SummaryBackground

As the HIV epidemic in sub-Saharan Africa matures, evidence about the age distribution of new HIV infections and how this distribution has changed over the epidemic is needed to guide HIV prevention. We aimed to assess trends in age-specific HIV incidence in six population-based cohort studies in eastern and southern Africa, reporting changes in mean age at infection, age distribution of new infections, and birth cohort cumulative incidence.

Methods

We used a Bayesian model to reconstruct age-specific HIV incidence from repeated observations of individuals' HIV serostatus and survival collected among population HIV cohorts in rural Malawi, South Africa, Tanzania, Uganda, and Zimbabwe, in a collaborative analysis of the ALPHA network. We modelled HIV incidence rates by age, time, and sex using smoothing splines functions. We estimated incidence trends separately by sex and study. We used estimated incidence and prevalence results for 2000–17, standardised to study population distribution, to estimate mean age at infection and proportion of new infections by age. We also estimated cumulative incidence (lifetime risk of infection) by birth cohort.

Findings

Age-specific incidence declined at all ages, although the timing and pattern of decline varied by study. The mean age at infection was higher in men (cohort mean 27·8–34·6 years) than in women (24·8–29·6 years). Between 2000 and 2017, the mean age at infection per cohort increased slightly: 0·5 to 2·8 years among men and −0·2 to 2·5 years among women. Across studies, between 38% and 63% (cohort medians) of the infections in women were among those aged 15–24 years and between 30% and 63% of infections in men were in those aged 20–29 years. Lifetime risk of HIV declined for successive birth cohorts.

Interpretation

HIV incidence declined in all age groups and shifted slightly to older ages. Disproportionate new HIV infections occur among women aged 15–24 years and men aged 20–29 years, supporting focused prevention in these groups. However, 40–60% of infections were outside these ages, emphasising the importance of providing appropriate HIV prevention to adults of all ages.

Funding

Bill & Melinda Gates Foundation.

IntroductionEastern and southern Africa are disproportionately affected by HIV, with an estimated 54% of 38 million people living with HIV worldwide and 43% of new worldwide infections in 2019.1UNAIDS
Seizing the moment: tackling entrenched inequalities to end epidemics. The estimated annual number of new infections in this region decreased from 1·5 million in 1997 to 730 000 in 2019.2UNAIDS
AIDSinfo: Africa–eastern and southern. However, this number still remains substantially higher than the UNAIDS 2020 target of 500 000 new infections among adults worldwide.1UNAIDS
Seizing the moment: tackling entrenched inequalities to end epidemics.Since the early years of the HIV epidemic, population-based cohort studies have been a key source of information about HIV epidemiology and trends in eastern and southern Africa.3Reniers G Wamukoya M Urassa M et al.Data resource profile: network for analysing longitudinal population-based HIV/AIDS data on Africa (ALPHA Network). The ALPHA network began in 2005 as a collaboration of independently conducted population-based health and demographic surveillance systems in eastern and southern Africa that assess HIV status.3Reniers G Wamukoya M Urassa M et al.Data resource profile: network for analysing longitudinal population-based HIV/AIDS data on Africa (ALPHA Network). Four cohort studies in the ALPHA network—Kisumu (Kenya),4Borgdorff 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. Manicaland (Zimbabwe),5Gregson S Garnett GP Nyamukapa CA et al.HIV decline associated with behavior change in eastern Zimbabwe. Rakai (Uganda),6Grabowski MK Serwadda DM Gray RH et al.HIV Prevention efforts and incidence of HIV in Uganda. and uMkhanyakude (South Africa)7Vandormael 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.—and eight studies collectively8

Slaymaker E, Todd J, Urassa M, et al. HIV incidence trends among the general population in eastern and southern Africa 2000 to 2014. International AIDS Conference. Amsterdam; July 23–27, 2018 (abstr TUAC0101).

