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10.1371/journal.pgph.0003723
PGPH-D-23-02270
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Beyond the 95s: What happens when uniform program targets are applied across a heterogenous HIV epidemic in Eastern and Southern Africa?
Beyond the 95s
https://orcid.org/0000-0003-4644-1568
Joseph Rachael H. Conceptualization Formal analysis Methodology Writing – original draft 1 *
Obeng-Aduasare Yaa Data curation Formal analysis Writing – review & editing 1
Achia Thomas Data curation Writing – review & editing 2
Agedew Abraham Visualization Writing – review & editing 1
Jonnalagadda Sasi Data curation Writing – review & editing 2
Katana Abraham Writing – review & editing 2
https://orcid.org/0000-0002-1370-2306
Odoyo Elijah J. Writing – review & editing 2
Appolonia Aoko Writing – review & editing 2
https://orcid.org/0000-0001-5121-877X
Raizes Elliot Writing – review & editing 3
Dubois Amy Writing – review & editing 1
https://orcid.org/0009-0006-0496-6410
Blandford John Writing – review & editing 1
Nganga Lucy Supervision Writing – review & editing 2
1 Division of Global HIV & TB, Global Health Center, U.S. Centers for Disease Control and Prevention, Pretoria, South Africa
2 Division of Global HIV & TB, Global Health Center, U.S. Centers for Disease Control and Prevention, Nairobi, Kenya
3 Division of Global HIV & TB, Global Health Center, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
Jacob Nisha Anne Sunny Editor
University of Cape Town, SOUTH AFRICA
The authors have declared that no competing interests exist.

* E-mail: vie5@cdc.gov
19 9 2024
2024
4 9 e00037234 12 2023
26 8 2024
https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

The UNAIDS 95-95-95 targets are an important metric for guiding national HIV programs and measuring progress towards ending the HIV epidemic as a public health threat by 2030. Nevertheless, as proportional targets, the outcome of reaching the 95-95-95 targets will vary greatly across, and within, countries owing to the geographic diversity of the HIV epidemic. Countries and subnational units with a higher initial prevalence and number of people living with HIV (PLHIV) will remain with a larger number and higher prevalence of virally unsuppressed PLHIV—persons who may experience excess morbidity and mortality and can transmit the virus to others. Reliance on achievement of uniform proportional targets as a measure of program success can potentially mislead resource allocation and progress towards equitable epidemic control. More granular surveillance information on the HIV epidemic is required to effectively calibrate strategies and intensity of HIV programs across geographies and address current and projected health disparities that may undermine efforts to reach and sustain HIV epidemic control even after the 95 targets are achieved.

The authors received no specific funding for this work. Data AvailabilityAll data used for the analysis are publicly available at https://naomi-spectrum.unaids.org/. The data used for the calculations is in the paper's tables.
Data Availability

All data used for the analysis are publicly available at https://naomi-spectrum.unaids.org/. The data used for the calculations is in the paper's tables.
==== Body
pmcIntroduction

In 2014 the Joint United Nations Programme on HIV/AIDS (UNAIDS) rolled out the “Fast-Track” strategy to end the AIDS epidemic by 2030 [1, 2]. Since then, 144 countries adopted the ambitious Fast-Track treatment targets, aiming that by 2020, 90% of people living with HIV know their HIV status, 90% of those who know their status are on antiretroviral treatment (ART), and 90% of those on ART achieve viral load suppression (i.e. the 90-90-90 targets); and further, by 2025, aiming to achieve the even more ambitious 95-95-95 targets (95% of people living with HIV know their status, 95% with known status on ART, 95% on ART virally suppressed). The latter targets equate to 85.7% of all PLHIV being on ART and virally suppressed.

With an estimated 20.6 million people living with HIV (PLHIV), the 21 countries in Eastern and Southern Africa (ESA) accounted for over 80% of PLHIV on the continent, 54% of all PLHIV, and 45% (670,000) of new HIV infections worldwide in 2021 [3]. Overall, in 2021, 90% of PLHIV in the region knew their status, 78% of all PLHIV were on ART and 73% of all PLHIV had suppressed viral load [3]. Six ESA countries achieved the 90-90-90 targets by 2020, with several having also met, or made substantial progress towards, the second and third 95 targets and epidemic control [4]. As countries strive to reach or sustain the 95-95-95 targets with international aid decreasing [4] and national health budgets stretched to meet competing demands, addressing gaps in health equity and equality has emerged among the highest priorities of the global HIV response [4–6].

