# Comparing modelled HIV incidence estimates with empirical HIV incidence observations in high-burden HIV African epidemic settings: systematic review and meta-regression

Oliver Stevens, Michelle Moffa, Joanne Hunt, Kyle Patel, Tihitna Aytenfisu, Adam Akullian, Rebecca Anderson, Peter Bock, Amelia Crampin, Sian Floyd, et al.

► To cite this version:

Oliver Stevens, Michelle Moffa, Joanne Hunt, Kyle Patel, Tihitna Aytenfisu, et al. Comparing modelled HIV incidence estimates with empirical HIV incidence observations in high-burden HIV African epidemic settings: systematic review and meta-regression. 2026. hal-05503914

HAL Id: hal-05503914

https://hal.science/hal-05503914v1

Preprint submitted on 10 Feb 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
1 Comparing modelled HIV incidence estimates with empirical HIV incidence

2 observations in high-burden HIV African epidemic settings: systematic review and

3 meta-regression

4 Oliver Stevens¹§, Michelle Moffa², Joanne H. Hunt², Kyle Patel², Tihitna Aytenfisu², Adam

5 Akullian³, Rebecca L Anderson¹, Peter Bock⁴,²⁷, Amelia C Crampin⁵,⁶,⁷, Sian Floyd⁷, Simon

6 Gregson¹,⁸, Richard J Hayes⁷, Collins Iwuji⁹,¹⁰, Ivan Kasamba¹¹, Daniel Kwaro¹²,¹³, Joseph

7 Larmarange¹⁴, Shahin Lockman¹⁵,¹⁶,¹⁷, Denna Michael¹⁸, Louisa Moorhouse¹, Joseph

8 Mugisha¹¹, Elphas Okango⁹, Maya L Petersen¹⁹, Victor Ssempijja²⁰,²¹, Emma Slaymaker²²,

9 Frank Tanser⁹,²³,²⁴, Cari van Schalkwyk²⁴, M. Kate Grabowski²,²⁵†, Jeffrey W. Imai-Eaton¹,²⁶†

10

1. MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom

11

12

2. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

13

14

3. Institute for Disease Modelling, Gates Foundation, Seattle, WA, USA

15

4. Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

16

17

5. Malawi Epidemiology and Intervention Research Unit, Chilumba, Malawi

18

6. School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom

19

7. Department of Infectious Disease Epidemiology and International Health, London School of Hygiene and Tropical Medicine, London, United Kingdom

20

21

8. Manicaland Centre for Public Health Research, Biomedical Research and Training Institute, Harare, Zimbabwe

22

23

9. Africa Health Research Institute, KwaZulu-Natal, South Africa

24

10. Department of Global Health and Infection, Brighton and Sussex Medical School, University of Sussex and University of Brighton, Brighton, United Kingdom

25

26

11. Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine, Uganda Research Unit, Entebbe, Uganda

27

28

12. Institute of Physiology, Center for Space Medicine and Extreme Environments, Charité - Universitätsmedizin Berlin, Berlin, Germany

29

30

13. Kenya Medical Research Institute, Kisumu, Kenya

31

14. Centre Population et Développement (Ceped), Université Paris Cité, Université Sorbonne Paris Nord, Institut de Recherche pour le Développement (IRD), Inserm, Paris, France

32

33

15. Botswana Harvard Health Partnership, Gaborone, Botswana

34

16. Department of Immunology and Infectious Diseases, Harvard T.H Chan School of Public Health, Boston, MA, USA

35

36

17. Division of Infectious Diseases, Brigham & Women's Hospital, Boston, MA, USA

37

18. National Institute for Medical Research (NIMR), Mwanza, Tanzania

38

19. School of Public Health, University of California, Berkeley, Berkeley, CA, USA

39

20. Rakai Health Sciences Program, Entebbe, Uganda

40

21. Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA

41

NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
22. Department of Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom

42

43

23. Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa

44

45

24. South African Centre for Epidemiological Modelling and Analysis (SACEMA), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa

46

47

48

25. Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA

49

26. Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

50

51

27. School of Medicine, Deakin University, Geelong, Australia

52

† Contributed equally

53

§ Correspondence to:

54

Oliver Stevens

55

MRC Centre for Global Infectious Disease Analysis

56

School of Public Health

57

Imperial College London

58

School of Public Health Building

59

Wood Lane

60

White City

61

London

62

W12 0BZ

63

64

o.stevens@imperial.ac.uk

65
## Abstract

**Background:** HIV incidence in sub-Saharan Africa has declined substantially since 2000 according to epidemic estimates published by UNAIDS. These estimates, derived by fitting mathematical models to national surveillance data, guide HIV programmes and epidemic response strategies. We assessed whether the level and age distribution of HIV incidence from modelled estimates were consistent with empirical HIV incidence observations, and whether incidence levels and trends were systematically different between study types, populations, and age groups.

**Methods:** We conducted an updated systematic review of adult HIV incidence data from sub-Saharan Africa published July 2019-February 2024 by searching Scopus, PubMed, Embase, and OVID databases, and combined with earlier systematic review data. We matched empirical incidence measurements between 1990-2023 to UNAIDS HIV incidence estimates by study area, sex, age group, and year. We used Bayesian mixed-effect Poisson regression to estimate (1) incidence rate ratios (IRR) between empirical observations and matched modelled incidence estimates adjusted for sex, year and study type/population; and (2) time trends in age-specific incidence from population-based cohort studies and household surveys.

**Results:** 3560 HIV empirical incidence measurements were included from 179 studies conducted in 21 countries, comprising 23,000 new infections and 3.1 million person-years. Incidence observations from nationally-representative household surveys (IRR 1.07 95%CI 0.68, 1.67) and population-representative study populations (IRR 0.98 95%CI 0.51, 1.89) were not significantly different from matched modelled estimates, and declined at the same rate as modelled estimates (annual aRR 0.99 95%CI 0.98, 1.01). Studies among pregnant women (IRR 2.60 95%CI 1.58, 4.28), control arms of clinical trials (IRR 3.01 95%CI 1.90, 4.77) and key populations (FSW IRR: 6.46 95%CI 4.18, 10.00; MSM 44.02 95%CI 27.35, 70.87) had significantly higher incidence than modelled total population incidence estimates.
92

Across population cohorts in Eastern and Southern Africa, HIV incidence among adults aged
15-49 declined by 75-90% between 2010-2023, and declined 7% (95%CI 4-10%) faster per
year among young adults 15-24 compared to age 25+ years. Modelled incidence declined
similarly to cohort data, but did not reflect the aging of the epidemic.

93

94

95

96

**Conclusion:** Observed incidence in population-representative studies in sub-Saharan Africa has declined steeply. Mathematical models that infer incidence from cross-sectional HIV surveillance data estimated the same incidence level and decline over time as population-representative studies. Studies with non-representative inclusion criteria had significantly higher incidence, including those among pregnant women and most HIV prevention/vaccine efficacy trials. The age pattern of incidence in modelled estimates should be reconsidered to capture the aging of the epidemic indicated by cohort studies.

