15-KP.Rmd

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# KP

**NOTE: The PrEP_CT, PrEP_NEW, KP_PREV, TX_CURR, TX_NEW, TX_PVLS (), TX_PVLS (N), HTS_TST, HTS_RECENT, and HTS_SELF indicators in the KP tab are related to Key Populations only and are not linked to other tabs that feature those indicators.**

This tab is provided to facilitate and inform (1) data-driven program
intent or relationships amongst indicators, where relevant, for KP
programming and (2) easy review of all KP-related targets by virtue of
having all KP-related targets in one tab. Importantly, pre-built
algorithms and pre-set assumptions are NOT included in this tab. As
such, entry of data into any columns labelled 'Assumptions' or
'Projected' MAY NOT automatically produce targets for the indicators
listed.

[Considerations as you complete and use this tab:]{.underline}

1.  As per the COP24 Guidance, baseline data to support target
    development can come from bio-behavioral surveys (BBS) and size
    estimates, especially to understand current PLHIV burden and program
    results. Use the most recent and reliable estimates available where
    possible. For example, population size estimates and survey data on
    knowledge of status can inform PP_PREV and subsequent clinical
    cascade targets. The COP24 Guidance Section 6.6.2 has substantial
    guidance on expectations of an effective KP program, and should be
    reviewed before setting KP targets.

2.  Where possible and relevant, use FY24 targets and, as available,
    FY23 results to inform FY25 targets (the 'Assumption' column for each
    indicator in the tab). But remember to consider expectations for
    scale-up based on current program needs and gaps. That is, FY22
    results may not be the most relevant and appropriate base from which
    to develop FY25 targets.

3.  As per COP24 Guidance, OUs should strive to ensure all KPs reached
    with KP programming (KP_PREV), who do not already know their HIV
    status are either tested for HIV or actively referred for HIV
    testing. Therefore, Target Setting Tools will be reviewed for the relationship
    between KP_PREV to HTS for KP, and if the relationship is
    substantially different from one to one, it will be important to
    discuss rationale and context with Chair and PPM.

4.  For clinical cascade indicators (HTS_TST, TX_NEW, etc.), consider
    the relationship amongst these indicators to ensure rates of linkage
    to treatment are in alignment with COP23 Guidance (i.e., high rates
    of linkage across all populations).

5.  Recognize that Key Population disaggregates are a SUBSET of the
    regular Age/Sex disaggregates. Each PSNU must have a total of
    relevant Age/Sex disaggregates of the same indicator for targeting
    process to be correct (e.g., 15+ Men for MSM). This is also an
    important factor to consider on the PSNU x IM tab. You may construct
    additional formulae in the far right of the tab to check this, but
    it will also be checked by the validation apps and the KP Validation
    tab.

6.  Therefore we have moved the KP tab earlier in the Target Setting Tool, and
    suggest that you start the KP tab early in the Target Setting Tool process, and
    regularly compare against Cascade, HTS, and PrEP tabs.

7.  Also note that IMs that do not provide actual clinical services
    cannot report TX_NEW or TX_CURR. While those IMs should track
    linkage in their own data systems, there is no relevant MER
    indicator for that data.

## KP: KP_ESTIMATES

```{r echo=FALSE, results='asis'}
sheet_name <- "KP"
section <- "KP_ESTIMATES"
columns <- col_seq("E", "G")
data <- prepare_table_data(sheet_name, columns)
for (t in table_seq(data)) {
  make_table(t, section)
  }
```

### DATIM Import

The following data points will be imported into DATIM from this section:

-   **Host Country Est. Total Size (FY24)** $KP\_ESTIMATES.Total.T$

-   **Host Country Est. KPLHIV (FY24)** $KP\_ESTIMATES.Pos.T$

-   **Host Country Est. HIV Prevalence (FY24) (%)**
    $KP\_ESTIMATES.Prev.T$

### Instructions

1.  Enter data directly into columns "Host Country Est. Total Size
    (FY24)", "Host Country Est. KPLHIV (FY24)", and "Host Country Est.
    HIV Prevalence (FY24) (%)". As mentioned above, these data should
    come from reliable, approved sources and then be pasted directly
    into the respective columns in this tab and used as reference when
    setting targets throughout the rest of the KP tab. All data from
    these three columns will be imported into DATIM.