Whereas the need to address health inequity among specific populations (e.g., adolescent girls and young women, children, female sex workers, men who have sex with men, and persons who inject drugs) has featured strongly in global guidance for HIV programs, the need to address health inequities among geographically defined subpopulations with high burden of HIV has received less attention [6, 7]. We used published estimates of the number of adult (aged 15 years and above) PLHIV in the Eastern and Southern Africa region to assess the absolute number and population-prevalence of people living with HIV who are expected to remain virally unsuppressed (not on ART, or on ART but not virally suppressed [viral load >1,000 copies/ml]) regionally and nationally after reaching the 95-95-95 targets. We further explored the expected outcome of reaching the 95-95-95 targets at sub-national levels in two country case studies, Kenya, and South Africa.

Methods

National and subnational data on the estimated number of PLHIV aged 15 years and above in 2022 were obtained from published UNAIDS modeled estimates [8, 9]. Population data on persons aged 15 years and above were obtained from published population estimates and projections [10] (S1 Table). We summed the overall number of PLHIV expected to be missed along each step of the 95-95-95 cascade to estimate the total number of virally unsuppressed PLHIV expected to remain in each country after meeting all three of the 95 targets. We then calculated the estimated prevalence of virally unsuppressed PLHV (viral load > 1,000 copies/ml); subsequently referred to as the “prevalence of virally unsuppressed PLHIV” expected to remain after reaching the 95-95-95 targets among the population of persons potentially susceptible to HIV infection. The population susceptible to HIV infection (i.e. HIV-negative population) was calculated by subtracting the total estimated number of PLHIV aged 15+ years from the total population aged 15+ years. We applied the same methodology to further calculate the number and prevalence of virally unsuppressed PLHIV at subnational levels in Kenya and South Africa using publicly available subnational HIV estimates and census data. Calculations were done using R version 4.3.0 [11]. Maps showing spatial variation in results among and within countries were generated using ArcGIS Enterprise version 10.6.1. This project was reviewed in accordance with CDC human research protection procedures and was determined to be non-research.

Results

Among the 21 countries included, 20 had national, and 15 had subnational estimates of PLHIV available. The number of virally unsuppressed PLHIV aged 15 years and above expected to remain after reaching all three 95 targets in the ESA region is 2,869,865 ranging from less than 2,000 in Comoros, Eritrea, and Mauritius to over 1.09 million in South Africa (Fig 1, Table 1). After reaching the 95-95-95 targets, eight countries are expected to remain with more than 100,000 virally unsuppressed PLHIV. The projected regional prevalence of virally unsuppressed PLHIV is 0.94%, and highest in Eswatini (4.03%), Lesotho (3.08%), South Africa (2.89%) and Botswana (2.87%).

10.1371/journal.pgph.0003723.g001 Fig 1 Estimated number and prevalence of virally unsuppressed people living with HIV aged 15+ years expected to remain after reaching the 95-95-95 targets by country, Eastern and Southern Africa Region.

Maps were created using a licensed ArcGIS by ESRI version 10.6.1 GIS Mapping Software, Location Intelligence & Spatial Analytics | Esri; Base map from: Office of the Geographer and Global Issues, U.S. Department of State. https://catalog.data.gov/dataset/large-scale-international-boundaries; June 14, 2024.

10.1371/journal.pgph.0003723.t001 Table 1 Estimated number and prevalence of virally unsuppressed people living with HIV aged 15+ years expected to remain after reaching the 95-95-95 targets by country, Eastern and Southern Africa Region.