have reported declines in the overall incidence of HIV in adults. Few studies, however, assessed changes in age-specific incidence in maturing HIV epidemics. A 2019 systematic review of data from ten countries in sub-Saharan Africa found that HIV incidence is decreasing in adolescent girls and young women (aged 15–24 years) in the general population, although the evidence to assess a trend was scarce.9Birdthistle I Tanton C Tomita A et al.Recent levels and trends in HIV incidence rates among adolescent girls and young women in ten high-prevalence African countries: a systematic review and meta-analysis.Combination HIV prevention interventions, including antiretroviral therapy (ART), medical male circumcision, condom use, and behaviour change, have been rolled out across eastern and southern Africa.1UNAIDS
Seizing the moment: tackling entrenched inequalities to end epidemics. HIV prevention initiatives increasingly aim to prioritise age groups at which most HIV infections occur. Most prominently, the Determined, Resilient, Empowered, AIDS-free, Mentored, and Safe (DREAMS) partnership, which is supported by the USA's President's Emergency Plan For AIDS Relief (PEPFAR) and was established in 2015, is a large-scale intervention focused on adolescent girls and young women in 15 countries in eastern and southern Africa.10UNAIDS
Decision-making aide for investments into HIV prevention programmes among adolescent girls and young women. Data about age-specific HIV incidence and its trends over the epidemic provide an evidence base for interventions, insight about how combined interventions affect the age distribution of new infections, and guidance to optimise future prevention efforts.Research in context

Evidence before this study

We searched PubMed, MEDLINE, and grey literature for English studies published between database inception and Nov 15, 2019 using the search terms “HIV”, “AIDS”, “incidence”, “age-specific”, “age patterns”, “proportion”, and “new infections”. We selected articles that reported on age-specific HIV incidence and proportion of HIV infections by age in eastern and southern Africa. Our search showed that several studies have reported HIV incidence in broad age categories (10–15 year age groups), though many estimates cover a single time period. A 2019 systematic review found evidence that HIV incidence is decreasing in adolescent girls and young women in eastern and southern Africa, but there was scarce evidence to assess a trend over time. UNAIDS estimates the proportion of new HIV infections in eastern and southern Africa among 15–24-year-olds to be 50% among women and 30% among men.

Added value of this study

Our analysis provides a detailed assessment of changes in age patterns of HIV incidence in six population-based cohort studies in eastern and southern Africa, using a Bayesian model that jointly estimates HIV incidence and mortality using serosurvey and vital status data. We found that age-specific incidence has decreased over time in these six studies. 38–63% of new infections among women were in 15–24-year-olds, and 30–63% of new infections in men were in 20–29-year-olds. The mean age at infection has started to increase in some studies as incidence has lowered, though changes were slight (−0·2 to 2·8 years). Although lifetime incidence has declined in most studies and sexes, substantial burden remains among women in uMkhanyakude, South Africa.

Implications of all the available evidence

As the HIV epidemic has matured, the relative contribution of different ages to new HIV infections has changed, supporting the importance of continuing to update HIV prevention strategies to reflect the changing disease burden. Age-targeting interventions for HIV prevention to adolescent girls and young women aged 15–24 years, as in the PEPFAR-supported DREAMS partnership, is supported by their high burden of HIV incidence, but variability between studies suggests that this is too narrow of a target in settings where age-specific incidence is less concentrated.

Most estimates of HIV incidence in population-based cohort studies rely solely on longitudinal observation of HIV seroconversion—ie, HIV-negative individuals who are prospectively followed up and re-tested for HIV. Given high levels of migration in and out of populations11Kate Grabowski M Lessler J Bazaale J et al.Migration, hotspots, and dispersal of HIV infection in Rakai, Uganda., 12McGrath N Eaton JW Newell ML Hosegood V Migration, sexual behaviour, and HIV risk: a general population cohort in rural South Africa. and of non-participation in HIV testing,13Larmarange J Mossong J Barnighausen T Newell ML Participation dynamics in population-based longitudinal HIV surveillance in rural South Africa. the reliance on individuals with a minimum of two test results discards substantial information about previous HIV incidence among individuals who are observed as HIV-positive upon enumeration into the cohort, and could lead to biased estimates of incidence over time. To incorporate data from all participants, we used a Bayesian model to jointly reconstruct age-specific HIV incidence and mortality using population-based HIV testing data and vital status information.

We aimed to assess trends in age-specific incidence over time in population-based cohort studies in Malawi, South Africa, Tanzania, Uganda, and Zimbabwe, reporting changes in mean age at infection, proportion of new infections in different age groups, and cumulative incidence in successive birth cohorts.

