Haeuser et al. BMC Medicine (2022) 20:488 https://doi.org/10.1186/s12916-022-02639-z RESEARCH ARTICLE Open Access Mapping age‑ and sex‑specific HIV prevalence in adults in sub‑Saharan Africa, 2000–2018 Emily Haeuser1*, Audrey L. Serfes1, Michael A. Cork1, Mingyou Yang1, Hedayat Abbastabar2, E. S. Abhilash3, Maryam Adabi4, Oladimeji M. Adebayo5, Victor Adekanmbi6, Daniel Adedayo Adeyinka7,8, Saira Afzal9,10, Bright Opoku Ahinkorah11, Keivan Ahmadi12, Muktar Beshir Ahmed13,14, Yonas Akalu15, Rufus Olusola Akinyemi16,17, Chisom Joyqueenet Akunna18,19, Fares Alahdab20, Fahad Mashhour Alanezi21, Turki M. Alanzi22, Kefyalew Addis Alene23,24, Robert Kaba Alhassan25, Vahid Alipour26,27, Amir Almasi‑Hashiani28, Nelson Alvis‑Guzman29,30, Edward Kwabena Ameyaw11, Saeed Amini31, Dickson A. Amugsi32, Robert Ancuceanu33, Davood Anvari34,35, Seth Christopher Yaw Appiah36,37, Jalal Arabloo26, Olatunde Aremu38, Mulusew A. Asemahagn39, Mohammad Asghari Jafarabadi40,41, Atalel Fentahun Awedew42, Beatriz Paulina Ayala Quintanilla43,44, Martin Amogre Ayanore45,46, Yared Asmare Aynalem47, Samad Azari48, Zelalem Nigussie Azene49, B. B. Darshan50, Tesleem Kayode Babalola51,52, Atif Amin Baig53, Maciej Banach54,55, Till Winfried Bärnighausen56,57, Arielle Wilder Bell58,59, Akshaya Srikanth Bhagavathula60,61, Nikha Bhardwaj62, Pankaj Bhardwaj63,64, Krittika Bhattacharyya65,66, Ali Bijani67, Zebenay Workneh Bitew68,69, Somayeh Bohlouli70, Obasanjo Afolabi Bolarinwa51, Archith Boloor71, Ivana Bozicevic72,73, Zahid A. Butt74,75, Rosario Cárdenas76, Felix Carvalho77, Jaykaran Charan78, Vijay Kumar Chattu79,80, Mohiuddin Ahsanul Kabir Chowdhury81,82, Dinh‑Toi Chu83, Richard G. Cowden84, Saad M. A. Dahlawi85, Giovanni Damiani86,87, Eugene Kofuor Maafo Darteh88, Aso Mohammad Darwesh89, José das Neves90,91, Nicole Davis Weaver1, Diego De Leo92, Jan‑Walter De Neve56, Kebede Deribe93,94, Keshab Deuba95,96, Samath Dharmaratne1,97,98, Mostafa Dianatinasab99,100, Daniel Diaz101,102, Alireza Didarloo103, Shirin Djalalinia104, Fariba Dorostkar105, Eleonora Dubljanin106, Bereket Duko107,108, Maha El Tantawi109, Shaimaa I. El‑Jaafary110, Babak Eshrati111, Sharareh Eskandarieh112, Oghenowede Eyawo113, Ifeanyi Jude Ezeonwumelu114,115, Sayeh Ezzikouri116, Farshad Farzadfar117, Nazir Fattahi118, Nelsensius Klau Fauk119,120, Eduarda Fernandes121, Irina Filip122,123, Florian Fischer124, Nataliya A. Foigt125, Masoud Foroutan126,127, Takeshi Fukumoto128, Mohamed M. Gad129,130, Abhay Motiramji Gaidhane131, Birhan Gebresillassie Gebregiorgis47, Ketema Bizuwork Gebremedhin132, Lemma Getacher133, Keyghobad Ghadiri134,135, Ahmad Ghashghaee136, Mahaveer Golechha137, Mohammed Ibrahim Mohialdeen Gubari138, Harish Chander Gugnani139,140, Rafael Alves Guimarães141, Mohammad Rifat Haider142, Arvin Haj‑Mirzaian143,144, Samer Hamidi145, Abdiwahab Hashi146, Soheil Hassanipour147,148, Hadi Hassankhani149,150, Khezar Hayat151,152, Claudiu Herteliu153,154, Hung Chak Ho155, *Correspondence: ehaeuser@uw.edu 1 Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA Full list of author information is available at the end of the article © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​ mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Haeuser et al. BMC Medicine (2022) 20:488 Page 2 of 24 Ramesh Holla50, Mostafa Hosseini156,157, Mehdi Hosseinzadeh158,159, Bing‑Fang Hwang160, Segun Emmanuel Ibitoye161, Olayinka Stephen Ilesanmi162,163, Irena M. Ilic164, Milena D. Ilic165, Rakibul M. Islam166, Chidozie C. D. Iwu167, Mihajlo Jakovljevic168,169, Ravi Prakash Jha170,171, John S. Ji172, Kimberly B. Johnson1, Nitin Joseph173, Vasna Joshua174, Farahnaz Joukar147,148, Jacek Jerzy Jozwiak175, Leila R. Kalankesh176, Rohollah Kalhor177,178, Naser Kamyari179, Tanuj Kanchan180, Behzad Karami Matin118, Salah Eddin Karimi181, Gbenga A. Kayode182,183, Ali Kazemi Karyani118, Maryam Keramati184, Ejaz Ahmad Khan185, Gulfaraz Khan186, Md Nuruzzaman Khan187, Khaled Khatab188,189, Jagdish Khubchandani190, Yun Jin Kim191, Adnan Kisa192,193, Sezer Kisa194, Jacek A. Kopec195,196, Soewarta Kosen197, Sindhura Lakshmi Koulmane Laxminarayana198, Ai Koyanagi199,200, Kewal Krishan201, Barthelemy Kuate Defo202,203, Nuworza Kugbey204, Vaman Kulkarni173, Manasi Kumar205,206, Nithin Kumar173, Dian Kusuma207,208, Carlo La Vecchia209, Dharmesh Kumar Lal210, Iván Landires211,212, Heidi Jane Larson1,213, Savita Lasrado214, Paul H. Lee215, Shanshan Li216, Xuefeng Liu217,218, Afshin Maleki219,220, Preeti Malik221,222, Mohammad Ali Mansournia156, Francisco Rogerlândio Martins‑Melo223, Walter Mendoza224, Ritesh G. Menezes225, Endalkachew Worku Mengesha226, Tuomo J. Meretoja227,228, Tomislav Mestrovic229,230, Andreea Mirica153, Babak Moazen56,231, Osama Mohamad232, Yousef Mohammad233, Abdollah Mohammadian‑ Hafshejani234, Reza Mohammadpourhodki235, Salahuddin Mohammed236,237, Shafiu Mohammed238,239, Ali H. Mokdad1,98, Masoud Moradi118, Paula Moraga240, Sumaira Mubarik241, Getaneh Baye B. Mulu242, Lillian Mwanri243, Ahamarshan Jayaraman Nagarajan244,245, Mukhammad David Naimzada246,247, Muhammad Naveed248, Javad Nazari249, Rawlance Ndejjo250, Ionut Negoi251,252, Frida N. Ngalesoni253, Georges Nguefack‑Tsague254, Josephine W. Ngunjiri255, Cuong Tat Nguyen256, Huong Lan Thi Nguyen256, Chukwudi A. Nnaji257,258, Jean Jacques Noubiap259, Virginia Nuñez‑Samudio260,261, Vincent Ebuka Nwatah262,263, Bogdan Oancea264, Oluwakemi Ololade Odukoya265,266, Andrew T. Olagunju267,268, Babayemi Oluwaseun Olakunde269, Bolajoko Olubukunola Olusanya270, Jacob Olusegun Olusanya270, Ahmed Omar Bali271, Obinna E. Onwujekwe272, Orish Ebere Orisakwe273, Nikita Otstavnov246, Stanislav S. Otstavnov246,274, Mayowa O. Owolabi275,276, P. A. Mahesh277, Jagadish Rao Padubidri278, Adrian Pana153,279, Ashok Pandey280,281, Seithikurippu R. Pandi‑Perumal282, Fatemeh Pashazadeh Kan283, George C. Patton284,285, Shrikant Pawar286, Emmanuel K. Peprah287, Maarten J. Postma288,289, Liliana Preotescu290,291, Zahiruddin Quazi Syed131, Navid Rabiee292,293, Amir Radfar294, Alireza Rafiei295,296, Fakher Rahim297, Vafa Rahimi‑Movaghar298, Amir Masoud Rahmani299, Kiana Ramezanzadeh143, Juwel Rana300,301, Chhabi Lal Ranabhat302,303, Sowmya J. Rao304, David Laith Rawaf305,306, Salman Rawaf307,308, Reza Rawassizadeh309, Lemma Demissie Regassa310, Nima Rezaei311,312, Aziz Rezapour26, Mavra A. Riaz313, Ana Isabel Ribeiro314, Jennifer M. Ross315,1,316, Enrico Rubagotti317,318, Susan Fred Rumisha319,320, Godfrey M. Rwegerera321, Sahar Saeedi Moghaddam117, Rajesh Sagar322, Biniyam Sahiledengle323, Maitreyi Sahu1, Marwa Rashad Salem324, Hossein Samadi Kafil325, Abdallah M. Samy326, Benn Sartorius98,327,328, Brijesh Sathian329,330, Abdul‑Aziz Seidu88,331, Amira A. Shaheen332, Masood Ali Shaikh333, Morteza Shamsizadeh334, Wondimeneh Shibabaw Shiferaw47, Jae Il Shin335, Roman Shrestha336, Jasvinder A. Singh337,338, Valentin Yurievich Skryabin339, Anna Aleksandrovna Skryabina340, Shahin Soltani118, Mu’awiyyah Babale Sufiyan341, Takahiro Tabuchi342, Eyayou Girma Tadesse343, Nuno Taveira344,345, Fisaha Haile Tesfay346,347, Rekha Thapar173, Marcos Roberto Tovani‑Palone348,349, Gebiyaw Wudie Tsegaye350, Chukwuma David Umeokonkwo351, Bhaskaran Unnikrishnan352, Jorge Hugo Villafañe353, Francesco S. Violante354,355, Bay Vo356, Giang Thu Vu357, Yohannes Dibaba Wado358, Yasir Waheed359, Richard G. Wamai360,361, Yanzhong Wang362, Paul Ward363, Nuwan Darshana Wickramasinghe364, Katherine Wilson365, Sanni Yaya366,367, Paul Yip368,369, Naohiro Yonemoto370,371, Chuanhua Yu241, Mikhail Sergeevich Zastrozhin372,373, Yunquan Zhang374,375, Zhi‑Jiang Zhang376, Simon I. Hay1,98, Laura Dwyer‑Lindgren1,98 and on behalf of Local Burden of Disease sub-Saharan Africa HIV Prevalence Haeuser et al. BMC Medicine (2022) 20:488 Page 3 of 24 Abstract 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 sex- specific 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 coun‑ tries in SSA, from years 2000 to 2018, at a 5 × 5-km resolution and presented among second administrative level (typi‑ cally 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. Keywords: HIV, Mapping, Africa, Geostatistics, Spatial statistics, HIV prevalence, Demographics Background the world is not on track to meet these goals [2, 7, 8], Four decades after its discovery, human immunode- and progress may stall further as a consequence of the ficiency virus (HIV) continues to impact millions of COVID-19 pandemic [9]. people worldwide, remains one of the leading causes Differences in HIV prevalence both within and between of morbidity and mortality globally [1, 2] and incurs nations in SSA have been well-documented [10–14], as billions of dollars annually in direct health care costs have differences between sexes [2, 12–14] and age groups and indirect socioeconomic costs [3]. In sub-Saharan [2]. These differences have also changed over time [1, Africa (SSA) in 2019, an estimated 26 million people 10], impacted in part by the onset, duration, location, were living with HIV [2]. In recent years, international and demographic targeting of different prevention and bodies have set goals to end the HIV epidemic: in 2014, treatment interventions [15–17]. Epidemiologically tar- the Joint United Nations Programme on HIV/AIDS geted interventions are understood to be more effec- (UNAIDS) introduced the “95-95-95” targets—that tive compared to homogeneous interventions [18] and by 2030, 95% of people living with HIV globally would are increasingly important at a time when the future of know their status, 95% of all people with diagnosed HIV funding for HIV prevention and treatment is both uncer- infection would receive sustained antiretroviral therapy, tain and highly variable [19, 20], particularly in the wake and 95% of people living with HIV receiving antiretro- of disruptions related to the COVID-19 pandemic [21]. viral therapy (ART) would be virally suppressed [4, 5]. Evidence suggests that interventions are most effective The United Nations Sustainable Development Goals when tailored to account for differences in the intensity also call for an end to the AIDS epidemic by 2030 [6]. of the epidemic by geographic location [14, 22], sex [23], Unfortunately, despite a significant increase in ART and age [24]. Locally and demographically precise HIV coverage over the last 20 years and major progress in prevalence information, however, is necessary in order to terms of reductions in HIV incidence and mortality maximize the benefit of such methods; at present, such [1], the latest estimates and projections indicate that information in SSA is lacking. Haeuser et al. BMC Medicine (2022) 20:488 Page 4 of 24 HIV prevalence estimates stratified by age and sex are optimize the contemporaneousness of the estimates and available at the national level through the Global Bur- to account for data availability—there were relatively den of Disease (GBD) [2] and from UNAIDS [25]. Both few large-scale seroprevalence surveys conducted sources also provide subnational estimates at the first before 2000, and most seroprevalence surveys focus on administrative level (e.g., province, state) in select coun- adults, with little reporting outside the 15–59 years age tries. Recently, Dwyer-Lindgren et al. [10] presented range. We produced estimates for sex rather than gender aggregated adult HIV prevalence estimates for the years binaries because sex is more predominantly reported 2000–2017 at local scales in SSA, generalizing estimates in the available data sources. Due to data availability for males and females combined, and across ages 15–49 limitations we were unable to produce prevalence years. Some studies have gone further to present subna- estimates for sex minority individuals outside the male/ tional prevalence estimates separated by sex [26–29] or female binary. The 43 countries analyzed were also age [30]; however, these studies focused on single coun- selected according to data availability—Mauritania tries, and/or presented estimates for only one point in was excluded as there were no HIV prevalence data time, without describing any temporal trajectories in available. We included six countries—Djibouti, Guinea- prevalence. To our knowledge, no previous studies have Bissau, Madagascar, Somalia, South Sudan and Sudan— presented age- and sex-specific HIV prevalence estimates where no seroprevalence survey data were available, across SSA at local scales over time. but where sentinel surveillance data collected from We built upon the HIV prevalence model from Dwyer- antenatal care clinic (ANC) attendees (described below) Lindgren et al. [10] to produce HIV prevalence estimates were available. The implications of these and other for 43 countries in SSA for males and females ages 15–59 limitations are expanded upon in the “Methodological years, stratified into nine 5-year age groups, for the advantages and limitations” section in the “Discussion” years spanning 2000 to 2018. Countries, age groups, and section. time period were selected according to data availability. The methodology used here largely parallels that previ- We expanded upon existing Bayesian spatiotemporal ously used to map adult HIV prevalence in SSA [10], with methods to model these estimates at a 5 × 5-km the incorporation of modifications necessary to model by resolution and present them here aggregated to the age and sex, and improvements related to the inclusion of second administrative level (which varies by country but spatially aggregated data and ANC data (Fig. 1). We used is typically equivalent to e.g., districts, municipalities), a 5 × 5-km grid for consistency with this previous analy- which is the level typically considered most relevant to sis; to align with the resolution available for pre-existing policymakers and stakeholders. Prevalence estimates covariates incorporated in this analysis; and for flexibility for all demographic groups at all levels of geographic in aggregating these estimates to other levels of interest aggregation, as well as number of people living with HIV (e.g., first- and second-level administrative subdivisions, (count estimates), are publicly available from the Global such as states or districts, respectively, or more aggre- Health Data Exchange (https://​ghdx.​healt​hdata.​org/​ gated age groups such as reproductive ages [commonly record/​ihme-​data/​sub-​sahar​an-​africa-​hiv-​preva​lence-​ 15-49]) using grid-cell-level estimates of age- and sex- geosp​atial-​estim​ates-​2000-​2018) and through a user- specific population from Worldpop [32]. These popula- friendly data visualization tool (http://​vizhub.​healt​hdata.