 	 	 	After reaching 1st 95	After reaching 2nd 95	After reaching 3rd 95	After reaching all 95-95-95 targets	
 	Estimated number of PLHIV aged 15+ years	National prevalence of HIV, age 15+ years	Remain un-diagnosed	Know HIV status but not on ART	On ART but not virally suppressed	Total no. of virally non-suppressed PLHIV	Population denominator*	Prevalence virally non-suppressed PLHIV	
Angola	278,549	1.42%	13,927	13,231	12,570	39,728	19,574,219	0.20%	
Botswana	345,055	19.58%	17,253	16,390	15,571	49,213	1,713,277	2.87%	
Comoros	140	0%	7	7	6	20	506,880	0.00%	
Eritrea	12,000	0.60%	600	570	542	1,712	2,169,680	0.08%	
Eswatini	216,083	27.17%	10,804	10,264	9,751	30,819	764,458	4.03%	
Ethiopia	573,538	0.91%	28,677	27,243	25,881	81,801	63,266,546	0.13%	
Kenya	1,309,914	4.14%	65,496	62,221	59,110	186,826	31,447,763	0.59%	
Lesotho	266,871	20.98%	13,344	12,676	12,043	38,062	1,233,981	3.08%	
Madagascar	57,000	0.30%	2,850	2,708	2,572	8,130	17,473,980	0.05%	
Malawi	949,975	7.98%	47,499	45,124	42,868	135,490	11,769,331	1.15%	
Mauritius	12,000	1.10%	600	570	542	1,712	1,072,680	0.16%	
Mozambique	2,278,754	12.14%	113,938	108,241	102,829	325,007	18,450,997	1.76%	
Namibia	208,155	12.62%	10,408	9,887	9,393	29,688	1,619,880	1.83%	
Rwanda	228,772	2.80%	11,439	10,867	10,323	32,629	8,132,303	0.40%	
South Africa	7,652,395	17.40%	382,620	363,489	345,314	1,091,423	37,755,782	2.89%	
South Sudan	1,632,461	2.00%	8,318	7,902	7,507	23,727	7,132,561	0.33%	
Tanzania	1,352,968	4.71%	81,623	77,542	73,665	232,830	34,462,677	0.68%	
Uganda	1,344,900	5.51%	67,648	64,266	61,053	192,967	24,340,286	0.79%	
Zambia	1,235,851	12.00%	67,245	63,883	60,689	191,816	11,020,044	1.74%	
Zimbabwe	1,632,461	12.08%	61,793	58,703	55,768	176,263	10,050,707	1.75%	
Eastern and Southern Africa	19,955,382	 	1,006,087	955,784	907,994	2,869,865	303,958,032	0.94%	
*Population denominator = Sum of total susceptible (HIV-negative) population aged 15+ years and the total number of PLHIV aged 15+ years who remain virally unsuppressed after reaching the 95-95-95 targets.

The implications of national achievement of the 95 targets differ greatly at subnational level. In Kenya, the projected outcome of meeting the 95-95-95 targets nationally leaves 186,826 virally unsuppressed PLHIV, yielding a national prevalence of virally unsuppressed PLHIV of 0.59% (Fig 2, Table 2). Approximately 50% (91,359) of the remaining unsuppressed PLHIV reside in 7 of the 47 subnational units (counties): Homa Bay, Kisumu, Siaya, Migori, Mombasa, Nakuru and Nairobi. The prevalence of unsuppressed PLHIV is expected to be 2-fold higher than the regional estimate for ESA, and over 3-fold higher than the Kenya national estimate in four counties in western Kenya around Lake Victoria, namely, Kisumu (2.16%), Homa Bay (2.15%), Migori (2.03%) and Siaya (1.92%).

10.1371/journal.pgph.0003723.g002 Fig 2 Estimated number and prevalence of virally unsuppressed people living with HIV aged 15+ years expected to remain after reaching the 95-95-95 targets by subnational unit, Kenya.

Maps were created using a licensed ArcGIS by ESRI version 10.6.1 GIS Mapping Software, Location Intelligence & Spatial Analytics | Esri, Desktop Help 10.0 - Redistribution rights (arcgis.com); Base map from: Esri. "Kenya_4_Counties_2022_Nov”[basemap]. Kenya_4_Counties_2022_Nov (FeatureServer)(arcgis.com); (June 14, 2024).

10.1371/journal.pgph.0003723.t002 Table 2 Estimated number and prevalence of virally unsuppressed people living with HIV aged 15+ years expected to remain after reaching the 95-95-95 targets by subnational unit, Kenya.

