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19-TX_TB_PREV.Rmd
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---
editor_options:
markdown:
wrap: 72
---
\blandscape
# TX_TB_PREV
## TX_TB_PREV: TX_TB (D)
**TX_TB (D):** Number of ART patients who were screened for TB at least
once during the semiannual reporting period.
Note: Targets set across this tab are set at Coarse Age Bands,
aggregating incoming data from any finer age bands to \<15 or 15+.
```{r echo=FALSE, results='asis'}
sheet_name <- "TX_TB_PREV"
section <- "TX_TB (D)"
columns <- col_seq("F", "O")
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:
- **New on ART, TB Screen + (FY25)** $TX\_TB.D.New.Pos.T$
- **New on ART, TB Screen -- (FY25)** $TX\_TB.D.New.Neg.T$
- **Already on ART, TB Screen + (FY25)** $TX\_TB.D.Already.Pos.T$
- **Already on ART, TB Screen -- (FY25)** $TX\_TB.D.Already.Neg.T$
### Instructions
1. Review Targeted coverage rates of TB testing both for those New on
ART as well as for those Already on ART. These will both come
pre-populated at 100% coverage, though can be adjusted as needed.
Red highlights indicate percentages over 100%, or under 0%, or may
also indicate where values have been left blank but are necessary
for further steps; yellow highlights indicate percentages less than
100%.
2. Review and adjust Estimated TB Screen Positivity Rates, both for
those New on ART as well as for those Already on ART. These will
both come prepopulated based on FY23 Results in DATIM. Red
highlights indicate percentages over 100%, or under 0%, or may also
indicate where values have been left blank but are necessary for
further steps.
3. For historical context, review FY25 Targets for TX_NEW and TX_CURR.
Follow hyperlinks to see and adjust source data as needed.
4. Review modeled targets for the following columns. See below for
additional information.
a. New on ART, TB Screen +
b. New on ART, TB Screen --
c. Already on ART, TB Screen +
d. Already on ART, TB Screen --
### TX_TB (D) Disaggregates (FY25)
The Target Setting Tool will set FY25 targets for TX_TB (D) as laid out below.
Targets will be set for those New on ART and screened positive for TB as
follows, rounded to the nearest integer: $$
{TX\_ TB.D.New.Pos}_{t}\ = \ {TX\_ NEW}_{t}\ \times \ {Targeted\ Coverage:\ New\ on\ ART}_{t}\ \times \\
{TB\ Screen\ Positivity\ Rate:\ New\ on\ ART}_{t}
$$
FY25 targets for those Already on ART, but found negative for TB will be
set as follows, rounded to the nearest integer:
$$
{TX\_ TB.D.New.Neg}_{t}\ = \ ({TX\_ NEW}_{t}\ \times \ {Targeted\ Coverage:\ New\ on\ ART}_{t})\ - \\
{TX\_ TB.D.New.Pos}_{t}
$$
Similarly, targets for those Already on ART and screened positive for TB
will be set as follows, rounded to the nearest integer:
$$
{TX\_ TB.D.Already.Pos}_{t}\ = \ {TX\_ CURR}_{t}\ \times \ {Targeted\ Coverage:\ Already\ on\ ART}_{t}\ \times \\
{TB\ Screen\ Positivity\ Rate:\ Already\ on\ ART}_{t}
$$
And finally, targets for those Already on ART and screened negative for
TB will be set as below, rounding to the nearest integer:
$$
{TX\_ TB.D.Already.Neg}_{t}\ = \ ({TX\_ CURR}_{t}\ \times \ {Targeted\ Coverage:\ Already\ on\ ART}_{t})\ - \\
{TX\_ TB.D.Already.Pos}_{t}
$$ \## TX_TB_PREV: TB_PREV (D)
**TB_PREV (D):** Number of ART patients who are expected to complete a
course of TB preventive therapy during the reporting period (for
programs using continuous IPT, this includes only the patients who are
scheduled to complete the first 6 months of therapy).
```{r echo=FALSE, results='asis'}
sheet_name <- "TX_TB_PREV"
section <- "TB_PREV (D)"
columns <- col_seq("P", "X")
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:
- **TB_PREV (D): New on ART (FY25)** $TB\_PREV.D.New.T$
- **TB_PREV (D): Already on ART (FY25)** $TB\_PREV.D.Already.T$
### Instructions
1. Review "Cumulative Previous Completion of TPT (Results FY17-22)" as
well as "TB_PREV (N) - New on ART (FY24 Targets)" and "TB_PREV (N) -
Already on ART (FY24 Targets)". These three columns will help
calculate "Already on ART who have likely completed TPT in last 5
years (FY25) (%)".
2. Review "Already on ART who have likely completed TPT in last 5 years
(FY25) (%)" which is the summation of the previous three columns
from step 1 above, divided by the "TX_CURR (FY24)" value from column
G of the TX_TB (D) section.
3. Review "Est. \# Already on ART Eligible for TPT (FY25)" which is
equivalent to "Already on ART, TB Screen -" set in the TX_TB (D)
section, less the projected number of these who have likely already
received TPT in the previous few years.
4. Review both "% TPT Initiation Rate - New on ART (FY25) (%)" and "%
TPT Initiation Rate - Already on ART (FY25) (%)" which are defaulted
to 100%. "% TPT Initiation Rate - New on ART (FY25) (%)" will flag
yellow if it is less than 100% while "% TPT Initiation Rate -
Already on ART (FY25) (%)" will flag if it is set less than 90%.
5. Review modeled targets for TB_PREV (D) New on ART --- set by
multiplying "New on ART, TB Screen -" and "% TPT Initiation Rate -
New on ART (FY25) (%)" --- and TB_PREV (D) Already on ART --- set by
multiplying "Est. \# Already on ART Eligible for TPT (FY25)" by "%
TPT Initiation Rate - Already on ART (FY25) (%)"
## TX_TB_PREV: TB_PREV (N)
**TB_PREV (N):** Number of ART patients who completed a course of TB
preventive therapy during the reporting period (for continuous IPT
programs, this includes the patients who have completed the first 6
months of isoniazid preventive therapy (IPT)).
```{r echo=FALSE, results='asis'}
sheet_name <- "TX_TB_PREV"
section <- "TX_PREV (N)"
columns <- col_seq("Y", "AC")
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:
- **TB_PREV (N): New on ART (FY25)** $TB\_PREV.N.New.T$
- **TB_PREV (N): Already on ART (FY25)** $TB\_PREV.N.Already.T$
### Instructions
1. For historical context, review FY24 targets from DATIM for TB_PREV
(N) for those New on ART and those Already on ART.
2. Review Targeted TPT completion rates, which will default to 90%, but
can be adjusted as needed, taking into account persons who (1) are
already on TB preventative therapy (2) will likely screen negative
(3) will be medically ineligible for TPT (4) will be on TPT by the
end of COP19. Note that data in this column will NOT be imported
into DATIM. Red highlights indicate percentages over 100% or less
than 0%; yellow highlights indicate percentages less than 90%.
3. Review modeled targets for TB_PREV (N) New on ART and Already on
ART, set by multiplying TB_PREV (D) New on ART and TB_PREV (D)
Already on ART, respectively, by the targeted TPT completion rates
set in step 2. Return to step 2 or previous sections to adjust
driving assumptions.
4. Review projected rates of change between FY24 targets and planned
FY25 targets to identify cases where rates of change indicate
significant departures from historic trends.
\elandscape
\newpage