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16-PrEP.Rmd
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16-PrEP.Rmd
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---
editor_options:
markdown:
wrap: 72
---
\blandscape
# PrEP
## PrEP: 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 <- "PrEP"
section <- "PrEP_NEW"
columns <- col_seq("F", "L")
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:
- **Newly on PrEP (FY25)** $PrEP\_NEW.T$
### Instructions
1. Think through population specific target of PrEP with in country
data, and population specific targets for AGYW, KP, zero-discordant
couples and other at risk group, in line with COP22 Guidance.
2. Review the PrEP_NEW section which will be populated with assumptions
of FY24 Targets set at other points in the Data Pack for
"HTS_TST_NEG", "Host Country Est. PLHIV", "AGYW(PREV (D)", and
"AGYW_PREV (N)". Also think through KP PrEP strategy and history.
3. Review data pulled from DATIM showing "PrEP_NEW (FY23 Results)", and
"PrEP_NEW (FY24 Targets)".
4. Manually enter targets for "Newly on PrEP (FY25)".
## PrEP: 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 <- "PrEP"
section <- "PrEP_CT"
columns <- col_seq("M", "O")
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 Continuing on PrEP (FY25)** $PrEP\_CT.T$
### Instructions
1. Think through population specific target of PrEP with in country
data, and population specific targets for AGYW, KP, zero-discordant
couples and other at risk groups, in line with COP22 Guidance. For
each of these populations think through how groups may continue to
use PrEP and remain at risk, go off PrEP due to change in risk, or
return to PrEP due to a change in risk. Note that PrEP_CT is a
revised and different indicator from PrEP_CURR. While PrEP_CURR
historical targets and results are provided for context and
assistance, they do not necessarily provide direct insight into
PrEP_CT targets.
2. Manually enter targets for "PrEP_CT (FY25)".
## PrEP: PrEP_CT - HIV Testing
**PrEP_CT # of Tests:** 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 <- "PrEP"
section <- "PrEP_CT - HIV Testing"
columns <- col_seq("P", "Q")
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 - # HIV tests administered (FY25)** $PrEP\_CT.TestResult.T$
### Instructions
1. The number of HIV Tests administered for PrEP_CT will be calculated by multiplying
the FY24 Target for PrEP_CT by the # of PrEP follow-up visits per year.
This will be prepopulated with 4 under the assumption that a test will be administered
once a quarter, but this value can be adjusted as needed to determine the final
target for column Q.
2. This target for FY25 PrEP_CT - # HIV tests administered will then pull directly
into the HTS tab in which PrEP_CT tests will all fall under the roll-up for
TotalHTS_TST_NEG (FY25).
\elandscape
\newpage