 	 	 	After reaching	After reaching	After reaching	After reaching	
1st 95 	2nd 95 	3rd 95 	all 95-95-95 targets 	
Subnational Unit 	Estimated number of PLHIV aged 15+ years 	HIV prevalence (%)	Remain undiagnosed 	Know HIV status, not on ART 	On ART, not virally suppressed 	Total no. of virally non-suppressed PLHIV 	Population denominator* 	Prevalence virally non-suppressed PLHIV 	
Central	119,495 	2.99% 	5,975 	5,676 	5,392 	17,043 	3,977,974 	0.43% 	
Kiambu 	65,438 	3.62% 	3,272 	3,108 	2,953 	9,333 	1,800,642 	0.52% 	
Kirinyaga 	11,586 	2.45% 	579 	550 	523 	1,652 	471,689 	0.35% 	
Murang’a 	17,723 	2.28% 	886 	842 	800 	2,528 	776,495 	0.33% 	
Nyandarua 	7,386 	1.65% 	369 	351 	333 	1,053 	445,668 	0.24% 	
Nyeri 	17,362 	3.57% 	868 	825 	783 	2,476 	483,478 	0.51% 	
Coast 	113,196 	4.01% 	5,660 	5,377 	5,108 	16,145 	2,805,399 	0.58% 	
Kilifi 	21,735 	2.40% 	1,087 	1,032 	981 	3,100 	903,425 	0.34% 	
Kwale 	23,085 	4.44% 	1,154 	1,097 	1,042 	3,292 	516,215 	0.64% 	
Lamu 	1,731 	1.82% 	87 	82 	78 	247 	94,854 	0.26% 	
Mombasa 	56,559 	6.42% 	2,828 	2,687 	2,552 	8,067 	872,960 	0.92% 	
Taita-Taveta 	8,420 	3.47% 	421 	400 	380 	1,201 	241,526 	0.50% 	
Tana River 	1,667 	0.94% 	83 	79 	75 	238 	176,418 	0.13% 	
Eastern 	130,880 	2.73% 	6,544 	6,217 	5,906 	18,667 	4,776,826 	0.39% 	
Embu 	10,184 	2.22% 	509 	484 	460 	1,453 	457,188 	0.32% 	
Isiolo 	1,991 	1.24% 	100 	95 	90 	284 	160,356 	0.18% 	
Kitui 	29,958 	3.98% 	1,498 	1,423 	1,352 	4,273 	748,288 	0.57% 	
Machakos 	35,224 	3.33% 	1,761 	1,673 	1,589 	5,024 	1,053,961 	0.48% 	
Makueni 	19,266 	2.78% 	963 	915 	869 	2,748 	690,362 	0.40% 	
Marsabit 	1,340 	0.49% 	67 	64 	60 	191 	274,163 	0.07% 	
Meru 	25,723 	2.31% 	1,286 	1,222 	1,161 	3,669 	1,108,721 	0.33% 	
Tharaka	7,194 	2.53% 	360 	342 	325 	1,026 	283,787 	0.36% 	
Nithi 	
Nairobi	140,251 	4.44% 	7,013 	6,662 	6,329 	20,003 	3,137,702 	0.64% 	
North Eastern	3,029 	0.21% 	151 	144 	137 	432 	1,466,100 	0.03% 	
Garissa 	1,480 	0.23% 	74 	70 	67 	211 	648,345 	0.03% 	
Mandera 	981 	0.24% 	49 	47 	44 	140 	411,669 	0.03% 	
Wajir 	568 	0.14% 	28 	27 	26 	81 	406,087 	0.02% 	
Nyanza 	432,991 	11.00% 	21,650 	20,567 	19,539 	61,755 	3,875,668 	1.59% 	
Homa Bay 	100,073 	14.76% 	5,004 	4,753 	4,516 	14,273 	663,638 	2.15% 	
Kisii 	30,346 	3.73% 	1,517 	1,441 	1,369 	4,328 	808,314 	0.54% 	
Kisumu 	113,236 	14.79% 	5,662 	5,379 	5,110 	16,150 	749,349 	2.16% 	
Migori 	92,275 	13.94% 	4,614 	4,383 	4,164 	13,161 	648,782 	2.03% 	
Nyamira 	14,385 	3.64% 	719 	683 	649 	2,052 	392,680 	0.52% 	
Siaya 	82,676 	13.23% 	4,134 	3,927 	3,731 	11,792 	612,904 	1.92% 	
Rift Valley 	214,541 	2.59% 	10,727 	10,191 	9,681 	30,599 	8,239,727 	0.37% 	
Baringo 	4,668 	1.14% 	233 	222 	211 	666 	407,707 	0.16% 	
Bomet 	10,019 	1.79% 	501 	476 	452 	1,429 	559,395 	0.26% 	
Elgeyo-	5,781 	1.99% 	289 	275 	261 	825 	290,045 	0.28% 	
Marakwet	
Kajiado 	23,829 	3.10% 	1,191 	1,132 	1,075 	3,399 	764,464 	0.44% 	
Kericho 	17,503 	2.89% 	875 	831 	790 	2,496 	602,715 	0.41% 	
Laikipia 	6,309 	1.75% 	315 	300 	285 	900 	358,585 	0.25% 	
Nakuru 	55,481 	3.70% 	2,774 	2,635 	2,504 	7,913 	1,490,503 	0.53% 	
Nandi 	14,073 	2.39% 	704 	668 	635 	2,007 	587,718 	0.34% 	
Narok 	12,157 	1.85% 	608 	577 	549 	1,734 	655,696 	0.26% 	
Samburu 	3,329 	1.90% 	166 	158 	150 	475 	175,002 	0.27% 	
Trans Nzoia 	14,588 	2.32% 	729 	693 	658 	2,081 	626,085 	0.33% 	
Turkana 	12,099 	2.12% 	605 	575 	546 	1,726 	568,278 	0.30% 	
Uasin Gishu 	32,320 	3.95% 	1,616 	1,535 	1,458 	4,610 	813,889 	0.57% 	
West Pokot 	2,384 	0.70% 	119 	113 	108 	340 	339,643 	0.10% 	
Western 	155,531 	4.87% 	7,777 	7,388 	7,018 	22,183 	3,168,367 	0.70% 	
Bungoma 	43,303 	4.15% 	2,165 	2,057 	1,954 	6,176 	1,037,912 	0.60% 	
Busia 	41,182 	7.23% 	2,059 	1,956 	1,858 	5,874 	564,040 	1.04% 	
Kakamega 	51,645 	4.33% 	2,582 	2,453 	2,330 	7,366 	1,185,615 	0.62% 	
Vihiga 	19,400 	5.06% 	970 	921 	875 	2,767 	380,800 	0.73% 	
Kenya 	1,309,914 	4.14% 	65,496 	62,221 	59,110 	186,826 	31,447,763 	0.59% 	
*Population denominator = Sum of the total susceptible (HIV-negative) population aged 15+ years and the total number of PLHIV aged 15+ years who remain virally unsuppressed after reaching the 95-95-95 targets.

