TX_NEW
Number of adults and children newly enrolled on antiretroviral therapy (ART) (Full Details)
Result: 154,736 (through Q2)
Target: 490,871
Achievement: 31.52%
Facilities Reporting: 2,837
Treatment Program
The charts below bring together the indicators associated with PEPFAR's treatment program. Not all indicators are active in each country and the charts represent only PEPFAR data rather than a complete picture of all treatment activities in the country.
59.34% of Estimated PLHIV are on ART with support from PEPFAR
Regularly updated viral suppression data are still lacking for all countries. The 90-90-90 targets require 72% of PLHIV to be virally suppressed.'
So what?
Shows progress towards the 90-90-90 targets that have been set by UNAIDS. Viral Suppression data are not currently available from PEPFAR. Advocates can use this indicator to identify locations where treatment coverage isn't reaching the 90-90-90 goals for 2020.
What this shows
The 90-90-90 cascade aims to have 90% of PLHIV diagnosed, 90% of those diagnosed on ART treatment (TX_CURR), and 90% of those on treatment with a suppressed viral load (TX_VIRAL). The 90-90-90 cascade has multiple purposes: First, to ensure that people are accessing treatment to keep them healthy; Second, to help realize the prevention benefits that come when PLHIV are on treatment and have a suppressed viral load. People with suppressed viral loads are unlikely to transmit HIV to their partners.
Note that the TX_CURR indicator for people currently on treatment displayed here only includes the number of people on treatment at health facilities that are currently receiving support from PEPFAR. There are more more people on treatment supported solely from government or other donors/payors. The number of people on treatment with PEPFAR support also does not mean that PEPFAR is solely covering the costs of treatment. PEPFAR programs are integrated with domestic ministries of health, Global Fund, and other support in the country.
How these data are used
PEPFAR uses these cascades to monitor their progress in helping to reach the 90-90-90 targets. Where PEPFAR is a major contributor to treatment in the country, the targets PEPFAR is setting should align closely with those of UNAIDS - particularly in districts that are considered 'scale-up' or 'achieved'. During it's annual target setting process, PEPFAR heavily utilizes PLHIV estimates and treatment coverage to determine where gaps in treatment coverage are - including at different age groups - to make decisions about how programs will be structured for the following year.
Good use of these data (example)
A good use of these data is to determine whether PEPFAR's support is substantially increasing the number of people on treatment in the country or the districts selected. Also, whether PEPFAR's targets are aligning to the coverage goals required.
Bad use of these data (example)
A bad use of these data is to suggest that the number of people on treatment here is necessarily all the people on treatment in the country or district. Different country programs and different districts have different investments from PEPFAR that may undermine the specific utility of this chart.
Desirable trends
When viewed over time, the middle bar should continually be moving upward.
In FY2024, 95.72% of people newly diagnosed have been initiated on treatment
Of 161,652 people newly diagnosed in PEPFAR programs, 154,736 (95.72%) were initiated on treatment
So what?
To meet expectations, a minimum of 90% or more of those being newly diagnosed must be initiated on treatment in short order. Same day initiation is now part of national policy in all countries with major PEPFAR programs.
Linkage rates over time can also be seen in the chart below. Contrasting those newly on treatment (TX_NEW) with NET_NEW (the number of people initiated on treatment minus those who have been lost to follow-up, transferred out, or died while on treatment) is important for monitoring whether progress is being made towards the 90-90-90 targets.
Advocates should pay very close attention to whether progress is being made in improving linkage rates and retention rates.
What this shows
HTS_TST_POS shows the number of people who were newly diagnosed as living with HIV. TX_NEW shows the number of people who were newly initiated on treatment. NET_NEW shows the increase or decrease in the number of people cumulatively on treatment accounting for those newly initiated (TX_NEW) minus those lost to follow-up, transferred out, or died while on treatment.
How these data are used
PEPFAR and countries have adopted policies to increase rapid initiation onto treatment of all people identified as HIV positive. Linkage here is called 'proxy linkage' because PEPFAR's data don't track individual patients, but linkage is instead calculated from how many people were reported as newly starting on treatment (TX_NEW) divided by the number of people reported as newly diagnosed (HTS_TST_POS). Because this doesn't track individuals, it's impossible to say whether the people who started on treatment are the same as those who newly tested positive, but in general, this is a good assumption. It's possible for people to get tested positive multiple times in different locations. It's also possible for people to start on treatment in a different quarter from when they were first identified. Linkage rates often fluxuate and should always be looked at over time to identify a trend, rather than looking at just one quarter worth of data.
