will likely be the case in settings with very high life-expectancies (usually middle and high income countries) or very low life-expectancies (usually low income countries and in emergencies). In a setting of very high life-expectancy you may want to restrict eligibility - to persons aged 65 years or older, for example. A local definition of older people is likely to be available. In a setting with very low life-expectancy, very few people are aged 60 years or older. For example: Figure 1.3: Population pyramid for a setting with low life-expectancy It is common in such setting for there to be a local definition of older people. This will usually be “persons aged 50 years or older” or “persons aged 55 years or older”. 1.5 Age distribution, eligibility criteria, and sample design The age distribution of the population and the survey eligibility criteria will affect the sample design in terms of the number of communities that you will need to sample (\\(m\\)) and the number of older persons (\\(n\\)) that can be sampled from each community. The overall sample size for a RAM-OP sample should be at least \\(n = 192\\) usually collected as \\(n = 12\\) eligible subjects sampled from \\(m = 16\\) communities. If older people make up a very small proportion (i.e. much less than 5%) of the total population and / or the average population of communities is small then you will usually need to sample more than \\(m = 16\\) communities in order to get about \\(n = 192\\) older people in the overall sample. This is likely to occur when there are fewer than 20 to 25 older people in a community of average size. You can calculate the number of older people that you would expect to be living in a community of average size using the following formula: \\[n_{\\text{aged 60+ in an average village}} ~ = ~ \\text{average village population}_{\\text{all ages}} ~ \\times ~ \\frac{\\text{percentage of population}_{\\text{aged 60+}}}{100}\\] If this is below about 20 people then you should consider how you will collect the required overall sample size. Three approaches may be used: Relax the eligibility criteria: You may decide to define older people as “persons aged 50 years or older” or “persons aged 55 years or older”. This may double the size of the eligible population and make the sample easier to collect. This approach is only reasonable if life-expectancy is low. Increase the number of communities that you plan to sample: You may choose to collect your sample as \\(n = 7\\) eligible subjects sampled from \\(m = 30\\) communities giving an expected overall sample size of \\(n = 210\\). This would be a very good sample. The disadvantage of this approach is that survey costs increase with the number of communities that are sampled, because a lot of survey time and vehicle costs are spent on travelling to and from the selected communities. Take a “top-up” sample only when you need to: The basic procedure when a selected community is small and likely to contain fewer than \\(n = 12\\) older people is to collect data on all older people in the selected community using a door-to-door census. If the within-community sample size is much smaller than the required one then a “top-up” sample is taken from the nearest neighbouring community using the map-segment-sample method (or a door-to-door census if this community is also small). The advantage of this approach is that travelling time and survey costs are better controlled. If the proportion of older people is not very small and / or communities are large then you should have no problems achieving the overall sample size. 1.6 Practical sampling 1.6.1 The first stage sample - list-based sampling The first stage sample can be drawn from a list of all communities. The list-based sample is a simple systematic sample taken from a complete list of communities in the survey area sorted by one or more non- overlapping spatial factors (such as administrative units or electoral wards) in the survey area. Population proportional sampling (PPS) is not used since this would concentrate the sample in the larger communities. Below is a worked example of how a RAM-OP first stage, list-based sample can be drawn from a survey area composed of 67 villages. Step 1: Calculate the sampling interval by dividing the total villages in the survey area (67 villages) with the number of villages to be drawn from the sample (16 villages). \\[\\text{Sampling Interval} ~ = ~ \\left \\lfloor ~ \\frac{N_{\\text{villages}}}{N_{\\text{sample}}} ~ \\right \\rfloor ~ = ~ \\left \\lfloor ~ \\frac{67}{16} ~ \\right \\rfloor ~ \\approx ~ \\left \\lfloor ~ 4.19 ~ \\right \\rfloor ~ \\approx ~ 4\\] The sampling interval needs to be a whole number. Remember to always round down when calculating the sampling interval to the nearest whole number. Step 2: Choose a random starting point between 1 and sampling interval. In this example, this would be a random number between 1 and 4. A random number can be selected through simple lottery (i.e., draw from a lot of 4 numbered from 1 to 4). A standard spreadsheet software can also be used to draw the random number using the RANDBETWEEN function as follows: RANDBETWEEN(1, 4) Step 3: Using the random starting point and the sampling interval, select the sampling villages from a list of all villages organised/sorted by a non-overlapping spatial factor such as district or sub-district. Figure 1.4: Selection of sampling villages using lists 1.6.2 The first stage sample - map-based sampling An alternative approach to list-based sampling is to use map-based sampling. The map-based (CSAS) sample selects communities from the centre of squares of a grid drawn over a map. The map must be sufficiently well made and of sufficiently large scale to show the position of all communities in the survey area. A square grid is drawn over the map. The size of the grid squares should be small enough so that the number of squares covering the survey area is the same as (or very similar to) the number of communities that you plan to sample. You may need to experiment with different grid sizes to achieve this. Figure 1.6 shows an example map and grid with \\(m = 16\\) grid squares. The sample is drawn by selecting the community that is located closest to the centre of each grid square: Figure 1.5: Selection of sampling villages using maps If two or more villages are located the same distance from the centre of a grid square then a single village is picked at random, by tossing a coin for example. Figure 1.7 shows the sample selected by this process for the area shown in Figure 1.6. Figure 1.6: Drawing a square grid over the map Figure 1.7: Drawing the first-stage CSAS sample Both the list-based and the map-based (CSAS) sampling methods spread the sample of communities evenly across the entire survey area. Each community has an equal chance of being included in the sample. Population proportional sampling (PPS) is not used since this would concentrate the sample in the larger communities. The same method can be used when sampling in urban contexts. Figure 1.8 shows a sample drawn from a list of census enumeration areas sorted by administrative district. Figure 1.9 shows a sample drawn using the map- based (CSAS) method. In both cases the primary sampling units (PSUs) are census enumeration areas. Figure 1.8: Example of an urban sample (list-based) Figure 1.9: Example of an urban sample (map-based) Note: In this example twenty-one (21) blocks have been selected. It can be difficult to achieve exactly the number of blocks that you need when using this type of sample. It is best to select more rather than fewer blocks than you need Here we would take our sample as \\(n = 10\\) individuals from \\(m = 21\\) blocks (overall \\(n = 210\\)). 1.6.3 The second stage (within-community) sample The second stage (within-community) sample uses a map-segment-sample approach: Map: Make a rough map of the community to be sampled. It is helpful to think of communities as being made of ribbons (i.e. lines of dwellings located along roads, tracks, or rivers) and clusters of dwellings. Here is an example of a ribbon of dwellings: Figure 1.10: Example of a ribbon of dwellings Here is an example of a cluster of dwellings: Figure 1.11: Example of a cluster of dwellings Segment: Divide the community into ribbon and cluster segments defined by the physical layout of the community being sampled. Sample: Ribbons and clusters are sampled in different ways: Ribbons are sampled using systematic sampling. Clusters are sampled using a random walk method. Note: If a small community is selected that is likely to have fewer than the required number of eligible persons then all eligible persons in that community are sampled by moving door-to-door. 1.6.4 Mapping the community - single and multiple clusters Some communities consist of a single cluster of dwellings: Figure 1.12: Example of a cluster of dwellings or a set of clusters of dwellings: Figure 1.13: Example of a set of clusters of dwellings For communities (or parts of communities) structured in this way we use a sampling method called the random walk. 1.6.5 Mapping the community - ribbon communities Ribbon communities have dwellings arranged in a line: Figure 1.14: Dwellings arranged in a line or in a several lines: Figure 1.15: Dwellings arranged in several lines For communities (or parts of communities) structured in this way we use a sampling method called systematic sampling. 1.6.6 Mapping the community - mixed communities Some communities are a mixture of clusters and ribbons: Figure 1.16: Mixture of clusters and ribbons For mixed communities we use a mixture of the random walk method (in the clusters) and systematic sampling (along the ribbons). Segmentation involves dividing a community into several parts and taking part of the within-community sample from each segment. With simple communities, segmentation is not required and we take a single sample from the entire community using the appropriate sampling method. 1.6.7 Segmentation For more complicated communities we divide the community into several parts or segments, such as a community made up of several clusters: Figure 1.17: Example of a set of clusters of dwellings or a community made up of several ribbons: Figure 1.18: Dwellings arranged in several lines or a mixed community: Figure 1.19: Mixture of clusters and ribbons We take a small sample from each segment using the appropriate sampling method. For example, with a community made up of three segments: Figure 1.20: Community made up of three segments we would take one third of the overall sample from each segment. If the within-community sample size is twelve eligible subjects. we would sample four eligible subjects from each segment (i.e. \\(12 / 3 = 4\\)). Dividing the sample up in this way means that we will sample from every part of the community rather than just one part of the community. When taking the sample we use the random walk method to take part of the sample from clusters and the systematic sampling method to take part of the sample from ribbons. Segments should be either ribbons or clusters but should never contain both a ribbon and a cluster. This is because clusters and ribbons are sampled in different ways. A dwelling can only belong to one segment. Segments should not overlap. 