In South Africa the projected outcome of meeting the 95-95-95 targets leaves 1,091,423 virally unsuppressed PLHIV, translating to a national prevalence of unsuppressed PLHIV of 2.89%. Outcomes vary widely across the nine provinces and 52 municipalities (districts) (Fig 3, Table 3). Fifteen (29%) districts remain with fewer than 10,000 virally unsuppressed PLHIV, 31 (60%) with 10,000–39,000, and 6 (11%) with over 40,000. Nearly one-half (26 districts) have a projected prevalence of virally unsuppressed PLHIV of 3.0% and above—≥ 3-fold higher than the regional estimate for ESA—with highest expected prevalence in districts in KwaZulu Natal Province (range 3.48%–4.69%).

10.1371/journal.pgph.0003723.g003 Fig 3 Estimated number and prevalence of virally unsuppressed people living with HIV aged 15+ years expected to remain after reaching the 95-95-95 targets by subnational unit, South Africa.

Maps were created using a licensed ArcGIS by ESRI version 10.6.1 GIS Mapping Software, Location Intelligence & Spatial Analytics | Esri, Desktop Help 10.0 - Redistribution rights (arcgis.com); Base map from: Esri. “SouthAfrica_5_Districts_2022_Nov”[basemap]. SouthAfrica_5_Districts_2022_Nov (FeatureServer) (arcgis.com); (June 14, 2024).

10.1371/journal.pgph.0003723.t003 Table 3 Estimated number and prevalence of virally unsuppressed people living with HIV aged 15+ years expected to remain after reaching the 95-95-95 targets by subnational unit, South Africa.

