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Details regarding the data sources used and their availability can be found in Additional file 2: Supplemental Tables 1-5 and online via the Global Health Data Exchange (https:// ghdx. healt hdata. org/ record/ ihme-​data/ sub-​sahar an-​africa-​hiv-​preva lence-​geosp atial-​estim ates-​2000-​2018). Estimates can also be further explored through the Global Health Data Exchange, as well as via our online visualization tool (http:// vizhub. healt hdata. org/ lbd/ hiv-​prevdisagg). Administrative boundaries were modified from the Database for Global Administrative Areas (GADM) dataset [77]. Populations were retrieved from WorldPop [32]. This study complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) recommendations [31]. All maps and figures presented in this study are generated by the authors; no permissions are required for publication. All computer code is available online and can be found at (https:// github. com/ ihmeuw/ lbd/ tree/ hiv_ prev-​africa-​2020).SUPPLEMENTARY INFORMATION 1. Compliance with the Guidlines for Accurate and Transparent Health Estimates Reporting (GATHER). 2. HIV data sources and data processing. 3. Covariate and auxiliary data. 4. Statistical model. 5. References. ADDITIONAL FILE 2 : Supplemental tables. TABLE S1. HIV seroprevalence survey data. TABLE S2. ANC sentinel surveillance data. TABLE S3. HIV and covariates surveys excluded from this analysis. TABLE S4. Sources for preexisting covariates. TABLE S5. HIV covariate survey data. TABLE S6. Fitted model parameters. ADDITIONAL FILE file 3 : Supplemental figures. FIGURE S1. Prevalence of male circumcision. FIGURE S2. Prevalence of signs and symptoms of sexually transmitted infections. FIGURE S3. Prevalence of marriage or living as married. FIGURE S4. Prevalence of partner living elsewhere among females. FIGURE S5. Prevalence of condom use during most recent sexual encounter. FIGURE S6. Prevalence of sexual activity among young females. FIGURE S7. Prevalence of multiple partners among males in the past year. FIGURE S8. Prevalence of multiple partners among females in the past year. FIGURE S9. HIV prevalence predictions from the boosted regression tree model. FIGURE S10. HIV prevalence predictions from the generalized additive model. FIGURE S11. HIV prevalence predictions from the lasso regression model. FIGURE S12. Modeling regions. FIGURE S13. Age- and sex-specific vs. adult prevalence modeling. FIGURE S14. Data sensitivity. FIGURE S15. Model specification validation. FIGURE S16. Modeled and re-aggregated adult prevalence comparison. FIGURE S17. HIV prevalence raking factors for males. FIGURE S18. HIV prevalence raking factors for females. FIGURE S19. Age-specific HIV prevalence in males, 2000. FIGURE S20. Age-specific HIV prevalence in females, 2000. FIGURE S21. Agespecific HIV prevalence in males, 2005. FIGURE S22. Age-specific HIV prevalence in females, 2005. FIGURE S23. Age-specific HIV prevalence in males, 2010. FIGURE S24. Age-specific HIV prevalence in females, 2010. FIGURE S25. Age-specific HIV prevalence in males, 2018. FIGURE S26. Age-specific HIV prevalence in females, 2018. FIGURE S27. Age-specific uncertainty interval range estimates in males, 2000. FIGURE S28. Age-specific uncertainty interval range estimates in females, 2000. FIGURE S29. Age-specific uncertainty interval range estimates in males, 2005. FIGURE S30. Agespecific uncertainty interval range estimates in females, 2005. FIGURE S31. Age-specific uncertainty interval range estimates in males, 2010. FIGURE S32. Age-specific uncertainty interval range estimates in females, 2010. FIGURE S33. Age-specific uncertainty interval range estimates in males, 2018. FIGURE S34. Age-specific uncertainty interval range estimates in females, 2018. FIGURE S35. Change in HIV prevalence in males, 2000-2005. FIGURE S36. Change in HIV prevalence in females, 2000-2005. FIGURE S37. Change in HIV prevalence in males, 2005-2010. FIGURE S38. Change in HIV prevalence in females, 2005-2010. FIGURE S39. Change in HIV prevalence in males, 2010-2018. FIGURE S40. Change in HIV prevalence in females, 2010-2018. FIGURE S41. Space mesh for geostatistical models. ADDITIONAL FILE 4 : Supplemental results.1. README. 2. Prevalence range across districts. 3. Prevalence range between sexes. 4. Prevalence range between ages. 5. Age-specific district ranges.BACKGROUND : Human immunodeficiency virus and acquired immune deficiency syndrome (HIV/AIDS) is still among the leading causes of disease burden and mortality in sub-Saharan Africa (SSA), and the world is not on track to meet targets set for ending the epidemic by the Joint United Nations Programme on HIV/AIDS (UNAIDS) and the United Nations Sustainable Development Goals (SDGs). Precise HIV burden information is critical for effective geographic and epidemiological targeting of prevention and treatment interventions. Age- and sex-specific HIV prevalence estimates are widely available at the national level, and region-wide local estimates were recently published for adults overall. We add further dimensionality to previous analyses by estimating HIV prevalence at local scales, stratified into sexspecific 5-year age groups for adults ages 15–59 years across SSA. METHODS : We analyzed data from 91 seroprevalence surveys and sentinel surveillance among antenatal care clinic (ANC) attendees using model-based geostatistical methods to produce estimates of HIV prevalence across 43 countries in SSA, from years 2000 to 2018, at a 5 × 5-km resolution and presented among second administrative level (typically districts or counties) units. RESULTS : We found substantial variation in HIV prevalence across localities, ages, and sexes that have been masked in earlier analyses. Within-country variation in prevalence in 2018 was a median 3.5 times greater across ages and sexes, compared to for all adults combined. We note large within-district prevalence differences between age groups: for men, 50% of districts displayed at least a 14-fold difference between age groups with the highest and lowest prevalence, and at least a 9-fold difference for women. Prevalence trends also varied over time; between 2000 and 2018, 70% of all districts saw a reduction in prevalence greater than five percentage points in at least one sex and age group. Meanwhile, over 30% of all districts saw at least a five percentage point prevalence increase in one or more sex and age group. CONCLUSIONS : As the HIV epidemic persists and evolves in SSA, geographic and demographic shifts in prevention and treatment efforts are necessary. These estimates offer epidemiologically informative detail to better guide more targeted interventions, vital for combating HIV in SSA.en© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License.MappingAfricaGeostatisticsSpatial statisticsHIV prevalenceDemographicsHuman immunodeficiency virus (HIV)SDG-03: Good health and well-beingAcquired immune deficiency syndrome (AIDS)Antenatal care clinic (ANC)Sustainable development goals (SDGs)Sub-Saharan Africa (SSA)Article