1.6.8 Sample dwellings All segments should be sampled. If, for example, there are five segments in a community: Figure 1.21: Community made up of five segments and the within-community sample size is twelve eligible subjects, then you would plan to sample two eligible subjects from each segment (i.e. \\(12 / 5 = 2.4\\) rounded down to two) and, if necessary, return to the largest segment to complete the sample. All segments should be sampled, even if this means that you take a larger sample than you expected to. Remember that different types of segment are sampled in different ways: Dwellings in cluster segments are sampled using a method called the random walk. This involves sampling houses by walking in random directions within the cluster. Dwellings in ribbon segments are sampled using a method called systematic sampling. This involves sampling houses at regular intervals along the ribbon. We will look at each of these sampling methods in turn. 1.6.9 Random walk sampling The random walk method is used to sample dwellings in cluster segments. Sampling proceeds as follows: Move to the approximate centre of the cluster. Select a random direction by spinning a bottle on the ground. The neck indicates the sampling direction. This is the direction you should walk in order to sample a dwelling. Walk in the sampling direction counting the dwellings that you pass. Sample the third dwelling. If there are no eligible persons in the selected dwelling then sample the nearest dwelling with an eligible person. Sample all eligible persons in the selected dwelling. Apply the survey questionnaire for all eligible persons in the selected dwelling. Select the next dwelling to sample by spinning a bottle and walking in the indicated direction. Count the dwellings you pass. Sample the third dwelling. If there are no eligible persons in the selected dwelling then sample the nearest dwelling with an eligible person. Sample all eligible persons in the selected dwelling. If you reach the edge of the cluster segment then return to the centre of the cluster and repeat step (2) above. Remember to keep count of the number of eligible persons sampled from the segment. Stop sampling in the segment when you have sampled the required number of eligible persons from the segment. Since you sample all eligible persons in a selected dwelling, you may sample a few more eligible persons than expected. This is OK. Always sample all eligible persons in a selected dwelling. If, when you have sampled all segments, you have not sampled twelve eligible persons, you should return to the largest segment to finish sampling using the appropriate sampling method. The random walk method is illustrated in Figure 1.22. Figure 1.22: Random walk sampling in a cluster segment 1.6.10 Systematic sampling The systematic sampling method is used to sample houses in ribbon segments. Sampling proceeds as follows: Move to one end of the ribbon segment. Walk to the other end of the segment counting the houses that you pass. Calculate the step size by dividing the number of dwellings in the segment by the required sample size for the segment. Use the whole number part of the result only. Do not round up. Pick a random number between one and the step size. This is your starting point. Select the first dwelling to sample by walking along the segment counting the dwellings that you pass and sample the dwelling indicated by the starting point. If there are no eligible persons in the selected dwelling then sample the nearest dwelling in any direction with an eligible person. Sample all eligible persons in the selected dwelling. Select the next dwelling to sample by walking along the segment. Count the dwellings that you pass. Sample the dwelling indicated by the step size. If there are no eligible persons in the selected dwelling then sample the nearest dwelling in any direction with an eligible person. Sample all eligible persons in the selected dwelling. Stop sampling in the segment when you reach the end of the ribbon segment. This may mean that you sample extra eligible persons. This is OK. Do not stop sampling from a ribbon until you reach the end of the ribbon. If, when you have sampled all segments, you have not sampled twelve eligible persons, you should return to the largest segment to finish sampling using the appropriate sampling method. The systematic sampling method is illustrated in Figure 1.23. Figure 1.23: Systematic sampling in a ribbon segment 1.6.11 Sampling in urban settings In urban areas the first stage sample is taken by replacing sub-districts with “sections” and communities with city blocks. Examples of sections may be administrative districts/sub-districts or electoral wards. Figure 1.24: Administrative divisions in an urban setting Census enumeration areas (EAs) are usually city blocks. Central statistics offices can usually provide lists of EAs by “section” and large-scale maps of EAs selected for sampling (See Figure 1.25 and Figure 1.26). These maps make it easy to locate EAs and their boundaries. The sample of EAs can be decided using list-based or map-based (CSAS) sampling. In these settings, eligible persons may be sampled by moving from door-to-door. All dwellings in the selected block are sampled and all eligible persons in the selected dwellings are sampled. This means that all eligible persons in a selected block are sampled. If city blocks are large then a type of systematic sampling may be used. With this method a rough map of the streets in the block is made and the number of doorways on each street is counted and copied onto the rough street map (as shown in Figure 1.27). The total number of doorways on all streets is calculated. A step size is calculated by dividing the total number of doorways on all streets by the number of dwellings to be sampled. A systematic sample along a route around the block that includes all streets in the block is taken. Streets can be sampled in any order. If you find that you have sampled all streets but have not yet sampled the required number of eligible persons then you should return to the street with the largest number of houses to collect the remainder of the sample. The number of blocks to be sampled will depend on the expected number of eligible persons in each block. You should aim for an overall sample size of about \\(n = 192\\). You should not sample fewer than \\(m = 16\\) blocks. Figure 1.25: Enumeration area map for a city block in Freetown, Sierra Leone Figure 1.26: Enumeration area map for a city block in Addis Ababa, Ethiopia Figure 1.27: Systematic sampling in a city block When useful lists and maps are not available then satellite imagery available though free services such as Google Earth (http://earth.google.com) may be used. The quality (resolution) of the images available from these services is variable but is usually good enough to allow you to segment the town into small areas of approximately equal volume (approximately the same number of dwellings) in each: Figure 1.28: Segmenting a town into smaller sampling areas When creating segments using maps or satellite images it is a good idea to use main roads, rivers, canals, railway lines, public parks, etc as boundaries. This simplifies the segmentation process and also simplifies fieldwork by making areas and their boundaries easier to locate and sample. The first stage sample can be list-based (such as where each area is numbered in a systematic north to south and east to west order and a systematic sample taken) or map-based (CSAS). Larger scale “maps” of blocks to be sampled can also me made using satellite imagery (see Figure 1.29). Figure 1.29: A large scale “map” of a city block made from satellite imagery "],
+["indicators.html", "2 Indicators 2.1 The RAM-OP indicator set 2.2 A note on data management and data analysis", " 2 Indicators 2.1 The RAM-OP indicator set RAM-OP surveys collect and report on data for a broad range of indicators relevant to older people. These indicators cover the following dimensions: Demography and situation Food intake Severe food insecurity Disability Activities of daily living Mental health and well-being Dementia Health and health-seeking behaviour Sources of income Water, sanitation, and hygiene Anthropometry and screening coverage Visual impairment Data for a small group of miscellaneous indicators are also collected and reported. The RAM-OP indicator set has been designed on a modular basis. Each module is a set of indicators relating to a single dimension from the list given above and is collected using a dedicated set of questions and measurements. This means that the RAM-OP questionnaire also consists of a set of modules. Whenever possible, RAM-OP uses standard and validated indicators and question sets. Indicators are described below, showing the questionnaire components that are used to collect and record the data required, and flowcharts of the process used to derive indicators from the collected data. Standard symbols are used. For example: A non-standard symbol is used to show recode operations. A recode operation shows changes that are made to data so that it can be used to derive indicators without having to show many decision nodes in the flowchart. They are also used to specify what should be done with missing or out-of-range values. For example: 2.1.1 Demography and situation The demography and situation indicators are used to describe the survey sample and are derived from this questionnaire component: Each of the questions yields a separate indicator: 2.1.2 Food intake Food-intake indicators are derived from this questionnaire component. This data can be queried to yield a large number of useful indicators. There are three related sets of diet-related indicators: meal frequency food groups consumed / dietary diversity indicators of nutrient consumption. The indicator hierarchy is: The data on the number of meals taken in the previous twenty-four hours forms a meal frequency score. Food intake data from each subject is combined into a dietary diversity score. The dietary diversity score is a crude measure of food security. The dietary diversity score ranges between zero (i.e. no food groups) and eleven (i.e. eleven food groups). Higher values of the dietary diversity sore are associated with better food security. The meal frequency score and the dietary diversity score follow: Swindale A, Bilinsky P, Household Dietary Diversity Score (HDDS) for measurement of household food access: Indicator guide.,Washington DC, Food and Nutrition Technical Assistance (FANTA) Project, 2006 Kennedy G, Ballard T, Dop MC, Guidelines for Measuring Household and Individual Dietary Diversity, Rome, Food and Agricultural Organization, 2010 The data on the types of food consumed in the previous twenty-four hours are analysed in order to determine the diet’s content of specific micronutrients that are important for older people. This also follows Swindale &amp; Bilinsky (2006) and Kennedy et al (2010), and: World Health Organisation, The management of nutrition in major emergencies, Geneva, WHO, 2000 2.1.3 Meal frequency The meal frequency score indicator is the answer given to the first food intake question: Meal frequency is a crude measure of food security. Higher values of meal frequency are associated with better food security. 2.1.4 Food groups and dietary diversity Questions relating to the consumption of individual food items / food types are combined to create food groups and the number of food groups consumed are counted to create a dietary diversity score: The consumption of the eleven individual food groups and the dietary diversity score are reported separately. The dietary diversity score is a crude measure of food security. The dietary diversity score ranges between zero (no food groups) and eleven (eleven food groups). Higher values of the dietary diversity score are associated with better food security. 2.1.5 Indicators of nutrient consumption Overview Questions and combinations of questions relating to the consumption of individual food items and food types can be used to determine whether the reported diet is likely to be provide sufficient nutrients of various types: Each indicator is formed using logical “or” operations (i.e. the indicator is true if any of the constituent foods are consumed). For example, the indicator for the consumption of iron rich foods: requires the consumption of one or more of green leafy vegetables, organ meats, meat, or fish and shellfish. Consumption of any of these foods is sufficient to indicate that the survey subject consumes iron rich food. 2.1.5.1 Protein rich foods Indicators of consumption of protein rich foods from animal sources, plant source, and any / all sources are calculated as: 2.1.5.2 Vitamin A rich foods Indicators of consumption of vitamin A rich foods from animal sources, plant source, and any / all sources are calculated as: 2.1.5.3 Iron rich foods An indicator of consumption of iron rich foods from any / all sources is calculated as: 2.1.5.4 Calcium rich foods An indicator of consumption of calcium rich foods from any / all sources is calculated as: 2.1.5.5 Zinc rich foods An indicator of consumption of zinc rich foods from any / all sources is calculated as: 2.1.5.6 Vitamin B rich foods Indicators of consumption of vitamin B rich foods from any / all sources are calculated as: Note that the vitamin B complex indicator requires that at least one food from each of the B1, B2, B3, B6, and B12 rich food combinations is consumed. 