 	 	 	After reaching 1st 95	After reaching 2nd 95	After reaching 3rd 95	After reaching all 95-95-95 targets	
Subnational Unit	Estimated no. PLHIV aged 15+ years	HIV prevalence (%)	Remain undiagnosed	Know HIV status, not on ART	On ART, not virally suppressed	Total no. virally non-suppressed PLHIV	Population denominator*	Prevalence virally non-suppressed PLHIV	
Eastern Cape Province	868,185	18.5%	43,409	41,239	39,177	123,825	4,027,238	3.07%	
    Alfred Nzo District	105,157	20.7%	5,258	4,995	4,745	14,998	436,250	3.44%	
    Amathole District	95,046	17.9%	4,752	4,515	4,289	13,556	456,053	2.97%	
    Buffalo City Metropolitan	120,808	19.5%	6,040	5,738	5,451	17,230	531,907	3.24%	
    Chris Hani District	90,656	19.0%	4,533	4,306	4,091	12,930	409,713	3.16%	
    Oliver Tambo District	232,589	23.6%	11,629	11,048	10,496	33,173	847,326	3.92%	
    Joe Gqabi District	42,317	18.0%	2,116	2,010	1,910	6,035	197,755	3.05%	
    Nelson Mandela Bay	126,821	18.4%	6,341	6,024	5,723	18,088	829,236	2.18%	
    Sarah Baartman District	54,791	14.8%	2,740	2,603	2,472	7,815	318,998	2.45%	
    Free State Province	400,276	19.4%	20,014	19,013	18,062	57,089	1,778,361	3.21%	
    Lejweleputswa District	94,784	20.1%	4,739	4,502	4,277	13,519	404,948	3.34%	
    Thabo Mofutsanyane	113,022	21.8%	5,651	5,369	5,100	16,120	445,035	3.62%	
    District	
    Fezile Dabi District	69,696	19.2%	3,485	3,311	3,145	9,940	312,891	3.18%	
    Mangaung Metropolitan	109,947	17.4%	5,497	5,222	4,961	15,681	541,877	2.89%	
    Xhariep District	12,827	15.0%	641	609	579	1,829	73,611	2.48%	
    Gauteng Province	1,800,724	14.4%	90,036	85,534	81,258	256,828	10,726,635	2.39%	
    City of Johannesburg	711,023	14.7%	35,551	33,774	32,085	101,410	4,159,706	2.44%	
    Metropolitan	
    City of Tshwane	344,530	11.5%	17,227	16,365	15,547	49,139	2,582,677	1.90%	
    Metropolitan	
    Ekurhuleni Metropolitan	530,480	16.8%	26,524	25,198	23,938	75,660	2,712,327	2.79%	
    Sedibeng District	731,014	14.2%	5,176	4,918	4,672	14,766	628,672	2.35%	
    West Rand District	747,968	14.9%	5,558	5,280	5,016	15,855	643,252	2.46%	
    KwaZulu Natal Province	1,949,499	24.1%	97,475	92,601	87,971	278,047	6,953,144	4.00%	
    eThekwini Metropolitan	645,186	21.0%	32,259	30,646	29,114	92,020	2,647,386	3.48%	
    Harry Gwala District	75,117	22.9%	3,756	3,568	3,390	10,714	281,642	3.80%	
    King Cetshwayo District	180,842	28.3%	9,042	8,590	8,160	25,793	550,502	4.69%	
    Ugu District	140,644	26.0%	7,032	6,681	6,347	20,059	464,323	4.32%	
    uMgungundlovu District	229,654	27.7%	11,483	10,909	10,363	32,754	714,067	4.59%	
    Uthukela District	119,695	26.1%	5,985	5,686	5,401	17,071	393,857	4.33%	
    Zululand District	147,574	26.7%	7,379	7,010	6,659	21,048	475,934	4.42%	
    Amajuba District	90,340	23.0%	4,517	4,291	4,077	12,885	337,871	3.81%	
    iLembe District	124,487	25.9%	6,224	5,913	5,617	17,755	412,661	4.30%	
    Umkhanyakude District	117,662	27.2%	5,883	5,589	5,309	16,782	371,923	4.51%	
    Umzinyathi District	78,298	22.2%	3,915	3,719	3,533	11,167	302,977	3.69%	
    Limpopo Province	682,578	17.2%	34,129	32,422	30,801	97,353	3,406,584	2.86%	
    Capricorn District	150,837	17.1%	7,542	7,165	6,807	21,513	760,696	2.83%	
    Mopani District	161,120	20.2%	8,056	7,653	7,271	22,980	687,588	3.34%	
    Sekhukhune District	112,749	14.5%	5,637	5,356	5,088	16,081	666,659	2.41%	
    Vhembe District	153,859	15.8%	7,693	7,308	6,943	21,944	835,664	2.63%	
    Waterberg District	104,013	19.6%	5,201	4,941	4,694	14,835	455,977	3.25%	
    Mpumalanga Province	735,931	20.9%	36,797	34,957	33,209	104,962	3,021,013	3.47%	
    Ehlanzeni District	300,316	23.2%	15,016	14,265	13,552	42,833	1,114,169	3.84%	
    Gert Sibande District	235,240	24.8%	11,762	11,174	10,615	33,551	817,366	4.10%	
    Nkangala District	200,375	15.8%	10,019	9,518	9,042	28,578	1,089,478	2.62%	
    Northern Cape Province	107,485	13.8%	5,374	5,106	4,850	15,330	667,472	2.30%	
    Frances Baard District	41,433	16.9%	2,072	1,968	1,870	5,909	210,586	2.81%	
    John Taolo Gaetsewe	27,044	17.2%	1,352	1,285	1,220	3,857	135,327	2.85%	
    District	
    Namakwa District	4,732	6.6%	237	225	214	675	61,637	1.10%	
    Pixley ka Seme District	13,297	10.5%	665	632	600	1,896	108,798	1.74%	
    Zwelentlanga Fatman	20,979	11.9%	1,049	997	947	2,992	151,124	1.98%	
    Mgcawu District	
    North West Province	524,486	17.8%	26,224	24,913	23,667	74,805	2,534,153	2.95%	
    Bojanala Platinum District	268,067	18.5%	13,403	12,733	12,097	38,233	1,247,310	3.07%	
    Dr Kenneth Kaunda	109,260	19.0%	5,463	5,190	4,930	15,583	494,766	3.15%	
    District	
    Ngaka Modiri Molema	99,852	16.2%	4,993	4,743	4,506	14,241	531,105	2.68%	
    District	
    Dr Ruth Segomotsi	47,307	15.6%	2,365	2,247	2,135	6,747	260,973	2.59%	
    Mompati District	
    Western Cape Province	583,231	10.8%	29,162	27,704	26,318	83,183	4,641,182	1.79%	
    City of Cape Town	399,320	11.1%	19,966	18,968	18,019	56,953	3,089,927	1.84%	
    Metropolitan	
    Cape Winelands District	66,742	9.3%	3,337	3,170	3,012	9,519	614,897	1.55%	
    Central Karoo District	3,115	5.9%	156	148	141	444	45,605	0.97%	
    Garden Route District	49,476	10.7%	2,474	2,350	2,233	7,057	398,689	1.77%	
    Overberg District	27,016	11.9%	1,351	1,283	1,219	3,853	195,586	1.97%	
    West Coast District	37,562	10.9%	1,878	1,784	1,695	5,357	296,478	1.81%	
South Africa Overall	7,652,395	17.4%	382,620	363,489	345,314	1,091,423	37,755,782	2.89%	
*Population denominator = Sum of total susceptible (HIV-negative) population aged 15+ years and the total number of PLHIV aged 15+ years who remain virally unsuppressed after reaching the 95-95-95 targets.