NET_NEW provides a good indication of whether people are staying on treatment at a rate that's growing the total number of people on treatment. Importantly, all people who were initiated on treatment are included in this number, even though they may fall off of treatment shortly after initiating. Retention information in the graph below provides more details.
Good use of these data (example)
Linkage rates should hover around 90% or more. Linkage rates that are considerably below that should lead to an investigation about what is leading to low linkage of those testing positive to being initated onto treatment.
Desirable trends
HTS_TST_POS and TX_NEW should be as close to equal as possible and stay there over many quarters. NET_NEW may fluxuate some, but should in general closely match increases or decreases of TX_NEW.
2.53% of people tested for HIV in the most recent quarter were HIV+
Different modalities of testing (PMTCT sites, TB sites) can be evaluated separately. Yield targets are based off PEPFAR's targets for testing in each modality on the postives expected to be found.
So what?
Testing yields are the percentage of tests which are positive. While high testing yields may suggest that PEPFAR is running programs that are very good at identifying those most in need of getting tested, it may also suggest that they are undertesting in other groups. It's important that overall numbers of people being identified as positive (HTS_TST_POS) remains high or even increases even if testing yields are increased.
What this shows
PEPFAR measures yields as the percentage of HIV Tests (HTS_TST) that are positive (HTS_TST_POS).
Yield = HTS_TST_POS/HTS_TST
Testing yields can also be calculated for individuals tested in different facilities:- TB clinics (TB_STAT_POS/TB_STAT)
- PMTCT clinics (PMTCT_STAT_POS/PMTCT_STAT)
- Infants (PMTCT_HEI_POS/PMTCT_EID)
How these data are used
PEPFAR is pushing to increase testing yields to increase efficiencies. HIV testing is inexpensive as single events, but in large numbers becomes very expensive, especially if people who are negative and at low risk for HIV are repeatedly requesting or being tested. PEPFAR monitors yields to push implementing partners to put in place policies on testing to that target individuals at increased risk of HIV and disincentivize testing of people and populations at low risk of HIV.
Good use of these data (example)
PEPFAR identifies very low yields in a facility that is testing all patients regardless of risk level. With some training, the facility begins asking patients questions about their HIV exposure risk before offering testing. As a result, they conduct fewer HIV tests, but continue identifying the same number of positive individuals.
Bad use of these data (example)
PEPFAR sets a target of achieving 6% testing yields in a given district. As a result, overall testing numbers decline considerably, but the yield increases. Before the intervention, the district was identifying about 200 people per month as HIV positive. After, reducing the number of tests, it's only identifying about 130 people per month, but the facilities have now met their testing yield target.
Desirable trends
Testing yields should only see significant fluxuation if there are programmatic changes that are made. As PEPFAR rolls out index testing (testing the partners of those who recently tested postive - also called Voluntary Assisted Partner Notification (VAPN)) yields may increase, but very high yields may be cause for concern as it may indicate coercion is taking place.
In FY2024, 154,736 people have been newly initiated onto ART
Initiating people onto ARV treatment is the core driver of PEPFAR's programming and meeting these targets are critical for attainment of the 90-90-90 targets established by UNAIDS.
So what?
Steady progress in getting people on treatment is necessary. Quarterly shortfalls that are corrected quickly may be ok, but each subsequent quarter of underperformance puts the program further behind track to meet the long-term coverage goals on which the targets are based.
What this shows
PEPFAR programs have set aggressive targets to meet the 90-90-90 goals. It is important that the program meets these not only in aggregate numbers for the country as a whole, but also for each population they work with. Programs collect treatment and other data by age and sex, and performance should be evaluated for each population.
How these data are used
PEPFAR's TX_NEW targets are based on estimates of how many people need to be enrolled onto treatment in order to meet the 90-90-90 goals. They are highly ambitious in many places, but that ambition is reactive to the level of unreached need and to interrupt the cycle of new HIV infections among individuals unaware of their status and not on treatment. If the program is not reaching it's TX_NEW target, evaluation should be done on which districts are not making the necessary progress. Also, determining whether linkage rates for people being newly diagnosed are low, or if the program is just failing to diagnose enough people with HIV to meet the treatment goals. If so, the design of the testing campaigns should be assessed.
95.66% of people tested positve for HIV in the most recent quarter were linked to ARV Treatment
Linkage rates are a proxy indicator. PEPFAR's data do not track individuals, so linkage doesn't reflect the rate individuals are linked to treatment, but an approximation instead.
