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AbstractIntroductionWeaknesses in care programmes providing anti‐retroviral therapy (ART) persist and are often instigated by late HIV diagnosis and poor linkage to care. We investigated the potential for a home‐based counselling and testing (HBCT) campaign to be improved through the optimal timing and enhancement of testing rounds to generate greater health outcomes at minimum cost.MethodsUsing a mathematical model of HIV care calibrated to longitudinal data from The Academic Model Providing Access To Healthcare (AMPATH) in Kenya, we simulated HBCT campaigns between 2016 and 2036, assessing the impact and total cost of care for each, for a further 20 years.ResultsWe find that simulating five equally spaced rounds averts 1.53 million disability‐adjusted life‐years (DALYs) at a cost of $1617 million. By altering the timing of HBCT rounds, a four‐round campaign can produce greater impact for lower cost. With "front‐loaded" rounds, the cost per DALY averted is reduced by 12% as fewer rounds are required ($937 vs. $1060). Furthermore, improvements to HBCT coverage and linkage to care avert over two million DALYs at a cost per DALY averted of $621 (41% less than the reference scenario).ConclusionsCountries implementing HBCT can reduce costs by optimally timing rounds and generate greater health outcomes through improving linkage, coverage, and retention. Tailoring HBCT campaigns to individual settings can enhance patient outcomes for minimal cost.

AbstractIntroductionThe World Health Organization recommends full disclosure of HIV‐positive status to adolescents who acquired HIV perinatally (APHIV) by age 12. However, even among adolescents (aged 10–19) already on antiretroviral therapy (ART), disclosure rates are low. Caregivers often report the child being too young and fear of disclosure worsening adolescents' mental health as reasons for non‐disclosure. We aimed to identify the predictors of disclosure and the association of disclosure with adherence, viral suppression and mental health outcomes among adolescents in sub‐Saharan Africa.MethodsAnalyses included three rounds (2014–2018) of data collected among a closed cohort of adolescents living with HIV in Eastern Cape, South Africa. We used logistic regression with respondent random‐effects to identify factors associated with disclosure, and assess differences in ART adherence, viral suppression and mental health symptoms between adolescents by disclosure status. We also explored differences in the change in mental health symptoms and adherence between study rounds and disclosure groups with logistic regression.ResultsEight hundred and thirteen APHIV were interviewed at baseline, of whom 769 (94.6%) and 729 (89.7%) were interviewed at the second and third rounds, respectively. The proportion aware of their HIV‐positive status increased from 63.1% at the first round to 85.5% by the third round. Older age (adjusted odds ratio [aOR]: 1.27; 1.08–1.48) and living in an urban location (aOR: 2.85; 1.72–4.73) were associated with disclosure between interviews. There was no association between awareness of HIV‐positive status and ART adherence, viral suppression or mental health symptoms among all APHIV interviewed. However, among APHIV not aware of their status at baseline, adherence decreased at the second round among those who were disclosed to (N = 131) and increased among those not disclosed to (N = 151) (interaction aOR: 0.39; 0.19–0.80). There was no significant difference in the change in mental health symptoms between study rounds and disclosure groups.ConclusionsAwareness of HIV‐positive status was not associated with higher rates of mental health symptoms, or lower rates of viral suppression among adolescents. Disclosure was not associated with worse mental health. These findings support the recommendation for timely disclosure to APHIV; however, adherence support post‐disclosure is important.