Discussion

Our analysis demonstrates, that although the UNAIDS 95-95-95 targets are an important metric for guiding and monitoring national HIV programs, the application of uniform proportional targets across the geographically heterogeneous HIV epidemic in ESA fails to fully address health inequities. Using HIV estimates for 2022 we show that if all countries had reached all three of the UNAIDS 95-95-95 targets, those with a higher initial HIV prevalence and number of PLHIV would remain with a greater number and prevalence of virally unsuppressed PLHIV—essentially, a greater number and prevalence of PLHIV who, without treatment, may have poor health outcomes and can transmit the virus. We further demonstrate that these limitations, and corresponding concerns about equitable epidemic control, persist when applied across subnational units in the case studies of Kenya and South Africa.

Kenya, with approximately 1,310,000 million PLHIV aged 15+ years, had a UNAIDS target achievement of 96-89-94 in 2021 [4]. With annual HIV incidence of 1.17 per 1,000 adults aged 15–49 years, and estimated new HIV infections (~35,000) falling below deaths among PLHIV (~36,000) [4], Kenya is among numerous countries in ESA nearing both the 95-95-95 targets and widely used definitions of HIV epidemic control [4, 12]. Our analysis highlights that despite these promising national metrics, geographic disparities in the remaining burden of HIV will persist at county-level, particularly in western Kenya around Lake Victoria. These counties will need to achieve a population-level viral load suppression that exceeds 85.7% (i.e., exceeds the 95-95-95 targets) to reach a prevalence of unsuppressed PLHIV equivalent to the national projection. For example, Kisumu County, would need approximately 96% (108,706) of its 113,236 PLHIV to be virally suppressed—nearly a 99-99-99 target achievement—to reach the current projected national prevalence of 0.59%.

In South Africa, with an estimated 7.5 million PLHIV, 94% are aware of their HIV status; however, gaps remain in treatment uptake among those who know their status (76%), and to a lesser degree, viral load suppression among those on ART (92%). Nationally, the number of new HIV infections in 2021 was 210,000, corresponding to an HIV incidence of 6.9 per 1,000 adults aged 15–49 years [3]. Owing to the sheer magnitude of the HIV epidemic, the expected number and prevalence of virally unsuppressed PLHIV after reaching the 95-95-95 targets in South Africa far exceeds the projected remaining burden for other countries in the region. Overall, 94.4% (7,224,438/7,652,395) of South Africa’s PLHIV would need to be virally suppressed, a 98-98-98 national target achievement, to reach a prevalence of unsuppressed PLHIV equivalent to the ESA regional projection (1.03%). Differences are even more pronounced at subnational levels, where, after reaching the 95s (85.7% population VL suppression) some provinces (KwaZulu-Natal, Gauteng) are expected to remain with a larger number and prevalence of virally unsuppressed PLHIV than some countries had nationally at baseline before applying the 95-95-95 target cascade.