2.1.6 Severe food insecurity An indicator of severe food insecurity (hunger) is derived from this questionnaire component: and is calculated as: This indicator is the Household Hunger Scale (HHS) and is a simple, well-validated, and widely used indicator of severe food insecurity: Ballard T, Coates J, Swindale A, Deitchler M, Household Hunger Scale: Indicator Definition and Measurement Guide, Washington DC, FANTA-2 Bridge, FHI 360, 2011 Ruel MT, Ballard TJ, Deitchler M, Measuring and Tracking the Access Dimension of Food Security: Available Indicators and Recommendations for Future Investments, Global Nutrition Report 2014: Technical Note 6, Washington DC, International Food Policy Research Institute, 2014 2.1.7 Disability Indicators of disability across six different domains are derived from this questionnaire component: Individual disability indicators are reported for each domain (i.e. vision, hearing, mobility, remembering, self-care, and communication) of disability in the Washington Group’s short set of question designed to identify people with a disability in a census or survey format: http://www.washingtongroup-disability.com https://www.cdc.gov/nchs/washington_group/wg_documents.htm Overall disability prevalence indicators are also reported. Indicators of disability in each domain are calculated as: Overall disability prevalence indicators are calculated as: P0 = 1 if no domain has D1 = 1, else = 0 (no disability in any domain) P1 = 1 if at least one domain has D1 = 1, else = 0 P2 = 1 if at least one domain has D2 = 1, else = 0 P3 = 1 if at least one domain has D3 = 1, else = 0 PM = 1 if at more than one domain has D1 = 1, else = 0 (M stands for “Multiple”) 2.1.8 Activities of daily living Indicators of how well the subject copes with activities of daily living are derived from this questionnaire component: Individual independence indicators are reported for each dimension (i.e. bathing, dressing, toilet, mobility, continence, and eating) of daily living activities. A composite indicator of the degree of independence (i.e. how well the subject can cope with activities of daily living) is also reported. This indicator is the Katz Index of Independence in Activities of Daily Living (or the Katz Index of ADL for short) and is a simple, well-validated, and widely used indicator of how well the subject can cope with activities of daily living: Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW, Studies of illness in the aged. The Index of ADL: A standardized measure of biological and psychosocial function, JAMA, 185(12), 1963, pp. 914-9 Katz S, Down TD, Cash HR, Grotz, RC, Progress in the development of the index of ADL, The Gerontologist, 10(1), 1970, pp. 20-30 Katz S, Assessing self-maintenance: Activities of daily living, mobility and instrumental activities of daily living, JAGS, 31(12), 1983, pp. 721-726 The Katz Index of ADL ranges between zero (complete dependence) and six (independence). The seventh question of this module, which is not part of the Katz Index of ADL, is reported separately and indicates whether the subject has someone to help them with activities of daily living: Activities of Daily Living a7 Is someone taking care of you or helping you with everyday activities such as shopping, cooking, bathing and dressing? 1 = Yes; 2 = No [__] It is not possible to know if the help available completely meets a subject’s needs, but we can identify the proportion of subjects needing help with one or more activities of daily living who also report not having someone to help them: This is an indicator of unmet need. Indicators of how well the subject can cope with activities of daily living and probable unmet need are calculated as: 2.1.9 Mental health and well-being Indicators of mental health and well being are derived from this questionnaire component: A score is calculated. This is the Kessler K6 Psychological Distress Scale. The score ranges from zero (indicating no psychological distress) to twenty-four (indicating severe psychological distress). A score of thirteen or more indicates serious psychological distress. The Kessler K6 Psychological Distress Scale is a widely recommended, widely used, accurate, reliable, and simple measure of psychological distress: Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek, DK, Normand SLT, et al, “Short screening scales to monitor population prevalences and trends in non-specific psychological distress”, Psychological Medicine, 32(6), 2002, pp. 959–976 Kessler RC, Barker PR, Colpe LJ, Epstein JF, Gfroerer JC, Hiripi E, “Screening for Serious Mental Illness in the General Population”, Archives of General Psychiatry, 60(2), 2003, pp. 184-189 Indicators of mental health and well-being are calculated as: 2.1.10 Dementia An indicator of probable dementia is derived from this questionnaire component: The indicator of probable dementia is calculated as: This indicator is derived from the Community Screening Instrument for Dementia (CSID) developed by the 10/66 Dementia Research Group. This is a simple, validated, and widely used indicator of probable dementia: Prince M, et al, “A brief dementia screener suitable for use by non-specialists in resource poor settings - The cross-cultural derivation and validation of the brief Community Screening Instrument for Dementia”, International Journal of Geriatric Psychiatry, 26(9), 2011, pp. 899–907 2.1.11 Health and health-seeking behaviour Indicators of health and health-seeking behaviour for chronic and acute conditions are derived from this questionnaire component: Indicators of health and health-seeking behaviour for chronic conditions are calculated as: Indicators of health and health-seeking behaviour for acute conditions are calculated as: 2.1.12 Sources of income Indicators related to sources of income are derived from this questionnaire component: and are calculated as: The grouped income sources (i.e. m2a, m2b, etc.) and individual income sources may vary between settings. The questionnaire component shown above has proved suitable for use in Ethiopia, South Sudan, and Tanzania. 2.1.13 Water, sanitation, and hygiene Indicators relating to water, sanitation, and hygiene (WASH) are derived from this questionnaire component: Indicators are calculated following: WHO / UNICEF, Core Questions on Drinking-water and Sanitation for Household Surveys, Geneva, WHO / UNICEF, 2006 Indicators relating to water, sanitation, and hygiene (WASH) are calculated as: 2.1.14 Anthropometry and screening coverage Indicators relating to anthropometry and screening coverage are derived from this questionnaire component: And are calculated as: Raw MUAC data (i.e. not MUAC class) is collected, entered, and analysed. This requires that an adult MUAC tape (i.e. capable of measuring MUAC to 450 mm) is used. The presence of bilateral oedema is assessed by pressing with your thumbs both feet of the older person for three seconds and checking whether this creates a lasting depression or “pit” on both feet. Bilateral pitting oedema in older people may not be “nutritional” oedema (as is almost always the case with children). Older people with bilateral pitting oedema should be advised to consult a doctor. The prevalence of GAM, MAM, and SAM are estimated using a PROBIT estimator. This type of estimator provides better precision than a classic estimator at small sample sizes: World Health Organisation, Physical Status: The use and interpretation of anthropometry. Report of a WHO expert committee, WHO Technical Report Series 854, WHO, Geneva, 1995 Dale NM, Myatt M, Prudhon C, Briend, A, “Assessment of the PROBIT approach for estimating the prevalence of global, moderate and severe acute malnutrition from population surveys”, Public Health Nutrition, 1–6. doi:10.1017/S1368980012003345, 2012 Blanton CJ, Bilukha, OO, “The PROBIT approach in estimating the prevalence of wasting: revisiting bias and precision”, Emerging Themes in Epidemiology, 10(1), 2013, p. 8 The PROBIT estimator is described in Box 1. MUAC-based case definitions for acute malnutrition are used: GAM MUAC &lt; 210 mm MAM 185 mm ≤ MUAC &lt; 210mm SAM MUAC &lt; 185mm These are standard case definitions for acute malnutrition in adults and recommended by HelpAge International for use in older people in humanitarian contexts. Note : MUAC in adults should be measured on the non-dominant arm. This is usually the left arm. The importance of high levels of accuracy and precision at the individual level is of lesser importance in survey work compared to case-finding or diagnosis in clinical contexts, for example. This means that a simple rule such as “Always measure MUAC on the left arm” may be used. An estimate of GAM prevalence can be made using a classic estimator: \\[\\text{prevalence} = \\frac{\\text{number of respondents with MUAC &lt; 210 mm}}{\\text{total number of respondents}}\\] The estimate of GAM prevalence made from the RAM-OP survey data is made using a PROBIT estimator. The PROBIT function is also known as the inverse cumulative distribution function. This function converts parameters of the distribution of an indicator (e.g. the mean and standard deviation of a normally distributed variable) into cumulative percentiles. This means that it is possible to use the normal PROBIT function with estimates of the mean and standard deviation of indicator values in a survey sample to predict (or estimate) the proportion of the population falling below a given threshold. For example, for data with a mean MUAC of 256 mm and a standard deviation of 28 mm the output of the normal PROBIT function for a threshold of 210 mm is 0.0502 meaning that 5.02% of the population are predicted (or estimated) to fall below the 210 mm threshold. Both the classic and the PROBIT methods can be thought of as estimating area: The principal advantage of the PROBIT approach is that the required sample size is usually smaller than that required to estimate prevalence with a given precision using the classic method. The PROBIT method assumes that MUAC is a normally distributed variable. If this is not the case then the distribution of MUAC is transformed towards normality. The prevalence of SAM is estimated in a similar way to GAM. The prevalence of MAM is estimated as the difference between the GAM and SAM prevalence estimates: \\[\\widehat{MAM prevalence} = \\widehat{GAM prevalence} - \\widehat{SAM prevalence}\\] 2.1.15 Visual impairment An indicator of visual impairment is derived from this questionnaire component: And is calculated as: The “illiterate E” or “tumbling E” (the preferred term) is a validated and widely used method for measuring visual acuity: Taylor HR, “Applying new design principles to the construction of an illiterate E chart”, American Journal of Optometry &amp; Physiological Optics, 55:348, 1978 Kaiser PK, “Prospective Evaluation of Visual Acuity Assessment: A Comparison of Snellen Versus ETDRS Charts in Clinical Practice (An AOS Thesis)”, Transactions of the American Ophthalmological Society, 107: 311–324, 2009 The size of the “E” used: as well as the distance used for the test (two metres) and the indicator calculation apply the WHO case definition of visual impairment (i.e. visual acuity &lt; 6 / 18). The tumbling E card should be laminated (i.e. plastic coated and have a two metre cord attached which helps to ensure that the visual acuity test is performed at the correct distance (See Figure 2.1). After demonstrating to the respondent what the test is about (i.e. the subject should indicate which direction the branches of the ‘E’ are pointing), the test is administered at a distance of two meters, turning the card in four different directions, and asking the person to indicate which direction the branches of the “E” is pointing. If the subject wears glasses, they are allowed to use them during the test if they want to. Note : If the person is unable to correctly answer at least three times out of four, they have a visual impairment. A simple visual acuity test such as the ‘tumbling E’ test also does not indicate anything about an underlying disease such as glaucoma or the need for reading spectacles (presbyopia). These conditions are common in people aged 60 years or older. Subjects failing the visual acuity test should be counselled to visit an ophthalmologist for a detailed eye examination. Figure 2.1: Equipment used to measure visual acuity 2.1.16 Miscellaneous indicators Data for a small group of miscellaneous indicators are also collected and reported. These are derived from these questions: Hunger – Ration - Relief f6 Are you or anyone in your household receiving a food ration on a regular basis? 1 = Yes; 2 = No [__] f7 Have you or another member of your household received non-food relief items such as soap, bucket, water container, bedding, mosquito net, clothes, or plastic sheet in the previous four weeks? 