​ tion estimates were also used to estimate the number of org/​lbd/​hiv-​prev-​disagg). people living with HIV in each demographic group. All analyses were conducted in R version 3.6.1 [33]. Figure 2 Methods provides an overview of the analytic process, described Overview in more depth below. Additional details are available in This ecological study follows the Guidelines for Additional file 1. Accurate and Transparent Health Estimates Reporting (GATHER) [31] (Additional file 1: Section 1). This Data analysis relies secondary data sources to provide HIV data estimates of HIV prevalence on a 5 × 5-km grid in 43 We compiled a geolocated dataset of 304,672 countries in SSA for males and females ages 15–59 observations from 91 seroprevalence surveys from years residing at each location, stratified into five-year 37 countries and 10,351 observations from sentinel age bins (i.e., ages 15–19, 20–24, 25–29, 30–34, 35–39, surveillance among antenatal care clinic attendees (ANC 40–44, 45–49, 50–54, 55–59), with annual resolution data) in 43 countries (Additional file 2: Tables S1-S2; from year 2000 to 2018 inclusive, calibrated to national Fig. 1). Data from seroprevalence surveys were originally estimates from the GBD [2]. The period of 2000–2018 in the form of survey microdata (that is, individual-level and the age range of 15–59 years were selected to survey responses) or survey reports (Additional file 2: Haeuser et al. BMC Medicine (2022) 20:488 Page 5 of 24 Fig. 1 HIV prevalence data by region and country. a HIV seroprevalence survey data and b ANC sentinel surveillance data used in this analysis, by region and country. Color indicates the data source. AIS, AIDS Indicator Survey; DHS, Demographic and Health Survey; MICS, Multiple Indicator Cluster Survey; PHIA, Population-based HIV Impact Assessment Survey. Shape type indicates whether a data source is age-specific and has point (GPS) or polygon location information. Size indicates the relative effective sample size for each source. A full list of data sources with additional details about data type (such as survey microdata and survey reports) and geographical details are provided in Additional file 2: Tables S1-S5 Table S1). For surveys with available microdata, we gender is a fluid social construct. In the absence of quality extracted variables related to age, sex, HIV blood test data, however, we could not disaggregate estimates by result, location, and year, as well as survey weights, where gender at this time. After subsetting by age, we collapsed available. We excluded rows with missing information the age-specific data into 5-year age bins (hereafter on any of these variables, and subset the data to ages referred to as “ages”) by sex. We did this by calculating 15–59 years. For data coded by gender rather than sex, the weighted age- and sex-specific HIV prevalence at we treated these data as if they were sex-specific rather the finest spatial resolution available. Ideally, this was at than gender-specific. We recognize that sex and gender the level of global positioning system (GPS) coordinates are not interchangeable: sex is a biological variable, while that represent the location of a survey cluster. In most Haeuser et al. BMC Medicine (2022) 20:488 Page 6 of 24 Fig. 2 Analytical process overview. The process used to produce age- and sex-specific HIV prevalence estimates in sub-Saharan Africa involved three main parts. In the data-processing steps (green), data were identified, extracted, and prepared for use in the HIV prevalence model and in covariate models. In the modeling phase (orange), we used these data and covariates in a stacked generalization ensemble model and spatiotemporal Gaussian process model. In the post-processing phase (blue), we calibrated the prevalence estimation to match GBD 2019 estimates at the national level, aggregated prevalence estimates to the first- and second-level administrative subdivisions in each country, and calculated the number of people living with HIV (PLHIV) surveys, GPS coordinates are randomly displaced information regarding the age range covered, to be used (typically by 2–5 km depending on the setting and the in our modeling process as described below. We also survey series [34]) in order to protect respondent’s only included sex-specific data. For more information on confidentiality. In instances where GPS coordinates were excluded surveys see Additional file 2: Table S3. not available, the smallest areal unit (termed a “polygon”) Data that were spatially aggregated (i.e., polygon data) possible was used instead. These typically represented and/or age-aggregated required additional processing. an administrative subdivision. For surveys without Although we ultimately modeled HIV prevalence at microdata but for which estimates with some subnational the level of the observation, be it point or polygon, age- resolution were provided in a report, we extracted these specific or age-aggregated, our modeling process initially estimates with information about the sample size and specified HIV prevalence at the point-, time-, age-, and location. GPS coordinates were not available for these sex-specific level. Because of this, it was necessary reports, so these data were exclusively matched to that we disaggregate the age-aggregated and polygon polygons. In most reports, age ranges larger than 5 years survey data to be location- and age-specific. We did this were reported. Among these, we retained data reported by distributing polygon data to pixels proportional to for age ranges that corresponded exactly to one or more population. Specifically, for each polygon, we generated of the 5-year age bins used in this model; for example, points at the centroid of each 5 × 5-km pixel falling we included surveys covering age ranges 15–49 years, or within that polygon and replicated that observation’s 15–24 years, but excluded those covering age ranges such HIV prevalence and sample size at the location of each as 18–24 years. For age-aggregated data, we retained of those centroids. Age-aggregated point data were Haeuser et al. BMC Medicine (2022) 20:488 Page 7 of 24 similarly disaggregated by replicating the HIV prevalence covariates were constructed explicitly for this analysis and sample size once for each year-age group covered owing to their known association with HIV prevalence in the overall age range. In the cases of age-aggregated and data availability: (1) prevalence of male circumcision polygon data, these two processes were combined. Next, (all forms); (2) prevalence of self-reported sexually each of the disaggregated, location- and age-specific rows transmitted infection (STI) symptoms; (3) prevalence of data associated with a given aggregated observation of marriage or living with a partner as married; (4) were assigned weights proportional to the age- and prevalence of one’s current partner living elsewhere sex-specific population residing at that location for the among females; (5) prevalence of condom use at last given year, derived from WorldPop [32]. Weights per sexual encounter; (6) prevalence of reporting ever having observation all summed to one. This process substantially had intercourse among young females; and (7) and (8) increased the size of the dataset. To reduce the associated prevalence of multiple partners in the past year for males computational burden when fitting the model, in cases and for females, respectively. We updated the covariates where at least one row within an observation was given constructed for this analysis to incorporate newly a weight of less than half of one divided by the number of available data but utilized the original statistical methods locations and/or ages in that observation, we successively (Additional file 1: Section 3.2; Additional file 2: Table S5; dropped the lowest-weighted locations and/or ages until Additional file 3: Figs. S1-S8). reaching a maximum of 1% of the observation’s weight dropped. Remaining locations and/or ages within that observation were then reweighted to maintain a total Model and estimation weight of one. Data that were not aggregated (i.e., age- Covariate stacking specific point observations) were each assigned a weight An ensemble covariate modeling approach (“stacking”) of one. was implemented to capture possible nonlinear ANC data were primarily derived from national HIV interactions among the covariates across space and time estimate files developed by national teams and compiled [37]. In this approach, three sub-models were fitted to and shared via UNAIDS [35] and supplemented with the HIV survey data with the covariates as explanatory data derived from sentinel surveillance country reports predictors: generalized additive models [38], boosted (Additional file 2: Table S2). We extracted information regression trees [39], and lasso regression [40]. Each from these sources on HIV prevalence and sample size sub-model was fitted using fivefold cross-validation to by site and year. Sites were geolocated to specific GPS avoid overfitting, and the out-of-sample predictions coordinates where possible and otherwise to a polygon from across the five folds were compiled into a single that represents an administrative subdivision. The ANC set of predictions that were used to fit the geostatistical data available for this analysis were not age-specific. model described below. In addition, each sub-model was Because ANC data included only pregnant females, also fitted to the full dataset to generate a complete set of we assumed the age range of these data to be that of in-sample predictions that were subsequently used when females with non-zero fertility rates in SSA according generating predictions from the geostatistical model to GBD 2019 [36], that is, females ages 15–54 years. We (Additional file 3: Figs. S9-S11). Because the covariates disaggregated ANC data to the age and location level used here were neither age-specific nor (for most) sex- as we did for age-aggregated or polygon survey data. specific, we fit these sub-models at that same age- and However, specific locations and ages were weighted sex-aggregated level as the HIV-specific covariates, by number of births rather than population size. The modeling HIV prevalence data aggregated across ages number of births for a given age and location was 15–49 and males and females. The age range 15–49 years estimated as the product of the location-, age-, and sex- was used in this case because of its more common usage specific population, again derived from WorldPop [32], in seroprevalence surveys compared to the 15–59 years and the national fertility rate, derived from GBD 2019 range, allowing us to retain more data for the stacking estimates [36]. model. Polygon data were excluded from stacking models due to their incongruity with the configurations Covariates needed for the different sub-models. The ANC data were This analysis included the same covariates as the also excluded due to known sampling biases, which are previous analysis [10]. This included five pre-existing described in the Additional file 1: Section 4.2. covariates: (1) travel time to the nearest settlement of more than 50,000 inhabitants; (2) total population; Geostatistical model (3) night-time lights; (4) urbanicity; and (5) malaria This model was fit in Template Model Builder (TMB) incidence (Additional file 2: Table S4). In addition, eight [41]. Owing to computational constraints, and to Haeuser et al. BMC Medicine (2022) 20:488 Page 8 of 24 allow for regional differences in the relationships We used the stochastic partial differential equation [43] between covariates and HIV prevalence, as well as approach to approximate the continuous spatiotemporal differences in the temporal, spatial, and demographic Gaussian random field (Z1, j). Sensitivity analyses were autocorrelation in HIV prevalence, separate models carried out to compare this model configuration to others were fitted for four regions (Additional file 3: Fig. with differing pj specification configurations, as well as S12). We modeled HIV prevalence stratified by space, to several other model and data specifications, and are time, age, and sex using a generalized linear mixed- described in detail in the Additional file 1: Section 4.3, effects model. To simultaneously model point- and Additional file 3: Figs. S13-S15, and the “Discussion” polygon-level observations, as well as both age-specific section. We then specified observation-level (i) prevalence: and age-aggregated observations, we specified the ( (∑ ( )) ( ) pi = logit−1 logit ) data likelihood at the observation level (i), which ptransformed,j ⋅ wj + 𝛽2 + Us[i] ⋅ IANC + 𝜖i accommodated all of these. We modeled the number of HIV-positive individuals (Yi) among a sample (Ni) for a pi was calculated as the sum of disaggregated preva- given observation as a binomial variable: lence (ptransformed, j) estimates multiplied by their respec- tive population (or in the case of ANC data, birth) weights (wj), plus the incorporation of additional ANC- ( ) Yi ∼ Binomial Ni , pi related transformations and bias corrections (β2, Us[i], Logit-transformed prevalence was however first specified and IANC described below), and an observation-level at the space, time, age, and sex-disaggregated level (j): uncorrelated error term (ϵi): ( ) logit pj = 𝛽0 + 𝜷 1 X j + Z1,j + Z2,j + Z3,c[j] ϵi ∼ Normal 0, 𝜎i2 ( ) ( ) Z1,j ∼ GP 0, 𝛴 1,space ⊗ 𝛴 1,time In cases where data were already disaggregated spatially ( ) Z2,j ∼ GMRF 0, 𝛴2,time ⊗ ( 𝛴2,age)⊗ 𝛴2,sex Z3,c[j] ∼ GMRF 0, 𝛴3,c and by age, wj = 1. HIV prevalence as measured by sentinel surveillance We specified logit-transformed prevalence at the of ANC clinic attendees is known to be biased as a disaggregated level (pj) as a linear combination of: measure of HIV prevalence in the general adult female population [44], because it only covers pregnant females • A regional intercept (β0); who attend ANC, compared to all adult females [45, • Covariates and associated regression parameters (β1Xj); 46]. Additionally, fertility rates differ between ­ HIV+ - • Random effects correlated across space and time, (Z1, j); and ­HIV females, with the exact relationship varying • Random effects correlated across time, age, and sex, by age [47], thereby impacting age-specific ANC clinic (Z2, j); visitation rates. To address this, for ANC data we • Country-specific (c) random effects correlated across transformed prevalence among pregnant females based age, (Z3, c[j]). on the underlying prevalence among all females and the age-specific fertility-rate ratio ­(HIV+ fertility/HIV- The random effects capturing correlations between fertility). For ANC data, space, time, age, and sex included: ( ) pj ⋅ FRRj ptransformed,j = ( ) • Z1, j: a Gaussian process with mean 0 and a covariance pj ⋅ FRRj + 1 − pj matrix given by the Kronecker product of a spatial Matérn covariance function [42] (Σ1, space) and a Fertility rate ratios (FRRj) were derived from GBD 2019 temporal first-order autoregressive covariance function fertility estimates [36], taken at the national level except (Σ1, time); in cases where subnational estimates were available (in • Z2, j: a Gaussian Markov Random Field with mean Ethiopia, Nigeria, and South Africa). For survey data, 0 and a covariance matrix given by the Kronecker ptransformed,j = pj product of first-order autoregressive covariance functions for time (Σ2, time), age (Σ2, age), and sex (Σ2, sex); To allow for additional ANC-related bias at the obser- • Z3, c[j]: a Gaussian Markov Random Field with mean 0 vation level (i), in instances where data in our model were and a covariance matrix given by country-specific first- derived from ANC sentinel surveillance (where IANC = 1 order autoregressive covariance functions for age (Σ3, c). for ANC data, and IANC = 0 for all other data) our model incorporated a fixed term (β2) that captured overall mean Haeuser et al. BMC Medicine (2022) 20:488 Page 9 of 24 bias in the ANC data, and a random effect (Us[i]) for a number of people living with HIV for a given age and given ANC site s that captured spatial differences in the sex in each grid cell were derived by multiplying esti- extent of this bias: mated prevalence in each grid cell by the correspond- ( ) ing population estimate from WorldPop [32], which 2 Us[i] ∼ Normal 0, 𝜎site[i] was also calibrated to match GBD 2019 [36] (Addi- tional file 1: Section 4.4; complete estimates of people Fitted model parameters are detailed in Additional living with HIV are available along with all prevalence file 2: Table S6. From each fitted model, we generated estimates at (https://​ghdx.​healt​hdata.​org/​record/​ihme-​ 1000 draws from the approximated joint posterior data/ ​ s ub- ​ s ahar ​ a n- ​ a frica- ​ h iv- ​ p reva ​ l ence- ​ g eosp ​ a tial-​ distribution of all model parameters and used these to estim​ates-​2000-​2018)). construct 1000 draws of pj, setting IANC to 0. Fivefold Although the model makes predictions for all locations cross-validation was used to assess model performance covered by available covariates, all final model outputs and to compare a number of alternative models for which land cover was classified as barren or sparsely (Additional file 3: Figs. S13-S15). We also compared the vegetated according to European Space Agency Climate re-aggregated adult-level estimates from our final model Change Initiative satellite data [48] and for which total to those from the results of an age- and sex-aggregated population density was less than 10 individuals per 1 × counterpart (Additional file 3: Fig. S16). 