97

98

99

100

101

102
103

## Introduction

104

105

106

107

108

HIV epidemic estimates, published annually by UNAIDS, indicate that HIV incidence in Africa has declined by 69% between 2010-2024, coinciding with expansion of biomedical and behavioural HIV prevention and treatment interventions.¹,² Accurate assessments of HIV incidence are important for tracking progression of the HIV epidemic, assessing the effectiveness and impact of programmes, and guiding and optimising the response.

109

110

111

112

113

114

115

116

117

National-level estimates of HIV incidence trends in high HIV burden African epidemics are derived by fitting mathematical models to national surveillance data.³,⁴ Most African countries use the Estimates and Projections Package (EPP) within the Spectrum modelling software to estimate incidence.⁵,⁶ EPP infers incidence primarily from trends in cross-sectional measures of HIV prevalence from nationally-representative household surveys, HIV prevalence among pregnant women,⁷ and routine data on the number of people receiving antiretroviral treatment (ART). The model reconstructs the historical incidence trajectory most consistent with observed prevalence data, accounting for natural HIV disease progression and survival,⁸ impacts of ART on survival,⁹ and demographic changes.

118

119

120

121

122

123

124

125

Direct empirical measures of HIV incidence with national coverage, for example from prospective cohort studies¹⁰,¹¹ or cross-sectional recency measures,¹²⁻¹⁵ are infrequent, geographically limited, and require large sample sizes. Complete national reporting of new HIV diagnoses, used as the primary data to monitor HIV incidence trends in other global regions with smaller HIV epidemics and long-standing robust routine case-based surveillance,¹⁶ has only recently begun in African countries. Ability to interpret trends in HIV diagnoses as indicative of underlying HIV incidence trends is limited by high rates of re-testing and re-diagnosis¹⁷ and changing HIV testing coverage and strategies.

126

127

Reviewing the accuracy of estimated HIV incidence levels and trends to guide programmes and long-term strategies is timely at the current juncture of African countries’ responses to
128 HIV. The past decade has focused on ambitious programme scale-up to attain UNAIDS 95-
95-95 testing and treatment targets.¹⁸ Attention is now turning towards understanding what is
129 required to reach targeted 90% incidence reductions by 2030 and to sustain effective long-
130 term declines in new infections.¹⁹ In this context, reviewing evidence for incidence levels and
131 trends is critical to evaluate progress, better understand the impact of programmes,
132 determine what strategies should be continued, and identify where and among whom new
133 approaches are required.
134

135 While modelled estimates and data suggest declining HIV incidence, research studies in
136 specific locations or population groups continue to report high HIV incidence rates, with
137 uncertainty about progress over time.²⁰ Empirical incidence observations from non-nationally
138 representative sources are not used to directly calibrate modelled incidence estimates
139 reported by UNAIDS, but provide opportunities to review and triangulate modelled
140 estimates.²⁰ Here, we updated a previous systematic review of empirical HIV incidence
141 measurements in sub-Saharan Africa from 2010-2019²¹ with data published 2019-2024 to
142 create a comprehensive pooled dataset. We compared empirical incidence observations
143 with area-age-sex-time matched modelled estimates to address (1) whether mathematical-
144 model derived estimates of HIV incidence are consistent with empirical HIV incidence
145 observations, and (2) whether empirical incidence data indicate systematic differences or
146 changes over time in the age distribution of new infections compared to modelled estimates.
147

Data and methods

148 The analysis consisted of three components: (1) an updated systematic review to collate
149 empirical HIV incidence measurements in sub-Saharan Africa at the finest spatial scale,
150 calendar period, sex, and age group stratification available; (2) matching empirical HIV
151 observations to modelled estimates by subnational location, year, sex, and age group; and
152 (3) regressing empirical HIV incidence observations against matched modelled estimates to
153 identify any evidence of systematic differences.
154
## Systematic review

We performed a systematic literature review to identify studies reporting HIV incidence from sub-Saharan Africa published between 23rd July 2019 and 22nd February 2024. The review was completed in November 2024 and the analysis in December 2024. We searched Scopus, PubMed, Embase, and OVID databases for peer-reviewed articles reporting directly observed (i.e., empirical) estimates of HIV incidence measured through either repeat HIV testing or cross-sectional HIV incidence testing of blood samples. We used the following search terms "HIV", "incidence" and "Africa" as medical subject heading (MESH) terms.

Titles and abstracts were screened independently by two reviewers (four reviewers in total; MM, JH, TA, KP). Discrepancies were first resolved through discussion and consensus; if agreement could not be reached, a third reviewer adjudicated the final decision. Each observation had: a study type, risk population, geographic area, start and end dates, lower and upper age bounds, and sex, and reported one or more of: HIV incidence rate per 100 person-years, number of person-years, HIV seroconversions, total number of participants, and number of HIV-positive participants at baseline. For randomised controlled trials, incidence estimates were recorded separately for control and intervention arms. The PRISMA checklist in Supplementary Table S2 and Supplementary Text S1 includes additional systematic search details.

## Data processing

Empirical incidence observations from a 2021 systematic review by Joshi et al. on incidence data from sub-Saharan Africa published from 2010–2019²² and the systematic review conducted during this analysis were combined. All studies were re-reviewed to identify whether the study population was representative of the resident population of the study area.

Observations were excluded if they: were total population studies that used risk-based inclusion criteria (e.g. serodiscordant couples, individuals with STIs), were from intervention arms or clusters of randomised controlled trials, duplicated another observation reporting on
the same individuals (Supplementary Text S2), were secondary analyses that duplicated primary reports or where primary reports were outside the literature search time period, measured incidence using the BED cross-sectional assay⁴⁸, were from studies among populations prescribed pre-exposure prophylaxis (PrEP) or recruited from PrEP programmes, or were from studies replaced by more recent or more finely stratified data (see Figure 1). For inclusion in regression analysis, empirical observations required reported numbers of HIV infections and person-years observed. Observations missing person-years were calculated or imputed (Supplementary Text S2).

Study start and end dates were rounded to the nearest year, and the midpoint year was used for matching to modelled estimates. Lower and upper age bounds were rounded to the nearest five-year age. Lower age bounds below age 15 were assigned to age 15. Observations missing an upper age bound were assigned to age 49.