Methods Data sourcesThe ALPHA network is comprised of ten ongoing population-based demographic surveillance studies across southern and eastern Africa that conduct HIV serosurveys.3Reniers G Wamukoya M Urassa M et al.Data resource profile: network for analysing longitudinal population-based HIV/AIDS data on Africa (ALPHA Network). Six of these studies have collected demographic and HIV data on individuals aged 15 years and older since at least the early 2000s and are included in this analysis: Karonga (Malawi), uMkhanyakude (South Africa), Kisesa (Tanzania), Masaka and Rakai (Uganda), and Manicaland (Zimbabwe).Each study conducts demographic surveillance involving enumeration of all household residents, births, deaths, and migrations in geographically defined populations. This is usually done via household visits at regular intervals. HIV serosurveys are done at different frequencies for each study (between annually and about every 3 years) and have different testing protocols and participation rates. Details of data collection and serological testing in each study are described elsewhere.5Gregson S Garnett GP Nyamukapa CA et al.HIV decline associated with behavior change in eastern Zimbabwe., 14Tanser F Hosegood V Barnighausen T et al.Cohort Profile: Africa Centre Demographic Information System (ACDIS) and population-based HIV survey., 15Crampin AC Dube A Mboma S et al.Profile: the Karonga Health and Demographic Surveillance System., 16Asiki G Murphy G Nakiyingi-Miiro J et al.The general population cohort in rural south-western Uganda: a platform for communicable and non-communicable disease studies., 17Kishamawe C Isingo R Mtenga B et al.Health & Demographic Surveillance System Profile: The Magu Health and Demographic Surveillance System (Magu HDSS)., 18Sewankambo NK Wawer MJ Gray RH et al.Demographic impact of HIV infection in rural Rakai District, Uganda.Participants from each of the included cohorts gave written informed consent to participate and for their data to be analysed by researchers within the organisation collecting the data and by their collaborators.3Reniers G Wamukoya M Urassa M et al.Data resource profile: network for analysing longitudinal population-based HIV/AIDS data on Africa (ALPHA Network)., 5Gregson S Garnett GP Nyamukapa CA et al.HIV decline associated with behavior change in eastern Zimbabwe., 14Tanser F Hosegood V Barnighausen T et al.Cohort Profile: Africa Centre Demographic Information System (ACDIS) and population-based HIV survey., 15Crampin AC Dube A Mboma S et al.Profile: the Karonga Health and Demographic Surveillance System., 16Asiki G Murphy G Nakiyingi-Miiro J et al.The general population cohort in rural south-western Uganda: a platform for communicable and non-communicable disease studies., 17Kishamawe C Isingo R Mtenga B et al.Health & Demographic Surveillance System Profile: The Magu Health and Demographic Surveillance System (Magu HDSS)., 18Sewankambo NK Wawer MJ Gray RH et al.Demographic impact of HIV infection in rural Rakai District, Uganda. The conduct of each longitudinal study is regularly reviewed by the relevant local ethics committee and the appropriate approvals were obtained. The London School of Hygiene & Tropical Medicine's ethics committee approved the pooled analysis of the secondary data.Further details on the ALPHA network have been published previously.3Reniers G Wamukoya M Urassa M et al.Data resource profile: network for analysing longitudinal population-based HIV/AIDS data on Africa (ALPHA Network). Model descriptionWe used a Bayesian model to simultaneously reconstruct age-specific incidence and mortality by sex in each cohort. By jointly modelling HIV incidence and survival after seroconversion, we incorporated data from study participants who were HIV-positive at first study visit or participated in only one serosurvey. Modelling the distribution of survival by age at infection enables these data to inform the timing and age of HIV seroconversion. These additional data increase the precision of age-specific incidence trends compared with typical cohort-based estimates of HIV incidence, which use only observations from participants with a HIV-negative test at baseline and a follow-up test.19Modeling methods for estimating HIV incidence: a mathematical review.