1-km in 2015 were masked for improved clarity when communicating with data specialists and policymakers. Post‑estimation Maps were generated in R using the ggplot2 [49] package To take advantage of the more structured modeling version 3.3.0. approach and additional national-level data used by GBD 2019 [2], we performed post hoc calibration of Results our estimates to the corresponding national-level GBD Geographic variation estimates. For each country, year, age bin, and sex in our We found large differences in the spatial and demographic analysis, we defined a “raking factor” equal to the ratio distribution of estimated HIV prevalence in SSA that were of the GBD estimate for this country-year-age-sex to the masked in demographically aggregated estimates (Figs. 3 population-weighted posterior mean HIV prevalence in and 4; Additional file 3: Figs. S19-S34). This was particu- all corresponding grid cells (Additional file 3: Figs. S17- larly striking among middle and older age groups. For S18). These raking factors were then used to scale each example, in the year 2018, the maximum estimated HIV draw of HIV prevalence for each grid cell within that prevalence in any second-level administrative unit for GBD geography, year, age, and sex. Point estimates for adults ages 15–59 years was 35.4% in Umgungundlovu each grid cell were calculated as the mean of the scaled in the Kwazulu Natal province, South Africa (95% uncer- draws, and 95% uncertainty intervals were calculated tainty interval (UI), 22.3–46.3%). However, estimated as the 2.5th and 97.5th percentiles of the scaled draws. prevalence reached up to 59.4% [46.5–71.2%], almost 1.7 Grid cells that crossed international borders within times higher, for females ages 35–39 years within that modeling regions were fractionally allocated to mul- same location. Across all second-level administrative tiple countries in proportion to the covered area dur- units, age groups, and sexes, females ages 35–39 in Nki- ing this process. In cases where subnational (i.e., first longo in Lubombo, Eswatini, had the highest estimated administrative level) estimates were available from the HIV prevalence in the year 2018, at 62.5% [50.1–74.5%]. GBD, that is, for Ethiopia, Nigeria and South Africa, we Geographic variation within countries was also more calibrated to those estimates rather than those at the dramatic in our demographically disaggregated results. national level. Uncertainty in GBD estimates was not Across SSA countries, the median absolute differ- accounted for in this calibration. ence between second-level administrative units with In addition to estimates of HIV prevalence on a 5 × the lowest and highest estimated prevalence within a 5-km grid, we constructed estimates of HIV prevalence given country in 2018 was 3.5 times greater when con- for first- and second-level administrative subdivisions. sidered across ages and sexes, than when estimated for We did this by calculating age- and sex-specific popu- all adults combined (11.2 percentage points versus 3.2 lation-weighted averages of prevalence for all grid cells percentage points). This difference in within-country within a given area. This process was carried out for prevalence range between demographically aggregated each of the 1000 posterior draws (after calibration to versus disaggregated estimates varied greatly between GBD), with final point estimates derived from the mean countries. For example, in Mozambique, this range of these draws and uncertainty intervals from the 2.5th across second-level administrative units was 30.1 per- and 97.5th percentiles. Additionally, estimates of the centage points [16.7–46.3] for combined adults and Haeuser et al. BMC Medicine (2022) 20:488 Page 10 of 24 Fig. 3 HIV prevalence in sub-Saharan Africa in 2018 at the second administrative level for a subset of modeled demographic groups from the lower, middle, and upper age ranges: a all adults, ages 15–59 years; b males and c females ages 15–19 years; d males and e females ages 35–39 years; and f males and g females ages 55–59 years. Maps reflect national boundaries, land cover, lakes, and population; areas with fewer than ten people per 1 × 1 km, and classified as barren or sparsely vegetated, are colored light gray. Countries colored in dark gray were not included in the analysis 56.9 percentage points [37.4–78.2] (or 1.9 times larger) than males (Fig. 5). In 2018, for prevalence aggregated for estimates across ages and sexes. In Lesotho, on the across ages 15–59 years, in no second-level adminis- other hand, this range was 8.2 times larger for estimates trative units was estimated prevalence higher among across ages and sexes compared to adults combined males compared to females. The absolute difference (51.6 percentage points [40.1–63.5] versus 6.3 percent- in estimated prevalence in 2018 between females and age points [1.4–11.5]). Overall, countries in Eastern males reached a maximum of 15.0 percentage points (in SSA tended to see greater such discrepancies compared Umkhanyakude, in KwaZulu-Natal, South Africa, with to other regions; here, the median absolute difference 36.3% [24.7–46.8%] estimated prevalence in females between second-level administrative units was 4.4 compared to 21.3% [13.1–28.7%] estimated preva- times greater when considered across ages and sexes lence in males), for a female to male prevalence ratio than for all adults combined (14.0 versus 3.2 percentage of 1.7 [1.5–1.9]. Countries in Central SSA, where over- points). For complete geographic variation compari- all prevalence was lower than in other SSA regions, sons within each country, including uncertainty esti- tended to see the largest disparity between females mates, see Additional file 4. and males in terms of relative differences. Estimated prevalence among females in Central SSA ranged up Variation between males and females to a maximum of 2.7 [1.84–4.2] times greater than Across SSA and across the years 2000–2018, estimated estimated prevalence in males in 2018 (in San Anto- HIV prevalence was generally higher among females nio de Palé, in Annobón, Equatorial Guinea, with 8.3% Haeuser et al. BMC Medicine (2022) 20:488 Page 11 of 24 Fig. 4 Relative uncertainty in HIV prevalence, 2018. Overlapping population-weighted quartiles of HIV prevalence (constructed separately for each demographic group) and relative 95% uncertainty in 2018 at the 5 × 5-km grid cell level for select demographic groups: a all adults, ages 15–59 years; b males and c females ages 15–19 years; d males and e females ages 35–39 years; and f males and g females ages 55–59 years. Relative uncertainty is defined as the ratio of the width of the 95% uncertainty interval to the mean estimate. Maps reflect national boundaries, land cover, lakes, and population; areas with fewer than ten people per 1 × 1 km, and classified as barren or sparsely vegetated, are colored light gray. Countries colored in dark gray were not included in the analysis [2.1–21.4%] prevalence in females compared to 3.1% prevalence was 1.5, and the median absolute difference [0.8–8.1%] prevalence in males). Across Central SSA was 1.5 percentage points. We did not note substantial second-level administrative units, the median ratio differences in within-country variations in prevalence between female and male estimated prevalence was between females and males in either 2000 or 2018 in 2.2, compared to the all-SSA median ratio of 1.6. The any region. For complete comparisons between sexes greatest absolute differences were seen in Eastern SSA, by second-level administrative unit, including uncer- where the median absolute difference between female tainty estimates, see Additional file 4. and male estimated prevalence was 1.9 percentage points in 2018, compared to the all-SSA median abso- Variation between age groups lute difference of 0.9 percentage points. These differ- Prevalence within second-level administrative units was ences between female and male prevalence in 2018 also highly variable across age groups (Fig. 6), and rela- were less than those observed in the year 2000, when tive variation in prevalence between age groups in 2018 the median ratio between female and male estimated tended to be higher in males. Comparing estimated Haeuser et al. BMC Medicine (2022) 20:488 Page 12 of 24 Fig. 5 Differences in estimated prevalence between males and females ages 15–59 years at the second administrative level in 2018, calculated as a the ratio of estimated prevalence among females to prevalence among males and b the absolute difference in estimated prevalence between females and males. Maps reflect national boundaries, land cover, lakes, and population; areas with fewer than ten people per 1 × 1 km, and classified as barren or sparsely vegetated, are colored light gray. Countries colored in dark gray were not included in the analysis prevalence across age groups within a given second- 4043) in 31 of 43 countries. For both males and females, level administrative unit in 2018, the ratio between high- the age group with the highest estimated prevalence est and lowest prevalence among age groups tended to tended to vary more across Eastern SSA compared to be larger among males compared to females (median other regions. ratio across all SSA second-level administrative units of Within-country variation between second-level admin- 14.4 for males, and 9.3 for females). For males, this ratio istrative units was relatively consistent across age groups. between highest and lowest estimated prevalence among The ratio of maximum to minimum estimated prevalence age groups was smaller in Central SSA compared to other among districts within each country was lowest for ages regions (median ratio of 8.3) and was largest in Western 35–39 years (median ratio of 4.3 across countries) and SSA (median ratio of 21.7). There was little regional dif- highest for ages 15–19 years (median ratio of 4.8 across ference for females. The sexes also differed in changes in countries) in 2018. Slightly larger differences were seen this ratio between years, where it decreased over time for between age groups in Eastern and Southern SSA, with males (with a median ratio in 2000 of 52.7) but increased lower variation in middle-age groups and greater within- over time for females (median ratio in 2000 of 5.6). For country variation in younger age groups. The maximum- complete age variation comparisons by second-level to-minimum within-country prevalence ratio in Eastern administrative unit, including uncertainty estimates, see SSA was lowest for adults ages 40–44 years (median Additional file 4. ratio of 5.4 across Eastern SSA countries) and highest Across SSA, the age group with the highest estimated for adults ages 15–19 years (median ratio of 6.7 across prevalence in any given second-level administrative unit Eastern SSA countries). These same age groups also rep- in 2018 was always between ages 35 and 54 years for resented the highest and lowest ratios in Southern SSA males and between 30 and 49 years for females (Fig. 6). countries, with median values of 2.0 in adults ages 40–44 In 2018, males ages 45–49 years most commonly had years and 2.8 in adults ages 15–19 years. the highest estimated prevalence across all age groups in a given second-level administrative unit, at 46.8% of Variation over time second-level administrative units (1894 of 4043) from Estimated change in prevalence over time among all within 23 of 43 countries. Females ages 40–44 years adults masked broad differences between specific age had the highest estimated prevalence across age groups and sex groups (Fig. 7; Additional file 3: Figs. S35-S40). in 63.8% of second-level administrative units (2581 of Large temporal changes were much more common Haeuser et al. BMC Medicine (2022) 20:488 Page 13 of 24 Fig. 6 Differences in prevalence between age groups in the year 2018 at the second administrative level, calculated as the ratio of estimated prevalence between the age groups with highest and lowest prevalence, for a males b and females; and the age groups with highest prevalence for c males d and females in 2018. Maps reflect national boundaries, land cover, lakes, and population; areas with fewer than ten people per 1 × 1 km, and classified as barren or sparsely vegetated, are colored light gray. Countries colored in dark gray were not included in the analysis when considering sexes and age groups, compared to administrative units. On the other hand, 37.7% (1523 of all adults combined. Between the years 2000 and 2018, 4043) of second-level administrative units experienced among all adults ages 15–59 years, estimated HIV prev- an increase in estimated HIV prevalence greater than alence increased by more than 5.0 percentage points in 5.0 percentage points in that timeframe in at least one only 3.7% (151 out of 4043) of second-level administra- sex and age group, and 70.9% (2867 of 4043) of second- tive units across SSA and decreased by more than 5.0 level administrative units saw a decrease greater than percentage points in 7.9% (321 of 4043) of second-level 5.0 percentage points in at least one sex and age group. Haeuser et al. BMC Medicine (2022) 20:488 Page 14 of 24 Fig. 7 Change in HIV prevalence at the second administrative level between 2000 and 2018 for a subset of modeled demographic groups from the lower, middle, and upper age ranges: a all adults, ages 15–59 years; b males and c females ages 15–19 years; d males and e females ages 35–39 years; and f males and g females ages 55–59 years. Maps reflect national boundaries, land cover, lakes, and population; areas with fewer than ten people per 1 × 1 km, and classified as barren or sparsely vegetated, are colored light gray. Countries colored in dark gray were not included in the analysis The distribution of districts with large increases or younger age groups, and greater increases among older decreases in prevalence over time also varied greatly age groups. For example, for females ages 25–29 years, by region. All regions saw a decrease of greater than 5.0 we found that estimated prevalence decreased by at least percentage points in estimated prevalence for at least 1.0 percentage point in the year 2018 compared to 2000 one sex and age group in a majority of second-level in more than 73.3% of second-level administrative units administrative units between 2000 and 2018: 61.2% for in SSA (2965 of 4043) and increased by at least 1.0 per- Central SSA, (393 out of 642), 70.9% (1160 out if 1635) for centage point in only 2.4% (99 of 4043) of all second-level Western SSA, 71.0% (1032 out of 1452) for Eastern SSA, administrative units. Conversely, among females ages and 90.1% (283 out of 314) for Southern SSA. However, 50–54 years, estimated prevalence decreased between Southern SSA also had a very high proportion of second- 2000 and 2018 by at least 1.0 percentage point in just level administrative units seeing an increase of greater 11.8% (477 of 4043) of second-level administrative units than 5.0 percentage points in that same time frame, at but increased by at least 1.0 percentage point in 40.1% 92.0% (289 of 314), while only a minority of second-level (1622 of 4043) of second-level administrative units. We administrative units saw similar increases in the other found this trend to be similar across regions. For com- regions. plete comparisons of prevalence over time for each sec- We found diverging overall trends between age groups ond-level administrative unit, age, and sex, including over time, with greater decreases over time among uncertainty estimates, see Additional file 4. Haeuser et al. BMC Medicine (2022) 20:488 Page 15 of 24 Discussion males and females, marital status) in different countries The results of this study, the first to present age- and [59]. In addition to understanding local patterns in HIV sex-specific HIV prevalence estimates across sub- prevalence, effective interventions also need to consider, Saharan Africa at local scales, emphasize the interactions if not focus directly on, locally important risk factors and of geographic and demographic differences in HIV determinants of HIV infection and mortality [60, 61]. prevalence, going beyond previous research focused Our estimates point to many local shifts in HIV prev- on either aspect individually. Just as previous work alence over time. A multitude of factors can affect HIV demonstrated how much geographic variability is prevalence trends at the local level over time, from local masked in national prevalence estimates [10], we show changes in prevention interventions to shifts in the over- here that demographically aggregated estimates mask all demographics of an area, but one particularly impor- important variation in the age and sex distributions of tant factor is local scale-up of ART [62, 63]. Increases in HIV prevalence at a local level, which in turn provide