2.  Where these data may not be available, the absence of this data will
    not adversely impact target-setting within the Target Setting Tool for Key
    Populations.**\
    **

## KP: PrEP_CT

**PrEP_CT:** Number of individuals, excluding those newly enrolled, that
return for a follow up visit or re-initiation visit to receive
pre-exposure prophylaxis (PrEP) to prevent HIV during the reporting
period.

```{r echo=FALSE, results='asis'}
sheet_name <- "KP"
section <- "PrEP_CT"
columns <- col_seq("H", "I")
data <- prepare_table_data(sheet_name, columns)
for (t in table_seq(data)) {
  make_table(t, section)
  }
```

### DATIM Import

The following data points will be imported into DATIM from this section:

-   **PrEP_CT - KeyPop** (FY25) $PrEP\_CT.KP.T$

### Instructions

1.  For historical context, review column "PrEP_CURR - KeyPop (FY24
    Targets)", which will come pre-populated with FY23 targets for
    PREP_CURR as currently reported in DATIM.

2.  Manually enter FY25 PrEP_CT targets in the column titled, "PrEP_CT -
    KeyPop (FY25)".

**NOTE:** The PrEP_CT targets here on the KP tab are not linked to those
on the PrEP tab, but should nonetheless represent a subset of the total
PrEP_CT targets. Be sure to review KP targets against total population
targets in the KP Validation tab to ensure total population targets do
not exceed total population targets set on the PrEP tab. It may in fact
be easier to set KP PrEP targets, other PrEP targets (like AGYW), and
then set the general PrEP target.

NOTE: Historical PrEP_CURR targets and results are provided for context,
but do not necessarily directly inform the targets for the new indicator
PrEP_CT. See PrEP_CT on PrEP tab.

## KP: PrEP_NEW

**PrEP_NEW:** Number of individuals who have been newly enrolled on
antiretroviral pre-exposure prophylaxis (PrEP) to prevent HIV infection
in the reporting period.

```{r echo=FALSE, results='asis'}
sheet_name <- "KP"
section <- "PrEP_NEW"
columns <- col_seq("J", "K")
data <- prepare_table_data(sheet_name, columns)
for (t in table_seq(data)) {
  make_table(t, section)
  }
```

### DATIM Import

The following data points will be imported into DATIM from this section:

-   **PrEP_NEW - KeyPop (FY25)** $PrEP\_NEW.KP.T$

### Instructions

1.  For historical context, review column "PrEP_NEW - KeyPop (FY24
    Targets)", which will come pre-populated with FY24 targets for
    PREP_NEW as currently reported in DATIM.

2.  Manually enter FY24 PrEP_NEW targets in the column titled,
    "PrEP_NEW - KeyPop (FY25)".

**NOTE:** PrEP_NEW targets here on the KP tab are not linked to those on
the PrEP tab, but should nonetheless represent a subset of the total
PrEP_NEW targets. Be sure to review KP targets against total population
targets in the KP Validation tab to ensure total population targets do
not exceed total population targets set on the PrEP tab. It may in fact
be easier to set KP PrEP targets, other PrEP targets (like AGYW), and
then set the general PrEP target.

## KP: KP_PREV

**KP_PREV:** Number of key populations reached with individual and/or
small group-level HIV prevention interventions designed for the target
population.

```{r echo=FALSE, results='asis'}
sheet_name <- "KP"
section <- "KP_PREV"
columns <- col_seq("L", "M")
data <- prepare_table_data(sheet_name, columns)
for (t in table_seq(data)) {
  make_table(t, section)
  }
```

### DATIM Import

The following data points will be imported into DATIM from this section:

-   **KP_PREV (FY25)** $KP\_PREV.T$

### Instructions

1.  For historical context, review column "KP_PREV (FY24 Targets)",
    which will come pre-populated with FY23 targets for KP_PREV as
    currently reported in DATIM.