Both case studies underscore the value of subnational estimates of PLHIV [9] and need for a more granular approach to defining and assessing progress towards ending HIV as a public health threat within and across geographically diverse HIV epidemics. In areas with generalized HIV epidemics, such as the ESA, the impact of achieving the 95-95-95 treatment targets by 2030, and recently expanded set of HIV prevention targets by 2025, is estimated using the Goals Age-Structured Model (Goals-ASM), described elsewhere [13]. Briefly, the Goals-ASM model incorporates data on behaviors, epidemiological factors, and biomedical and behavioral interventions that can influence the probability of HIV transmission, together with data on HIV prevalence, key populations size estimates and intervention coverage, all stratified by age and sex, to generate estimates of expected trends in new infections and AIDS-related deaths. Indicators are generated at national level and aggregated up to produce a global impact estimate, which is validated against results from other models [13, 14].

Whereas the Goals-ASM model of the global HIV epidemic focuses on national and regional outcomes, our analysis points to a critical need for models that assess the prevention and treatment intervention coverage required at the subnational level to effectively, and equitably, reduce the number and prevalence of virally unsuppressed PLHIV, new HIV infections and deaths across a geographically diverse epidemic. Finer detail could assist national HIV programs to effectively calibrate strategies and the intensity of programing across geographic areas, and to address current and projected health disparities that may undermine efforts to reach and sustain HIV epidemic control even after the 95 targets are achieved.

Population-level surveys are key sources of data on incidence and prevalence of HIV, knowledge of HIV status, prevalence of viral load non-suppression in the population of PLHIV, and prevalence of behavioral factors that can affect the risk of HIV transmission—these data are essential for monitoring program impact and gaps, and as a source of national and subnational HIV model inputs and assumptions [9, 15, 16]. Numerous countries in ESA region have supported one or more periodic (approximately every 5 years) national population-based HIV serological and behavioral surveys in the last 10 years, in some cases oversampling geographic areas with high burden of HIV to provide subnational estimates of key HIV indicators [17–19]. The relative infrequency and cost of national surveys in an era of a rapidly evolving HIV response limits timely access to valuable data for decision-making, an issue that could be addressed by more frequent population surveys economized to focus on geographic areas and subpopulations with greatest burden of HIV and/or greatest potential barriers to accessing care [18]. HIV case surveillance systems provide ongoing longitudinal data on key outcomes (HIV diagnosis, viral load status, mortality) among persons with HIV infection who have accessed care. Case surveillance data can be used to monitor HIV epidemics, inform HIV programing and guide rapid public health action at a granular level [20]; however, the status of implementation of case surveillance, including collection of mortality events, varies widely across countries [21]. Expansion of effective HIV case surveillance systems [21], including linkage to high quality vital registration data [22], together with more frequent localized population surveys could improve the availability of timely, granular data needed to guide HIV programing, effectively track, model, and control the HIV epidemic, and address existing and emergent inequities at subnational levels.

Limitations

Our analysis has limitations. Firstly, we used census projections for 2018–2021; any differences between projected and actual population aged 15 years could result in an over- or under-estimation of the calculated prevalence of virally unsuppressed PLHIV. Secondly, this analysis relied on modeled estimates of the number of PLHIV. Published HIV estimates and associated credible intervals are generated using a robust, standardized process [9], but are nevertheless impacted by the quality and timeliness of model inputs for each country. Finally, as our analysis applied the 95-95-95 targets to current national and subnational estimates targets to calculate projected outcomes, it did not account for longitudinal changes in population structure, transmission dynamics, migration [23, 24] or the public health response (e.g. expanded access to pre-exposure prophylaxis), which over time could impact the course of the HIV epidemic in the general population, key and priority populations (i.e., female sex workers, men who have sex with men, people who inject drugs, adolescent girls and young women). Models tailored to the local (geographic) context would lend further insight into how these may impact projected outcomes. Despite these limitations, the underlying principle that health inequities result when a uniform set of targets is applied across a heterogeneous HIV epidemic remains unchanged.

Conclusions

The UNAIDS 95-95-95 targets are an important metric for guiding and monitoring national HIV programs. Our analysis demonstrates that reliance on uniform targets across a geographically diverse HIV epidemic can lead to remarkably different outcomes, and potentially mislead program strategies, resource allocation, and progress towards equitable epidemic control. More granular surveillance information on the HIV epidemic could assist national HIV programs to effectively calibrate strategies and intensity of programing across geographic areas to address current and projected health disparities that may undermine efforts to reach and sustain HIV epidemic control even after the 95 targets are achieved.

Supporting information

S1 Table Data sources and available data.

Description of data sources and available data for calculation of the number and prevalence of virally unsuppressed people living with HIV by country.

(DOCX)
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