Systematic review data were supplemented by additional data where more recent or more granular data (with respect to geographic location, sex, age, or time) were available from other published sources, secondary analysis datasets, or by request from study teams (Supplementary Table S3). These included additional data from: 23 nationally-representative Population HIV Impact Assessment (PHIA) surveys conducted in 16 countries; six population-representative cohort studies (the Africa Health Research Institute [AHRI; uMkhanyakude, South Africa],²³ Manicaland General Population Cohort [Manicaland, Zimbabwe]²⁴, Kyamulibwa General Population Cohort [Masaka, Uganda]²⁵, Rakai Community Cohort Study [RCCS; Rakai, Uganda]²⁶, Kisesa [Mwazna, Tanzania]²⁷; and the KEMRI-Siaya [Siaya, Kenya]²⁸ and Karonga²⁹ [Karonga, Malawi] Health and Demographic Surveillance Sites); four population-representative universal testing and treatment (UTT) community-randomised HIV prevention trials: HPTN071 [PopART; Zambia, South Africa]³⁰, Ya Tsie/Botswana Combination Prevention Project [BCPP; Botswana] ³¹,³², Treatment as Prevention [ANRS 12249 TasP; KwaZulu-Natal, South Africa]³³, and SEARCH [Uganda,
Kenya]³⁴; and incidence estimates among female sex workers (FSW) and men who have sex with men (MSM) from recent systematic reviews.³⁵,³⁶ For PHIA household surveys, national incidence by sex and five-year age group were calculated from survey datasets using the standard Kassanjee et al. estimator³⁷ with the 'svyincid' R package³⁸, incorporating survey-specific assumptions about mean duration of recent infection and false-recent ratio and survey design.

### UNAIDS modelled HIV incidence estimates

Modelled HIV incidence estimates were from UNAIDS Global HIV Epidemic Estimates published in July 2024.³⁹ National HIV incidence trend estimates were extracted from UNAIDS Spectrum files⁴⁰, and provincial HIV estimates for South Africa were extracted from Thembisa v4.7.⁴¹ Methods for the EPP and Spectrum models are described elsewhere.⁴²,⁴³ Briefly, EPP-Spectrum infers the HIV incidence rate among ages 15-49 years from national surveillance data, which is apportioned to all adults (15+) by sex and five-year age group according to incidence rate ratios.⁴⁴,⁴⁵ Countries may calibrate the age pattern of new infections to age-specific HIV prevalence data from nationally representative household surveys or use a default age pattern.⁴⁴,⁴⁶ In 2024 UNAIDS estimates, 58/74 sub-Saharan African Spectrum files specified a constant age pattern of new infections over time. EPP-Spectrum provides an option to include cross-sectional incidence observations from PHIA surveys in calibration (Supplementary Table S5), but does not calibrate to empirical incidence observations from cohort studies, control arms of intervention trials, or other studies measuring HIV incidence. For South Africa, Thembisa estimates age-sex stratified incidence rates from age-sex stratified HIV prevalence data. District-level HIV incidence estimates by sex and five-year age group for 2023 were extracted from UNAIDS subnational estimates.⁴⁷

### Matching empirical observations to district-level HIV estimates
Observations recorded at the national- and provincial-level were hand-matched to UNAIDS subnational estimates areas. Observations recorded at any other level (e.g. named district, city, or hospital) were geocoded to a GPS location using the Google Maps API and assigned to a UNAIDS subnational area.⁴⁹ Subnational estimates from UNAIDS were available for 2023 only. To match to empirical HIV incidence observations over time, subnational estimates were extrapolated backwards in time parallel to age/sex-matched national or provincial HIV incidence trajectories from Spectrum 1970-2023 (see Supplementary Text S3 for temporal extrapolation source for each country).

## Regression analysis

Regression analyses used Bayesian mixed-effect Poisson regression weighted by observed person years. For the primary analysis on estimating the difference between empirical incidence observations and modelled estimates by study type, we modelled the empirical log incidence rate relative to modelled estimates with area/sex/age/year matched total population log incidence as a regression offset. We specified a linear model for log incidence rate with fixed effects for sex, year, and study recruitment or population; smoothing random effects over time by sex and study recruitment; and unstructured study-level random effects.

For the sub-analysis to assess changes in the age patterns of HIV incidence over time, we used sex/age stratified data from population cohorts and PHIA surveys. Using population cohort data, we modelled the log incidence rate among young adults (15-24 years) and older adults (25-49 years) over time as a linear model including fixed effects for sex, age group, year, three linear interactions between each of sex, age, and year, smoothing random effects over time by study cohort and age group, and unstructured study-level random effects by age group. Estimates were centred on calendar year 2014, the median study year. Using PHIA survey data, we modelled age patterns of incidence with fixed effects for sex, age group (<25 and 25-49 years-old), year, linear interactions between each of sex,
256

age, and year, random intercepts for country, and random slopes for country over year and
binary age group. See Supplementary Text S4 for full model specifications.

257

258

We compared incidence level, incidence trend, and the proportion of new infections under
age 25 from cohort studies and PHIA survey data with national modelled estimates from
Spectrum/Thembisa.

259

260

261

Ninety-five percent uncertainty ranges were generated by combining 1000 posterior samples
of the outcome variable. Analyses were conducted in R version 4.2.0 using the R package
R-INLA (v23.4.24).⁵⁰ This study received ethical approval from the Imperial College London
Research and Ethics Committee (#7045035). Analytical datasets are available at
https://zenodo.org/records/18482588.

262

263

264

265

266

## Results

267

3560 empirical incidence observations from 179 studies conducted in 21 countries were
included in analysis, reflecting 23,000 infections and 3.1 million person-years. Seventy three
percent of studies (131/179) were from five countries (South Africa: N=51 studies; Kenya:
N=35; Uganda: N=17; Tanzania and Zimbabwe: N=14; Supplementary Table S6).

268

269

270

271

Observations were primarily from prospective observational cohort studies and UTT trials
(n=2528; 71%), and household surveys (n=668; 19%). Forty-one studies estimated
incidence using cross-sectional assays of which 23 were PHIA surveys. Twenty-six studies
recorded incidence measurements in West and Central Africa, of which only seven were
among the total population (non-FSW/MSM). Nearly all observations were stratified by sex
(99%; 3501/3551), with an even sex distribution (women 51%; men 48%). Median study year
was 2014 (interquartile range [IQR] 2006-2016).

272

273

274

275

276

277

278

### *Empirical incidence relative to modelled estimates*

279

Observed incidence was highly correlated with matched modelled estimates (log scale
correlation coefficient [R] 0.83; Supplementary Figure S1) and had the same rate of decline

280
over time (annual aRR 0.99 95%CI 0.98, 1.01). There was no statistically significant
difference between matched modelled estimates and empirical observations from
population-representative cohorts and UTT trial control clusters (adjusted rate ratio
[empirical:model aRR] 0.95 95%CI 0.49, 1.86), nationally-representative household surveys
(aRR 1.01 95%CI 0.64, 1.59), or other household survey-based studies (aRR 1.25 95%CI
0.79, 1.96; Table 1; Figure 2). Studies with non-representative inclusion criteria had
significantly higher incidence than matched modelled estimates: control arms of clinical trials
were three-times higher (aRR 2.91 95%CI 1.80, 4.69), and studies among pregnant and
post-partum women were 2.5-times higher (aRR 2.44 95%CI 1.47, 4.06), as were other
studies with any inclusion criteria (aRR 2.38 95%CI 1.46, 3.87). HIV incidence among FSW
and MSM was significantly higher than matched total population incidence estimates (FSW
aRR 6.46 95%CI 4.18, 10.00; MSM aRR 44.02 95%CI 27.35, 70.87).