2.  Manually enter FY25 KP_PREV targets in the column titled, "KP_PREV
    (FY25)".

## KP: TX_CURR

**TX_CURR:** Number of adults and children currently receiving
antiretroviral therapy (ART).

```{r echo=FALSE, results='asis'}
sheet_name <- "KP"
section <- "TX_CURR"
columns <- col_seq("N", "S")
data <- prepare_table_data(sheet_name, columns)
for (t in table_seq(data)) {
  make_table(t, section)
  }
```

### DATIM Import

The following data points will be imported into DATIM from this section:

-   **TX_CURR - KeyPop (FY25)** $TX\_CURR.KP.T$

### Instructions

1.  Review columns "TX_CURR - KeyPop (FY23 Results)" and "TX_CURR - \>
    KeyPop (FY24 Targets)", which will be imported from DATIM for \>
    reference.

2.  Manually enter TX_CURR targets in the column titled, "TX_CURR - \>
    KeyPop (FY25)". Be prepared to explain target setting processes \>
    and justify variations from previous years if asked during or \>
    prior to COP meetings.

3.  Review "TX_NET_NEW - KeyPop (FY25)", which will be set by taking the
    \> difference between "TX_CURR - KeyPop (FY25)" and "TX_CURR -
    KeyPop \> (FY24 Targets)" and be used as further reference in
    setting KP \> TX_NEW.

NOTE: TX_CURR targets here on the KP tab are not linked to those on the
Cascade tab, but should nonetheless represent a subset of the total
TX_CURR targets. Be sure to review KP targets against total population
targets in the KP Validation tab to ensure total population targets do
not exceed total population targets set on the Cascade tab.

## KP: TX_NEW (N)

**TX_NEW:** Number of adults and children newly enrolled on
antiretroviral therapy (ART).

```{r echo=FALSE, results='asis'}
sheet_name <- "KP"
section <- "TX_NEW"
columns <- col_seq("T", "Z")
data <- prepare_table_data(sheet_name, columns)
for (t in table_seq(data)) {
  make_table(t, section)
  }
```

### DATIM Import

The following data points will be imported into DATIM from this section:

-   **TX_NEW - KeyPop (FY25)** $TX\_NEW.KP.T$

### Instructions

1.  Review column "TX_NEW - KeyPop (FY24 Targets)", which will come
    pre-populated with FY24 targets for reference.

2.  Review and adjust the columns "Proportion of TX_NET_NEW from New ART
    Initiation (FY25) (%)", "Targeted Retention Rate - Already on ART
    (FY25) (%)", and "Targeted Retention Rate - New on ART (FY25) (%)",
    which will be prepopulated with 100%, 98%, and 98% respectively.
    These columns serve similar roles along the KP Cascade as seen on
    the Cascade tab.

3.  Review modeled FY25 targets for TX_NEW -- KeyPop, which are
    initially set by multiplying the FY25 target for TX_CURR -- KeyPop
    by first the "Proportion of TX_NET_NEW from New ART Initiation
    (FY25) (%)", and then the "Targeted Retention Rate - New on ART
    (FY25) (%)". However, due to wide variation in KP programming, this
    value can be overwritten and manually adjusted as needed without
    further approval from PPMs or DUIT Liaisons.

NOTE: TX_NEW targets here on the KP tab are not linked to those on the
Cascade tab, but should nonetheless represent a subset of the total
TX_NEW targets. Be sure to review KP targets against total population
targets in the KP Validation tab to ensure total population targets do
not exceed total population targets set on the Cascade tab.**\
**

## KP: TX_PVLS (D) & TX_PVLS (N)

**TX_PVLS (D):** Number of ART patients with a VL result documented in
the medical or laboratory records/LIS within the past 12 months