AbstractIntroductionThe Case Surveillance and Vital Registration (CSAVR) model within Spectrum estimates HIV incidence trends from surveillance data on numbers of new HIV diagnoses and HIV‐related deaths. This article describes developments of the CSAVR tool to more flexibly model diagnosis rates over time, estimate incidence patterns by sex and age group and by key population group.MethodsWe modelled HIV diagnosis rate trends as a mixture of three factors, including temporal and opportunistic infection components. The tool was expanded to estimate incidence rate ratios by sex and age for countries with disaggregated reporting of new HIV diagnoses and AIDS deaths, and to account for information on key populations such as men who have sex with men (MSM), males who inject drugs (MWID), female sex workers (FSW) and females who inject drugs (FWID). We used a Bayesian framework to calibrate the tool in 71 high‐income or low‐HIV burden countries.ResultsAcross countries, an estimated median 89% (interquartile range [IQR]: 78%–96%) of HIV‐positive adults knew their status in 2019. Mean CD4 counts at diagnosis were stable over time, with a median of 456 cells/μl (IQR: 391–508) across countries in 2019. In European countries reporting new HIV diagnoses among key populations, median estimated proportions of males that are MSM and MWID was 1.3% (IQR: 0.9%–2.0%) and 0.56% (IQR: 0.51%–0.64%), respectively. The median estimated proportions of females that are FSW and FWID were 0.36% (IQR: 0.27%–0.45%) and 0.14 (IQR: 0.13%–0.15%), respectively. HIV incidence per 100 person‐years increased among MSM, with median estimates reaching 0.43 (IQR: 0.29–1.73) in 2019, but remained stable in MWID, FSW and FWID, at around 0.12 (IQR: 0.04–1.9), 0.09 (IQR: 0.06–0.69) and 0.13% (IQR: 0.08%–0.91%) in 2019, respectively. Knowledge of HIV status among HIV‐positive adults gradually increased since the early 1990s to exceed 75% in more than 75% of countries in 2019 among each key population.ConclusionsCSAVR offers an approach to using routine surveillance and vital registration data to estimate and project trends in both HIV incidence and knowledge of HIV status.

IntroductionHealth and Demographic Surveillance Systems (HDSS) are important sources of population health data in sub-Saharan Africa, but the recording of pregnancies, pregnancy outcomes, and early mortality is often incomplete.
ObjectiveThis study assessed HDSS pregnancy reporting completeness and identified predictors of unreported pregnancies that likely ended in adverse outcomes.
MethodsThe analysis utilized individually-linked HDSS and antenatal care (ANC) data from Siaya, Kenya for pregnancies in 2018-2020. We cross-checked ANC records with HDSS pregnancy registrations and outcomes. Pregnancies observed in the ANC that were missing reports in the HDSS despite a data collection round following the expected delivery date were identified as likely adverse outcomes, and we investigated the characteristics of such individuals. Clinical data were used to investigate the timing of HDSS pregnancy registration relative to care seeking and gestational age, and examine misclassification of miscarriages and stillbirths.
ResultsFrom an analytical sample of 2,475 pregnancies observed in the ANC registers, 46% had pregnancy registrations in the HDSS, and 89% had retrospectively reported pregnancy outcomes. 1% of registered pregnancies were missing outcomes, compared to 10% of those lacking registration. Registered pregnancies had higher rates of stillbirth and perinatal mortality than those lacking registration. In 77% of cases, women accessed ANC prior to registering the pregnancy in the HDSS. Half of reported miscarriages were misclassified stillbirths. We identified 141 unreported pregnancies that likely ended in adverse outcomes. Such cases were more common among those who visited ANC clinics during the first trimester, made fewer overall visits, were HIV-positive, and outside of formal union.
ConclusionsRecord linkage with ANC clinics revealed pregnancy underreporting in HDSS, resulting in biased measurement of perinatal mortality. Integrating records of ANC usage into routine data collection can augment HDSS pregnancy surveillance and improve monitoring of adverse pregnancy outcomes and early mortality.

AbstractIntroductionSeveral HIV risk scores have been developed to identify individuals for prioritized HIV prevention in sub‐Saharan Africa. We systematically reviewed HIV risk scores to: (1) identify factors that consistently predicted incident HIV infection, (2) review inclusion of community‐level HIV risk in predictive models and (3) examine predictive performance.MethodsWe searched nine databases from inception until 15 February 2021 for studies developing and/or validating HIV risk scores among the heterosexual adult population in sub‐Saharan Africa. Studies not prospectively observing seroconversion or recruiting only key populations were excluded. Record screening, data extraction and critical appraisal were conducted in duplicate. We used random‐effects meta‐analysis to summarize hazard ratios and the area under the receiver‐operating characteristic curve (AUC‐ROC).ResultsFrom 1563 initial search records, we identified 14 risk scores in 13 studies. Seven studies were among sexually active women using contraceptives enrolled in randomized‐controlled trials, three among adolescent girls and young women (AGYW) and three among cohorts enrolling both men and women. Consistently identified HIV prognostic factors among women were younger age (pooled adjusted hazard ratio: 1.62 [95% confidence interval: 1.17, 2.23], compared to above 25), single/not cohabiting with primary partners (2.33 [1.73, 3.13]) and having sexually transmitted infections (STIs) at baseline (HSV‐2: 1.67 [1.34, 2.09]; curable STIs: 1.45 [1.17; 1.79]). Among AGYW, only STIs were consistently associated with higher incidence, but studies were limited (n = 3). Community‐level HIV prevalence or unsuppressed viral load strongly predicted incidence but was only considered in 3 of 11 multi‐site studies. The AUC‐ROC ranged from 0.56 to 0.79 on the model development sets. Only the VOICE score was externally validated by multiple studies, with pooled AUC‐ROC 0.626 [0.588, 0.663] (I2: 64.02%).ConclusionsYounger age, non‐cohabiting and recent STIs were consistently identified as predicting future HIV infection. Both community HIV burden and individual factors should be considered to quantify HIV risk. However, HIV risk scores had only low‐to‐moderate discriminatory ability and uncertain generalizability, limiting their programmatic utility. Further evidence on the relative value of specific risk factors, studies populations not restricted to "at‐risk" individuals and data outside South Africa will improve the evidence base for risk differentiation in HIV prevention programmes.PROSPERO NumberCRD42021236367