1 = Yes; 2 = No [__] Activities of Daily Living a8 Do you have problems chewing food? 1 = Yes; 2 = No [__] and are calculated as: 2.2 A note on data management and data analysis This section has described how RAM-OP data is used to create a broad set of indicators. If you do not want to use the standard RAM-OP software to do this then you can use this information to create data entry systems and data management scripts for your favoured database or statistical analysis software. See the sections on RAM-OP datasets and RAM-OP questionnaire for more compact information on variable names and codes that you may find helpful. It is important to note that data analysis procedures need to account for the sample design. All major statistical analysis software can do this (details vary). There are two things to note: The RAM-OP sample is a two-stage sample. Subjects are sampled from a small number of primary sampling units (PSUs). The RAM-OP sample is not prior weighted. This means that you will need to provide per-PSU sampling weights. These are usually the populations of the PSU. You will need to specify this sample design to your statistical analysis software. If you fail to do this then your analysis may produce estimates that place undue weight to observations from smaller communities with confidence intervals with lower than nominal coverage (i.e. they will be too narrow). The standard RAM-OP software uses blocked weighted bootstrap estimation approach: Blocked : The block corresponds to the PSU or cluster. Weighted : The RAM-OP sampling procedure does not use population proportional sampling to weight the sample prior to data collection as is done with SMART type surveys. This means that a posterior weighting procedure is required. The standard RAM-OP software uses a “roulette wheel” algorithm to weight (i.e. by population) the selection probability of PSUs in bootstrap replicates. A total of m' PSUs are sampled with-replacement from the survey dataset where m' is the number of PSUs in the survey sample. Individual records within each PSU are then sampled with-replacement. A total of n’ records are sampled with-replacement from each of the selected PSUs where n' is the number of individual records in a selected PSU. The resulting collection of records replicates the original survey in terms of both sample design and sample size. A large number of replicate surveys are taken (the standard RAM-OP software uses \\(r = 399\\) replicate surveys but this can be changed). The required statistic (e.g. the mean of an indicator value) is applied to each replicate survey. The reported estimate consists of the 50th (point estimate), 2.5th (lower 95% confidence limit), and the 97.5th (upper 95% confidence limit) percentiles of the distribution of the statistic observed across all replicate surveys. The blocked weighted bootstrap procedure is outlined in Figure 2.2. The principal advantages of using a bootstrap estimator are: Bootstrap estimators work well with small sample sizes. The method is non-parametric and uses empirical rather than theoretical distributions. There are no assumptions of things like normality to worry about. The method allows estimation of the sampling distribution of almost any statistic using only simple computational methods. The standard RAM-OP data analysis software is described in the section Standard RAM-OP software. Figure 2.2: The blocked weighted bootstrap used by the standard RAM-OP software "],
+["questionnaire.html", "3 The RAM-OP questionnaire", " 3 The RAM-OP questionnaire Modules of the RAM-OP questionnaire are presented in the RAM-OP indicators section of this manual. The entire RAM-OP questionnaire is presented in the following pages. This questionnaire is composed of many tested and validated components. The order of the questions and the format of the questionnaire have been tested in several settings (Chad, Dadaab Camps, South Sudan, Ethiopia, and Tanzania) over a period of three years. It is strongly recommended that you do not change the questionnaire, other than translating it into a language other than English and necessary localisation (i.e. adapting the questions to meet the language, cultural, and other requirements of a specific target population in order to ensure that the words, names, terms, and concepts used are culturally appropriate and understandable to them), unless you are very sure of what you are doing. Modifying the questionnaire may have one or more of the following consequences: Modifying the order of the questions or adding questions : The links with the data entry, data checking, and data analysis software will be broken. You will have to modify the software to accommodate your changes. Modifying the variable names : The links with the data entry, data checking, and data analysis software will be broken. You will have to modify the software to accommodate your changes. Modifying the content or the phrasing of questions : All questions have been tested and are formulated for accuracy and reliability (precision). Modifying them may lead to loss of accuracy (bias) and precision When translating the questionnaire you should check if validated question sets for each indicator module are already available in your local language. This is likely to be the case for the food intake, severe food insecurity, activities of daily living, mental health and well-being, dementia, water / sanitation / hygiene, and visual impairment indicator modules. There may also be local language training modules and guidelines available for these modules. Localisation is recommended for: Food groups : Remove inappropriate foodstuffs and give examples of local foodstuffs. Income sources : Review income types and income categories. The question numbers used on the questionnaire are the names of variables used in the RAM-OP data entry, data checking, and data analysis software. Leaving these as they are will be helpful if you intend to use the RAM-OP data-entry and data-analysis software. The questionnaire can be downloaded (in ODT and PDF format) from http://www.brixtonhealth.com/qesRAMOP.zip "],
+["datasets.html", "4 Datasets", " 4 Datasets This section details the RAM-OP datasets. The information presented here is of most use if you decide not to use the RAM-OP data entry and data checking software. You might, for example, decide to enter survey data using spreadsheet software such as Microsoft Excel. If you do this and want to use the RAM-OP data analysis software then you will need to export the data as a comma-separated-value (CSV) file with the same variable names, variable types and lengths, and using the same codes as shown in the tables in this section. For the main RAM-OP survey dataset these are the same variable names, variable types, variable lengths, codes, and in the same order as shown on the standard RAM-OP questionnaire. There are two RAM-OP datasets: The main RAM-OP survey dataset : This is the data collected by the survey questionnaire. The dataset definition for the main RAM-OP dataset is shown in Figure 4.1. The PSU dataset : This a short and narrow file with one record per PSU and just two variables: psu The PSU identifier. This must use the same coding system used to identify PSUs that is used in the main RAM-OP dataset. pop The population of the PSU. The PSU dataset is used during data-analysis to weight data by PSU population. If you do not know population sizes (as might be the case in emergencies) then you can collect this data: When you visit the PSU (i.e. from community leaders or health centres). When you visit the PSU as a doorway count or roof count. Using recent satellite imagery as a roof count. Relative population sizes can be used. If no better data is available then it is reasonable to use a simple semi-quantitative assessment such as: Type of place Population range* Features Record population as … Hamlet \\(&lt;\\) 1,000 Very small local market or no market 1 Village 1,000 – 4,000 Market and small shops serving the village and the surrounding hamlets 2 Town \\(&gt;\\) 4,000 Large market, many shops (some specialised), guest houses, bus station, government offices 4 *These ranges may need to be adjusted to match local circumstances. The PSU dataset must be in comma-separated-value (CSV) format (see Figure 4.2) for use with the RAM-OP data analysis software. Figure 4.1: Main RAM-OP dataset definition The RAM-OP data analysis requires that the main RAM-OP survey dataset is supplied in either an EpiInfo v6.xx or EpiData (REC) format or in a comma-separated-value (CSV) format file The RAM-OP data analysis requires that the PSU dataset is supplied in a comma-separated-value (.CSV) format file. Figure 4.2 shows an example of a PSU dataset in comma-separate-value (CSV) format. Figure 4.2: An example comma-separated-value (CSV) format file (the example is for a RAM-OP PSU dataset) Note that the first line of a CSV format file gives the names of the variables (e.g. these are psu and pop for the PSU dataset) separated by commas. Subsequent lines contain data with items separated by commas and with one record per line. CSV format files can be created using a plain text editor (e.g. Notepad) or with a spreadsheet application such as Microsoft Excel™. If you use a spreadsheet application then you will have to be careful: Variable names and data items must be separated by commas (not tab characters or semi-colon characters). Numbers with decimal places must use the full-stop character as the decimal separator. In some settings a spreadsheet application may want to use the comma character as the decimal separator. Avoid using accented characters in the names of and in the data entered into text variables. These characters can sometimes confuse the RAM-OP data analysis software. A CSV file should contain only plain text, number, and commas without formatting. Do not use a word processor application such as Microsoft Word™ to create or edit a CSV file. If you have problems using a CSV file then you should check and edit the file using a plain text-editor such as Notepad or a dedicated CSV editor such as Ron’s Editor (http://www.ronsplace.eu/Products/RonsEditor) Remember to backup your data before editing it. "],
+["practical.html", "5 Practical Fieldwork 5.1 Authorisations and clearances 5.2 Working with a local partner 5.3 Translating the questionnaire 5.4 Supervisors, enumerators, and data entry staff 5.5 Training of enumerators 5.6 Survey logistics 5.7 Data collection 5.8 Survey planning 5.9 Daily survey activities", " 5 Practical Fieldwork This section is intended to guide you through the different steps leading up to the fieldwork once the survey location has been identified, and gives some tips on how the fieldwork might be organised. 5.1 Authorisations and clearances Before implementing the survey, you will need to get all the authorisations relevant to the country in which you plan to work. These could include: Clearance from the national nutrition cluster or the equivalent structure co-ordinating national assessment activities. In humanitarian contexts, this might be the only clearance that you will need at the national level. Ethical approval : This is obtained from the country’s national ethical committee (or equivalent). Some NGOs and UNOs also have ethical committees and you may also need to submit your survey plans to them for ethical approval. It may be necessary to work with both national and local ethical committees. The process of gaining ethical approval can take several months. It is important to note that RAM-OP surveys are needs assessments rather than experiments upon human subjects. This means that ethical clearance may not be required for RAM-OP surveys, or that it can be given by the chair of the appropriate ethical review committee without the need for a full meeting of the ethical review committee. It is a good idea to check this with the chair of the appropriate committees to see if permissions can be expedited. Getting ethical clearance is often very useful when applying for other permission as it shows that some technical quality assurance has been done. Authorisation from the appropriate government departments at various levels (i.e. national, regional, and at the level where you are going to implement the survey). Authorisation of the authority managing the survey site should be sought. For example, a survey in a refugee camp will need the authorisation of UNHCR, the national administrative authority in charge of refugees and displaced persons, and the agency in charge of the camp management. In some settings you may also need to obtain authorisation from other government departments such as the Ministry of Health, the Department of Rural Affairs, or the Department of Social Affairs. Authorisation from the administrative authorities at local level : Make sure that all levels of the local administration are informed about what you intend to do (i.e. what, where, and when). It is essential to meet with the local administrative and health authorities prior to the survey. This is done to avoid problems with permissions and to involve them in the implementation of the survey. Describe the survey and explain what might be expected from their staff. You might, for example, need some help in identifying the exact location and boundaries of villages and hamlets in rural areas, or blocks and sections of towns in urban areas. You might need translators or guides to travel with the enumerators, and you might need facilitators to introduce you to village executives. Make sure that you share the results of the survey with them once it is available. Security clearance : Be aware of the potential security problems in the survey area. Inform all agencies with security responsibilities in the area about the dates and locations of the survey. The police or the army may have to be specifically informed. You may also need to negotiate access with non-state actors. Field staff should be provided with copies of official documents (in the local language) proving that they are authorised to carry out survey work in the specific area between specific dates. They will have to carry this document with them at all times during the fieldwork and present it on request to local authorities and study subjects. It can also be useful to give a copy of this and other official documents to village leaders on arrival at the survey location. 