So what?
Linkage rates of at least 90% should be the expectation. With the introduction of test and start and same-day initation onto treatment, programs should be designed to rapidly get people onto treatment. Where they are not, it suggests that patients are not receiving standard of care services. Linkage rates of at least 90% are essential to achieve the overall 90-90-90 goals.
What this shows
Proxy linkage is calculated here by dividing new on treatment (TX_NEW) by the number of newly identified positives (HTS_TST_POS):
Linkage = TX_NEW/HTS_TST_POS
Linkage rates can be skewed if individuals test in multiple sites without disclosing that they had tested positive elsewhere, or if people who had tested positive in a previous quarter were only initiated on treatment in the next quarter. While these can be concerns, any significant changes should be able to be explained by a change in program, not just because the data are an approximation.
Where linkage rates are greater than 100% it suggests that people who were diagnosed in other districts or were not initiated on treatment when they were diagnosed have now been initatied on treatment. While this pattern is understandable for short periods of time, it should be investigated if linkage rates are consistently above 100%. Likewise, when targets are set at greater than 100%, it must suggest that the PEPFAR team anticipates a considerable number of pre-ART patients will be initiated on treatment. In these circumstances, advocates should ask for details on the number of pre-ART patients that are left to enroll.
How these data are used
Linkage rates are used by PEPFAR to monitor whether the program is being successful at linking those individuals who test positive to rapid initiation on to treatment. Where linkage rates are low, these should lead to investigations into which facilities and locations are not meeting the 90% linkage expecations. Facility level data in this area can be very useful for determining whether there are specific patterns within a district that are particularly low.
Desirable trends
Because linkage rates experience fluxuation in any given quarter, trends over time are more important to assess. Ideally, linkage rates should be increasing towards 95% in the long term.
People Retained on Treatment
Retained on treatment here is measured based on quarterly TX_CURR results against TX_NEW.
So what?
Retention rates should be above 90% and generally above 95%. Maintaining people on treatment is essential and systems should be in place at all facilities to rapidly track and support individuals who have not collected medicines at their appointment.
What this shows
PEPFAR used to maintain a separate indicator (TX_RET) that measures the number of people retained on treatment 12 months after first being initiated. The chart above is a different measure of retention based on patients who have been on treatment longer than 6 months. The retention rate here is calculated from the total number of people on treatment (TX_CURR) each quarter, measured with the number initiated on treatment (TX_NEW) and then annualized for consistent interpretation:
Retentionquarter = (1-(TX_CURRprior quarter + TX_NEW - TX_CURR)/TX_CURRprior quarter)4
Importantly, because people who initiate treatment are not 'eligible' to drop out of TX_CURR reporting for at least 6 months, this measure should not be conflated with LTFU for those newly being initiated.
Where retention rates are above 100%, there are a few potential explanations:
- Patients who were previously on treatment were recruited back onto treatment;
- PEPFAR began reporting TX_CURR results at facilities or districts that were not being reported in the previous quarter;
- There's a data quality problem.
Any time retention rates spike, PEPFAR should be able to provide an explanation for that spike.
How these data are used
This indicator is not frequently used by PEPFAR throughout the year. However, PEPFAR does set expectations for retention rates when doing annual planning and target setting. These are generally set at 90% (and sometimes lower). Where the targets are set below 90%, it implies a considerable pessimism about the ability of the program to retain patients.
Retention rates can also be cross compared with the NET_NEW indicator to understand more about whether retention is leading to significant gains in people accessing treatment.Desirable trends
Retention rates at this stage should be relatively flat. See trends over time is important to determine whether an individual quarter is an abberation. In some cases, the retention rate can fluxuate because some facilities didn't report in the prior quarter(s), but then started reporting again.
Data Table
This table shows all the data available by either district level or implementing partner level for the selected year. The table can be sorted by any of the columns by clicking on the header.