ART coverage and reduced treatment costs have repeat- much clearer insights into the evolution of the HIV edly been associated with large demographic shifts epidemic in SSA. among people living with HIV [64] due to its success Many intervention methods are commonly used in the in reducing HIV mortality, leading to greatly increas- fight against the HIV epidemic, and variation in their ing numbers of people living with HIV over the age of efficacy and implementation has likely contributed to 50 years; our results reflect this trend. Given evidence the prevalence trends presented here. Cost-efficiency is a pointing to differences between younger and older ART consistent priority and is generally maximized by using patients in rates of CD4 cell count decline [65], immune targeted, integrated interventions [50]. For example, HIV reconstitution rates [66], and risk of associated non-com- prevention via behavioral and biomedical interventions municable diseases [67, 68], among other health metrics based on local prevalence rates, HIV testing, and [69], it is necessary that treatment plans for older patients treatment initiation may be priorities for some age groups be specifically tailored for their age group. Our results [51], while long-term ART retention and comorbidity highlight those locations with large existing populations care may require more emphasis for others [52]. Barriers of people living with HIV for ages 50–59 years, and those to access to care often differ between geographic and seeing rapid growth of HIV prevalence in that demo- demographic groups, where in some cases barriers may graphic group. At the same time, the minimal change be logistical (e.g., geographic isolation and programmatic in estimated prevalence over time among the youngest fragmentation [53]) or social (e.g., lack of information, age groups suggests that continued and even expanded stigmatization, homophobia [54]), and require different efforts in HIV prevention for adolescents and young intervention methods. Males and females are also often adults still need to be maintained as a priority across the targeted using different points of contact. For example, continent. HIV testing has been recommended for all females Despite the significant progress made through this attending antenatal care clinics [55], whereas for males analysis in describing HIV burden in SSA, prevalence the provision of self-, home-based, and mobile testing estimates mask complex and varied relationships between compared to facility-based testing may be more useful HIV incidence and mortality, as well as migration for testing and subsequent uptake of care [56–58]. and seasonal mobility. It is difficult to determine, for Effective targeting of these interventions requires local, example, if a dramatic decrease in HIV prevalence in an demographically specific HIV burden information, such area is due to reduced incidence, increased mortality, as provided in the estimates presented here. Countries or differences in the immigration and emigration rates may similarly use this burden information to prioritize of ­HIV+ and H ­ IV- individuals. Primary data for all subnational and demographically specific treatment three of these metrics are not widely available for SSA, needs. This resource may also be useful in program adding additional complexity to the interpretation evaluation efforts and thus aid the development of more of our estimates. Importantly, no estimates of these successfully tailored interventions. indicators are consistently available at local scales for Variation in the social determinants driving HIV specific demographic groups. Furthermore, local data incidence and mortality, and thus HIV prevalence, related to diagnosis, treatment, and viral suppression are also an important consideration when assessing rates are also limited, despite these metrics lying at the inequalities in HIV prevalence between locations and heart of the UNAIDS 95-95-95 goals [4]. While very demographic groups. While prevalence among females informative, difficulties can still arise in intervention is consistently higher than prevalence among males, for decision-making built around HIV prevalence estimates example, these differences can be attributed to different alone, without understanding their underlying drivers. exposure to risk factors (such as age at first sex between Improved surveillance of HIV prevalence, incidence, Haeuser et al. BMC Medicine (2022) 20:488 Page 16 of 24 and mortality, combined with reliable population computational abilities. Our modeling strategy also and migration estimates and information on local assumed no difference in the probability that an H ­ IV+ - programs, are necessary to fully understand the versus an H ­ IV pregnant woman would access antenatal complexities of the region’s HIV epidemic. Clearly, even care and therefore be included in ANC surveillance. with the development of more comprehensive burden Due to limited data availability, we delineated estimates information, any modeled estimates should only be in this analysis using a male/female binary. We recognize used for intervention purposes in conjunction with local that this approach does not allow for investigation of HIV program knowledge. prevalence among gender and sex diverse people, despite the disproportionate burden of HIV commonly seen Methodological advantages and limitations among these populations [73]. Further, we recognize that The methods used in this analysis build upon those many data sources do not provide the option to select a previously used by Dwyer-Lindgren et al. to model adult sex other than “male” or “female,” gender options beyond HIV prevalence [10]. While this analysis does improve “man” or “woman,” and often conflate gender with sex. In upon and have advantages over the previous methods in the future, we hope that high-quality data on HIV preva- some ways, it faces some of the same, as well as some lence for gender and sexual diverse people will be more new limitations. As with the previous study, and as widely available, so we can produce estimates beyond with all modeling studies, the quality of our estimates females and males. is highly dependent on the quality and coverage of our We note that our results include unprecedentedly high input data. Despite constructing a large database of HIV prevalence estimates for certain population subsets. prevalence data, coverage gaps and small sample sizes in In most cases, we do not believe these estimates are some locations can be associated with imprecision and/ implausible. For example, we estimated prevalence or large uncertainty intervals in some of our prevalence among middle- and older-aged females to be up to estimates (Additional file 3: Figs. S27-S34). Additionally, 59.2% [45.9–73.0%] in Umgungundlovu in KwaZulu- the location information associated with the data Natal, South Africa in 2018. Previous research has compiled for this analysis is subject to some error. estimated prevalence for females adults of all ages In order to protect respondent confidentiality, most combined in Umgungundlovu in 2017 to be 46.6% surveys that collect GPS coordinates perform some type [43.8–49.5%] [74]. As we have shown that prevalence of random displacement on those coordinates prior to in middle- and older-aged females tended to be higher releasing data for secondary analysis: for example, GPS than all-ages prevalence, we believe our estimates coordinates for Demographic and Health Surveys (DHS) for middle- and older-aged females during this time are displaced by up to 2 km for urban clusters, up to 5 period in this location to be reasonable, especially with km for most rural clusters, and up to 10 km in a random uncertainty intervals taken into consideration. In rare 1% of rural clusters [34]. Past research has found that cases, however, our methods yielded estimates which displacement can degrade the predictive power of a we were unable to support through the literature. For geostatistical model, however this effect was found to example, for males ages 35–39 and 40–44 years in be modest, and researchers concluded that relatively Nyatike in Migori, Kenya, we estimated prevalence in accurate mapping can be undertaken at a 5 × 5-km the year 2000 to be 77.8% [50.2–100.0%] and 78.7% resolution even with GPS displacement [70]. [50.0–100.0%], respectively. It is unlikely true prevalence The approximate integration method we use in this in that area and year was this high (though given the analysis better handles uncertainty estimation and easily large uncertainty intervals associated with these values, accommodates not only polygon data but age-aggregated it is probable that true prevalence does fall within those data as well, compared to the polygon resampling ranges). We note, however, that the high estimates in this method that has been used elsewhere [10, 71, 72]. At the area and surrounding second-level administrative units same time, given the large number of dimensions being were predominantly associated with the earlier years in modeled, as well as the high data input count produced our time series—we believe the more recent estimates by our data disaggregation technique, we found that in Nyatike to be more realistic [75]. In these locations, current matrix packages, as well as our computational decreases in prevalence over time may therefore also be facilities, could not accommodate a Gaussian process overestimated. These instances were rare. that accounted for the covariance of a complete space- A combination of data limitations and model time-age-sex Kronecker product. We therefore focused complexity ultimately led to large uncertainty intervals on the interactions between space, time, age, and sex that around our estimates. Given that our 95% coverage we believed would be most relevant in terms of capturing estimates in model validation were consistently higher important variability in these dimensions, within our than expected (Additional file 3: Figs. S14-S16), this Haeuser et al. BMC Medicine (2022) 20:488 Page 17 of 24 indicates that these uncertainty intervals may be larger S20. Age-specific HIV prevalence in females, 2000. Figure S21. Age- than appropriate. Wide uncertainty can limit the utility specific HIV prevalence in males, 2005. Figure S22. Age-specific HIV prev‑ alence in females, 2005. Figure S23. Age-specific HIV prevalence in males, of our estimates in terms of informing HIV policies, 2010. Figure S24. Age-specific HIV prevalence in females, 2010. Figure and reducing this uncertainty through improved data S25. Age-specific HIV prevalence in males, 2018. Figure S26. Age-specific coverage will be an important consideration in future HIV prevalence in females, 2018. Figure S27. Age-specific uncertainty interval range estimates in males, 2000. Figure S28. Age-specific uncer‑ iterations of this model. We were also unable to account tainty interval range estimates in females, 2000. Figure S29. Age-specific for all sources of uncertainty such as uncertainty in the uncertainty interval range estimates in males, 2005. Figure S30. Age- WorldPop estimates used in many stages of our modeling specific uncertainty interval range estimates in females, 2005. Figure S31. Age-specific uncertainty interval range estimates in males, 2010. Figure and estimation processes and uncertainty in covariates. 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. Conclusions Figure S36. Change in HIV prevalence in females, 2000-2005. Figure S37. HIV continues to impose enormous human and financial Change in HIV prevalence in males, 2005-2010. Figure S38. Change in HIV costs [3] on SSA, decades since its emergence. Financial prevalence in females, 2005-2010. Figure S39. Change in HIV prevalence in males, 2010-2018. Figure S40. Change in HIV prevalence in females, and logistical disruptions and discontinuities due to 2010-2018. Figure S41. Space mesh for geostatistical models. the impacts of COVID-19, as well as changes in ART Additional file 4: Supplemental results.1. README. 2. Prevalence range adherence, are likely to present new barriers [21, 76] to across districts. 3. Prevalence range between sexes. 4. Prevalence range the UNAIDS 95-95-95 goals [4]. This analysis provides between ages. 5. Age-specific district ranges. important insight into the nuances of HIV burden in SSA, offering information that is critical to the development of Acknowledgements targeted interventions. LBD sub-Saharan Africa HIV Prevalence Collaborators S Afzal acknowledges support of the Pakistan Society of Medical Infectious Diseases and King Edward Medical University to access the relevant data of HIV from various sources. T W Bärnighausen was supported by the Alexander Abbreviations von Humboldt Foundation through the Alexander von Humboldt Professor AIDS: Acquired immune deficiency syndrome; ANC: Antenatal care; ART​: award, funded by the German Federal Ministry of Education and Research. Antiretroviral therapy; GATHER: Guidelines for Accurate and Transparent Health F Carvalho and E Fernandes acknowledge support from Fundação para a Estimates Reporting; GBD: Global Burden of Disease; GPS: Global positioning Ciência e a Tecnologia (FCT), I.P., in the scope of the project UIDP/04378/2020 system; HIV: Human immunodeficiency virus; SDGs: Sustainable Development and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences Goals; SSA: Sub-Saharan Africa; TMB: Template Model Builder; UNAIDS: Joint - UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute United Nations Programme on HIV/AIDS. for Health and Bioeconomy - i4HB; FCT/MCTES (Ministério da Ciência, Tecnologia e Ensino Superior) through the project UIDB/50006/2020. K Deribe Supplementary Information acknowledges support by the Wellcome Trust [grant number 201900/Z/16/Z] as part of his International Intermediate Fellowship. C Herteliu and A Pana The online version contains supplementary material available at https://​doi.​ are partially supported by a grant of the Romanian National Authority for org/​10.​1186/​s12916-​022-​02639-z. Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III- P4-ID-PCCF-2016-0084. Claudiu Herteliu is partially supported by a grant Additional file 1: Supplemental information.1. Compliance with of the Romanian Ministry of Research Innovation and Digitalization, MCID, the Guidlines for Accurate and Transparent Health Estimates Reporting project number ID-585-CTR-42-PFE-2021. Y J Kim acknowledges support (GATHER). 2. HIV data sources and data processing. 3. Covariate and auxil‑ by the Research Management Centre, Xiamen University Malaysia [No. iary data. 4. Statistical model. 