5.2 Working with a local partner It is often very useful to prepare and carry out the survey in collaboration with one or more local partners, such as a local NGO, the local health authority, or the camp management agency in a refugee camp. If feasible, you should recruit a representative of your local partner as a “survey facilitator” with responsibility for liaising with the national and local stakeholders. This person will support your survey preparation with the following: At national or regional level, support the endorsement of the survey objectives by the national authorities, and facilitation in obtaining the relevant authorisations and clearances. At local level, be the link between you and the local communities informing health staff and village leaders in the areas where the enumerators are going to sample households. This information should be disseminated before the survey starts and reiterated a day or two before teams travel to survey locations either by telephone or by personal visits. Provide you with a list of useful contacts (with telephone numbers) for each of the areas covered by the survey. This list should be shared with all program staff. Identify local guides or translators to support the teams in the field. In-depth knowledge of the survey area, useful for checking the location of the villages to be surveyed on a map. Information about travel and security constraints, travel distances and times, and assist in formulating the survey travel plan. Support with the survey logistics, such as renting vehicles, renting accommodation and training venues, where to purchase food and drinks, where to have forms and questionnaires printed / copied, etc. Help with the referral of malnourished or sick older people identified during the survey by liaising with community services, ambulance services, and relevant health facilities as needed. The local partner will also help you disseminate the results of the survey to the various stakeholders, and might be involved in response plans following the assessment. 5.3 Translating the questionnaire Precision and accuracy are improved by translating the questionnaire in the local language appropriate to the survey area before data is collected. This allows enumerators to ask questions using the same language and terminology in every interview. Thorough training of the enumerators in applying the questionnaire will also improve the precision and accuracy of your survey results. A translated questionnaire may also be a requirement for getting the ethical clearance for the survey. We advise you to use an iterative translation process and use: Standard language if available : Most indicators used in RAM-OP have question sets available in different languages. You can check for these online. You may need to alter some language to account for local dialects and idioms but using standard language, when it is available, can save you a lot of time and effort Knowledgeable lead translators : You need to use people who know the target language and culture but are also fluent in the starting language of the questionnaire. Forward translation and back translation : The questionnaire is translated from English, for example, into the local language by one person or team (this is forward translation) and is then translated back into the original language by another person or team (this is back translation). The back translated questionnaire is then checked against the original questionnaire. Differences are then analysed and a new translation produced. You may need to go through this process several times until a satisfactory version of the translated questionnaire is reached. Your survey staff to provide language and to pilot (i.e. test) questionnaire components as they are translated. Piloting can be done with community members and by role-playing between survey staff. Test interviews and group discussions usually help to improve the language used in the questionnaire. Your intended survey population to help you make sure that the language you are using is simple and to the point. Test interviews and group discussions usually help to improve the language used in the questionnaire. Having enumerators translate the English language questionnaire (for example) each time they apply the questionnaire is not a good option and should be avoided. 5.4 Supervisors, enumerators, and data entry staff The more survey teams you recruit and use, the quicker the survey will be finished. However, the number of teams should be linked to your capacity for supervision. Also, having a large number of teams usually means that you will need a large number of vehicles and drivers. This can be hard to achieve and hard to manage. We recommend that you recruit three teams of two enumerators with one supervisor per team. The duties of supervisors and enumerators are: Supervisors have to take all necessary actions to ensure the accuracy of the collected data, particularly: Checking equipment before departure and when leaving the survey site. Travelling with a team every day, to observe and correct the enumerators’ work. Introducing teams to local leaders. Ensuring households and subjects are selected properly, that the interviews are conducted with respect and thoroughness, and that measurements are taken and recorded accurately. Enumerators are in charge of implementing the field procedures: Identifying the households to survey. Apply the questionnaires to older people. Measure MUAC, oedema, and visual acuity and complete questionnaires. If each team can complete a single PSU per day (this is the minimum you can expect from a team) then the survey may be completed in six days (i.e. three PSUs per day for five days plus one PSU on the last day). This will depend on context and on the teams’ expertise. It is often possible for a team to reach more than one location per day, such as in cities or camps where sectors and blocks are close to each other and travelling time is not high. You will often find that survey data can be collected in just four or five days. It is important not to rush data collection. It is also important to supervise the teams from day one in order to ensure they follow the proper sampling procedures and applying the questionnaires correctly. It is advisable to enlist more enumerators to be trained than the minimum number needed. This will ensure that you have sufficient enumerators should you find, during training, that some recruits cannot perform their duties well enough. It will also provide additional trained staff should you need to cover for absences, due to illness for example. Make sure that you enlist both male and female trainees. You will also need to recruit data entry staff. The workload for the data entry staff is usually between about thirty-six and seventy-two questionnaires per day. 5.5 Training of enumerators Training the enumerators is a crucial step to ensuring the quality of the data collection. At the end of training each enumerator should be able to: Explain the objectives of the survey. Sample households and older people in the survey area following the appropriate field procedures. Introduce themselves to older people in a polite and respectful manner. Apply the questionnaire smoothly and efficiently. Properly measure MUAC, check for bilateral pitting oedema, and properly measure visual acuity. Complete the questionnaire neatly and without making mistakes (including the correct numbering of PSU, households and individual subjects). Advise the subject or their family in case there is a need for referral, such as to a health facility. A typical first RAM-OP training course will last for five days: Day 1 Presentation of your organisation (mission, code of conduct, etc.) Objectives of the survey How are we going to do it? Questionnaire : First reading and explanations Recap Day 2 Field procedures Job descriptions Measurements: MUAC, oedema, visual acuity (practice on each other) Questionnaire : Role-playing Lessons learned Recap Day 3 Measurements: MUAC, oedema, visual acuity (practice on ten older people) Testing the questionnaire with ten older people Lessons learned Recap Day 4 Questionnaire : Role-playing Field procedures : Recap and group work Recap Day 5 Field test : Practical field procedures, etc. in one community Lessons learned from field test Recap Additional notes: Practising the questionnaire : This is very important. Each training day should contain some work on the questionnaire. Particular attention is paid to the content and function of each question set, the numbering system for the PSU, household, and subject, the meaning / intention of each question set, skip / jump patterns, coding, ranges, and checking for completeness and consistency. Extensive use should be made of role-playing (in pairs and in groups) and testing with eligible subjects. Care needs to be taken to ensure that all field staff have extensive practice in working with the questionnaire. Practising measurements : This is very important. Most training days should contain some work on measurement. Care needs to be taken to ensure that all field staff have extensive practice in taking all measurements. Standardisation of measurement : A formal standardisation exercise for MUAC measurement is not required. The format of such an exercise does, however, provide a useful framework for training enumerators to measure MUAC with acceptable accuracy and precision by: Comparisons of measurements made by different enumerators on the same person to explore accuracy (bias). Comparisons of measurements made by different enumerators on the same person with the measurements made by the training supervisor to explore accuracy (bias). Repeated measurements on the same persons by the same enumerator to explore repeatability (precision). Ten older people : The survey facilitator should be able to mobilise ten older people (women and men) to participate in questionnaire and measurement exercises. Make sure to explain to these volunteers what is going to happen to them. The exercises will last a half day (maybe longer) and may be quite tiring for older persons. Make sure they are comfortably accommodated, organise their transport to and from the training venue (which should be easily accessible and so avoiding stairs), indoors or under shelter, with chairs or benches. Provide safe drinking water and possibly tea, coffee and snacks. It is also advisable to provide them with a small ex gratia payment. The field test : This will put the trainees in to field conditions but without the stress of having to take a full sample. It is a “dummy run” of all survey activities. Divide the trainees into teams assigning a supervisor to each team. The test area should not be one of the areas to be sampled for the survey and can be close to the training venue. At the end of the training week, you should be able to select the best enumerators and divide them in to teams, balancing genders and personalities, as well as strengths and weaknesses. 5.6 Survey logistics Thorough logistics preparation is essential to the smooth implementation of a survey. Transportation is particularly important. 5.6.1 Transportation Ideally you will need one car and driver per survey team. Depending on the area, you may be able to reduce this number and organise the survey travel plan so that vehicles can be pooled between the teams. You will need strong cars (ones adapted to the terrain) and reliable and safe drivers ready to work flexible hours. It is very useful to brief the teams and the drivers about the management of the cars: Who gives instructions to the drivers on a daily basis. One person per car should be in charge. Who is checking the car log book every morning and every evening. What to do in case of an accident. All staff should be given the contact telephone numbers of all the drivers Safety and security procedures should be followed thoroughly, such as use of seat belts, speed limits, prohibitions on “racing”, carrying of water, first aid kits, and spare wheels. 