13-TB.Rmd

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# TB

## TB: TB_STAT (D)

**TB_STAT (D):** Total number of new and relapsed TB cases, during the
reporting period.

```{r echo=FALSE, results='asis'}
sheet_name <- "TB"
section <- "TB_STAT (D)"
columns <- col_seq("F", "H")
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:

-   **TB_STAT (D) (FY25)** $TB\_STAT.D.T$

### Instructions

1.  For historical context, review FY23 targets for TB_STAT (D),
    including in the Target Setting Tool reflective of data reported in DATIM.

2.  Review and adjust the Estimated Change in Incidence to reflect most
    reliable projections of TB trends into FY24. This value defaults to
    0%, though this should not be interpreted as a suggested
    epidemiological estimate. If the incidence of TB is expected to
    remain unchanged from FY23, this value should remain at 0%; if the
    incidence is expected to double, the cell should read "100%".

3.  Review FY24 Targets for TB_STAT (D) and return to step 2 to adjust
    driving assumptions as necessary. In the case services are planned
    in FY24 where these were not provided in FY23, you may manually
    enter FY24 targets in this column.

## TB: TB_STAT (N)

**TB_STAT (N):** Number of new and relapsed TB cases with documented HIV
status, during the reporting period.

```{r echo=FALSE, results='asis'}
sheet_name <- "TB"
section <- "TB_STAT (N)"
columns <- col_seq("I", "Q")
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:

-   **Known HIV Status, Positive (FY25)** $TB\_STAT.N.KnownPos.T$

-   **Newly Tested, Positive (FY25)** $TB\_STAT.N.New.Pos.T$

-   **Newly Tested, Negative (FY25)** $TB\_STAT.N.New.Neg.T$

### Instructions

1.  Review historic data for TB_STAT (N): New Positives from FY23
    Targets for context.

2.  Review and adjust Targeted TB_STAT Coverage. This defaults to 100%,
    reflecting that 100% of new and relapsed TB cases know their HIV
    status, but this rate can be adjusted as needed. Red highlights
    indicate percentages over 100%; yellow highlights indicate
    percentages under 100%.

3.  Review FY22 Results for (a) Estimated % TB clients with already
    Known HIV Positive status, and (b) Estimated Positivity Rate among
    Newly Tested TB clients.

4.  Review FY24 projections for (a) Estimated % TB clients with already
    Known HIV Positive status, and (b) Estimated Positivity Rate among
    Newly Tested TB clients. These data default to remain static from
    FY22 results trends, but can be adjusted as necessary. Red
    highlights indicate percentages over 100%; yellow highlights
    indicate percentages different from FY22 results.

5.  Review modeled targets for Total TB_STAT (N), Known HIV Status,
    Positive, Newly Tested, Positive, and Newly Tested, Negative, and
    return to steps 1-4 to adjust driving assumptions as needed. See
    below for additional information.

### Total TB_STAT (N)

Total TB_STAT (N) targets are modeled as follows, rounding to the
nearest integer:

$$
{TB\_ STAT.N}_{t}\  = \ {TB\_ STAT.D}_{t}\  \times \ {Targeted\ TB\_ STAT\ Coverage}_{t}
$$

### Known HIV Status, Positive

Known HIV Status, Positive targets are modeled as follows, rounding to
the nearest integer:

$$
{TB\_ STAT.N.KnownPos}_{t}\  = \ {TB\_ STAT.N}_{t}\  \times \\
{Estimated\ \%\ TB\ clients\ already\ Known\ HIV\ Positive}_{t}
$$

### Newly Tested

Targets for TB_STAT (N): Newly Tested, Positive are modeled as follows,
rounding to the nearest integer:

$$
{TB\_ STAT.N.New.Pos}_{t}\  = \ ({TB\_ STAT.N}_{t}\  - \ {TB\_ STAT.N.KnownPos}_{t})\  \times \\
\text{Estimated Positivity Rate among Newly Tested}_{t}
$$

Based on these and targets for Known HIV Status, Positive, targets for
Newly Tested, Negative are modeled as a remainder, as follows:

$$
{TB\_ STAT.N.New.Neg}_{t}\  = \ {TB\_ STAT.N}_{t}\  - \ {TB\_ STAT.N.KnownPos}_{t}\  - \\
{TB\_ STAT.N.New.Pos}_{t}
$$

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## TB_STAT_ART: TB_ART

**TB_ART:** Proportion of HIV-positive new and relapsed TB cases on ART
during TB treatment.

```{r echo=FALSE, results='asis'}
sheet_name <- "TB"
section <- "TB_ART"
columns <- col_seq("R", "T")
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:

-   **Already on ART (FY25)** $TB\_ART.Already.T$

-   **New on ART (FY25)** $TB\_ART.New.T$

### Instructions

1.  Review Targeted ART Linkage Rate for linkage between TB_STAT (N)
    Newly Tested, Positive and TB_ART New on ART. This rate is locked in
    step with ART Linkage Rates set on the Cascade Tab, which default to
    95%; return to that tab to adjust this rate, though note that this
    will alter linkage rates across all modalities.

2.  Review modeled targets for Already on ART and New on ART, returning
    to the previous sections for TB_STAT (D) and TB_STAT (N) to adjust
    driving assumptions.

### Already on ART

For the purposes of COP21 target setting in the Target Setting Tool, FY24 targets
for TB_ART Already on ART are set assuming that 100% of those TB clients
with already known HIV positive status are already on ART. In other
words, the following holds true in the Target Setting Tool:

$$
{TB\_ ART.Already}_{t}\  = \ {TB\_ STAT.N.KnownPos}_{t}
$$

### New on ART

FY24 Targets for TB_ART New on ART are based largely on TB_STAT Newly
Identified HIV positive TB clients as follows, rounding to the nearest
integer:

$$
{TB\_ ART.New}_{t}\  = \ {TB\_ STAT.N.New.Pos}_{t}\  \times \ \text{Targeted ART Linkage Rate}_{t}
$$

## TB: Testing Rationalization

```{r echo=FALSE, results='asis'}
sheet_name <- "TB"
section <- "Testing Rationalization"
columns <- col_seq("U", "Z")
data <- prepare_table_data(sheet_name, columns)
for (t in table_seq(data, max_col = 4)) {
  make_table(t, section)
  }
```

### DATIM Import

No data from this section will be imported into DATIM.

### Instructions

1.  Use this section of the TB tab to analyze how TB_STAT Newly Tested,
    Positives fit within the context of an overall testing strategy. In
    particular, consider how this modality contributes to total
    HTS_TST_POS in relation to HTS_INDEX, PMTCT_STAT, Post ANC1 testing,
    VMMC_CIRC, and all other HTS modalities.

2.  Review any cases where this section is highlighted red, indicating
    over- or under-allocation of HTS_TST_POS targets across contributing
    modalities. While these allocation issues may be more the result of
    a different modality(ies), analysis of these to confirm no
    adjustments to TB_STAT are warranted may prevent issues and
    additional work in other sections of the Target Setting Tool.

3.  Return to other tabs of the Target Setting Tool where issues flagged in this
    section require adjustment of either total HTS_TST_POS targets, or
    targets via other modalities. Similar Testing Rationalization
    sections can be also found in each of these other tabs of the
    Target Setting Tool. You may also use hyperlinks in column headers in this
    section to quickly navigate to the most relevant section of the
    Target Setting Tool.

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