Time trends in modelled estimates compared to population cohort studies and PHIA surveys
Observed HIV incidence in uMkhanyakude (South Africa), Kisesa (Tanzania), Rakai, and
Kayamulibwa (both Uganda) cohorts did not decline substantially between 2000-2010, while
incidence in Manicaland (Zimbabwe) declined steeply between 1999 and 2011 (Figure 3A).
Since 2010, HIV incidence among all adults aged 15-49 declined in all cohorts by between
75-90%. Modelled estimates were consistent with these trends in all cohorts except
uMkhanyakude (modelled incidence declines began earlier than observed in cohort data),
and were consistent with the incidence level in Siaya, Kisesa, and Manicaland cohorts
(Figure 3B).

National estimates of HIV incidence among adults aged 15-49 from PHIA surveys were
similar to national modelled Spectrum estimates in the same year (Figure 4A; correlation of
log incidence: R=0.90). The correspondence was higher in the first PHIA survey round
(2015-2019; R=0.94 [n=14]) than in the second PHIA round (2020-2022; R=0.56 [n=9];
Figure 4B). The 95% confidence intervals for survey incidence estimates contained modelled
estimates for 17/23 surveys. In the seven countries with two PHIA surveys, modelled and
survey estimates were similar, except in Zimbabwe and Tanzania where modelled incidence
was lower than in the second surveys (Figure 4C).

Sex and age patterns of HIV incidence from population cohorts and PHIA surveys

In population cohort studies, HIV incidence among young adults 15-24 years declined by an average of 7% per year (aRR 0.93 95%CI 0.90, 0.96); Figure 5A; Supplementary Table S7), compared to only 3% per year among age 25-49 years (interaction aRR: 0.96 per year; 95%CI: 0.94, 0.98). Between 2017-2022, around one-quarter of new infections were among adults aged 15-24 (median 25%; range 24-44%; derived from uMkhanyakude, Siaya, Kyamulibwa, and Rakai cohorts), and decreased over time (Figure 5B). In modelled estimates, 40-50% of new infections occurred among adults aged 15-24, higher than the latest available data from most cohorts, and did not reflect the aging pattern of incidence found in all cohort data (Supplementary Figure S2A). Pooled analysis of PHIA surveys estimated similar incidence declines to those of cohort studies among adults aged 15-24 (8% per year; 95%CI -7, 26%), but, dissimilar to cohort studies, indicated more rapid declines among adults 25-49 years (15% per year; 95%CI 1, 31%). Consequently, in PHIA surveys, the estimated proportion of new infections among adults aged 15-24 increased over time (interaction aRR per year among ages 25-49 0.92 95%CI 0.83, 1.02; Supplementary Table S8).

Incidence declined significantly faster among men than women in population-based cohorts (interaction aRR/annum 0.96, 95%CI 0.95, 0.97) and PHIA surveys (interaction aRR/annum 0.93, 95%CI 0.82, 1.04). From 2017-2022, women comprised three-quarters of new infections in population-based cohorts (median 72% range 69-74%; Figure 5C; Supplementary Figure S3). National modelled estimates assumed a female:male new HIV infection sex ratio of around 1.5:1 from 1995-2008. This was higher than the empirical sex ratio of new infections from Kisesa, Kyamulibwa, Manicaland, and Rakai cohorts. The latest
available data from uMkhanyakude, Kyamulibwa, Siaya, and Rakai cohorts 2017-2022
indicated a rapid increase in the sex ratio of new infections, reaching 4:1 in Kyamulibwa and
zero new male infections in the Siaya cohort 2020-2022 (Supplementary Figure S2B).

Discussion

Empirical observations of HIV incidence from population-representative studies (population-
based cohorts, UTT trial control clusters, and national household surveys), indicate steep
HIV incidence declines consistent with modelled incidence estimates. Studies with non-
representative inclusion criteria had significantly higher HIV incidence, including control arms
of clinical trials that often seek to enrol persons at risk of infection to test new interventions
and studies among pregnant and post-partum women. In all population-based cohort
studies, incidence declined by between 75-90% between 2010-2024 and the proportion of
new infections occurring among adults aged over 25 increased. The more rapid decline in
incidence among young adults and aging pattern of new infections indicated by cohort
studies was not reflected in national modelled estimates.

Modelled estimates, which infer HIV incidence using trends in HIV prevalence and treatment
coverage, indicate that HIV incidence in sub-Saharan Africa has fallen by median 75%
across countries since 2010, including in the highest burden epidemics,³⁹,⁵¹ coinciding with
the rapid scaling-up of HIV testing and treatment programmes. This is consistent with the
steep incidence declines observed across all population cohorts and the incidence trends
from successive national household surveys in Eastern and Southern Africa since
2010.²¹,⁵²,⁵³. Continued incidence declines are conditional on maintaining high levels of
population-level ART coverage and viral load suppression: modelling studies indicate that
widespread disruption to treatment would lead to a large and rapid increase in HIV incidence
and AIDS mortality.⁵⁴,⁵⁵
Collated incidence data also identified populations with disproportionately higher incidence, including female sex workers, men who have sex with men, pregnant and post-partum women, and participants in control arms of clinical studies, such as randomised HIV prevention and vaccine trials.¹⁹ These studies often sought to identify populations with disproportionate HIV risk to efficiently reach clinical endpoints in tests of novel interventions. The higher incidence may be driven by self-selection of participants who have identified their risk status, as indicated by high prevalence of study participants reporting recent transactional sex and sexually transmitted infections⁵⁶⁻⁵⁹ or by study inclusion criteria (e.g. minimum number of sex acts in last 30 days⁶⁰⁻⁶²). Given the roughly three-fold higher incidence among these study populations, the study recruitment strategies may also serve as templates for delivering new HIV prevention interventions to those who will benefit most, such as scale-up of long-acting injectable HIV pre-exposure prophylaxis.