5.6.2 Tools and equipment The equipment needed for a RAM-OP survey is minimal: MUAC tapes for adults (i.e. minimum 450 mm long, graduated in millimetres). A “tumbling E chart” to perform the visual acuity test. Pens, notebooks, clip-boards, etc. Questionnaires. Maps of the area showing PSU locations. PSU maps (if required). Official letters of authorisation to carry out the survey. Every morning during the survey, each team should receive a schedule of that day’s activities detailing the team’s objectives for the day giving PSU numbers, location, PSU maps, sample target sizes, local contact information, emergency contact list, etc. 5.7 Data collection Each team should be able to survey at least one PSU per day. No community mobilisation is necessary but community officials should be informed of your arrival in advance. Avoid sampling at special or busy times, such as holy days and market days. When arriving in the community, the teams should introduce themselves to community leaders and explain the conduct of the survey. The supervisor should collect and record information about the PSU’s total population. The team should then explore the boundaries of the community and perform mapping and segmentation as required. Eligible subjects are usually people aged 60 years and older (this may differ in some settings). We accept the respondents’ statement of their own age, and we do not challenge them if they appear younger or older than the age they declare. We usually do not ask for a proof of age. However, it might be useful to have compiled a list of events related to the past hundred years of the country’s history, as some older people do not remember their age, but remember living at the time of some remarkable events (independence, elections, wars, etc). Women often remember the age at which they had their children All eligible older people present in and belonging to the sampled household are interviewed and measured, even if the required quota of respondents has been reached. Older people visiting the household should only be included if they are residing there for more than two weeks. Person such as maids, houseboys, watchmen, and carers should be treated as part of the household (i.e. should be interviewed) if the sampled dwelling / compound is their principal place of residence. Older people should be interviewed in their houses. It is not good practice to have the older people in the community gathered in one place. When entering older people’s houses, the enumerators should always be polite, respectful, and attentive not to intrude on people’s privacy. Older people should not be intimidated into answering the questionnaire or being measured. They always have a right to refuse to answer some or all of the questions and to refuse measurements. Refusals should be noted on the questionnaire. Some of the questions are of an intimate nature. Be careful not to administer the questionnaire in an open space where everybody present can hear the answers. This may embarrass the respondent or cause the respondent to answer wrongly. This is important as a “wrong answer” will tend to hide need and will bias the survey results. Some older women may object to having their MUAC taken by a man (or vice versa). This is why it is advisable that the team has both male and female members. This not always possible, but older people are often more free from prejudices than the younger members of their community. It is rare for an older woman to refuse to expose her arms or feet before a male enumerator, and also rare that an older man objects to being measured by a female enumerator. 5.8 Survey planning Here is a typical timetable of survey activities: Several weeks in advance Identify survey area Recruit local partner Obtain ethical clearance Obtain technical approval from relevant bodies Obtain permissions and letters from appropriate authorities Start translation of questionnaire Obtain maps / lists as required Obtain map(s) of the survey area One month in advance Advertise staff positions Recruit and contract supervisors Define first stage sample from list / map Make (draft) survey travel plans Obtain permissions for travel Book training venue Arrange staff accommodation (if required) Continue translation of questionnaire Begin sourcing equipment Identify potential suppliers and contractors One week before the training Recruit enumerators Purchase equipment Book vehicles and drivers Review and print / copy training manual Print translated questionnaire for the training Print “daily program” forms Finalise survey travel plans Disseminate travel plans to local authorities (with survey facilitator) Arrange logistics for the training During the training Arrange for ten older people to participate in training Revise the questionnaire with the trainees Print revised version of questionnaire for the field test (c. 50 copies) Arrange the logistics for the field test (vehicles, drivers, equipment) Review logistics arrangements with the trainees At the end of the training Provide each trainee with a certificate of attendance Pay the training incentives Review and amend questionnaire from the feedback of the field test Print survey questionnaires (c. 250 copies) Recruit enumerators and data entry staff During the survey Train the data entry clerk (day one) using the results of the field test Manage survey activities (see below) Data entry and cleaning At the end of the survey Complete data entry Provide all staff with certificates of participation Pay incentives Thank the local authorities Party for all staff Data analysis and reporting Disseminate results 5.9 Daily survey activities Here is a list of typical survey activities: Morning Brief the teams on the day’s objectives Provide feedback on the previous day (success and failures, correction of mistakes made in field procedures or data collection). Discuss problems with supervisors Provide water and snacks (or food allowance) Provide forms, questionnaires, maps (as required) Evening Check the data entry with the data-entry staff Identify problematic questionnaires Identify common data collection problems Plan the next day’s programme with the facilitator and the supervisors Discuss the problems met during the day and their resolution (particularly mistakes that they have observed in the field procedures or in the data collection). Prepare the day programme forms for the next day Check that there are enough forms, questionnaires, maps for the next day "],
+["software.html", "6 RAM-OP Software 6.1 Data entry 6.2 Data analysis", " 6 RAM-OP Software 6.1 Data entry A data entry system for RAM-OP data using EpiData has been developed. This software can be downloaded from http://www.brixtonhealth.com/enterRAMOP.zip. This software runs on the Windows operating system. It does not need to be installed and can be run from a USB drive. The data entry system provides facilities for entering data, interactive checking of data as it is entered, batch checking of entered data, data summary, and data export. The system creates files that can be read by the standard RAM-OP data analysis software. Documentation of Epidata can be found here http://www.epidata.dk/index.htm. 6.2 Data analysis This manual covers analysing your data using the RAnalyticFlow workflow. An RAnalyticFlow workflow may be thought of as an “app” that makes it easy to analyse your survey data. To use the RAnalyticFlow workflow you must install: The R Language for Data-Analysis and Graphics (R) : This is the “engine” which does all the work of analysing your data. You can get the R installation program from: http://cran.r-project.org. Following are links to download operating software-specific versions of R: Download R for Linux Download R for (Mac) OS X Download R for Windows R packages (libraries of functions needed to work with the RAnalyticFlow workflow) : You can install these from within R using the Package Installer function within R. The libraries needed are: Package Comments rJava Required: Used by RAnalyticFlow JavaGD Required: Used by RAnalyticFlow codetools Required: Used by RAnalyticFlow foreign Required: Opens EpiData (REC) files car Required: Used for PROBIT estimator ggplot2 Desirable: Provides many plotting functions data.table Desirable: Speeds up working with large dataset The Package Installer function can be called in R using the following command: install.packages(c(&quot;rJava&quot;, &quot;JavaGD&quot;, &quot;codetools&quot;, &quot;foreign&quot;, &quot;car&quot;, &quot;ggplot2&quot;, &quot;data.table&quot;), repos = &quot;https://cloud.r-project.org/&quot;) The repos argument in the R command above specifies the CRAN mirror from which you to download the package/s you want to install. Here we specify the cloud-based mirror for CRAN provided by RStudio. If unspecified, the installation process will prompt you to select a mirror from which to download packages from. If you already know the URL of the CRAN mirror you want to use, specify this in the repos argument. Note that RAnalyticFlow may require you to have Java installed. Check the instructions on the RAnalyticFlow download page and on this starter guide. All of this software is open source and free to download, copy, and use. It will run on Windows, Mac OS X, and Linux (and other UNIX-like) operating systems. Your ICT department should be able to help you with installing this software. In addition you will also need a copy of the RAnalyticFlow workflow and supporting files. These are available from: http://www.brixtonhealth.com/ramOP.rflow.zip You may need to extract the file from the ZIP archive before use if this is not done automatically. Before starting to analyse your data you should create a project directory or project folder. This is just a normal folder or directory that can be created using your usual file manager (e.g. Windows ExplorerTM in WindowsTM or the FinderTM in Macintosh OS-XTM). The project directory or project folder should contain: Your PSU file (here we assume this file is called testPSU.csv but it could have any name). This file must be a comma-separated-value (CSV) file. Your survey data file (here we assume this file is called testSVY.rec but it could have any name). This file can be an EpiData (REC) file or a comma-separated-value (CSV) file. The language file (always called ramOP.language.csv). This file provides text that is used in reports and graphics. The purpose of this file is to make the data analysis software produce reports in any language. This file must be a comma-separated-value (CSV) file. A copy of the file ramOP.rflow. When you have created the project directory or project folder with the required files you can start RAnalyticFlow. Note: The testSVY.rec and testPSU.csv files are example data files and are distributed with the RAnalyticFlow workflow. You can use these files to practice analysing data using RAnalyticFlow, and as examples of RAM-OP survey data and PSU files. Before you start work you will need to create a project for your survey: Click the New Project… button Give your project a useful (i.e. descriptive and memorable) name. This might be a name that describes the survey. For example, if the survey was done in the Kereinik locality of West Darfur in December 2015 you might use the name WD.Kereinik.Dec2015.RAMOP Give the location of your project directory or project folder. This is the directory or folder which contains your survey data file, your PSU file, the RAM-OP language file, and a copy of RAMOP.rflow (see previous page). The location of the project directory or project folder (labelled “Path” by the software) that RAnalyticFlow selects automatically will almost always be wrong. You need to specify this manually. Click the OK button Double click the item named ramOP.rflow shown in the file manager pane of the RAnalyticFlow window. This will open the data-analysis workflow which will be shown in the workflow viewer / editor window of the RAnalyticFlow window. Once you have opened the workflow you need to initialise it (i.e. load libraries, useful analysis function, and initialise the workspace for a new analysis): Once this is done, you should: Retrieve your survey data. This can be in EpiData (REC) format or CSV format. Select and run the appropriate Survey Data node and select the survey data file. Retrieve the PSU date data. Select and run the PSU Data node and select your PSU file. Produce the survey report and graphics. Select and run the Report node. This will take some time to complete because the analysis uses computer intensive techniques to make best use of the available data. The Report node/icon will have black lines around it has completed running the report. When the analysis is complete your project directory or project folder should contain eight new files: The files ending in .png are graphics files. The names of these files are intended to be informative. For example: The file ending in .report.csv is the survey report file (see below). There may also be a file ending in .afp. This is a project information file used by RAnalyticFlow and can be ignored. The file ending in .report.csv is the survey report file. This file can be opened, formatted and edited in a spreadsheet program such as Microsoft Excel or OpenOffice Calc: In the illustration above, proportions have been formatted as percentages with one decimal place, means have been formatted as numbers with two decimal places, and column titles have been formatted as bold text. You can edit the labels as you see fit. If you plan to do several RAM-OP surveys then you may want to edit the language file (always called ramOP.language.csv). This file provides text that is used in reports and graphics. The purpose of this file is to make the data analysis software produce reports in any language. This file must be a comma-separated-value (CSV) file. "],