We jointly fit the model to individual records from demographic surveillance (whether an individual is living in the study area and, if yes, whether they are alive) and serosurveillance (HIV status if available). The model was specified by three hazard functions across time and age:

λ(t, a): HIV incidence rate at time t and age a;ϕ(t, a, u)=ω(a-u, u)·hART(t): HIV-related death rate for time t, age a, and duration of infection u; μ(t, a): natural (non-HIV) mortality rate at time t and age a

We modelled HIV incidence hazard as a generalised additive model with an interaction between time and age,20Brizzi F Birrell PJ Plummer MT et al.Extending Bayesian back-calculation to estimate age and time specific HIV incidence. logλ(t,a)=f(t) + f(a) + f(t,a), where f(t) and f(a) specify average time and age pattern for incidence and the interaction f(t,a) allows for changes in the incidence age pattern over time. We modelled non-HIV mortality additively with average time and age patterns,21Global estimation of child mortality using a Bayesian B-spline bias-reduction model. log μ(t,a)=g(t) + g(a). Each of the terms of the generalised additive models were represented by penalised B-splines (p-splines) with knots every 5 years in age and time.22Flexible smoothing with B-splines and penalties. The p-splines used are flexibly modelled splines that penalise first-order differences between adjacent coefficients to smooth between terms.We modelled HIV survival by age at infection with a Weibull hazard function (ω(a – u,u)) based on age at infection a–u and time since infection u.23Todd J Glynn JR Marston M et al.Time from HIV seroconversion to death: a collaborative analysis of eight studies in six low and middle-income countries before highly active antiretroviral therapy. HIV mortality hazard is modified by the ratio hART(t) in the ART era (table), such that in pre-ART era, hART(t)=1, and in the ART era, hART(t) is estimated from the data on the basis of observed declines in the mortality of people living with HIV after ART became available. Diffuse prior distributions were specified for all spline coefficients and the variance on differences in spline coefficients; smoothness penalties were shared for men and women within each study, but spline coefficients were a priori conditionally independent between sexes.

TableData availability and model inputs for individuals aged ≥15 years, ALPHA network 1989–2017

Data are for the survival cohort, except HIV-positive test data (ever collected). Additional details are given in the appendix (pp 14–19). ART=antiretroviral therapy.

The data for each individual consist of the dates first observed in demographic surveillance and last observed alive in the study or date of death, and a collection of HIV status observations from rounds of HIV serosurveys. HIV status observations defined an interval in which each individual could have seroconverted. For a specific seroconversion date in this interval, the likelihood for an observed individual is the probability of surviving to time last observed or died, conditional on having survived HIV-free to time of seroconversion and survived with HIV from seroconversion to time last observed or died. The model integrates over all possible seroconversion times from last HIV-negative observation to first HIV-positive observation. There are four cases of seroconversion intervals: (1) those HIV-positive at first observation (left-censored), for whom the seroconversion interval starts at age 10 years (start of the model); (2) those HIV-negative at last observation (right-censored) could seroconvert anytime from time last observed HIV-negative until last observation or death in demographic surveillance; (3) those with a HIV-negative and HIV-positive test (interval-censored) who seroconverted between these two tests; and (4) those with no HIV testing data who could have seroconverted anytime between first observation to last observation or death. By considering all four cases described, we used all HIV serostatus information to inform HIV incidence estimates, rather than exclusively using the cohort of seroconverters.

We reconstructed HIV incidence from the introduction of HIV in each study population, but since demographic surveillance and HIV serosurveillance only began several years into the epidemic, we added auxiliary information to specify an early prevalence of 0% before the HIV epidemic started in each study.24The History of the HIV/AIDS Epidemic in Africa. For this auxiliary information, we inserted a hypothetical cross-sectional HIV serosurvey of 451 adults, aged 15–60 years by 0·1 of a year, with all HIV-negative individuals (year in appendix p 17).For computation, the model and data were discretised to 0·2 year time-steps. We estimated the model using Hamiltonian Monte Carlo, No-U-Turn samplers in the Stan programming language (version 2.19).25Carpenter B Gelman A Hoffman MD et al.Stan: a probabilistic programming language. We ran four independent chains for 500 iterations, and combined the latter 250 iterations of chains to calculate summary statistics of the posterior distribution. Statistical analysis

We estimated (1) age-specific incidence over time, (2) mean age at infection over time, (3) proportion of infections occurring within 5-year age groups, (4) narrowest age ranges that capture 25%, 50%, and 75% of new infections, and (5) cumulative incidence (lifetime risk of infection) by birth cohort.