Both cohort studies and PHIA surveys provided consistent evidence of declining HIV incidence among young adults 15-24 years. Cohort studies indicated a concurrent shift towards an increasing age at HIV seroconversion. National models estimated a 51% decline in new infections among young adults from 2010-2020 in sub-Saharan Africa, similar to the 54% decline from cohort studies⁵¹ and the 8% per year rate of decline from household surveys. The impact of intense programmatic focus to reduce HIV infections among young adults was visible across studies⁶³,⁶⁴: incidence among young women is lower than incidence in women over age 25 in six of seven cohorts and, since 2017, as few as one in four infections occurred among young adults within South African, Ugandan, and Kenyan cohorts. Modelled estimates, however, did not reflect the sustained shift towards older age of infection; the default Spectrum model assumption of a constant age pattern of incidence over time should be reconsidered. As incidence among men continues to decline faster than among women, benefitting from high levels of female viral load suppression and medical circumcision, up to half of all new HIV infections within cohort studies now occur in women
over age 25.<sup>52,53,65,66</sup> Similar to recent phylogenetic studies of transmission patterns<sup>63,65</sup>, this analysis suggests programmes should ensure HIV prevention service prioritisation and uptake among those at higher risk of acquisition across all ages including women above 25, and seek to narrow the gap in viral load suppression among men to prevent transmission to female partners.

This analysis was subject to several limitations. First, historical subnational incidence trajectories were assumed to be parallel to national or provincial incidence trajectories which may not reflect true subnational historical trends. Though this uncertainty was not reflected in regression analyses, most observations used a closely matched provincial incidence trajectory for extrapolation rather than national estimates. Second, empirical observations were matched to modelled incidence estimates which represent the average incidence level in an area, though study sites may have been specifically selected for urban or rural locations with higher or lower incidence levels than the area average.<sup>53</sup>

## Conclusion

Direct HIV incidence measurements provided empirical support for reported declines since 2010 as shown in modelled estimates. Incidence from modelled estimates was consistent in level and trend with population-representative studies, while studies with non-representative inclusion criteria had significantly higher incidence, including those among pregnant women and the control arms of most HIV prevention/vaccine efficacy trials, identifying potential opportunities for impactful delivery of new HIV prevention technologies. New infections are increasingly spread across age ranges including among women over age 25 years; programmes should ensure they are engaged by HIV prevention services and seek to increase viral load suppression among men. The assumed age pattern of incidence in modelled estimates should be reconsidered to reflect the aging of the epidemic indicated by cohort studies.
408

Acknowledgements

409

We thank the participants of the UNAIDS Reference Group on Estimates, Modelling and Projections meeting on HIV incidence in May 2024.

410

411

Funding

412

This research was funded by UNAIDS. OS and JWI-E acknowledge funding support from the Bill and Melinda Gates Foundation (INV-005576), and the MRC Centre for Global Infectious Disease Analysis (reference MR/R015600/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 programme supported by the European Union.

413

414

415

416

417

418

The study sponsor(s) had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.

419

420
<figure><img src="image_2.png" /><figcaption>**Figure 1 – Systematic review flowchart.** Existing systematic reviews among the total population (Joshi et al.²¹), female sex workers (Jones and Anderson et al.⁶⁷), and men who have sex with men (Stannah et al.⁶⁸).</figcaption></figure>
426

427

<table><caption>**Table 1: Regression results**</caption><thead><tr><th>Parameter</th><th>Incidence rate ratio<br>(95% CI)</th></tr></thead><tbody><tr><td><strong>Study recruitment type/population (N studies)</strong></td><td></td></tr><tr><td>Matched modelled estimates</td><td>1.00 (Reference)</td></tr><tr><td>Studies representative of the total adult population</td><td></td></tr><tr><td>Population cohort studies and UTT trials [control clusters] (N=10)</td><td>0.95 (0.49, 1.86)</td></tr><tr><td>Nationally-representative household surveys (N=27)</td><td>1.01 (0.64, 1.59)</td></tr><tr><td>Other household surveys (N=23)</td><td>1.25 (0.79, 1.96)</td></tr><tr><td>Studies not representative of the total adult population</td><td></td></tr><tr><td>Clinical trials, excluding UTT trials [control arms] (N=21)</td><td><strong>2.91 (1.80, 4.69)</strong></td></tr><tr><td>Pregnant/post-partum women (N=21)</td><td><strong>2.44 (1.47, 4.06)</strong></td></tr><tr><td>Other studies (N=10)</td><td><strong>2.38 (1.46, 3.87)</strong></td></tr><tr><td>Convenience sample from total population (N=2)</td><td>1.42 (0.41, 4.94)</td></tr><tr><td>Female sex workers (N=28)</td><td><strong>6.46 (4.18, 10.00)</strong></td></tr><tr><td>Men who have sex with men (N=22)</td><td><strong>44.02 (27.35, 70.87)</strong></td></tr><tr><td><strong>Sex</strong></td><td></td></tr><tr><td>Female</td><td>1.00 (Reference)</td></tr><tr><td>Male</td><td>0.99 (0.92, 1.08)</td></tr><tr><td>Year [reference year = 2014]</td><td>0.99 (0.98, 1.01)</td></tr><tr><td><strong>Random effect precision parameter</strong></td><td><strong>Estimate (95% CI)</strong></td></tr><tr><td>Study (IID)</td><td>0.93 (0.73, 1.19)</td></tr><tr><td>Sex-specific time trend (RW2)‡</td><td>26.43 (11.50, 63.06)</td></tr><tr><td>Study type-specific time trend (RW2)‡</td><td>16.66 (9.71, 28.42)</td></tr></tbody></table>

428

429

430

431

432

433

434

435

Incidence rate ratios represent empirical HIV incidence compared to modelled incidence reported by UNAIDS in 2024, matched by subnational study area, sex, age group, and year.

Regression coefficients in bold have 95% credible intervals that do not include no difference (incidence rate ratio = 1). UTT = Universal Testing and Treatment trials. ‡Time trend interaction terms are centred on year 2014 (median study year), such that incidence rate ratios for study and sex effects reflect estimates in year 2014.
## Population representative studies

<figure><img src="image_3.png" /></figure>

## Non-representative studies

<figure><img src="image_4.png" /></figure>

## Key populations

<figure><img src="image_5.png" /></figure>
437

438

439

440

441

442

443

**Figure 2: Incidence rate ratio between empirical HIV incidence observations and matched modelled incidence estimates by sex and study type.** Points are sized by observed person-years from empirical incidence observations. Mixed-effects Poisson model regression line shown in blue with 95% uncertainty bounds in light blue shading. Black dashed line represents an incidence rate ratio of 1, the line of equality. The upper limit for y-axis scale for total population studies is 15, and 1000 for key population studies.
<figure><img src="image_6.png" /><figcaption>**Figure 3: HIV incidence trends in population-representative cohort studies.** (A) Trends in adult incidence ages 15-49 over time normalised to observed HIV incidence in 2010. Each point represents measured HIV incidence in that year and lines represent smoothing cubic</figcaption></figure>
splines separately for each study. (B) Comparison of HIV incidence trends in cohort data