+["conclusion.html", "7 Conclusion", " 7 Conclusion We live in an ageing world, where people aged 60 or over will be 2 billion or about 22% of the world’s population by 2050. Currently, two in three people aged 60 years or older live in developing countries. By 2050, nearly four in five older people will be living in the developing world. The changing demographics of ageing combined with the increasing number of disasters will exert a disproportionate impact on the world’s oldest and poorest. In this context, identifying the needs of older people as accurately as possible is a necessity. More and more donors and UN agencies are now willing to include older people in their programmes. Age markers, to complement gender markers, will be disseminated very soon RAM-OP is offering a fast, robust, reliable, tested and user-friendly way of assessing the needs of older people. It can be used in humanitarian situations as well as in development contexts. The modular structure of RAM-OP allows for adaptations, making it exhaustive or limited to essential indicators according to the immediate needs. As more organisations start to use it, RAM-OP will evolve and improve. New versions of RAM-OP can be created (for example, RAM-OP for refugee or displaced people camps). We wish that a greater number of actors will start using RAM-OP and make it their own. "]
 ]
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@@ -208,7 +215,7 @@ <h1>
 
             <section class="normal" id="section-">
 <div id="software" class="section level1">
-<h1><span class="header-section-number">Chapter 6</span> RAM-OP Software</h1>
+<h1><span class="header-section-number">6</span> RAM-OP Software</h1>
 <div id="data-entry" class="section level2">
 <h2><span class="header-section-number">6.1</span> Data entry</h2>
 <p>A data entry system for RAM-OP data using EpiData has been developed.</p>
@@ -223,11 +230,11 @@ <h2><span class="header-section-number">6.2</span> Data analysis</h2>
 <p>To use the <strong>RAnalyticFlow</strong> workflow you must install:</p>
 <ul>
 <li><p><strong>The <em>R Language for Data-Analysis and Graphics</em> (R)</strong> : This is the <em>“engine”</em> which does all the
-work of analysing your data. You can get the R installation program from: <a href="http://cran.r-project.org" class="uri">http://cran.r-project.org</a>. Following are links to download operating sofware-specific versions of R:</p>
+work of analysing your data. You can get the R installation program from: <a href="http://cran.r-project.org">http://cran.r-project.org</a>. Following are links to download operating software-specific versions of R:</p>
 <ul>
 <li><p><a href="https://cran.r-project.org/bin/linux/">Download R for Linux</a></p></li>
 <li><p><a href="https://cran.r-project.org/bin/macosx/">Download R for (Mac) OS X</a></p></li>
-<li><p><a href="https://cran.r-project.org/bin/windows/">Download R for Windowx</a></p></li>
+<li><p><a href="https://cran.r-project.org/bin/windows/">Download R for Windows</a></p></li>
 </ul></li>
 <li><p><strong>R packages</strong> (libraries of functions needed to work with the <strong>RAnalyticFlow</strong> workflow) : You can install these from within <strong>R</strong> using the Package Installer function within R. The libraries needed are:</p></li>
 </ul>
@@ -274,14 +281,14 @@ <h2><span class="header-section-number">6.2</span> Data analysis</h2>
 </tbody>
 </table>
 <p>The Package Installer function can be called in R using the following command:</p>
-<pre class="sourceCode r" id="cb2"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1"><span class="kw">install.packages</span>(<span class="kw">c</span>(<span class="st">&quot;rJava&quot;</span>, <span class="st">&quot;JavaGD&quot;</span>, <span class="st">&quot;codetools&quot;</span>, </a>
-<a class="sourceLine" id="cb2-2" data-line-number="2">                   <span class="st">&quot;foreign&quot;</span>, <span class="st">&quot;car&quot;</span>, <span class="st">&quot;ggplot2&quot;</span>, <span class="st">&quot;data.table&quot;</span>), </a>
-<a class="sourceLine" id="cb2-3" data-line-number="3">                   <span class="dt">repos =</span> <span class="st">&quot;https://cloud.r-project.org/&quot;</span>)</a></code></pre>
-<p>The <code>repos</code> argument in the R command above specifies the CRAN mirror from which you to download the package/s you want to install. Here we specify the cloud-based mirror for CRAN provided by RStudio. If unspecified, the installiation process will prompt you to select a mirror from which to download packages from. If you already know the URL of the CRAN mirror you want to use, specify this in the <code>repos</code> argument.</p>
+<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">install.packages</span>(<span class="kw">c</span>(<span class="st">&quot;rJava&quot;</span>, <span class="st">&quot;JavaGD&quot;</span>, <span class="st">&quot;codetools&quot;</span>, 
+                   <span class="st">&quot;foreign&quot;</span>, <span class="st">&quot;car&quot;</span>, <span class="st">&quot;ggplot2&quot;</span>, <span class="st">&quot;data.table&quot;</span>), 
+                   <span class="dt">repos =</span> <span class="st">&quot;https://cloud.r-project.org/&quot;</span>)</code></pre>
+<p>The <code>repos</code> argument in the R command above specifies the CRAN mirror from which you to download the package/s you want to install. Here we specify the cloud-based mirror for CRAN provided by RStudio. If unspecified, the installation process will prompt you to select a mirror from which to download packages from. If you already know the URL of the CRAN mirror you want to use, specify this in the <code>repos</code> argument.</p>
 <p>Note that <strong>RAnalyticFlow</strong> may require you to have <strong>Java</strong> installed. Check the instructions on the <strong>RAnalyticFlow</strong> <a href="http://r.analyticflow.com/en/download/">download page</a> and on this <a href="http://download.ef-prime.com/ranalyticflow/3.1.5/readme.html">starter guide</a>.</p>
 <p>All of this software is open source and free to download, copy, and use. It will run on Windows, Mac OS X, and Linux (and other UNIX-like) operating systems. Your ICT department should be able to help you with installing this software.</p>
 <p>In addition you will also need a copy of the <strong>RAnalyticFlow</strong> workflow and supporting files. These are available from:</p>
-<p><a href="http://www.brixtonhealth.com/ramOP.rflow.zip" class="uri">http://www.brixtonhealth.com/ramOP.rflow.zip</a></p>
+<p><a href="http://www.brixtonhealth.com/ramOP.rflow.zip">http://www.brixtonhealth.com/ramOP.rflow.zip</a></p>
 <p>You may need to extract the file from the ZIP archive before use if this is not done automatically.</p>
 <p><img src="figures/dirStructureRAF.png" width="703" style="display: block; margin: auto;" /></p>
 <p>Before starting to analyse your data you should create a project directory or project folder. This is just a normal folder or directory that can be created using your usual file manager (e.g. Windows ExplorerTM in WindowsTM or the FinderTM in Macintosh OS-XTM). The project directory or project folder should contain:</p>
@@ -299,7 +306,7 @@ <h2><span class="header-section-number">6.2</span> Data analysis</h2>
 <ol style="list-style-type: decimal">
 <li><p>Click the <strong>New Project…</strong> button</p></li>
 <li><p>Give your project a useful (i.e. descriptive and memorable) name. This might be a name that describes the survey. For example, if the survey was done in the Kereinik locality of West Darfur in December 2015 you might use the name <strong>WD.Kereinik.Dec2015.RAMOP</strong></p></li>
-<li><p>Give the location of your project directory or project folder. This is the directory or folder which contains your survey data file, your PSU file, the RAM-OP language file, and a copy of <strong>RAMOP.rflow</strong> (see previous page). The location of the project directory or project folder (labeled “Path” by the software) that <strong>RAnalyticFlow</strong> selects automatically will almost always be wrong. You need to specify this manually.</p></li>
+<li><p>Give the location of your project directory or project folder. This is the directory or folder which contains your survey data file, your PSU file, the RAM-OP language file, and a copy of <strong>RAMOP.rflow</strong> (see previous page). The location of the project directory or project folder (labelled “Path” by the software) that <strong>RAnalyticFlow</strong> selects automatically will almost always be wrong. You need to specify this manually.</p></li>
 <li><p>Click the <strong>OK</strong> button</p></li>
 </ol>
 <p><img src="figures/openWorkflowRAF.png" width="703" style="display: block; margin: auto;" /></p>
@@ -309,7 +316,7 @@ <h2><span class="header-section-number">6.2</span> Data analysis</h2>
 <p><img src="figures/runWorkflowRAF.png" width="703" style="display: block; margin: auto;" /></p>
 <p>Once this is done, you should:</p>
 <ol style="list-style-type: decimal">
-<li><p>Retrieve your survey data. This can be in EpiDat (REC) format or CSV format. Select and run the
+<li><p>Retrieve your survey data. This can be in EpiData (REC) format or CSV format. Select and run the
 appropriate <strong>Survey Data</strong> node and select the survey data file.</p></li>
 <li><p>Retrieve the PSU date data. Select and run the <strong>PSU Data</strong> node and select your PSU file.</p></li>
 <li><p>Produce the survey report and graphics. Select and run the <strong>Report</strong> node. This will take some time to complete because the analysis uses computer intensive techniques to make best use of the available data. The Report node/icon will have black lines around it has completed running the report.</p></li>
@@ -347,13 +354,13 @@ <h2><span class="header-section-number">6.2</span> Data analysis</h2>
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@@ -366,6 +373,10 @@ <h2><span class="header-section-number">6.2</span> Data analysis</h2>
 "link": null,
 "text": null
 },
+"history": {
+"link": null,
+"text": null
+},
 "download": ["ramOPmanual.pdf", "ramOPmanual.epub"],
 "toc": {
 "collapse": "section"
@@ -380,7 +391,7 @@ <h2><span class="header-section-number">6.2</span> Data analysis</h2>
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diff --git a/docs/style.css b/docs/style.css
index 5f8345c..6ffd539 100644
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+++ b/docs/style.css
@@ -1,4 +1,24 @@
-.caption {
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diff --git a/figures/ramOP.png b/figures/ramOP.png
new file mode 100644
index 0000000..1702533
Binary files /dev/null and b/figures/ramOP.png differ
diff --git a/images/caution.png b/images/caution.png
new file mode 100644
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Binary files a/images/warning.png and b/images/warning.png differ
diff --git a/index.Rmd b/index.Rmd
index ad8db77..ee928f9 100644
--- a/index.Rmd
+++ b/index.Rmd
@@ -14,8 +14,8 @@ link-citations: TRUE
 links-as-notes: FALSE
 colorlinks: TRUE
 fontsize: 12pt
-url: https\://validmeasures.org/ramOPmanual
-github-repo: validmeasures/ramOPmanual
+url: https\://rapidsurveys.io/ramOPmanual
+github-repo: rapidsurveys/ramOPmanual
 cover-image: figures/coverImage.jpg
 description: "Rapid assessment method for older people (RAM-OP) manual"
 ---
@@ -34,7 +34,7 @@ Despite these potential vulnerabilities in humanitarian situations, older people
 
 HelpAge International, VALID International, and Brixton Health, with financial assistance from the Humanitarian Innovation Fund (HIF), have developed a Rapid Assessment Method for Older People (RAM-OP) that provides accurate and reliable estimates of the needs of older people. The method uses simple procedures, in a short time frame (i.e. about two weeks including training, data collection, data entry, and data analysis), and at considerably lower cost than other methods. The RAM-OP method is based on the following principles:
 
-* Use of a familiar “household survey” design employing a two-stage cluster sample design optimised to allow the use of a small primary sample ( m ≥ 16 clusters) and a small overall ( n ≥ 192) sample.