District | HTS_TST | HTS_TST_POS | Yield | TX_NEW | Linkage | TX_CURR | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Result | Target | (%) | Result | Target | (%) | Result | Result | Target | (%) | Result | Result | Target | (%) | |
Alfred Nzo | 173,433 | 220,533 | 78.64% | 2,949 | 6,746 | 43.71% | 1.70% | 2,751 | 6,433 | 42.76% | 93.29% | 88,287 | 95,645 | 92.31% |
Amajuba | 862 | 1,123 | 76.76% | 84 | 66 | 127.27% | 9.74% | 88 | 63 | 139.68% | 104.76% | 636 | 500 | 127.20% |
Amathole | 169,595 | 279,461 | 60.69% | 3,189 | 9,718 | 32.82% | 1.88% | 2,954 | 9,262 | 31.89% | 92.63% | 77,048 | 85,544 | 90.07% |
Bojanala Platinum | 352,322 | 829,069 | 42.50% | 6,067 | 27,416 | 22.13% | 1.72% | 6,054 | 26,098 | 23.20% | 99.79% | 162,135 | 200,064 | 81.04% |
Buffalo City | 166,329 | 421,981 | 39.42% | 3,642 | 13,550 | 26.88% | 2.19% | 3,214 | 12,890 | 24.93% | 88.25% | 78,877 | 95,823 | 82.32% |
Cape Winelands | 878 | 622 | 141.16% | 19 | 42 | 45.24% | 2.16% | 19 | 40 | 47.50% | 100.00% | 274 | 317 | 86.44% |
Capricorn | 243,543 | 535,287 | 45.50% | 4,385 | 15,965 | 27.47% | 1.80% | 3,852 | 15,203 | 25.34% | 87.84% | 98,245 | 117,035 | 83.94% |
Chris Hani | 113,121 | 288,798 | 39.17% | 3,438 | 9,875 | 34.82% | 3.04% | 3,247 | 9,401 | 34.54% | 94.44% | 73,064 | 82,785 | 88.26% |
City of Cape Town | 399,601 | 843,757 | 47.36% | 10,553 | 37,809 | 27.91% | 2.64% | 11,845 | 36,002 | 32.90% | 112.24% | 239,193 | 287,962 | 83.06% |
City of Johannesburg | 841,405 | 1,565,111 | 53.76% | 20,768 | 75,293 | 27.58% | 2.47% | 21,063 | 71,684 | 29.38% | 101.42% | 452,828 | 571,503 | 79.23% |
City of Tshwane | 423,709 | 827,135 | 51.23% | 12,576 | 40,297 | 31.21% | 2.97% | 11,158 | 38,365 | 29.08% | 88.72% | 255,740 | 308,862 | 82.80% |
Dr Kenneth Kaunda | 127,188 | 267,255 | 47.59% | 2,817 | 9,940 | 28.34% | 2.21% | 2,576 | 9,467 | 27.21% | 91.44% | 71,828 | 84,717 | 84.79% |
Dr Ruth Segomotsi Mompati | 6 | 0 | ||||||||||||
Ehlanzeni | 300,879 | 708,882 | 42.44% | 9,245 | 23,502 | 39.34% | 3.07% | 8,602 | 22,430 | 38.35% | 93.04% | 270,961 | 296,719 | 91.32% |
Ekurhuleni | 608,573 | 1,021,209 | 59.59% | 15,203 | 47,693 | 31.88% | 2.50% | 13,906 | 45,420 | 30.62% | 91.47% | 333,610 | 394,512 | 84.56% |
Fezile Dabi | 909 | 828 | 109.78% | 168 | 51 | 329.41% | 18.48% | 183 | 48 | 381.25% | 108.93% | 538 | 380 | 141.58% |
Frances Baard | 484 | 661 | 73.22% | 13 | 35 | 37.14% | 2.69% | 18 | 33 | 54.55% | 138.46% | 495 | 260 | 190.38% |
Garden Route | 245 | 255 | 96.08% | 7 | 17 | 41.18% | 2.86% | 10 | 17 | 58.82% | 142.86% | 115 | 138 | 83.33% |
Gert Sibande | 204,753 | 350,022 | 58.50% | 4,252 | 11,867 | 35.83% | 2.08% | 4,290 | 11,312 | 37.92% | 100.89% | 160,345 | 173,365 | 92.49% |
Harry Gwala | 116,810 | 119,613 | 97.66% | 1,890 | 4,250 | 44.47% | 1.62% | 1,669 | 4,054 | 41.17% | 88.31% | 64,294 | 70,963 | 90.60% |
King Cetshwayo | 128,176 | 235,258 | 54.48% | 4,220 | 8,643 | 48.83% | 3.29% | 3,719 | 8,244 | 45.11% | 88.13% | 133,052 | 149,473 | 89.01% |