(orange) with subnational modelled estimates extrapolated backwards proportional to

national or subnational HIV incidence trends (blue).
<figure><img src="image_7.png" /><figcaption>**Figure 4: HIV incidence estimates from PHIA surveys.** (A) Comparison of HIV incidence among adults aged 15-49 years in PHIA surveys and national modelled estimates. (B) Scatter plot of PHIA survey HIV incidence and modelled estimates. Points are sized proportionate to survey sample size and vertical lines represent 95% confidence intervals. Blue line and shaded area represents mixed-effects Poisson regression estimate and 95% credible interval. Diagonal dotted line is the line of equality. (C) Trends in HIV incidence in the seven countries with two PHIA surveys (yellow) compared to national modelled estimates (blue).</figcaption></figure>
460 PHIA surveys conducted in Cote d'Ivoire (CIV), Cameroon (CMR), Nigeria (NGA), Ethiopia

461 (ETH), Kenya (KEN), Rwanda (RWA), Tanzania (TZA), Uganda (UGA), Botswana (BWA),

462 Lesotho (LSO), Mozambique (MOZ), Malawi (MWI), Namibia (NAM), eSwatini (SWZ),

463 Zambia (ZMB), and Zimbabwe (ZWE)
A

Population-based cohorts

<figure><img src="image_8.png" /></figure>

B

PHIA surveys: national incidence

<figure><img src="image_9.png" /></figure>

C

<figure><img src="image_10.png" /></figure>
Figure 5: Age patterns of incidence in population-based cohorts and PHIA surveys.

(A) Ratio of HIV incidence rate among age 25-49 years to 15-24 years from population-based cohort studies stratified by sex. Cohort-specific data and mixed effect regression line represented by coloured points and lines. Y-axis shown on a log scale, and dotted line represents an incidence rate ratio of 1. (B): Age 25-49 : 15-24 incidence rate ratio from PHIA surveys stratified by sex. (C) Modelled estimates of the share of new adult HIV infections stratified by sex and age group over time. Estimates are shown for the period that data were available from each cohort. Estimates for Karonga, Kisesa, and Siaya cohorts shown in Supplementary Figure S3.
474

References

475

1. United Nations. End Inequalities. End AIDS. Global AIDS Strategy 2021-2026 | UNAIDS. Accessed May 5, 2021.

476

477

https://www.unaids.org/en/resources/documents/2021/2021-2026-global-AIDS-strategy

478

479

2. UNAIDS. *In Danger: UNAIDS Global AIDS Update 2022*; 2022.

480

3. Hallett TB, Stover J, Mishra V, Ghys PD, Gregson S, Boerma T. Estimates of HIV incidence from household-based prevalence surveys. *AIDS*. 2010;24(1):147-152.
doi:10.1097/QAD.0b013e32833062dc

481

482

483

4. Hallett TB, Zaba B, Todd J, et al. Estimating Incidence from Prevalence in Generalised HIV Epidemics: Methods and Validation. Ghys P, ed. *PLoS Med*. 2008;5(4):e80. doi:10.1371/journal.pmed.0050080

484

485

486

5. Eaton JW, Brown T, Puckett R, et al. The Estimation and Projection Package Age-Sex Model and the r-hybrid model: New tools for estimating HIV incidence trends in sub-Saharan Africa. *AIDS*. 2019;33(Suppl 3):S235-S244.
doi:10.1097/QAD.0000000000002437

487

488

489

490

6. Stover J, Glaubius R. Methods and Assumptions for Estimating Key HIV Indicators in the UNAIDS Annual Estimates Process. *J Acquir Immune Defic Syndr* (1988). 2024;95(1):E5-E12. doi:10.1097/QAI.0000000000003316

491

492

493

7. Sheng B, Marsh K, Slavkovic AB, Gregson S, Eaton JW, Bao L. Statistical models for incorporating data from routine HIV testing of pregnant women at antenatal clinics into HIV/AIDS epidemic estimates. *AIDS*. 2017;31(Suppl 1):S87-S94.
doi:10.1097/QAD.0000000000001428

494

495

496
497 8. Glaubius R, Kothegal N, Birhanu S, et al. Disease progression and mortality with untreated HIV infection: evidence synthesis of HIV seroconverter cohorts, 498 antiretroviral treatment clinical cohorts and population-based survey data. *J Int AIDS Soc.* 2021;24 Suppl 5(Suppl 5). doi:10.1002/JIA2.25784

501 9. Johnson LF, Anderegg N, Zaniewski E, et al. Global variations in mortality in adults after initiating antiretroviral treatment. *AIDS*. 2019;33:S283-S294.
502 doi:10.1097/QAD.0000000000002358
503

504 10. Justman J, Reed JB, Bicego G, et al. Swaziland HIV Incidence Measurement Survey (SHIMS): a prospective national cohort study. *Lancet HIV*. 2017;4(2):e83-e92.
505 doi:10.1016/S2352-3018(16)30190-4
506

507 11. Nsanzimana S, Remera E, Kanters S, et al. Household survey of HIV incidence in Rwanda: a national observational cohort study. *Lancet HIV*. 2017;4(10):e457-e464.
508 doi:10.1016/S2352-3018(17)30124-8
509

510 12. Rehle T, Johnson L, Hallett T, et al. A Comparison of South African National HIV Incidence Estimates: A Critical Appraisal of Different Methods. *PLoS One*. 2015;10(7).
511 doi:10.1371/JOURNAL.PONE.0133255
512

513 13. Kimanga DO, Ogola S, Umuro M, et al. Prevalence and incidence of HIV infection, trends, and risk factors among persons aged 15-64 years in Kenya: results from a nationally representative study. *J Acquir Immune Defic Syndr.* 2014;66 Suppl 1(Suppl 1). doi:10.1097/QAI.0000000000000124

517 14. Voetsch AC, Duong YT, Stupp P, et al. HIV-1 Recent Infection Testing Algorithm With Antiretroviral Drug Detection to Improve Accuracy of Incidence Estimates. *J Acquir Immune Defic Syndr.* 2021;87(Suppl 1):S73-S80.
518 doi:10.1097/QAI.0000000000002707
519
520
521 15. Facente SN, Grebe E, Maher AD, et al. Use of HIV Recency Assays for HIV Incidence
522 Estimation and Other Surveillance Use Cases: Systematic Review. JMIR Public
523 Health Surveill. 2022;8(3). doi:10.2196/34410

524 16. Mahiane SG, Marsh K, Glaubius R, Eaton JW. Estimating and projecting the number
525 of new HIV diagnoses and incidence in Spectrum's case surveillance and vital
526 registration tool. AIDS. 2019;33:S245-S253. doi:10.1097/QAD.0000000000002324

527 17. Maheu-Giroux M, Marsh K, Doyle CM, et al. National HIV testing and diagnosis
528 coverage in sub-Saharan Africa. AIDS. 2019;33:S255-S269.
529 doi:10.1097/QAD.0000000000002386

530 18. Frescura L, Godfrey-Faussett P, Ali Feizzadeh A, et al. Achieving the 95 95 95 targets
531 for all: A pathway to ending AIDS. PLoS One. 2022;17(8):e0272405.
532 doi:10.1371/JOURNAL.PONE.0272405

533 19. Beyrer C, Tomaras GD, Gelderblom HC, et al. Is HIV epidemic control by 2030
534 realistic? Lancet HIV. 2024;11(7):e489-e494. doi:10.1016/S2352-3018(24)00098-5

535 20. UNAIDS Reference Group on Estimates Modelling and Projections. Technical
536 Updates for UNAIDS-Supported Estimation Tools.; 2020.