AbstractIntroductionIdentifying strategies to further reduce AIDS‐related mortality requires accurate estimates of the extent to which mortality among people living with HIV (PLHIV) is due to AIDS‐related or non‐AIDS‐related causes. Existing approaches to estimating AIDS‐related mortality have quantified AIDS‐related mortality as total mortality among PLHIV in excess of age‐ and sex‐matched mortality in populations without HIV. However, recent evidence suggests that, with high antiretroviral therapy (ART) coverage, a growing proportion of excess mortality among PLHIV is non‐AIDS‐related.MethodsWe searched Embase on 22/09/2023 for English language studies that contained data on AIDS‐related mortality rates among adult PLHIV and age‐matched comparator all‐cause mortality rates among people without HIV. We extracted data on the number and rates of all‐cause and AIDS‐related deaths, demographics, ART use and AIDS‐related mortality definitions. We calculated the proportion of excess mortality among PLHIV that is AIDS‐related. The proportion of excess mortality due to AIDS was pooled using random‐effects meta‐analysis.ResultsOf 4485 studies identified by the initial search, eight were eligible, all from high‐income settings: five from Europe, one from Canada, one from Japan and one from South Korea. No studies reported on mortality among only untreated PLHIV. One study included only PLHIV on ART. In all studies, most PLHIV were on ART by the end of follow‐up. Overall, 1,331,742 person‐years and 17,471 deaths were included from PLHIV, a mortality rate of 13.1 per 1000 person‐years. Of these deaths, 7721 (44%) were AIDS‐related, an overall AIDS‐related mortality rate of 5.8 per 1000 person‐years. The mean overall mortality rate among the general population was 2.8 (95% CI: 1.8–4.0) per 1000 person‐years. The meta‐analysed percentage of excess mortality that was AIDS‐related was 53% (95% CI: 45–61%); 52% (43–60%) in Western and Central Europe and North America, and 71% (69–74%) in the Asia‐Pacific region.DiscussionAlthough we searched all regions, we only found eligible studies from high‐income countries, mostly European, so, the generalizability of these results to other regions and epidemic settings is unknown.ConclusionsAround half of the excess mortality among PLHIV in high‐income regions was non‐AIDS‐related. An emphasis on preventing and treating comorbidities linked to non‐AIDS mortality among PLHIV is required.