Lejweleputswa | 85,483 | 205,878 | 41.52% | 2,282 | 6,497 | 35.12% | 2.67% | 2,103 | 6,189 | 33.98% | 92.16% | 72,407 | 79,559 | 91.01% |
Mangaung | 1,058 | 687 | 154.00% | 55 | 44 | 125.00% | 5.20% | 56 | 42 | 133.33% | 101.82% | 445 | 330 | 134.85% |
Mopani | 197,511 | 581,007 | 33.99% | 3,913 | 17,641 | 22.18% | 1.98% | 3,481 | 16,797 | 20.72% | 88.96% | 114,477 | 137,500 | 83.26% |
Nelson Mandela Bay | 2,020 | 2,605 | 77.54% | 102 | 223 | 45.74% | 5.05% | 73 | 215 | 33.95% | 71.57% | 1,121 | 983 | 114.04% |
Ngaka Modiri Molema | 176,355 | 305,714 | 57.69% | 2,798 | 10,138 | 27.60% | 1.59% | 2,727 | 9,649 | 28.26% | 97.46% | 75,916 | 86,712 | 87.55% |
Nkangala | 268,399 | 503,522 | 53.30% | 4,861 | 16,203 | 30.00% | 1.81% | 5,023 | 15,430 | 32.55% | 103.33% | 132,993 | 153,484 | 86.65% |
Oliver Tambo | 187,465 | 492,641 | 38.05% | 6,152 | 16,668 | 36.91% | 3.28% | 5,730 | 15,892 | 36.06% | 93.14% | 150,046 | 167,426 | 89.62% |
Overberg | 259 | 457 | 56.67% | 4 | 16 | 25.00% | 1.54% | 2 | 16 | 12.50% | 50.00% | 116 | 129 | 89.92% |
Sarah Baartman | 248 | 345 | 71.88% | 7 | 23 | 30.43% | 2.82% | 6 | 23 | 26.09% | 85.71% | 167 | 183 | 91.26% |
Sedibeng | 147,182 | 340,225 | 43.26% | 3,401 | 11,406 | 29.82% | 2.31% | 3,319 | 10,859 | 30.56% | 97.59% | 93,972 | 106,004 | 88.65% |
Thabo Mofutsanyane | 125,481 | 203,803 | 61.57% | 2,902 | 6,693 | 43.36% | 2.31% | 2,778 | 6,387 | 43.49% | 95.73% | 100,226 | 106,981 | 93.69% |
Ugu | 117,835 | 267,116 | 44.11% | 2,467 | 8,725 | 28.28% | 2.09% | 2,649 | 8,318 | 31.85% | 107.38% | 103,819 | 120,197 | 86.37% |
Umzinyathi | 255 | 114 | 223.68% | 1 | 0 | 0.39% | 1 | 0 | 100.00% | 177 | 0 | |||
Uthukela | 109,622 | 193,910 | 56.53% | 2,300 | 6,681 | 34.43% | 2.10% | 2,251 | 6,369 | 35.34% | 97.87% | 101,719 | 111,412 | 91.30% |
Vhembe | 1,492 | 4,208 | 35.46% | 131 | 337 | 38.87% | 8.78% | 133 | 321 | 41.43% | 101.53% | 1,080 | 1,997 | 54.08% |
Waterberg | 233 | 430 | 54.19% | 11 | 23 | 47.83% | 4.72% | 14 | 23 | 60.87% | 127.27% | 124 | 184 | 67.39% |
West Coast | 570 | 993 | 57.40% | 4 | 43 | 9.30% | 0.70% | 11 | 41 | 26.83% | 275.00% | 286 | 329 | 86.93% |
West Rand | 580 | 0 | 102 | 0 | 17.59% | 146 | 0 | 143.14% | 391 | 0 | ||||
Xhariep | 9 | 131 | 6.87% | |||||||||||
Zululand | 136,801 | 269,393 | 50.78% | 3,575 | 9,527 | 37.52% | 2.61% | 3,564 | 9,086 | 39.23% | 99.69% | 128,293 | 135,640 | 94.58% |
Zwelentlanga Fatman Mgcawu | 66 | 0 | 2 | 0 | 3.03% | 66 | 0 | |||||||
eThekwini | 492,787 | 934,944 | 52.71% | 16,248 | 44,963 | 36.14% | 3.30% | 14,876 | 42,842 | 34.72% | 91.56% | 472,846 | 541,701 | 87.29% |
uMgungundlovu | 172,716 | 479,921 | 35.99% | 4,849 | 16,703 | 29.03% | 2.81% | 4,575 | 15,906 | 28.76% | 94.35% | 157,785 | 183,417 | 86.03% |
Data Download
All data in the database can be downloaded for further analysis. The links below provide downloads in xlsx or CSV files. Additionally, the entire database may be downloaded as zipped TSV files.