537 21. Joshi K, Lessler J, Olawore O, et al. Declining HIV incidence in sub-Saharan Africa: a
538 systematic review and meta-analysis of empiric data. J Int AIDS Soc. 2021;24(10):1-
539 11. doi:10.1002/jia2.25818

540 22. Joshi K, Lessler J, Olawore O, et al. Declining HIV incidence in sub-Saharan Africa: a
541 systematic review and meta-analysis of empiric data. J Int AIDS Soc. 2021;24(10):1-
542 11. doi:10.1002/jia2.25818
23. Gareta D, Baisley K, Mngomezulu T, et al. Cohort Profile Update: Africa Centre Demographic Information System (ACDIS) and population-based HIV survey. *Int J Epidemiol.* 2021;50(1):33-34. doi:10.1093/ije/dyaa264

24. Gregson S, Mugurungi O, Eaton J, et al. Documenting and explaining the HIV decline in east Zimbabwe: The Manicaland General Population Cohort. *BMJ Open*. 2017;7(10):1-12. doi:10.1136/bmjopen-2017-015898

25. Kasamba I, Mugisha J, Abaasa A, et al. Thirty years of change in HIV incidence among adults in the Kyamulibwa General Population Cohort in rural southwest Uganda, 1989-2021. *Int J Infect Dis.* 2023;129:125-134. doi:10.1016/J.IJID.2023.01.029

26. Nakigozi, Gertrude; Chang, Larry; Reynolds, Steven; Nalugoda, Fred; Kigozi, Godfrey; Quinn, Thomas; Gray, Ronald; Kisakye, Alice; Ndyanabo, Anthony; Ssekubugu, Robert; Serwadda, David; Wawer, Maria; Kagaayi, Joseph; Grabowski M. Rapidly declining HIV incidence amongst men and women in Rakai, Uganda. In: *CROI*. 2020.

27. Urassa M, Marston M, Mangya C, et al. Cohort Profile Update: Magu Health and Demographic Surveillance System, Tanzania. *Int J Epidemiol.* 2024;53(3). doi:10.1093/IJE/DYAE058

28. Odhiambo FO, Laserson KF, Sewe M, et al. Profile: The KEMRI/CDC health and demographic surveillance system-Western Kenya. *Int J Epidemiol.* 2012;41(4):977-987. doi:10.1093/ije/dys108

29. Crampin AC, Dube A, Mboma S, et al. Profile: The Karonga Health and Demographic Surveillance System. *Int J Epidemiol.* 2012;41(3):676-685. doi:10.1093/ije/DYS088
30. Hayes RJ, Donnell D, Floyd S, et al. Effect of Universal Testing and Treatment on HIV Incidence — HPTN 071 (PopART). *New England Journal of Medicine*. 2019;381(3):207-218. doi:10.1056/nejmoa1814556

31. Wirth KE, Gaolathe T, Pretorius Holme M, et al. Population uptake of HIV testing, treatment, viral suppression, and male circumcision following a community-based intervention in Botswana (Ya Tsie/BCPP): a cluster-randomised trial. *Lancet HIV*. 2020;7(6):e422-e433. doi:10.1016/S2352-3018(20)30103-X

32. Makhema J, Wirth KE, Pretorius Holme M, et al. Universal Testing, Expanded Treatment, and Incidence of HIV Infection in Botswana. *New England Journal of Medicine*. 2019;381(3):230-242. doi:10.1056/NEJMoa1812281

33. Iwuji CC, Orne-Gliemann J, Larmarange J, et al. Universal test and treat and the HIV epidemic in rural South Africa: a phase 4, open-label, community cluster randomised trial. *Lancet HIV*. 2018;5(3):e116-e125. doi:10.1016/S2352-3018(17)30205-9

34. Havlir D V., Balzer LB, Charlebois ED, et al. HIV Testing and Treatment with the Use of a Community Health Approach in Rural Africa. *New England Journal of Medicine*. 2019;381(3):219-229. doi:10.1056/nejmoa1809866

35. Jones HS, Anderson RL, Cust H, et al. HIV incidence among women engaging in sex work in sub-Saharan Africa: a systematic review and meta-analysis. *medRxiv*. Published online January 1, 2023;2023.10.17.23297108. doi:https://doi.org/10.1101/2023.10.17.23297108

36. Stannah J, Soni N, Keng J, et al. Trends in HIV testing, the treatment cascade, and HIV incidence among men who have sex with men in Africa: A systematic review and meta-regression analysis. *Lancet HIV*. Published online 2023. doi:10.1016/S2352-3018(23)00111-X
590

37. Kassanjee R, Pilcher CD, Busch MP, et al. Viral load criteria and threshold

591

optimization to improve HIV incidence assay characteristics. AIDS. 2016;30(15):2361-

592

2371. doi:10.1097/QAD.0000000000001209

593

38. jeffeaton/svyincid: Extend R survey package to analyse household survey-based HIV

594

incidence estimates. Accessed November 13, 2025.

595

https://github.com/jeffeaton/svyincid

596

39. UNAIDS. 2024 Global AIDS Report — The Urgency of Now: AIDS at a Crossroads.

597

40. UNAIDS. Spectrum file request – UNAIDS HIV Tools. Accessed August 30, 2022.

598

https://hivtools.unaids.org/spectrum-file-request/

599

41. Johnson LF, Dorrington RE, Moolla H. Progress towards the 2020 targets for HIV

600

diagnosis and antiretroviral treatment in South Africa. South Afr J HIV Med.

601

2017;18(1):1-8. doi:10.4102/sajhivmed.v18i1.694

602

42. Stover J, Glaubius R. Methods and Assumptions for Estimating Key HIV Indicators in

603

the UNAIDS Annual Estimates Process. J Acquir Immune Defic Syndr (1988).

604

2024;95(1):E5-E12. doi:10.1097/QAI.00000000000003316

605

43. Eaton JW, Brown T, Puckett R, et al. The Estimation and Projection Package Age-

606

Sex Model and the r-hybrid model: New tools for estimating HIV incidence trends in

607

sub-Saharan Africa. AIDS. 2019;33(Suppl 3):S235-S244.