For analyses of age distributions of new HIV infections, we applied modelled age-specific incidence rates and prevalence to the age structure of the resident population in the study areas enumerated through demographic surveillance (regardless of whether they participated in HIV serosurveys), with no smoothing between surveillance. Since Manicaland does not conduct continuous demographic surveillance, we standardised to the national population.26United Nations
World Population Prospects. Modelled HIV prevalence was applied to produce estimates of the population susceptible by time and age, to which we applied incidence hazards to calculate new infections by age. Mean age at infection was weighted by the population susceptible in each age group wt,a at each time t:

(∑aλ(t,a).a.wt,a∑aλ(t,a).wt,a)

Cumulative incidence by birth cohort was calculated by cumulating the hazard across age-specific and time-specific incidence estimates by birth year:1 – exp(−t·Σ(t,a) λ(t,a)).

We projected future cumulative incidence by birth cohort under two assumptions: (1) future age-specific incidence rates remain constant at the most recent estimated levels (2012 for Karonga and Manicaland, 2017 for uMkhanyakude, Rakai, Masaka, and Kisesa), and (2) age-specific HIV incidence rates continue to decline at the same study-specific and sex-specific rate estimated for the past 5 years among ages 15–54 years. Sex differences in the rate of incidence decline are reduced from the end of the data period, converging in 2022 after which the rate of decline is assumed to be the same in both sexes (appendix pp 31–32).

For each outcome, we present results for 2000–17 or end of the most recent HIV serosurveillance in each study (2012 in Karonga and Manicaland) for individuals aged 15–54 years due to limited serosurveillance in those younger than 15 years and older than 54 years. Results represent the median of 1000 posterior samples, with 95% credible intervals (95% CrIs) representing the 2·5th and 97·5th percentiles of these distributions. We used R statistical software (version 3.6) to analyse modelled outputs. Owing to substantial variability in the magnitude of HIV epidemics in the different studies, plotted results are presented side-by-side with different vertical axis ranges.

Analyses assessing sensitivity to assumptions about the type of HIV incidence hazard smoothing term, the population standardised to, and comparisons of modelled outputs to direct estimates from seroconverter cohorts are shown in the appendix (pp 34–53). Role of the funding source

The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

ResultsAge-specific incidence decreased over time across most age groups in these studies, though decreases were observed at different times (figure 1A). In 2000 and 2005, HIV incidence generally peaked between ages 20–24 years for women and 25–29 years for men (figure 1B). Over time, age-specific incidence flattened in Karonga, Kisesa, Manicaland, and Masaka. In Rakai and uMkhanyakude, the peaked incidence age pattern did not change. Recent incidence declines among women in uMkhanyakude were greatest below age 30 years. HIV prevalence trends by age group are shown in the appendix (p 29).

Figure 1Age-specific HIV incidence modelled estimates in six ALPHA network studies by age group (A) and by year (B)

Show full caption(A) Rakai does not include a line for age 45–54 years because the cohort has largely not done HIV tests in individuals older than 49 years. (B) Karonga, Manicaland, and uMkhanyakude do not include results for 2000 and 2015, 2015, and 2000, respectively, due to different start and end dates of HIV testing rounds. More details on serosurvey years are given in the appendix (p 17).The mean age at infection was higher in men than women in all studies, and the difference between the sexes remained similar over time (figure 2). In the most recent estimate (2012 for Karonga and Manicaland, 2017 for elsewhere), mean age at infection among men was 27·8 years (95% CrI 26·7–29·1) in uMkhanyakude, 29·2 (27·5–31·0) in Rakai, 31·1 (29·6–33·0) in Manicaland, 32·1 (30·4–34·4) in Karonga, 32·2 (29·5–35·6) in Masaka, and 34·6 (31·3–37·5) in Kisesa. Among women, most recent mean age at infection was 24·8 years (95% CrI 24·1–25·7) in uMkhanyakude, 25·5 (24·2–27·2) in Rakai, 27·3 (24·9–31·0) in Masaka, 27·7 (26·6–29·0) in Manicaland, 29·0 (27·3–31·1) in Karonga, and 29·6 (26·7–33·3) in Kisesa. The mean age at infection increased slightly from 2000 to most recent estimate in Kisesa (2·8 years in men, 2·5 in women), Masaka (1·8 years in men, 1·5 in women), Manicaland (1·5 years in men, 1·6 in women), Karonga (1·2 years in men, 1·2 in women), and Rakai men (0·7 years); women in Rakai showed little change (−0·2 years). Mean age at infection decreased between 2000 and 2017 in uMkhanyakude (−2·8 years in men, −2·2 in women), although the mean age at infection increased slightly between 2013 and 2017 (0·5 years in men, 0·3 in women).