**TX_PVLS (N):** Number of ART patients with suppressed VL results
(\<1,000 copies/mL) documented in the medical or laboratory results/LIS
within the past 12 months.

```{r echo=FALSE, results='asis'}
sheet_name <- "KP"
section <- "TX_PVLS (D)"
columns <- col_seq("AA", "AD")
data <- prepare_table_data(sheet_name, columns)
for (t in table_seq(data)) {
  make_table(t, section)
  }
```

```{r echo=FALSE, results='asis'}
sheet_name <- "KP"
section <- "TX_PVLS (N)"
columns <- col_seq("AE", "AF")
data <- prepare_table_data(sheet_name, columns)
for (t in table_seq(data)) {
  make_table(t, section)
  }
```

### DATIM Import

The following data points will be imported into DATIM from this section:

-   **TX_PVLS (D) - KeyPop (FY25)** $TX\_PVLS.D.KP.T$

-   **TX_PVLS (N) - KeyPop (FY25)** $TX\_PVLS.N.KP.T$

### Instructions

1.  Review and adjust the columns "% of TX_NEW Eligible for VL Test
    (FY25) (%)" and "Proportion of eligible w/ access to VL testing
    (FY25) (%)", which will be prepopulated with 70% and 100%,
    respectively. These columns serve similar roles along the KP Cascade
    as seen on the Cascade tab.

2.  Review modeled targets for "TX_PVLS (D) - KeyPop (FY25)", which will
    initially be set by multiplying the FY24 target TX_NEW -- KeyPop
    first by "% of TX_NEW Eligible for VL Test (FY25) (%)" and then by
    "Proportion of eligible w/ access to VL testing (FY25) (%)".
    However, due to wide variation in KP programming, this value can be
    overwritten and manually adjusted as needed without further approval
    from PPMs or DUIT Liaisons.

3.  Review and adjust the "Targeted VL Suppression Rate (FY25) (%)",
    which is set at a default 95% for all OUs, but can be changed with
    permission from your PPM and DUIT Liaisons. Decreasing the targeted
    suppression rate to any value below 95% will highlight the cell in
    Yellow, and in Red should it exceed 100% or drop below 0%.

4.  Review modeled targets for "TX_PVLS (N) -- KeyPop (FY25) (%)", which
    will initially be set by multiplying the Denominator Target for
    TX_PVLS -- KeyPop by the "Targeted VL Suppression Rate (FY25) (%)".

> NOTE: The KP tab TX_PVLS (D) and TX_PVLS (N) are not linked to the
> Cascade tab, therefore be sure to review KP targets against total
> population targets in the KP Validation tab to ensure Key Population
> targets do not exceed total population targets set on the Cascade tab.
## KP: HTS_TST

**HTS_TST:** Number of individuals who received HIV Testing Services
(HTS) and received their test results.

```{r echo=FALSE, results='asis'}
sheet_name <- "KP"
section <- "HTS_TST"
columns <- col_seq("AG", "AL")
data <- prepare_table_data(sheet_name, columns)
for (t in table_seq(data, max_col = 4)) {
  make_table(t, section)
  }
```

### DATIM Import

The following data points will be imported into DATIM from this section:

-   **HTS_TST KeyPop, Positive (FY25)** $HTS\_TST.KP.Pos.T$

-   **HTS_TST KeyPop, Negative (FY25)** $HTS\_TST.KP.Neg.T$

### Instructions

1.  Review "TX_NEW from Previously Diagnosed (FY25) (%)", which will
    come prepopulated at 0%, but can be adjusted as needed. Note that
    this column serves a similar role along the KP Cascade as seen in
    the Cascade tab.

2.  Review the number of "TX_NEW from Previously Diagnosed (FY25)",
    which is calculated by multiplying the rate from Step 1 by "TX_NEW -
    KeyPop (FY25)". Return to Step 1 to adjust this value.

3.  Review "TX_NEW from all other sources (FY24)", which will be set
    taking the difference of "TX_NEW - KeyPop (FY24)" and "TX_NEW from
    Previously Diagnosed (FY25)".

4.  The FY24 Targets for HTS_TST KeyPop Positive and Negative will be
    pulled from DATIM into this tab for added reference.