608

doi:10.1097/QAD.00000000000002437

609

44. Stover J, Glaubius R, Mofenson L, et al. Updates to the Spectrum/AIM model for

610

estimating key HIV indicators at national and subnational levels. AIDS. 2019;33(Suppl

611

3):S227-S234. doi:10.1097/QAD.00000000000002357
45. UNAIDS Reference Group on Estimates M. *Technical Updates for UNAIDS-Supported Estimation Tools*.; 2020. Accessed February 10, 2025. www.epidem.org

46. Stover J, Andreev K, Slaymaker E, et al. Updates to the spectrum model to estimate key HIV indicators for adults and children. *AIDS*. 2014;28 Suppl 4(4):S427-S434. doi:10.1097/QAD.0000000000000483

47. HIV sub-national estimates viewer. Accessed May 3, 2024. https://naomi-spectrum.unaids.org/

48. UNAIDS Reference Group on Estimates M and P. UNAIDS Reference Group on Estimates, Modelling and Projections' statement on the use of the BED-assay for the estimation of HIV-1 incidence for surveillance or epidemic monitoring. Published online 2005. Accessed January 15, 2025. https://epidem.org/wp-content/uploads/2005/12/BED-statement-2005.12.13.pdf

49. Combined Geographic Health Boundaries - Geographic-Data-Package-3 - AIDS Data Repository. https://adr.unaids.org/geographic-data-package-3/combine-geographic-health-boundaries

50. Rue H, Martino S, Chopin N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. *J R Stat Soc Series B Stat Methodol*. 2009;71(2):319-392. doi:10.1111/j.1467-9868.2008.00700.x

51. UNAIDS. Spectrum file request – UNAIDS HIV Tools. https://hivtools.unaids.org/spectrum-file-request/

52. Risher KA, Cori A, Reniers G, et al. Age patterns of HIV incidence in eastern and southern Africa: a modelling analysis of observational population-based cohort studies. *Lancet HIV*. 2021;8(7):e429. doi:10.1016/S2352-3018(21)00069-2
53. Vandormael A, Akullian A, Siedner M, de Oliveira T, Bärnighausen T, Tanser F. Declines in HIV incidence among men and women in a South African population-based cohort. *Nature Communications* 2019 10:1. 2019;10(1):1-10.
doi:10.1038/s41467-019-13473-y

54. Jewkes R, Nduna M, Levin J, et al. Impact of Stepping Stones on incidence of HIV and HSV-2 and sexual behaviour in rural South Africa: cluster randomised controlled trial. *BMJ. 2008;337(7666):391-395.* doi:10.1136/BMJ.A506

55. Gray GE, Bekker LG, Laher F, et al. Vaccine Efficacy of ALVAC-HIV and Bivalent Subtype C gp120-MF59 in Adults. *New England Journal of Medicine.* 2021;384(12):1089-1100.
doi:10.1056/NEJMOA2031499/SUPPL_FILE/NEJMOA2031499_DATA-SHARING.PDF

56. Phase 2b Efficacy Trial of Mosaic HIV-1 Vaccine Regimen in African Women (Imbokodo). Accessed January 9, 2025.
https://www.natap.org/2022/CROI/croi_159a.htm

57. Skoler-Karpoff S, Ramjee G, Ahmed K, et al. Efficacy of Carraguard for prevention of HIV infection in women in South Africa: a randomised, double-blind, placebo-controlled trial. *Lancet.* 2008;372(9654):1977-1987. doi:10.1016/S0140-6736(08)61842-5

58. Nel A, van Niekerk N, Kapiga S, et al. Safety and Efficacy of a Dapivirine Vaginal Ring for HIV Prevention in Women. *New England Journal of Medicine.* 2016;375(22):2133-2143.
doi:10.1056/NEJMOA1602046/SUPPL_FILE/NEJMOA1602046_DISCLOSURES.PD
F
659

59. Karim QA, Karim SSA, Frohlich JA, et al. Effectiveness and safety of tenofovir gel, an antiretroviral microbicide, for the prevention of HIV infection in women. *Science*. 2010;329(5996):1168-1174. doi:10.1126/SCIENCE.1193748

660

661

662

60. Delany-Moretlwe S, Lombard C, Baron D, et al. Tenofovir 1% vaginal gel for prevention of HIV-1 infection in women in South Africa (FACTS-001): a phase 3, randomised, double-blind, placebo-controlled trial. *Lancet Infect Dis*. 2018;18(11):1241-1250. doi:10.1016/S1473-3099(18)30428-6

663

664

665

666

61. Saul J, Bachman G, Allen S, Toiv NF, Cooney C, Beamon T. The DREAMS core package of interventions: A comprehensive approach to preventing HIV among adolescent girls and young women. *PLoS One*. 2018;13(12):e0208167. doi:10.1371/JOURNAL.PONE.0208167

667

668

669

670

62. Decision-making aide for investments into HIV prevention programmes among adolescent girls and young women Version for use in 2023 planning processes
Decision-making Aide for Investments into HIV Prevention Programmes among Adolescent Girls and Young Women. Published online 2023.

671

672

673

674

63. Monod M, Brizzi A, Galiwango RM, et al. Longitudinal population-level HIV epidemiologic and genomic surveillance highlights growing gender disparity of HIV transmission in Uganda. *Nature Microbiology* 2023 9:1. 2023;9(1):35-54. doi:10.1038/s41564-023-01530-8

675

676

677

678

64. Borgdorff MW, Kwaro D, Obor D, et al. HIV incidence in western Kenya during scale-up of antiretroviral therapy and voluntary medical male circumcision: a population-based cohort analysis. *Lancet HIV*. 2018;5(5):e241-e249. doi:10.1016/S2352-3018(18)30025-0

679

680

681
682

65. Hall M, Golubchik T, Bonsall D, et al. Demographics of sources of HIV-1 transmission in Zambia: a molecular epidemiology analysis in the HPTN 071 PopART study.

683

*Lancet Microbe.* 2024;5(1):e62-e71. doi:10.1016/S2666-5247(23)00220-3

684

685

66. Fellows IE, Hladik W, Eaton JW, Voetsch AC, Parekh BS, Shiraishi RW. Improving Biomarker-based HIV Incidence Estimation in the Treatment Era. *Epidemiology*.

686

2023;34(3):353. doi:10.1097/EDE.0000000000001604

687

688

67. Jones HS, Anderson RL, Cust H, et al. HIV incidence among women engaging in sex work in sub-Saharan Africa: a systematic review and meta-analysis. *Lancet Glob Health*.

689

2024;12(8):e1244-e1260. doi:10.1016/S2214-109X(24)00227-4

690

691

68. Stannah J, Soni N, Keng J, et al. Trends in HIV testing, the treatment cascade, and HIV incidence among men who have sex with men in Africa: A systematic review and meta-regression analysis. *Lancet HIV*. Published online 2023. doi:0.1016/S2352-3018(23)00111-X

692

693

694

695