5. References. XMUMRF/2020-C6/ITCM/0004]. S L Koulmane Laxminarayana acknowledges Additional file 2: Supplemental tables.Table S1. HIV seroprevalence institutional support by the Manipal Academy of Higher Education. K Krishan survey data. Table S2. ANC sentinel surveillance data. Table S3. HIV and acknowledges non-financial support from UGC Centre of Advanced Study, covariates surveys excluded from this analysis. Table S4. Sources for pre- CAS II, Department of Anthropology, Panjab University, Chandigarh, India. M existing covariates. Table S5. HIV covariate survey data. Table S6. Fitted Kumar would like to acknowledge NIH/FIC K43 TW010716-04. I Landires is model parameters. a member of the Sistema Nacional de Investigación (SNI), supported by the Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT), Panama. Additional file 3: Supplemental figures.Figure S1. Prevalence of male V Nuñez-Samudio is a member of the Sistema Nacional de Investigación circumcision. Figure S2. Prevalence of signs and symptoms of sexu‑ (SNI), which is supported by Panama’s Secretaría Nacional de Ciencia, ally transmitted infections. Figure S3. Prevalence of marriage or living Tecnología e Innovación (SENACYT). O O Odukoya was supported by the as married. Figure S4. Prevalence of partner living elsewhere among Fogarty International Center of the National Institutes of Health under the females. Figure S5. Prevalence of condom use during most recent sexual Award Number K43TW010704. The content is solely the responsibility of the encounter. Figure S6. Prevalence of sexual activity among young females. authors and does not necessarily represent the official views of the National Figure S7. Prevalence of multiple partners among males in the past year. Institutes of Health. Z Quazi Syed acknowledges support from JNMC, Datta Figure S8. Prevalence of multiple partners among females in the past Meghe Institute of Medical Sciences. A I Ribeiro was supported by National year. Figure S9. HIV prevalence predictions from the boosted regression Funds through FCT, under the ‘Stimulus of Scientific Employment – Individual tree model. Figure S10. HIV prevalence predictions from the generalized Support’ program within the contract CEECIND/02386/2018. A M Samy additive model. Figure S11. HIV prevalence predictions from the lasso acknowledges the support from a fellowship of the Egyptian Fulbright regression model. Figure S12. Modeling regions. Figure S13. Age- and Mission program and Ain Shams University. R Shrestha acknowledges sex-specific vs. adult prevalence modeling. Figure S14. Data sensitivity. support from NIDA K01 Award: K01DA051346. N Taveira acknowledges Figure S15. Model specification validation. Figure S16. Modeled and support from FCT and Aga Khan Development Network (AKDN) - Portugal re-aggregated adult prevalence comparison. Figure S17. HIV prevalence Collaborative Research Network in Portuguese speaking countries in Africa raking factors for males. Figure S18. HIV prevalence raking factors for (project reference: 332821690), and by the European & Developing Countries females. Figure S19. Age-specific HIV prevalence in males, 2000. Figure Haeuser et al. BMC Medicine (2022) 20:488 Page 18 of 24 Funding Clinical Trials Partnership (EDCTP), UE (project reference: RIA2016MC-1615). This work was primarily supported by grant OPP1132415 from the Bill & B Unnikrishnan acknowledges support from Kasturba Medical College, Melinda Gates Foundation. The funder of the study had no role in study Mangalore, Manipal Academy of Higher Education, Manipal. design, data collection, data analysis, data interpretation, writing of the report, or decision to publish. The corresponding authors had full access to all the Authors’ contributions data in the study and had final responsibility for the decision to submit for Individual author contributions are divided into the following categories: publication. managing the estimation or publication process; writing the first draft of the manuscript; primary responsibility for applying analytical methods to Availability of data and materials produce estimates; primary responsibility for seeking, cataloging, extracting, The findings of this study are supported by data available in public online or cleaning data; designing or coding figures and tables; providing data or repositories and data publicly available upon request of the data provider. critical feedback on data sources; developing methods or computational Details regarding the data sources used and their availability can be found machinery; providing critical feedback on methods or results; drafting the in Additional file 2: Supplemental Tables 1-5 and online via the Global Health manuscript or revising it critically for important intellectual content; and Data Exchange (https://​ghdx.​healt​hdata.​org/​record/​ihme-​data/​sub-​sahar​ managing the overall research process. Members of the core research team an-​africa-​hiv-​preva​lence-​geosp​atial-​estim​ates-​2000-​2018). Estimates can for this topic area had full access to the underlying data used to generate also be further explored through the Global Health Data Exchange, as well as estimates presented in this article. All other authors had access to and via our online visualization tool (http://​vizhub.​healt​hdata.​org/​lbd/​hiv-​prev-​ reviewed estimates as part of the research evaluation process, which includes disagg). Administrative boundaries were modified from the Database for additional stages of formal review. All authors read and approved the final Global Administrative Areas (GADM) dataset [77]. Populations were retrieved manuscript. Managing the estimation or publications process: LDL, EH, SIH. from WorldPop [32]. This study complies with the Guidelines for Accurate and Writing the first draft of the manuscript: EH. Primary responsibility for applying Transparent Health Estimates Reporting (GATHER) recommendations [31]. analytical methods to produce estimates: EH, MAC. Primary responsibility for All maps and figures presented in this study are generated by the authors; seeking, cataloging, extracting, or cleaning data; designing or coding figures no permissions are required for publication. All computer code is available and tables: ALS, MY. Providing data or critical feedback on data sources: ALS, online and can be found at (https://​github.​com/​ihmeuw/​lbd/​tree/​hiv_​ ESA, OMA, VA1, SA1, BOA, MBA, ROA, CJA, FA, FMA, TMA, RKA, AAH, NAG, DAA2, prev-​africa-​2020). DA, SCYA, JA, OA, MAA1, BPAQ, YAA, SA3, TKB, AAB, MB, TWB, ASB, SB, OAB, AB2, VKC, DTC, RGC, GD, AMD, JDN, MD, AD, FD, BD, BE, SE1, IF, TF, MMG, BGG, LG, KG, AG, MG, HCG, AHM1, SH2, HH, CH, HCH, MH2, SEI, CCDI, MJ, KBJ, FJ, JJJ, LRK, Declarations RK, NK1, SEK, GAK, MK1, MNK, KK1, JK, YJK, AK1, SK1, SLKL, KK2, BKD, NK2, MK2, DK, DKL, SL1, XL, MAM, FRMM, WM, RGM, EWM, BM, AMH, SM1, SM2, AHM2, Ethics approval and consent to participate MM, SM3, GBBM, AJN, MDN, MN, IN, FNN, GNT, JWN, CTN, HLTN, CAN, JJN, BO, Not applicable. ATO, BOO1, BOO2, JOO, AOB, OEO1, OEO2, MOO, MPA, JRP, AP1, AP2, FPK, SP, MJP, LP, ZQS, NR1, AR1, AR2, FR, VRM, AMR, KR, JR, CLR, SJR, DLR, SR, RR, LDR, Consent for publication NR2, MAR, ER, GMR, RS1, MS1, MRS, HSK, AMS, BS2, BS3, AAS3, MAS, JIS, RS2, Not applicable. JAS, VYS, AAS2, TT, NT, FHT, MRTP, CDU, BU, BV, GTV, YDW, YW1, SY, NY, CY, ZJZ, SIH. Developing methods or computational machinery: MY, DAA1, SA1, MBA, Competing interests TMA, AAH, DA, ASB, SB, DTC, AMD, MD, FD, BD, MH2, SEI, NK1, MK1, AK1, SM1, S Afzal reports leadership or fiduciary role in other board, society, committee AHM2, AOB, ZQS, NR1, AMR, CLR, RR, LDR, ER, AMS, BS2, TT, BV, KW, MSZ, SIH, or advocacy group, unpaid, with the Pakistan society of Community Medicine LDL. Providing critical feedback on methods or results: MY, HA, ESA, MA, OMA, & Public Health, the Pakistan Association of Medical Editors, and the Pakistan VA1, DAA1, SA1, BOA, KA, MBA, YA, ROA, CJA, FA, FMA, TMA, KAA, RKA, VA2, Society of Medical Infectious Diseases, all outside the submitted work. R AAH, NAG, EKA, SA2, DAA2, DA, SCYA, JA, OA, MAA1, MAJ, BPAQ, MAA2, SA3, Ancuceanu reports 5 payment or honoraria for lectures, presentations, ZNA, DBB, TKB, AAB, MB, TWB, AWB, ASB, NB, PB, KB, AB1, ZWB, SB, OAB, AB2, speakers bureaus, manuscript writing, or educational events from Avvie, IB, ZAB, JC, VKC, MAKC, DTC, RGC, SMAD, GD, EKMD, AMD, JDN, NDW1, JWDN, Sandoz, and B Braun, all outside the submitted work. T W Bärnighausen KD1, KD2, MD, DD, AD, SD2, FD, ED, BD, SE1, OE, IJE, FF1, NF, IF, FF2, NAF, MF, reports research grants from the European Union (Horizon 2020 and EIT TF, MMG, AMG, BGG, KBG, LG, AG, MG, MIMG, HCG, RAG, MRH, SH1, AH, SH2, Health), German Research Foundation (DFG), US National Institutes of Health, HH, KH, CH, HCH, RH, MH2, BFH, SEI, OSI, IMI, MDI, RMI, CCDI, MJ, RPJ, JSJ, VJ, German Ministry of Education and Research, Alexander von Humboldt FJ, JJJ, LRK, RK, NK1, TK, BKM, SEK, GAK, AKK, EAK, MNK, KK1, JK, YJK, AK1, SK1, Foundation, Else-Kröner-Fresenius-Foundation, Wellcome Trust, Bill & Melinda JAK, SK2, SLKL, AK2, KK2, BKD, NK2, VK, MK2, NK3, DK, CLV, DKL, HJL, SL1, SL2, Gates Foundation, KfW, UNAIDS, and WHO; consulting fees from KfW on the XL, PM1, MAM, FRMM, WM, RGM, EWM, TM, AM2, BM, OM, YM, AMH, RM, SM1, OSCAR initiative in Vietnam; participation on a Data Safety Monitoring Board SM2, AHM2, SM3, GBBM, LM, AJN, MDN, MN, JN, RN, IN, FNN, GNT, JWN, CTN, or Advisory Board with the NIH-funded study “Healthy Options” (PIs: Smith HLTN, CAN, JJN, VEN, BO, ATO, BOO1, BOO2, JOO, AOB, OEO1, OEO2, NO, SSO, Fawzi, Kaaya), Chair, Data Safety and Monitoring Board (DSMB), German MOO, MPA, JRP, AP1, SRPP, FPK, SP, EKP, MJP, LP, ZQS, NR1, AR1, AR2, FR, VRM, National Committee on the “Future of Public Health Research and Education,” AMR, JR, CLR, SJR, DLR, SR, RR, LDR, NR2, AR3, AIR, JMR, ER, SFR, GMR, SSM, RS1, Chair of the scientific advisory board to the EDCTP Evaluation, Member of the BS1, MS1, MRS, HSK, AMS, BS2, BS3, AAS3, AAS1, MAS, MS2, WSS, JIS, RS2, JAS, UNAIDS Evaluation Expert Advisory Committee, National Institutes of Health VYS, AAS2, SS, MBS, TT, EGT, NT, FHT, RT, MRTP, GWT, CDU, BU, JHV, FSV, BV, GTV, Study Section Member on Population and Public Health Approaches to HIV/ YDW, YW1, RGW, YW2, PW, NDW2, SY, PY, NY, CY, MSZ, YZ, SIH, LDL. Drafting AIDS (PPAH), US National Academies of Sciences, Engineering, and Medicine’s the work or revising is critically for important intellectual content: ALS, MY, Committee for the “Evaluation of Human Resources for Health in the Republic HA, OMA, VA1, DAA1, SA1, BOA, MBA, YA, ROA, FA, KAA, RKA, AAH, NAG, SA2, of Rwanda under the President’s Emergency Plan for AIDS Relief (PEPFAR),” DAA2, RA, JA, OA, MAA1, AFA, BPAQ, MAA2, ZNA, AAB, MB, TWB, AWB, ASB, KB, University of Pennsylvania (UPenn) Population Aging Research Center (PARC) SB, IB, RC, FC, VKC, DTC, RGC, GD, EKMD, JDN, NDW1, JWDN, KD1, SDD, MD, DD, External Advisory Board Member; leadership or fiduciary role in other board, AD, FD, MET, SIEJ, SE1, OE, SE2, NKF, EF, IF, FF2, NAF, MF, TF, MMG, AG, MIMG, society, committee or advocacy group, paid or unpaid, as co-chair of the RAG, AH, CH, HCH, RH, MH1, SEI, OSI, IMI, MDI, RMI, CCDI, MJ, RPJ, NJ, JJJ, GAK, Global Health Hub Germany (which was initiated by the German Ministry of EAK, GK, MNK, KK1, JK, YJK, AK1, SK1, SLKL, AK2, KK2, BKD, NK2, DK, CLV, IL, SL1, Health); all outside the submitted work. J das Neves reports grants or contracts PHL, SL2, AM1, PM1, FRMM, WM, RGM, EWM, TJM, TM, BM, OM, YM, RM, SM1, from Ref. 13605 – Programa GÉNESE, Gilead Portugal (PGG/002/2016 SM2, AHM2, PM2, LM, AJN, JN, IN, FNN, GNT, JWN, CTN, HLTN, JJN, VNS, VEN, – Programa GÉNESE, Gilead Portugal) outside the submitted work. L OOO, ATO, BOO1, BOO2, JOO, OEO1, NO, MOO, MPA, JRP, AP1, FPK, GCP, SP, Dwyer-Lindgren reports support for the present manuscript from the Bill & MJP, LP, ZQS, NR1, AR1, AR2, FR, VRM, CLR, SJR, DLR, SR, NR2, MAR, AIR, JMR, Melinda Gates Foundation through grant OPP1132415. I Filip reports other ER, GMR, RS1, BS1, MS1, MRS, HSK, AMS, RS2, JAS, VYS, AAS2, MBS, TT, EGT, NT, financial or non-financial interests from Avicenna Medical and Clinical MRTP, BU, JHV, FSV, GTV, YW1, RGW, YW2, PW, NDW2, SY, NY, MSZ, ZJZ, SIH, LDL. Research Institute, outside the submitted work. E Haeuser reports support for Managing the overall research enterprise: LDL, SIH, AHM2, GCP, and BS2. Haeuser et al. BMC Medicine (2022) 20:488 Page 19 of 24 the present manuscript from the Bill & Melinda Gates Foundation through the FDA Arthritis Advisory Committee; leadership or fiduciary role in other grant OPP1132415. C Herteliu reports grants from Romanian Ministry of board, society, committee or advocacy group, paid or unpaid, as a member of Research Innovation and Digitalization, MCID, for project number ID-585-CTR- the steering committee of OMERACT, an international organization that 42-PFE-2021 (Jan 2022-Jun 2023) “Enhancing institutional performance develops measures for clinical trials and receives arm’s length funding from 12 through development of infrastructure and transdisciplinary research pharmaceutical companies, with the Veterans Affairs Rheumatology Field ecosystem within socio-economic domain – PERFECTIS,” from Romanian Advisory Committee as Chair, and with the UAB Cochrane Musculoskeletal National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, for Group Satellite Center on Network Meta-analysis as a director and editor; stock project number PN-III-P4-ID-PCCF-2016-0084 (Oct 2018-Sep 2022) or stock options in TPT Global Tech, Vaxart pharmaceuticals, Atyu Biopharma, “Understanding and modelling time-space patterns of psychology-related Adaptimmune Therapeutics, GeoVax Labs, Pieris Pharmaceuticals, Enzolytics inequalities and polarization,” and project number PN-III-P2-2.1- Inc, Series Therapeutics, Tonix Pharmaceuticals, and Charlotte’s Web Holdings SOL-2020-2-0351 (Jun 2020-Oct 2020) “Approaches within public health Inc. and previously owned stock options in Amarin, Viking, and Moderna management in the context of COVID-19 pandemic,” and from the Ministry of pharmaceuticals; all outside the submitted work. N Taveira reports grants or Labour and Social Justice, Romania for project number “Agenda for skills contracts from FCT and Aga Khan Development Network (AKDN) – Portugal Romania 2020-2025”; all outside the submitted work. J J Jozwiak reports Collaborative Research Network in Portuguese speaking countries in Africa payment or honoraria for lectures, presentations, speakers bureaus, (Project reference: 332821690) and from European & Developing Countries manuscript writing, or educational events from Teva, Amgen, Synexus, Clinical Trials Partnership (EDCTP), UE (Project reference: RIA2016MC-1615), as Boehringer Ingelheim, Zentiva, and Sanofi as personal fees, all outside the payments made to their institution, all outside the submitted work. submitted work. J Khubchandani reports other financial interests from Teva Pharmaceuticals, all outside the submitted work. K Krishnan reports other Author details 1 non-financial support from UGC Centre of Advanced Study, CAS II, Institute for Health Metrics and Evaluation, University of Washington, Seattle, Department of Anthropology, Panjab University, Chandigarh, India, outside WA, USA. 2 Advanced Diagnostic and Interventional Radiology Research the submitted work. H J Larson reports grants or contracts from the Center, Tehran University of Medical Sciences, Tehran, Iran. 3 Department MacArthur Foundation and Merck to London School of Hygeine and Tropical of Botany, Sree Narayana Guru College Chelannur, Kozhikode, India. 4 Hamadan Medicine, and from the Vaccine Confidence Fund to the University of University of Medical Sciences, Hamadan, Iran. 5 College of Medicine, University Washington; payment or honoraria for lectures, presentations, speakers College Hospital, Ibadan, Ibadan, Nigeria. 6 Department of Population bureaus, manuscript writing, or educational events from Center for Strategic Medicine, Cardiff University, Cardiff, UK. 7 Department of Community Health and International Studies as payment to LSHTM for co-chairing HighLevel and Epidemiology, University of Saskatchewan, Saskatoon, SK, Canada. 8 Panel and from GSK as personal payment for developing training sessions and Department of Public Health, Federal Ministry of Health, Abuja, Nigeria. 9 lectures; leadership or fiduciary role in other board, society, committee or Department of Community Medicine, King Edward Memorial Hospital, advocacy group, pair, with the ApiJect Advisory Board; all outside the Lahore, Pakistan. 10 Department of Public Health, Public Health Institute, submitted work. O O Odukoya reports support for the present manuscript Lahore, Pakistan. 11 The Australian Centre for Public and Population Health from the Fogarty International Center of the National Institutes of Health Research (ACPPHR), University of Technology Sydney, Sydney, NSW, Australia. 12 under the Award Number K43TW010704. The content is solely the School of Public Health, Imperial College London, London, UK. 13 responsibility of the authors and does not necessarily represent the official Department of Epidemiology, Jimma University, Jimma, Ethiopia. 14 views of the National Institutes of Health. A Pans reports grants from Australian Center for Precision Health, University of South Australia, Adelaide, Romanian National Authority for Scientific Research and Innovation, SA, Australia. 15 Department of Medical Physiology, University of Gondar, CNDS-UEFISCDI, for project number PN-III-P4-ID-PCCF-2016-0084 (Oct Gondar, Ethiopia. 16 Institute for Advanced Medical Research and Training, 2018-Sep 2022) “Understanding and modelling time-space patterns of University of Ibadan, Ibadan, Nigeria. 17 Institute of Neuroscience, Newcastle psychology-related inequalities and polarization,” and project number University, Newcastle upon Tyne, UK. 18 Department of Public Health, The PN-III-P2-2.1-SOL-2020-2-0351 (Jun 2020-Oct 2020) “Approaches within public Intercountry Centre for Oral Health (ICOH) for Africa, Jos, Nigeria. 