5.  Review and adjust the "Targeted ART Linkage Rate (FY25) (%)", which
    is set at a default of 95% for all OUs. Change this value as needed,
    however, you [must seek permission]{.underline} from your assigned
    PPM and DUIT Liaisons before decreasing the targeted suppression
    rate to any value below 95%. Red highlights in this column indicate
    percentages above 100% or below 0%; yellow highlights indicate
    percentages that have been altered to drop below 95%.

6.  Set HTS_TST "Yield (FY25) (%)" which will resemble the Yield % that
    was set in the various modalities of the HTS tab and should be
    approached similarly.

7.  Review modeled FY25 targets for HTS_TST KeyPop, Positive, which are
    the product of "TX_NEW from all other sources (FY25)" and the rate
    set in "Targeted ART Linkage Rate (FY25) (%)". However, due to wide
    variation in KP programming, this value can be overwritten and
    manually adjusted as needed without further approval from PPMs or
    DUIT Liaisons.

8.  Lastly, review the modeled FY25 Targets for HTS_TST KeyPop,
    Negative, which will be calculated by first dividing the FY25 target
    for HTS_TST KeyPop, Positive by the Yield set in Step 5, and then
    subtracting the FY25 target for HTS_TST KeyPop, Positive. Due to
    wide variation in KP programming, this value can be overwritten and
    manually adjusted as needed without further approval from PPMs or
    DUIT Liaisons.

> NOTE: This HTS_TST on the KP tab is not linked to the HTS tab,
> therefore be sure to review KP targets against total population
> targets in the KP Validation tab to ensure Key Population targets do
> not exceed total population targets set on the Cascade tab.
## KP: HTS_RECENT

**HTS_RECENT:** Number of newly diagnosed HIV-positive persons aged
$\geq$ 15 years with a test for recent infection result during the
reporting period.

```{r echo=FALSE, results='asis'}
sheet_name <- "KP"
section <- "HTS_RECENT"
columns <- col_seq("AM", "AN")
data <- prepare_table_data(sheet_name, columns)
for (t in table_seq(data)) {
  make_table(t, section)
  }
```

### DATIM Import

The following data points will be imported into DATIM from this section:

-   **HTS_RECENT - KeyPop (FY25)** $HTS\_RECENT.KP.T$

### Instructions

1.  Review and adjust the "% of HTS_TST KeyPop Positives (FY25) (%)",
    which will be prepopulated at a default of 100%. This assumption
    resembles that of the % of Positives used to help set targets in the
    HTS_RECENT tab. Red highlights in this column indicate percentages
    over 100% or under 0%; yellow highlights indicate percentages that
    have been changed to be less than 100%.

2.  Review and adjust the modeled FY25 targets for HTS_RECENT - KeyPop,
    which are the product of the rate set in step 1, and the FY25
    Targets for HTS_TST KeyPop, Positives.

> NOTE: HTS_RECENT KeyPop is not linked to the HTS_RECENT tab. Be sure
> to review KP targets against total population targets in the KP
> Validation tab to ensure Key Population targets do not exceed total
> population targets set on the HTS_RECENT tab.
## KP: HTS_SELF

**HTS_SELF:** Number of individual HIV self-test kits distributed.

```{r echo=FALSE, results='asis'}
 sheet_name <- "KP"
 section <- "HTS_SELF"
 columns <- col_seq("AM", "AN")
 data <- prepare_table_data(sheet_name, columns)
 for (t in table_seq(data)) {
   make_table(t, section)
   }
```

### DATIM Import

The following data points will be imported into DATIM from this section:

-   **HTS_SELF - KeyPop (FY25)** $HTS\_SELF.KP.T$

### Instructions

1.  For historical context, review FY24 Targets for HTS_SELF -- KeyPop,
    which will be pulled from DATIM.

2.  Manually populate FY25 Targets for HTS_SELF - KeyPop.

NOTE: HTS_SELF on this tab is not linked to the HTS tab. Be sure to
review KP targets against total population targets in the KP Validation
tab to ensure Key Population targets do not exceed total population
targets set on the HTS tab.

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