+* Use of a familiar “household survey” design employing a two-stage cluster sample design optimised to allow the use of a small primary sample ($m ≥ 16$ clusters) and a small overall ($n ≥ 192$) sample.
 
 * Assessment of multiple dimensions of need in older people (including prevalence of global, moderate and severe acute malnutrition) using, whenever possible, standard and well-tested indicators and question sets.
 * Data analysis performed using modern computer-intensive methods to allow estimates of indicator levels to be made with useful precision using a small sample size.
diff --git a/preamble.tex b/preamble.tex
index ee70afd..e7c8e05 100644
--- a/preamble.tex
+++ b/preamble.tex
@@ -1,81 +1,90 @@
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-
-\newenvironment{rmddownload}
-  {\begin{tcolorbox}[width=\textwidth, 
-                     colback = {white}, 
-                     title = {\textbf{Download}}, 
-                     colbacktitle = lightgray,
-                     coltitle = black]
-  \begin{includegraphics}[scale = 1]{download.png}}
-  {\end{includegraphics}
-  \end{tcolorbox}}
\ No newline at end of file
+  {\begin{rmdblock}{warning}}
+  {\end{rmdblock}}
\ No newline at end of file
diff --git a/style.css b/style.css
index 5f8345c..6ffd539 100644
--- a/style.css
+++ b/style.css
@@ -1,4 +1,24 @@
-.caption {
+.rmdcaution, .rmdimportant, .rmdnote, .rmdtip, .rmdwarning {
+  padding: 1em 1em 1em 4em;
+  margin-bottom: 10px;
+  background: #f5f5f5 5px center/3em no-repeat;
+}
+.rmdcaution {
+  background-image: url("../images/caution.png");
+}
+.rmdimportant {
+  background-image: url("../images/important.png");
+}
+.rmdnote {
+  background-image: url("../images/note.png");
+}
+.rmdtip {
+  background-image: url("../images/tip.png");
+}
+.rmdwarning {
+  background-image: url("../images/warning.png");
+}
+p.caption {
   color: #777;
   margin-top: 10px;
 }
@@ -12,119 +32,35 @@ pre {
 pre code {
   white-space: inherit;
 }
-.rmdremind {
-  padding: 1em 1em 1em 4em;
-  margin-bottom: 10px;
-  background: #add8e6;
-  background: rgba(204,229,255,0.6);
-  border-radius: 15px;
-  position:relative;
-}
-.rmdremind:before {
-    content: "\f0f3";
-    font-family: FontAwesome;
-    left:10px;
-    position:absolute;
-    top:0px;
-    font-size: 45px;
+p.flushright {
+  text-align: right;
 }
-.rmdnote {
-  padding: 1em 1em 1em 4em;
-  margin-bottom: 10px;
-  background: #add8e6;
-  background: rgba(204,229,255,0.6);
-  border-radius: 15px;
-  position:relative;
-}
-.rmdnote:before {
-    content: "\f040";
-    font-family: FontAwesome;
-    left:10px;
-    position:absolute;
-    top:0px;
-    font-size: 45px;
-}
-.rmdexercise {
-  padding: 1em 1em 1em 4em;
-  margin-bottom: 10px;
-  background: #add8e6;
-  background: #ffffff;
-  border: 5px solid;
-  border-radius: 15px;
-  border-color: #add8e6;
-  position:relative;
-}
-.rmdexercise:before {
-    content: "\f15c";
-    font-family: FontAwesome;
-    left:10px;
-    position:absolute;
-    top:0px;
-    font-size: 45px;
-}
-.rmdinfo {
-  padding: 1em 1em 1em 4em;
-  margin-bottom: 10px;
-  background: #98fb98;
-  background: rgba(152,251,152,0.6);
-  border-radius: 15px;
-  position:relative;
-}
-.rmdinfo:before {
-    content: "\f129";
-    font-family: FontAwesome;
-    left:10px;
-    position:absolute;
-    top:0px;
-    font-size: 45px;
+blockquote > p:last-child {
+  text-align: right;
 }
-.rmdwarning {
-  padding: 1em 1em 1em 4em;
-  margin-bottom: 10px;
-  background: #ffff00;
-  background: rgba(255,255,0,0.6);
-  border-radius: 15px;
-  position:relative;
-}
-.rmdwarning:before {
-    content: "\f071";
-    font-family: FontAwesome;
-    left:10px;
-    position:absolute;
-    top:0px;
-    font-size: 45px;
-}
-.rmddownload {
-  padding: 1em 1em 1em 4em;
-  margin-bottom: 10px;
-  background: #ffff00;
-  background: rgba(204,229,255,0.6);
-  border-radius: 15px;
-  position:relative;
-}
-.rmddownload:before {
-    content: "\f019";
-    font-family: FontAwesome;
-    left:10px;
-    position:absolute;
-    top:0px;
-    font-size: 30px;
-}
-.rmdbox {
-  padding: 1em 1em 1em 4em;
-  margin-bottom: 10px;
-  background: #add8e6;
-  background: #ffffff;
-  border: 5px solid;
-  border-radius: 15px;
-  border-color: #add8e6;
-  position:relative;
-}
-.rmdbox:before {
-    content: "\f15c";
-    font-family: FontAwesome;
-    left:10px;
-    position:absolute;
-    top:0px;
-    font-size: 45px;
+blockquote > p:first-child {
+  text-align: inherit;
+}
+.header-section-number {
+  padding-right: .2em;
+  font-weight: 500;
+}
+.level1 .header-section-number {
+  display: inline-block;
+  border-bottom: 3px solid;
+}
+.level1 h1 {
+  border-bottom: 1px solid;
+}
+h1, h2, h3, h4, h5, h6 {
+  font-weight: normal;
+}
+h1.title {
+  font-weight: 700;
+}
+.smallcaps {
+  font-variant: small-caps;
+}
+.book .book-body .page-wrapper .page-inner section.normal strong {
+  font-weight: 600;
 }
diff --git a/toc.css b/toc.css
new file mode 100644
index 0000000..c28801b
--- /dev/null
+++ b/toc.css
@@ -0,0 +1,124 @@
+#TOC ul,
+#TOC li,
+#TOC span,
+#TOC a {
+  margin: 0;
+  padding: 0;
+  position: relative;
+}
+#TOC {
+  line-height: 1;
+  border-radius: 5px 5px 0 0;
+  background: #141414;
+  background: linear-gradient(to bottom, #333333 0%, #141414 100%);
+  border-bottom: 2px solid #0fa1e0;
+  width: auto;
+}
+#TOC:after,
+#TOC ul:after {
+  content: '';
+  display: block;
+  clear: both;
+}
+#TOC a {
+  background: #141414;
+  background: linear-gradient(to bottom, #333333 0%, #141414 100%);
+  color: #ffffff;
+  display: block;
+  padding: 19px 20px;
+  text-decoration: none;
+  text-shadow: none;
+}
+#TOC ul {
+  list-style: none;
+}
+#TOC > ul > li {
+  display: inline-block;
+  float: left;
+  margin: 0;
+}
+#TOC > ul > li > a {
+  color: #ffffff;
+}
+#TOC > ul > li:hover:after {
+  content: '';
+  display: block;
+  width: 0;
+  height: 0;
+  position: absolute;
+  left: 50%;
+  bottom: 0;
+  border-left: 10px solid transparent;
+  border-right: 10px solid transparent;
+  border-bottom: 10px solid #0fa1e0;
+  margin-left: -10px;
+}
+#TOC > ul > li:first-child > a {
+  border-radius: 5px 0 0 0;
+}
+#TOC.align-right > ul > li:first-child > a,
+#TOC.align-center > ul > li:first-child > a {
+  border-radius: 0;
+}
+#TOC.align-right > ul > li:last-child > a {
+  border-radius: 0 5px 0 0;
+}
+#TOC > ul > li.active > a,
+#TOC > ul > li:hover > a {
+  color: #ffffff;
+  box-shadow: inset 0 0 3px #000000;
+  background: #070707;
+  background: linear-gradient(to bottom, #262626 0%, #070707 100%);
+}
+#TOC .has-sub {
+  z-index: 1;
+}
+#TOC .has-sub:hover > ul {
+  display: block;
+}
+#TOC .has-sub ul {
+  display: none;
+  position: absolute;
+  width: 200px;
+  top: 100%;
+  left: 0;
+}
+#TOC .has-sub ul li a {
+  background: #0fa1e0;
+  border-bottom: 1px dotted #31b7f1;
+  filter: none;
+  display: block;
+  line-height: 120%;
+  padding: 10px;
+  color: #ffffff;
+}
+#TOC .has-sub ul li:hover a {
+  background: #0c7fb0;
+}
+#TOC ul ul li:hover > a {
+  color: #ffffff;
+}
+#TOC .has-sub .has-sub:hover > ul {
+  display: block;
+}
+#TOC .has-sub .has-sub ul {
+  display: none;
+  position: absolute;
+  left: 100%;
+  top: 0;
+}
+#TOC .has-sub .has-sub ul li a {
+  background: #0c7fb0;
+  border-bottom: 1px dotted #31b7f1;
+}
+#TOC .has-sub .has-sub ul li a:hover {
+  background: #0a6d98;
+}
+#TOC ul ul li.last > a,
+#TOC ul ul li:last-child > a,
+#TOC ul ul ul li.last > a,
+#TOC ul ul ul li:last-child > a,
+#TOC .has-sub ul li:last-child > a,
+#TOC .has-sub ul li.last > a {
+  border-bottom: 0;
+}
\ No newline at end of file