19 health management in the context of COVID-19 pandemic,” outside the Department of Public Health, Federal Ministry of Health, Garki, Nigeria. 20 submitted work. S R Pandi-Perumal reports royalties from Springer for editing Mayo Evidence-based Practice Center, Mayo Clinic Foundation for Medical services; stock or stock options in Somnogen Canada Inc as the President and Education and Research, Rochester, MN, USA. 21 Imam Abdulrahman Bin Faisal Chief Executive Officer; all outside the submitted work. A Radfar reports other University, Dammam, Saudi Arabia. 22 Health Information Management financial or non-financial interests from Avicenna Medical and Clinical and Technology Department, Imam Abdulrahman Bin Faisal University, Research Institute, outside the submitted work. A I Ribeiro reports grants or Dammam, Saudi Arabia. 23 Faculty of Health Sciences, Curtin University, Perth, contracts from National Funds through FCT, under the ‘Stimulus of Scientific WA, Australia. 24 Wesfarmers Centre of Vaccines and Infectious Diseases, Employment – Individual Support’ program within the contract Telethon Kids Institute, Perth, WA, Australia. 25 Institute of Health Research, CEECIND/02386/2018, outside the submitted work. J M Ross reports support University of Health and Allied Sciences, Ho, Ghana. 26 Health Management for the present manuscript from the Bill & Melinda Gates Foundation through and Economics Research Center, Iran University of Medical Sciences, Tehran, grant OPP1132415; grants or contracts from National Institutes of Health and Iran. 27 Department of Health Economics, Iran University of Medical Sciences, Firland Foundation as payments to their institution; consulting fees from Tehran, Iran. 28 Department of Epidemiology, Arak University of Medical United States Agency for International Development as personal payments, Sciences, Arak, Iran. 29 Research Group in Hospital Management and Health and from KNCV Tuberculosis Foundation as payments to their institution; all Policies, Universidad de la Costa (University of the Coast), Barranquilla, outside the submitted work. E Rubagotti reports payment or honoraria for Colombia. 30 Research Group in Health Economics, University of Cartagena, lectures, presentations, speakers bureaus, manuscript writing, or educational Cartagena, Colombia. 31 Department of Health Services Management, events from the Greenwich China Office and Unviersity Prince Mohammad VI, Khomein University of Medical Sciences, Khomein, Iran. 32 Department Morocco, all outside the submitted work. B Sartorius reports grants or of Maternal and Child Wellbeing, African Population and Health Research contracts from DHSC – GRAM Project; Leadership or fiduciary role in other Center, Nairobi, Kenya. 33 Pharmacy Department, Carol Davila University board, society, committee or advocacy group, paid or unpaid, as a member of of Medicine and Pharmacy, Bucharest, Romania. 34 Department of Parasitology, the GBD Scientific Council and a Member of WHO RGHS; all outside the Mazandaran University of Medical Sciences, Sari, Iran. 35 Department submitted work. J A Singh reports consulting fees from Crealta/Horizon, of Parasitology, Iranshahr University of Medical Sciences, Iranshahr, Iran. 36 Medisys, Fidia, PK Med, Two labs Inc, Adept Field Solutions, Clinical Care Department of Sociology and Social Work, Kwame Nkrumah University options, Clearview healthcare partners, Putnam associates, Focus forward, of Science and Technology, Kumasi, Ghana. 37 Center for International Health, Navigant consulting, Spherix, MedIQ, Jupiter Life Science LLC, UBM LLC, Trio Ludwig Maximilians University, Munich, Germany. 38 Department of Public Health, Medscape, WebMD, and Practice Point communications, and the Health, Birmingham City University, Birmingham, UK. 39 School of Public National Institutes of Health and the American College of Rheumatology; Health, Bahir Dar University, Bahir Dar, Ethiopia. 40 Department of Biostatistics payment or honoraria for participating in the speakers bureau for Simply and Epidemiology, Tabriz University of Medical Sciences, Tabriz, Iran. 41 Speaking; support for attending meetings and/or travel from the steering Department of Biostatistics and Epidemiology, Zanjan University of Medical committee of OMERACT, to attend their meeting every 2 years; participation Sciences, Zanjan, Iran. 42 Department of Surgery, Addis Ababa University, Addis on a Data Safety Monitoring Board or Advisory Board as an unpaid member of Ababa, Ethiopia. 43 The Judith Lumley Centre, La Trobe University, Melbourne, Haeuser et al. BMC Medicine (2022) 20:488 Page 20 of 24 VIC, Australia. 44 San Martin de Porres University, Lima, Peru. 45 Department and Zootechnics, Autonomous University of Sinaloa, Rosales, Culiacán, Mexico. 103 of Health Policy Planning and Management, University of Health and Allied Department of Community Medicine and Public Health, Urmia University Sciences, Ho, Ghana. 46 Department of Health Economics, Centre for Health of Medical Science, Urmia, Iran. 104 Development of Research and Technology Policy Advocacy Innovation & Research in Africa (CHPAIR-Africa), Accra, Ghana. Center, Ministry of Health and Medical Education, Tehran, Iran. 105 Department 47 Department of Nursing, Debre Berhan University, Debre Berhan, Ethiopia. of Medical Laboratory Sciences, Iran University of Medical Sciences, Tehran, 48 Hospital Management Research Center, Iran University of Medical Sciences, Iran. 106 Institute of Microbiology and Immunology, University of Belgrade, Tehran, Iran. 49 Department of Reproductive Health, University of Gondar, Belgrade, Serbia. 107 School of Public Health, Hawassa University, Hawassa, Gondar, Ethiopia. 50 Kasturba Medical College, Mangalore, Manipal Academy Ethiopia. 108 School of Public Health, Curtin University, Perth, WA, Australia. of Higher Education, Manipal, India. 51 Discipline of Public Health Medicine, 109 Pediatric Dentistry and Dental Public Health Department, Alexandria University of KwaZulu-Natal, Durban, South Africa. 52 Department University, Alexandria, Egypt. 110 Department of Neurology, Cairo University, of Community Health and Primary Care, University of Lagos, Lagos, Nigeria. Cairo, Egypt. 111 Preventive Medicine and Public Health Research Center, Iran 53 Unit of Biochemistry, Universiti Sultan Zainal Abidin (Sultan Zainal Abidin University of Medical Sciences, Tehran, Iran. 112 Multiple Sclerosis Research University), Kuala Terengganu, Malaysia. 54 Department of Hypertension, Center, Tehran University of Medical Sciences, Tehran, Iran. 113 Faculty of Health, Medical University of Lodz, Lodz, Poland. 55 Polish Mothers’ Memorial Hospital York University, Toronto, BC, Canada. 114 Institute for Health Science Research Research Institute, Lodz, Poland. 56 Heidelberg Institute of Global Health Germans Trias i Pujol, Autonomous University of Barcelona, Badalona, Spain. (HIGH), Heidelberg University, Heidelberg, Germany. 57 T.H. Chan School 115 IrsiCaixa AIDS Research Institute, Badalona, Spain. 116 Department of Public Health, Harvard University, Boston, MA, USA. 58 Department of Global of Virology, Pasteur Institute of Morocco, Casablanca, Morocco. 117 Non- Health and Social Medicine, Harvard University, Boston, MA, USA. communicable Diseases Research Center, Tehran University of Medical 59 Department of Social Services, Tufts Medical Center, Boston, MA, USA. Sciences, Tehran, Iran. 118 Research Center for Environmental Determinants 60 Department of Social and Clinical Pharmacy, Charles University, Hradec of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran. Kralova, Czech Republic. 61 Institute of Public Health, United Arab Emirates 119 Torrens University Australia, Adelaide, SA, Australia. 120 Institute of Resource University, Al Ain, United Arab Emirates. 62 Department of Anatomy, All India Governance and Social Change, Kupang, Indonesia. 121 Associated Laboratory Institute of Medical Sciences, Jodhpur, India. 63 Department of Community for Green Chemistry (LAQV), University of Porto, Porto, Portugal. 122 Psychiatry Medicine and Family Medicine, All India Institute of Medical Sciences, Jodhpur, Department, Kaiser Permanente, Fontana, CA, USA. 123 School of Health India. 64 School of Public Health, All India Institute of Medical Sciences, Sciences, A.T. Still University, Mesa, AZ, USA. 124 Institute of Public Health, Jodhpur, India. 65 Department of Statistical and Computational Genomics, Charité Universitätsmedizin Berlin (Charité Medical University Berlin), Berlin, National Institute of Biomedical Genomics, Kalyani, India. 66 Department Germany. 125 Institute of Gerontology, National Academy of Medical Sciences of Statistics, University of Calcutta, Kolkata, India. 67 Social Determinants of Ukraine, Kyiv, Ukraine. 126 Department of Medical Parasitology, Abadan of Health Research Center, Babol University of Medical Sciences, Babol, Iran. University of Medical Sciences, Abadan, Iran. 127 Faculty of Medicine, Abadan 68 Nutrition Department, St. Paul’s Hospital Millennium Medical College, Addis University of Medical Sciences, Abadan, Iran. 128 Department of Dermatology, Ababa, Ethiopia. 69 St. Paul’s Hospital Millennium Medical College, Addis Ababa, Kobe University, Kobe, Japan. 129 Department of Cardiovascular Medicine, Ethiopia. 70 Department of Veterinary Medicine, Islamic Azad University, Cleveland Clinic, Cleveland, OH, USA. 130 Gillings School of Global Public Kermanshah, Iran. 71 Department of Internal Medicine, Manipal Academy Health, University of North Carolina Chapel Hill, Chapel Hill, NC, USA. of Higher Education, Mangalore, India. 72 WHO Collaborating Centre for HIV 131 Department of Community Medicine, Datta Meghe Institute of Medical Strategic Information, University of Zagreb, Zagreb, Croatia. 73 Faculty of Public Sciences, Wardha, India. 132 Department of Nursing and Midwifery, Addis Health and Policy, London School of Hygiene & Tropical Medicine, London, UK. Ababa University, Addis Ababa, Ethiopia. 133 Department of Public Health, 74 School of Public Health and Health Systems, University of Waterloo, Debre Berhan University, Debre Berhan, Ethiopia. 134 Infectious Disease Waterloo, ON, Canada. 75 Al Shifa School of Public Health, Al Shifa Trust Eye Research Center, Kermanshah University of Medical Sciences, Kermanshah, Hospital, Rawalpindi, Pakistan. 76 Department of Health Care, Metropolitan Iran. 135 Pediatric Department, Kermanshah University of Medical Sciences, Autonomous University, Mexico City, Mexico. 77 Research Unit on Applied Kermanshah, Iran. 136 School of Public Health, Qazvin University of Medical Molecular Biosciences (UCIBIO), University of Porto, Porto, Portugal. Sciences, Qazvin, Iran. 137 Health Systems and Policy Research, Indian Institute 78 Department of Pharmacology, All India Institute of Medical Sciences, of Public Health, Gandhinagar, India. 138 Department of Family and Community Jodhpur, India. 79 Department of Community Medicine, Datta Meghe Institute Medicine, University Of Sulaimani, Sulaimani, Iraq. 139 Department of Medical Sciences, Sawangi, India. 80 Saveetha Medical College, Saveetha of Microbiology, Saint James School of Medicine, The Valley, Anguilla. University, Chennai, India. 81 James P Grant School of Public Health, BRAC 140 Department of Epidemiology, Saint James School of Medicine, The Valley, University, Dhaka, Bangladesh. 82 Department of Epidemiology Anguilla. 141 Faculty of Nursing, Federal University of Goiás, Goiânia, Brazil. and Biostatistics, University of South Carolina, Columbia, SC, USA. 83 Center 142 Department of Health Policy and Management, University of Georgia, for Biomedicine and Community Health, VNU-International School, Hanoi, Athens, GA, USA. 143 Department of Pharmacology, Shahid Beheshti University Vietnam. 84 Department of Psychology, University of the Free State, Park West, of Medical Sciences, Tehran, Iran. 144 Obesity Research Center, Shahid Beheshti South Africa. 85 Environmental Health Department, Imam Abdulrahman Bin University of Medical Sciences, Tehran, Iran. 145 School of Health Faisal University, Dammam, Saudi Arabia. 86 IRCCS Istituto Ortopedico Galeazzi and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, (Galeazzi Orthopedic Institute IRCCS), University of Milan, Milan, Italy. United Arab Emirates. 146 Department of Public Health, Jigjiga University, Jijiga, 87 Department of Dermatology, Case Western Reserve University, Cleveland, Ethiopia. 147 Gastrointestinal and Liver Diseases Research Center, Guilan OH, USA. 88 Department of Population and Health, University of Cape Coast, University of Medical Sciences, Rasht, Iran. 148 Caspian Digestive Disease Cape Coast, Ghana. 89 Department of Information Technology, University Research Center, Guilan University of Medical Sciences, Rasht, Iran. 149 School of Human Development, Sulaymaniyah, Iraq. 90 Institute for Research of Nursing and Midwifery, Tabriz University of Medical Sciences, Tabriz, Iran. and Innovation in Health, University of Porto, Porto, Portugal. 91 Institute 150 Independent Consultant, Tabriz, Iran. 151 Institute of Pharmaceutical of Biomedical Engineering (INEB), University of Porto, Porto, Portugal. Sciences, University of Veterinary and Animal Sciences, Lahore, Pakistan. 92 152 Australian Institute for Suicide Research and Prevention, Griffith University, Department of Pharmacy Administration and Clinical Pharmacy, Xian Mount Gravatt, QLD, Australia. 93 Wellcome Trust Brighton and Sussex Centre Jiaotong University, Xian, China. 153 Department of Statistics and Econometrics, for Global Health Research, Brighton and Sussex Medical School, Brighton, UK. Bucharest University of Economic Studies, Bucharest, Romania. 154 School 94 School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia. of Business, London South Bank University, London, UK. 155 Department 95 National Centre for AIDS and STD Control, Save the Children, Kathmandu, of Urban Planning and Design, University of Hong Kong, Hong Kong, China. Nepal. 96 Department of Global Public Health, Karolinska Institute, Stockholm, 156 Department of Epidemiology and Biostatistics, Tehran University of Medical Sweden. 97 Department of Community Medicine, University of Peradeniya, Sciences, Tehran, Iran. 157 Pediatric Chronic Kidney Disease Research Center, Peradeniya, Sri Lanka. 98 Department of Health Metrics Sciences, School Tehran University of Medical Sciences, Tehran, Iran. 158 Institute of Research of Medicine, University of Washington, Seattle, WA, USA. 99 Department and Development, Duy Tan University, Da Nang, Vietnam. 159 Department of Epidemiology, Maastricht University, Maastricht, Netherlands. of Computer Science, University of Human Development, Sulaymaniyah, Iraq. 100 160 Department of Epidemiology, Shiraz University of Medical Sciences, Shiraz, Department of Occupational Safety and Health, China Medical University, Iran. 101 Center of Complexity Sciences, National Autonomous University Taichung, Taiwan. 161 Department of Health Promotion and Education, of Mexico, Mexico City, Mexico. 102 Faculty of Veterinary Medicine University of Ibadan, Ibadan, Nigeria. 162 Department of Community Medicine, Haeuser et al. BMC Medicine (2022) 20:488 Page 21 of 24 University of Ibadan, Ibadan, Nigeria. 163 Department of Community Medicine, USA. 223 Campus Caucaia, Federal Institute of Education, Science University College Hospital, Ibadan, Ibadan, Nigeria. 164 Faculty of Medicine, and Technology of Ceará, Caucaia, Brazil. 224 Peru Country Office, United University of Belgrade, Belgrade, Serbia. 165 Department of Epidemiology, Nations Population Fund (UNFPA), Lima, Peru. 225 Forensic Medicine Division, University of Kragujevac, Kragujevac, Serbia. 166 Department of Epidemiology Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. 226 and Preventive Medicine, Monash University, Melbourne, VIC, Australia. Department of Reproductive Health and Population Studies, Bahir Dar 167 School of Health Systems and Public Health, University of Pretoria, Pretoria, University, Bahir Dar, Ethiopia. 227 Breast Surgery Unit, Helsinki University South Africa. 168 Institute of Advanced Manufacturing Technologies, Peter Hospital, Helsinki, Finland. 228 University of Helsinki, Helsinki, Finland. 