Figure 2Mean age at infection in six ALPHA network studies by sex

The proportions of new infections occurring in each 5-year age group was relatively stable over time and broadly similar across studies (figure 3). In all studies, for those aged 15–19 years, a higher proportion of infections occur among women (14–29%) than men (5–11%), whereas men have a greater proportion of infections among those aged 25–34 years (23–40% among women and 30–48% among men). In Kisesa, age at infection was more evenly distributed across ages than in other studies.

Figure 3Proportion of new HIV infections in six ALPHA network studies by age group

38–63% (cohort medians) of the infections in women were among those aged 15–24 years (figure 3). This value was above 50% in the most recent year for three of six studies: uMkhanyakude (63%, 95% CrI 59–67), Rakai (60%, 52–69), and Masaka (53%, 37–66). In the other three studies, this proportion was below 50%: Karonga (38%, 28–46), Kisesa (41%, 27–55), and Manicaland (42%, 35–49). Among men in the most recent year, while 15–24-year-olds represented 19–39% of infections in men, men aged 20–29 years represented more than 50% of infections in uMkhanyakude (63%, 54–69) and Rakai (52%, 43–61), and less than 50% in Manicaland (44%, 32–54), Karonga (40%, 30–49), Masaka (36%, 25–47), and Kisesa (30%, 18–43). The uncertainty ranges for these estimates show substantial uncertainty around the proportion of infections attributable to each age group. Uncertainty was largest in the youngest age group and in smaller studies.Across studies, the width of the age range within which 75% of new infections occurred in the most recent year was 13·2–22·2 years for women and 13·2–24·6 years for men (figure 4). For women in Rakai and uMkhanyakude, the narrowest band to capture 75% of infections lies, in most years, between ages 15 years and 30 years, the most recent estimate is 15·0–29·2 years in Rakai and 15·2–28·4 years in uMkhanyakude. Among women in Karonga, Kisesa, Manicaland, and Masaka, this 75% band extends slightly older at 15–35 years, with some changes over time in Manicaland and Masaka as infections in women have shifted to older ages. Most recent estimates are 15·0–34·8 years in Karonga, 15·0–37·2 years in Kisesa, 17·4–33·8 years in Manicaland, and 15·0–34·2 years in Masaka.

Figure 4Narrowest age bands accounting for 25%, 50%, and 75% of new HIV infections in six ALPHA network studies

Among men in the most recent estimates, the narrowest band to capture 75% of infections is in ages 20–35 years in Rakai and uMkhanyakude (most recent estimates are 18·4–35·8 years in Rakai and 19·6–32·8 years in uMkhanyakude), closer to ages 20–40 years in Karonga and Manicaland (most recent estimates are 21·6–40·8 years in Karonga and 21·0–38·6 years in Manicaland), and somewhat wider in Kisesa and Masaka (most recent estimates are 21·2–45·2 years in Kisesa and 17·8–42·4 years in Masaka; figure 4).The lifetime risk of HIV infection, measured by cumulative incidence of HIV infection between ages 15 years and 49 years, has declined for more recent birth cohorts in all studies; however, declines in women in uMkhanyakude occur later than for all other studies and sexes (figure 5). For the birth cohort turning 50 years old in 2020, the percentage of women who were infected with HIV (either deceased or surviving) was 28% (95% CrI 25–32) in Kisesa, 34% (31–38) in Karonga, 40% (32–51) in Rakai, 45% (36–48) in Masaka, 53% (51–56) in Manicaland, and 70% (67–72) in uMkhanyakude. In uMkhanyakude, we projected that cumulative incidence will peak for the cohort turning 50 years old in 2029 at 79% (77–81). For men, the cumulative incidence for the cohort turning 50 years old in 2020 was similar to women in the same population—ranging from 24% (21–28) in Kisesa to 53% (49–56) in Manicaland and 69% (66–71) in uMkhanyakude. By contrast, HIV prevalence at the most recent est