229 Clinical the Great St. Petersburg Polytechnic University, St. Petersburg, Russia. Microbiology and Parasitology Unit, Dr. Zora Profozic Polyclinic, Zagreb, 169 Institute of Comparative Economic Studies, Hosei University, Tokyo, Japan. Croatia. 230 University Centre Varazdin, University North, Varazdin, Croatia. 170 231 Department of Community Medicine, Dr. Baba Saheb Ambedkar Medical Institute of Addiction Research (ISFF), Frankfurt University of Applied College & Hospital, Delhi, India. 171 Department of Community Medicine, Sciences, Frankfurt, Germany. 232 Department of Radiation Oncology, University Banaras Hindu University, Varanasi, India. 172 Vanke School of Public Health, of California San Francisco, San Francisco, CA, USA. 233 Internal Medicine Tsinghua University, Beijing, China. 173 Department of Community Medicine, Department, King Saud University, Riyadh, Saudi Arabia. 234 Department Manipal Academy of Higher Education, Mangalore, India. 174 National Institute of Epidemiology and Biostatistics, Shahrekord University of Medical Sciences, of Epidemiology, Indian Council of Medical Research, Chennai, India. Shahrekord, Iran. 235 Department of Nursing, Mashhad University of Medical 175 Department of Family Medicine and Public Health, University of Opole, Sciences, Mashhad, Iran. 236 Department of Biomolecular Sciences, University Opole, Poland. 176 School of Management and Medical Informatics, Tabriz of Mississippi, Oxford, MS, USA. 237 Department of Pharmacy, Mizan-Tepi University of Medical Sciences, Tabriz, Iran. 177 Institute for Prevention University, Mizan, Ethiopia. 238 Health Systems and Policy Research Unit, of Non-communicable Diseases, Qazvin University of Medical Sciences, Ahmadu Bello University, Zaria, Nigeria. 239 Department of Health Care Qazvin, Iran. 178 Health Services Management Department, Qazvin University Management, Technical University of Berlin, Berlin, Germany. 240 Computer, of Medical Sciences, Qazvin, Iran. 179 Department of Biostatistics, Abadan Electrical, and Mathematical Sciences and Engineering Division, King Abdullah University of Medical Sciences, Abadan, Iran. 180 Department of Forensic University of Science and Technology, Thuwal, Saudi Arabia. 241 Department Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India. of Epidemiology and Biostatistics, Wuhan University, Wuhan, China. 181 242 Social Determinants of Health Research Center, Tabriz University of Medical Department of Pediatrics and Child Health, Debre Berhan University, Debre Sciences, Tabriz, Iran. 182 International Research Center of Excellence, Institute Berhan, Ethiopia. 243 College of Medicine and Public Health, Flinders University, of Human Virology Nigeria, Abuja, Nigeria. 183 Julius Centre for Health Sciences Adeaide, SA, Australia. 244 Research and Analytics Department, Initiative and Primary Care, Utrecht University, Utrecht, Netherlands. 184 Mashhad for Financing Health and Human Development, Chennai, India. 245 Department University of Medical Sciences, Mashhad, Iran. 185 Department of Epidemiology of Research and Analytics, Bioinsilico Technologies, Chennai, India. 246 and Biostatistics, Health Services Academy, Islamabad, Pakistan. Laboratory of Public Health Indicators Analysis and Health Digitalization, 186 Department of Medical Microbiology & Immunology, United Arab Emirates Moscow Institute of Physics and Technology, Dolgoprudny, Russia. University, Al Ain, United Arab Emirates. 187 Department of Population Science, 247 Experimental Surgery and Oncology Laboratory, Kursk State Medical Jatiya Kabi Kazi Nazrul Islam University, Mymensingh, Bangladesh. 188 Faculty University, Kursk, Russia. 248 Department of Biotechnology, University of Central of Health and Wellbeing, Sheffield Hallam University, Sheffield, UK. 189 College Punjab, Lahore, Pakistan. 249 Department of Pediatrics, Arak University of Arts and Sciences, Ohio University, Zanesville, OH, USA. 190 Department of Medical Sciences, Arak, Iran. 250 Department of Disease Control of Public Health, New Mexico State University, Las Cruces, NM, USA. 191 School and Environmental Health, Makerere University, Kampala, Uganda. 251 of Traditional Chinese Medicine, Xiamen University Malaysia, Sepang, Malaysia. Department of General Surgery, Carol Davila University of Medicine 192 School of Health Sciences, Kristiania University College, Oslo, Norway. and Pharmacy, Bucharest, Romania. 252 Department of General Surgery, 193 Department of Global Community Health and Behavioral Sciences, Tulane Emergency Hospital of Bucharest, Bucharest, Romania. 253 Ministry of Health, University, New Orleans, LA, USA. 194 Department of Nursing and Health Community Development, Gender, Elderly and Children, Dar es Salaam, Promotion, Oslo Metropolitan University, Oslo, Norway. 195 School Tanzania. 254 Department of Public Health, University of Yaoundé I, Yaoundé, of Population and Public Health, University of British Columbia, Vancouver, BC, Cameroon. 255 Department of Biological Sciences, University of Embu, Embu, Canada. 196 Arthritis Research Canada, Richmond, Canada. 197 Independent Kenya. 256 Institute for Global Health Innovations, Duy Tan University, Hanoi, Consultant, Jakarta, Indonesia. 198 Kasturba Medical College, Udupi, India. Vietnam. 257 South African Medical Research Council, Cape Town, South Africa. 199 258 Biomedical Research Networking Center for Mental Health Network School of Public Health and Family Medicine, University of Cape Town, Cape (CIBERSAM), San Juan de Dios Sanitary Park, Sant Boi de Llobregat, Spain. Town, South Africa. 259 Centre for Heart Rhythm Disorders, University 200 Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, of Adelaide, Adelaide, SA, Australia. 260 Unit of Microbiology and Public Health, Spain. 201 Department of Anthropology, Panjab University, Chandigarh, India. Institute of Medical Sciences, Las Tablas, Panama. 261 Department of Public 202 Department of Demography, University of Montreal, Montreal, QC, Canada. Health, Ministry of Health, Herrera, Panama. 262 Department of Pediatrics, 203 Department of Social and Preventive Medicine, University of Montreal, National Hospital, Abuja, Nigeria. 263 Department of International Public Health, Montreal, Canada. 204 University of Environment and Sustainable Development, University of Liverpool, Liverpool, UK. 264 Administrative and Economic Somanya, Ghana. 205 Department of Psychiatry, University of Nairobi, Nairobi, Sciences Department, University of Bucharest, Bucharest, Romania. Kenya. 206 Division of Psychology and Language Sciences, University College 265 Department of Community Health and Primary Care, University of Lagos, Idi London, London, UK. 207 Imperial College Business School, Imperial College Araba, Nigeria. 266 Department of Family and Preventive Medicine, University London, London, UK. 208 Faculty of Public Health, University of Indonesia, of Utah, Salt Lake City, UT, USA. 267 Department of Psychiatry and Behavioural Depok, Indonesia. 209 Department of Clinical Sciences and Community Health, Neurosciences, McMaster University, Hamilton, ON, Canada. 268 Department University of Milan, Milan, Italy. 210 Public Health Foundation of India, of Psychiatry, University of Lagos, Lagos, Nigeria. 269 Community Prevention Gurugram, India. 211 Unit of Genetics and Public Health, Institute of Medical and Care Services, National AIDS Control Committee, Abuja, Nigeria. 270 Centre Sciences, Las Tablas, Panama. 212 Ministry of Health, Herrera, Panama. for Healthy Start Initiative, Lagos, Nigeria. 271 Diplomacy and Public Relations 213 Department of Infectious Disease Epidemiology, London School of Hygiene Department, University of Human Development, Sulaymaniyah, Iraq. & Tropical Medicine, London, UK. 214 Department of Otorhinolaryngology, 272 Department of Pharmacology and Therapeutics, University of Nigeria Father Muller Medical College, Mangalore, India. 215 Department of Health Nsukka, Enugu, Nigeria. 273 University of Port Harcourt, Port Harcourt, Nigeria. Sciences, University of Leicester, Leicester, UK. 216 School of Public Health 274 Department of Project Management, National Research University Higher and Preventive Medicine, Monash University, Melbourne, VIC, Australia. School of Economics, Moscow, Russia. 275 Department of Medicine, University 217 Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA. of Ibadan, Ibadan, Nigeria. 276 Department of Medicine, University College 218 Department of Quantitative Health Science, Case Western Reserve Hospital, Ibadan, Ibadan, Nigeria. 277 Department of Respiratory Medicine, University, Cleveland, OH, USA. 219 Department of Environmental Health Jagadguru Sri Shivarathreeswara Academy of Health Education and Research, Engineering, Tehran University of Medical Sciences, Tehran, Iran. Mysore, India. 278 Department of Forensic Medicine and Toxicology, Manipal 220 Environmental Health Research Center, Kurdistan University of Medical Academy of Higher Education, Manipal, India. 279 Department of Health Sciences, Sanandaj, Iran. 221 Department of Pediatrics, Montefiore Medical Metrics, Center for Health Outcomes & Evaluation, Bucharest, Romania. Center, New York, NY, USA. 222 Department of Environmental Medicine 280 Research Department, Nepal Health Research Council, Kathmandu, Nepal. 281 and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, Research Department, Public Health Research Society Nepal, Kathmandu, Haeuser et al. BMC Medicine (2022) 20:488 Page 22 of 24 Nepal. 282 Saveetha Medical College and Hospitals, Saveetha University, Ahmadu Bello University, Zaria, Nigeria. 342 Cancer Control Center, Osaka Chennai, India. 283 Iran University of Medical Sciences, Tehran, Iran. International Cancer Institute, Osaka, Japan. 343 Department of Biomedical 284 Department of Pediatrics, University of Melbourne, Melbourne, VIC, Sciences, Arba Minch University, Arba Minch, Ethiopia. 344 University Institute Australia. 285 Population Health Theme, Murdoch Children’s Research Institute, “Egas Moniz”, Monte da Caparica, Portugal. 345 Research Institute for Medicines, Melbourne, VIC, Australia. 286 Department of Genetics, Yale University, New University of Lisbon, Lisbon, Portugal. 346 School of Public Health, Mekelle Haven, CT, USA. 287 School of Global Public Health, New York University, New University, Mekelle, Ethiopia. 347 Southgate Institute for Health and Society, York, NY, USA. 288 University Medical Center Groningen, University Flinders University, Adelaide, SA, Australia. 348 Department of Pathology of Groningen, Groningen, Netherlands. 289 School of Economics and Business, and Legal Medicine, University of São Paulo, Ribeirão Preto, Brazil. University of Groningen, Groningen, Netherlands. 290 National Institute 349 Modestum LTD, London, UK. 350 College of Medicine and Health Sciences, of Infectious Diseases, Bucuresti, Romania. 291 Department of Infectious Bahir Dar University, Bahir Dar, Ethiopia. 351 Department of Community Diseases, Carol Davila University of Medicine and Pharmacy, Bucharest, Medicine, Alex Ekwueme Federal University Teaching Hospital Abakaliki, Romania. 292 School of Engineering, Macquarie University, Sydney, NSW, Abakaliki, Nigeria. 352 Kasturba Medical College, Manipal Academy of Higher Australia. 293 Pohang University of Science and Technology, Pohang, South Education, Mangalore, India. 353 Clinical Research Department, IRCCS Korea. 294 College of Medicine, University of Central Florida, Orlando, FL, USA. Fondazione Don Carlo Gnocchi, Milan, Italy. 354 Department of Medical 295 Department of Immunology, Mazandaran University of Medical Sciences, and Surgical Sciences, University of Bologna, Bologna, Italy. 355 Occupational Sari, Iran. 296 Molecular and Cell Biology Research Center, Mazandaran Health Unit, Sant’Orsola Malpighi Hospital, Bologna, Italy. 356 Faculty University of Medical Sciences, Sari, Iran. 297 Metabolomics and Genomics of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam. Research Center, Tehran University of Medical Sciences, Tehran, Iran. 298 Sina 357 Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Trauma and Surgery Research Center, Tehran University of Medical Sciences, Ho Chi Minh City, Vietnam. 358 Population Dynamics and Sexual Tehran, Iran. 299 Future Technology Research Center, National Yunlin University and Reproductive Health, African Population and Health Research Center, of Science and Technology, Yunlin, Taiwan. 300 Department of Epidemiology, Nairobi, Kenya. 359 Foundation University Medical College, Foundation Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada. University Islamabad, Islamabad, Pakistan. 360 Department of Cultures, Societies 301 Research and Innovation Division, South Asian Institute for Social and Global Studies, Northeastern University, Boston, MA, USA. 361 School Transformation (SAIST), Dhaka, Bangladesh. 302 Research Department, Policy of Public Health, University of Nairobi, Nairobi, Kenya. 362 School of Population Research Institute, Kathmandu, Nepal. 303 Health and Public Policy Health and Environmental Sciences, King’s College London, London, UK. 363 Department, Global Center for Research and Development, Kathmandu, Centre for Health Policy Research, Torrens University Australia, Adelaide, SA, Nepal. 304 Department of Oral Pathology, Sharavathi Dental College Australia. 364 Department of community Medicine, Rajarata University of Sri and Hospital, Shimogga, India. 305 WHO Collaborating Centre for Public Health Lanka, Anuradhapura, Sri Lanka. 365 Department of Biostatistics, University Education and Training, Imperial College London, London, UK. 306 University of Washington, Seattle, WA, USA. 366 School of International Development College London Hospitals, London, UK. 307 Department of Primary Care and Global Studies, University of Ottawa, Ottawa, ON, Canada. 367 The George and Public Health, Imperial College London, London, UK. 308 Academic Public Institute for Global Health, University of Oxford, Oxford, UK. 368 Centre Health England, Public Health England, London, UK. 309 Department for Suicide Research and Prevention, University of Hong Kong, Hong Kong, of Computer Science, Boston University, Boston, MA, USA. 310 Department China. 369 Department of Social Work and Social Administration, University of Epidemiology and Biostatistics, Haramaya University, Harar, Ethiopia. of Hong Kong, Hong Kong, China. 370 Department 311 Research Center for Immunodeficiencies, Tehran University of Medical of Neuropsychopharmacology, National Center of Neurology and Psychiatry, Sciences, Tehran, Iran. 312 Network of Immunity in Infection, Malignancy Kodaira, Japan. 371 Department of Public Health, Juntendo University, Tokyo, and Autoimmunity (NIIMA), Universal Scientific Education and Research Japan. 372 Department of Bioengineering and Therapeutic Sciences, University Network (USERN), Tehran, Iran. 313 Faculty of Business and Management, of California San Francisco, San Francisco, CA, USA. 373 Addictology Universiti Sultan Zainal Abidin (Sultan Zainal Abidin University), Kuala Department, Russian Medical Academy of Continuous Professional Education, Terengganu, Malaysia. 314 Epidemiology Research Unit (EPIUnit), University Moscow, Russia. 374 School of Public Health, Wuhan University of Science of Porto, Porto, Portugal. 315 Department of Global Health, University and Technology, Wuhan, China. 375 Hubei Province Key Laboratory of Washington, Seattle, WA, USA. 316 Department of Medicine, University of Occupational Hazard Identification and Control, Wuhan University of Washington, Seattle, WA, USA. 317 African Genome Center, Mohammed VI of Science and Technology, Wuhan, China. 376 School of Medicine, Wuhan Polytechnic University (UM6P), Ben Guerir, Morocco. 318 Centro de University, Wuhan, China. Investigaciones en Anomalías Congénitas y Enfermedades Raras (Center for Research in Congenital Anomalies and Rare Diseases), Universidad ICESI Received: 26 April 2022 Accepted: 26 October 2022 (ICESI University), Cali, Colombia. 319 Malaria Atlas Project, Telethon Kids Institute, Perth, Australia. 320 Department of Health Statistics, National Institute for Medical Research, Dar es Salaam, Tanzania. 321 Department of Internal Medicine, University of Botswana, Gaborone, Botswana. 322 Department of Psychiatry, All India Institute of Medical Sciences, New Delhi, India. References 323 Department of Public Health, Madda Walabu University, Bale Robe, Ethiopia. 1. Frank TD, Carter A, Jahagirdar D, Biehl MH, Douwes-Schultz D, Larson SL, 324 Public Health and Community Medicine Department, Cairo University, Giza, et al. Global, regional, and national incidence, prevalence, and mortality Egypt. 325 Drug Applied Research Center, Tabriz University of Medical Sciences, of HIV, 1980–2017, and forecasts to 2030, for 195 countries and territories: Tabriz, Iran. 326 Department of Entomology, Ain Shams University, Cairo, Egypt. a systematic analysis for the Global Burden of Diseases, Injuries, and Risk 327 Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, Factors Study 2017. Lancet HIV. 2019;6:e831–59. UK. 328 Nuffield Department of Medicine, University of Oxford, Oxford, UK. 2. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 329 Geriatric and Long Term Care Department, Hamad Medical Corporation, diseases and injuries in 204 countries and territories, 1990–2019: a Doha, Qatar. 330 Faculty of Health & Social Sciences, Bournemouth University, systematic analysis for the Global Burden of Disease Study 2019. Lancet. Bournemouth, UK. 331 College of Public Health, Medical and Veterinary 2020;396:1204–22. Sciences, James Cook University, QLD, Townsville, Australia. 332 Public Health 3. Micah AE, Su Y, Bachmeier SD, Chapin A, Cogswell IE, Crosby SW, et al. Division, An-Najah National University, Nablus, Palestine. 333 Independent Health sector spending and spending on HIV/AIDS, tuberculosis, and Consultant, Karachi, Pakistan. 334 Faculty of Caring Science, Work Life, and Social malaria, and development assistance for health: progress towards Sus‑ Welfare, University of Borås, Borås, Sweden. 335 College of Medicine, Yonsei tainable Development Goal 3. Lancet. 2020;396:693–724. University, Seoul, South Korea. 336 Department of Internal Medicine, Yale 4. UNAIDS. Joint United Nations Programme on HIV/AIDS. 2020. https://​ University, New Haven, CT, USA. 337 School of Medicine, University of Alabama www.​unaids.​org/​en. at Birmingham, Birmingham, AL, USA. 338 Medicine Service, US Department 5. UNAIDS. Understanding Fast Track: accelerating action to end the AIDS of Veterans Affairs (VA), Birmingham, AL, USA. 339 Department No.16, Moscow epidemic by 2030. 2015. http://​www.​unaids.​org/​sites/​defau​lt/​files/​ Research and Practical Centre on Addictions, Moscow, Russia. 340 Department media_​asset/​201506_​JC2743_​Under​stand​ing_​FastT​rack_​en.​pdf. of Infectious Diseases and Epidemiology, Pirogov Russian National Research Medical University, Moscow, Russia. 341 Department of Community Medicine, Haeuser et al. BMC Medicine (2022) 20:488 Page 23 of 24 6. United Nations. Transforming our world: the 2030 agenda for sustainable 29. Tanser F, Bärnighausen T, Cooke GS, Newell M-L. Localized spatial development. New York: United Nations; 2015. clustering of HIV infections in a widely disseminated rural South African 7. Bekker L-G, Alleyne G, Baral S, Cepeda J, Daskalakis D, Dowdy D, et al. epidemic. Int J Epidemiol. 2009;38:1008–16. Advancing global health and strengthening the HIV response in the era 30. Bulstra CA, Hontelez JAC, Giardina F, Steen R, Nagelkerke NJD, Bär‑ of the Sustainable Development Goals: the International AIDS Society— nighausen T, et al. Mapping and characterising areas with high levels of Lancet Commission. Lancet. 2018;392:312–58. HIV transmission in sub-Saharan Africa: a geospatial analysis of national 8. Jones J, Sullivan PS, Curran JW. Progress in the HIV epidemic: identifying survey data. PLoS Med. 2020;17:e1003042. goals and measuring success. PLoS Med. 2019;16:e1002729. 31. Stevens GA, Alkema L, Black RE, Boerma JT, Collins GS, Ezzati M, et al. 9. Hogan AB, Jewell BL, Sherrard-Smith E, Vesga JF, Watson OJ, Whittaker C, Guidelines for accurate and transparent health estimates reporting: the et al. Potential impact of the COVID-19 pandemic on HIV, tuberculosis, GATHER statement. Lancet. 2016;388:e19–23. and malaria in low-income and middle-income countries: a modelling 32. Tatem AJ. WorldPop, open data for spatial demography. Sci Data. study. Lancet Glob Health. 2020;8:e1132–41. 2017;4:170004. 10. Dwyer-Lindgren L, Cork MA, Sligar A, Steuben KM, Wilson KF, Provost NR, 33. R Core Team. R: the R project for statistical computing. 2019. https://​ et al. Mapping HIV prevalence in sub-Saharan Africa between 2000 and www.r-​proje​ct.​org/. Accessed 8 July 2020. 2017. Nature. 2019;570:189–93. 34. Burgert C, Colston J, Roy T, Zachary B. Geographic displacement proce‑ 11. Larmarange J, Bendaud V. HIV estimates at second subnational level from dure and georeferenced data release policy for the Demographic and national population-based surveys. AIDS. 2014;28(Suppl 4):S469–76. Health Surveys; 2013. 12. Meyer-Rath G, McGillen JB, Cuadros DF, Hallett TB, Bhatt S, Wabiri N, et al. 35. UNAIDS. National HIV estimates file. 2019. https://​www.​unaids.​org/​en/​ Targeting the right interventions to the right people and places: the role dataa​nalys​is/​datat​ools/​spect​rum-​epp. of geospatial analysis in HIV program planning. AIDS. 2018;32:957–63. 36. GBD 2019 Demographics Collaborators. Global age-sex-specific fertility, 13. Cuadros DF, Li J, Branscum AJ, Akullian A, Jia P, Mziray EN, et al. Mapping mortality, healthy life expectancy (HALE), and population estimates the spatial variability of HIV infection in sub-Saharan Africa: effective infor‑ in 204 countries and territories, 1950–2019: a comprehensive demo‑ mation for localized HIV prevention and control. Sci Rep. 2017;7:9093. graphic analysis for the Global Burden of Disease Study 2019. Lancet. 14. Coburn BJ, Okano JT, Blower S. Using geospatial mapping to design 2020;396:1160–203. HIV elimination strategies for sub-Saharan Africa. Sci Transl Med. 37. Bhatt S, Cameron E, Flaxman SR, Weiss DJ, Smith DL, Gething PW. 2017;9:eaag0019. Improved prediction accuracy for disease risk mapping using Gaussian 15. Akullian A, Vandormael A, Miller JC, Bershteyn A, Wenger E, Cuadros D, process stacked generalization. J Royal Soc Interface. 2017;14:20170520. et al. Large age shifts in HIV-1 incidence patterns in KwaZulu-Natal, South 38. Hastie T, Tibshirani RJ. Generalized additive models. London: Chapman & Africa. Proc Natl Acad Sci U S A. 2021;118:e2013164118. Hall; 1990. 16. Khalifa A, Stover J, Mahy M, Idele P, Porth T, Lwamba C. Demographic 39. Friedman JH. Greedy function approximation: a gradient boosting change and HIV epidemic projections to 2050 for adolescents and young machine. Ann Stat. 2001;29:1189–232. people aged 15-24. Null. 2019;12:1662685. 40. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc 17. Faust L, Yaya S. The effect of HIV educational interventions on HIV-related B Methodol. 1996;58:267–88. knowledge, condom use, and HIV incidence in sub-Saharan Africa: a 41. Kristensen K, Nielsen A, Berg CW, Skaug H, Bell BM. TMB: automatic dif‑ systematic review and meta-analysis. BMC Public Health. 2018;18:1254. ferentiation and Laplace approximation. J Stat Softw. 2016;70:1–21. 18. Anderson S-J, Cherutich P, Kilonzo N, Cremin I, Fecht D, Kimanga D, et al. 42. Stein ML. Interpolation of spatial data: some theory for Kriging. New York: Maximising the effect of combination HIV prevention through prioritisa‑ Springer-Verlag; 1999. tion of the people and places in greatest need: a modelling study. Lancet. 43. Lindgren F, Rue H, Lindström J. An explicit link between Gaussian fields 2014;384:249–56. and Gaussian Markov random fields: the stochastic partial differential 19. Schneider MT, Birger M, Haakenstad A, Singh L, Hamavid H, Chapin A, equation approach. R Stat Soc. 2011;73:423–98. et al. Tracking development assistance for HIV/AIDS: the international 44. Zaba BW, Carpenter LM, Boerma JT, Gregson S, Nakiyingi J, Urassa M. response to a global epidemic. AIDS. 2016;30:1475–9. Adjusting ante-natal clinic data for improved estimates of HIV prevalence 20. Olakunde BO, Adeyinka DA, Ozigbu CE, Ogundipe T, Menson WNA, among women in sub-Saharan Africa. AIDS. 2000;14:2741–50. Olawepo JO, et al. Revisiting aid dependency for HIV programs in sub- 45. Gouws E, Mishra V, Fowler TB. Comparison of adult HIV prevalence from Saharan Africa. Public Health. 2019;170:57–60. national population-based surveys and antenatal clinic surveillance in 21. Jewell BL, Mudimu E, Stover J, ten Brink D, Phillips AN, Smith JA, et al. countries with generalised epidemics: implications for calibrating surveil‑ Potential effects of disruption to HIV programmes in sub-Saharan Africa lance data. Sex Transm Infect. 2008;84(Suppl 1):i17–23. caused by COVID-19: results from multiple mathematical models. Lancet 46. Marsh K, Mahy M, Salomon JA, Hogan DR. Assessing and adjusting for HIV. 2020;7:e629–40. differences between HIV prevalence estimates derived from national 22. Nagelkerke NJD, Jha P, de Vlas SJ, Korenromp EL, Moses S, Blanchard population-based surveys and antenatal care surveillance, with applica‑ JF, et al. Modelling HIV/AIDS epidemics in Botswana and India: impact tions for Spectrum 2013. AIDS. 2014;28:S497–505. of interventions to prevent transmission. Bull World Health Organ. 47. Marston M, Zaba B, Eaton JW. The relationship between HIV and fertility in 2002;80:89–96. the era of antiretroviral therapy in sub-Saharan Africa: evidence from 49 23. Long EF, Stavert RR. Portfolios of biomedical HIV interventions Demographic and Health Surveys. Trop Med Int Health. 2017;22:1542–50. in South Africa: a cost-effectiveness analysis. J Gen Intern Med. 48. ESA-CCI Project. Land cover classification gridded maps from 1992 to pre‑ 2013;28:1294–301. sent derived from satellite observations. 2020. https://​cds.​clima​te.​coper​ 24. Bershteyn A, Klein DJ, Eckhoff PA. Age-targeted HIV treatment and nicus.​eu/​cdsapp#​!/​datas​et/​satel​lite-​land-​cover?​tab=​overv​iew. Accessed primary prevention as a “ring fence” to efficiently interrupt the age pat‑ 30 Apr 2020. terns of transmission in generalized epidemic settings in South Africa. Int 49. Wickham H. ggplot2: elegant graphics for data analysis. New York: Health. 2016;8:277–85. Springer-Verlag; 2016. 25. Joint United Nations Programme on HIV/AIDS. AIDSinfo. UNAIDS; 2018. 50. McGillen JB, Anderson S-J, Dybul MR, Hallett TB. Optimum resource allo‑ http://​aidsi​nfo.​unaids.​org/. Accessed 18 June 2020. cation to reduce HIV incidence across sub-Saharan Africa: a mathematical 26. Okano JT, Blower S. Sex-specific maps of HIV epidemics in sub-Saharan modelling study. Lancet HIV. 2016;3:e441–8. Africa. Lancet Infect Dis. 2016;16:1320–2. 51. Hosek S, Pettifor A. HIV prevention interventions for adolescents. Curr 27. Messina JP, Emch M, Muwonga J, Mwandagalirwa K, Edidi SB, Mama HIV/AIDS Rep. 2019;16:120–8. N, et al. Spatial and socio-behavioral patterns of HIV prevalence in the 52. Schatz E, Seeley J, Negin J, Weiss HA, Tumwekwase G, Kabunga E, et al. Democratic Republic of Congo. Soc Sci Med. 2010;71:1428–35. “For us here, we remind ourselves”: strategies and barriers to ART access 28. Palk L, Blower S. Geographic variation in sexual behavior can explain and adherence among older Ugandans. BMC Public Health. 2019;19:131. geospatial heterogeneity in the severity of the HIV epidemic in Malawi. 53. De Neve J-W, Garrison-Desany H, Andrews KG, Sharara N, Boudreaux BMC Med. 2018;16:22. C, Gill R, et al. Harmonization of community health worker programs Haeuser et al. BMC Medicine (2022) 20:488 Page 24 of 24 for HIV: A four-country qualitative study in Southern Africa. PLoS Med. 74. Woldesenbet S, Kufa T, Lombard C, Manda S, Ayalew kassahun, Cheyip M, 2017;14:e1002374. et al. The 2017 national antenatal sentinel HIV survey key findings, South 54. Mbonu NC, van den Borne B, De Vries NK. Stigma of people with Africa. 2019. HIV/AIDS in Sub-Saharan Africa: a literature review. J Trop Med. 75. Ministry of Health, National AIDS Control Council. Kenya AIDS response 2009;2009:145891. progress report 2016. 2016. 55. World Health Organization. Consolidated guidelines on the use of antiret‑ 76. Jiang H, Zhou Y, Tang W. Maintaining HIV care during the COVID-19 roviral drugs for treating and preventing HIV infection: recommendations pandemic. Lancet HIV. 2020;7:e308–9. for a public health approach. 2013. https://​apps.​who.​int/​iris/​bitst​ream/​ 77. Global Administrative Areas. GADM maps and data. v.3.6. 2019. https://​ handle/​10665/​85321/​97892​41505​727_​eng.​pdf;​jsess​ionid=​FA819​AE8F0​ gadm.​org/. 65C68​5D2C4​CC768​FEE73​04?​seque​nce=1. Accessed 20 Mar 2021. 56. Sabapathy K, den Bergh RV, Fidler S, Hayes R, Ford N. Uptake of home- based voluntary HIV testing in sub-Saharan Africa: a systematic review Publisher’s Note and meta-analysis. PLoS Med. 2012;9:e1001351. Springer Nature remains neutral with regard to jurisdictional claims in 57. Sharma M, Ying R, Tarr G, Barnabas R. Systematic review and meta-analy‑ published maps and institutional affiliations. sis of community and facility-based HIV testing to address linkage to care gaps in sub-Saharan Africa. Nature. 2015;528:S77–85. 58. Hatzold K, Gudukeya S, Mutseta MN, Chilongosi R, Nalubamba M, Nkhoma C, et al. HIV self-testing: breaking the barriers to uptake of test‑ ing among men and adolescents in sub-Saharan Africa, experiences from STAR demonstration projects in Malawi, Zambia and Zimbabwe. J Int AIDS Soc. 2019;22:e25244. 59. Sia D, Onadja Y, Hajizadeh M, Heymann SJ, Brewer TF, Nandi A. What explains gender inequalities in HIV/AIDS prevalence in sub-Saharan Africa? Evidence from the demographic and health surveys. BMC Public Health. 2016;16:1136. 60. Dean HD, Fenton KA. Addressing social determinants of health in the prevention and control of HIV/AIDS, viral hepatitis, sexually transmitted infections, and tuberculosis. Public Health Rep. 2010;125(4_suppl):1–5. 61. Heestermans T, Browne JL, Aitken SC, Vervoort SC, Klipstein-Grobusch K. Determinants of adherence to antiretroviral therapy among HIV-positive adults in sub-Saharan Africa: a systematic review. BMJ Glob Health. 2016;1:e000125. 62. Tanser F, Bärnighausen T, Grapsa E, Zaidi J, Newell M-L. High coverage of ART associated with decline in risk of HIV acquisition in rural KwaZulu- Natal, South Africa. Science. 2013;339:966–71. 63. 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. Nat Commun. 2019;10:5482. 64. Hontelez JAC, de Vlas SJ, Baltussen R, Newell M-L, Bakker R, Tanser F, et al. The impact of antiretroviral treatment on the age composition of the HIV epidemic in sub-Saharan Africa. AIDS. 2012;26(Suppl 1 0 1):S19–30. 65. CASCADE Collaboration. Differences in CD4 cell counts at seroconversion and decline among 5739 HIV-1-infected individuals with well-estimated dates of seroconversion. J Acquir Immune Defic Syndr. 2003;34:76–83. 66. Goetz MB, Boscardin WJ, Wiley D, Alkasspooles S. Decreased recovery of CD4 lymphocytes in older HIV-infected patients beginning highly active antiretroviral therapy. AIDS. 2001;15:1576–9. 67. Mills EJ, Bärnighausen T, Negin J. HIV and aging--preparing for the chal‑ lenges ahead. N Engl J Med. 2012;366:1270–3. 68. Coetzee L, Bogler L, De Neve J-W, Bärnighausen T, Geldsetzer P, Vollmer S. HIV, antiretroviral therapy and non-communicable diseases in sub- Saharan Africa: empirical evidence from 44 countries over the period 2000 to 2016. J Int AIDS Soc. 2019;22:e25364. 69. Parikh SM, Obuku EA, Walker SA, Semeere AS, Auerbach BJ, Hakim JG, et al. Clinical differences between younger and older adults with HIV/ AIDS starting antiretroviral therapy in Uganda and Zimbabwe: a second‑ ary analysis of the DART trial. PLoS One. 2013;8:e76158. 70. Gething P, Tatem A, Bird T, Burgert-Brucker CR. Creating spatial interpola‑ tion surfaces with DHS data. Rockville: ICF International; 2015. 71. Golding N, Burstein R, Longbottom J, Browne AJ, Fullman N, Osgood- Zimmerman A, et al. Mapping under-5 and neonatal mortality in Africa, 2000–15: a baseline analysis for the Sustainable Development Goals. Lancet. 2017;390:2171–82. 72. Graetz N, Friedman J, Osgood-Zimmerman A, Burstein R, Biehl MH, Shields C, et al. Mapping local variation in educational attainment across Africa. Nature. 2018;555:48–53. 73. Blondeel K, Say L, Chou D, Toskin I, Khosla R, Scolaro E, et al. Evidence and knowledge gaps on the disease burden in sexual and gender minorities: a review of systematic reviews. Int J Equity Health. 2016;15:16.