AbstractIntroductionStrategies employing a single rapid diagnostic test (RDT) such as HIV self‐testing (HIVST) or "test for triage" (T4T) are proposed to increase HIV testing programme impact. Current guidelines recommend serial testing with two or three RDTs for HIV diagnosis, followed by retesting with the same algorithm to verify HIV‐positive status before anti‐retroviral therapy (ART) initiation. We investigated whether clients presenting to HIV testing services (HTS) following a single reactive RDT must undergo the diagnostic algorithm twice to diagnose and verify HIV‐positive status, or whether a diagnosis with the setting‐specific algorithm is adequate for ART initiation.MethodsWe calculated (1) expected number of false‐positive (FP) misclassifications per 10,000 HIV negative persons tested, (2) positive predictive value (PPV) of the overall HIV testing strategy compared to the WHO recommended PPV ≥99%, and (3) expected cost per FP misclassified person identified by additional verification testing in a typical low‐/middle‐income setting, compared to the expected lifetime ART cost of $3000. Scenarios considered were as follows: 10% prevalence using two serial RDTs for diagnosis, 1% prevalence using three serial RDTs, and calibration using programmatic data from Malawi in 2017 where the proportion of people testing HIV positive in facilities was 4%.ResultsIn the 10% HIV prevalence setting with a triage test, the expected number of FP misclassifications was 0.86 per 10,000 tested without verification testing and the PPV was 99.9%. In the 1% prevalence setting, expected FP misclassifications were 0.19 with 99.8% PPV, and in the Malawi 2017 calibrated setting the expected misclassifications were 0.08 with 99.98% PPV. The cost per FP identified by verification testing was $5879, $3770, and $24,259 respectively. Results were sensitive to assumptions about accuracy of self‐reported reactive results and whether reactive triage test results influenced biased interpretation of subsequent RDT results by the HTS provider.ConclusionsDiagnosis with the full algorithm following presentation with a reactive triage test is expected to achieve PPV above the 99% threshold. Continuing verification testing prior to ART initiation remains recommended, but HIV testing strategies involving HIVST and T4T may provide opportunities to maintain quality while increasing efficiency as part of broader restructuring of HIV testing service delivery.

AbstractIntroductionModel‐based estimates of key HIV indicators depend on past epidemic trends that are derived based on assumptions about HIV disease progression and mortality in the absence of antiretroviral treatment (ART). Population‐based HIV Impact Assessment (PHIA) household surveys conducted between 2015 and 2018 found substantial numbers of respondents living with untreated HIV infection. CD4 cell counts measured in these individuals provide novel information to estimate HIV disease progression and mortality rates off ART.MethodsWe used Bayesian multi‐parameter evidence synthesis to combine data on (1) cross‐sectional CD4 cell counts among untreated adults living with HIV from 10 PHIA surveys, (2) survival after HIV seroconversion in East African seroconverter cohorts, (3) post‐seroconversion CD4 counts and (4) mortality rates by CD4 count predominantly from European, North American and Australian seroconverter cohorts. We used incremental mixture importance sampling to estimate HIV natural history and ART uptake parameters used in the Spectrum software. We validated modelled trends in CD4 count at ART initiation against ART initiator cohorts in sub‐Saharan Africa.ResultsMedian untreated HIV survival decreased with increasing age at seroconversion, from 12.5 years [95% credible interval (CrI): 12.1–12.7] at ages 15–24 to 7.2 years (95% CrI: 7.1–7.7) at ages 45–54. Older age was associated with lower initial CD4 counts, faster CD4 count decline and higher HIV‐related mortality rates. Our estimates suggested a weaker association between ART uptake and HIV‐related mortality rates than previously assumed in Spectrum. Modelled CD4 counts in untreated people living with HIV matched recent household survey data well, though some intercountry variation in frequencies of CD4 counts above 500 cells/mm3 was not explained. Trends in CD4 counts at ART initiation were comparable to data from ART initiator cohorts. An alternate model that stratified progression and mortality rates by sex did not improve model fit appreciably.ConclusionsSynthesis of multiple data sources results in similar overall survival as previous Spectrum parameter assumptions but implies more rapid progression and longer survival in lower CD4 categories. New natural history parameter values improve consistency of model estimates with recent cross‐sectional CD4 data and trends in CD4 counts at ART initiation.

AbstractIntroductionAs prevalence of undiagnosed HIV declines, it is unclear whether testing programmes will be cost‐effective. To guide their HIV testing programmes, countries require appropriate metrics that can be measured. The cost‐per‐diagnosis is potentially a useful metric.MethodsWe simulated a series of setting‐scenarios for adult HIV epidemics and ART programmes typical of settings in southern Africa using an individual‐based model and projected forward from 2018 under two policies: (i) a minimum package of "core" testing (i.e. testing in pregnant women, for diagnosis of symptoms, in sex workers, and in men coming forward for circumcision) is conducted, and (ii) core‐testing as above plus additional testing beyond this ("additional‐testing"), for which we specify different rates of testing and various degrees to which those with HIV are more likely to test than those without HIV. We also considered a plausible range of unit test costs. The aim was to assess the relationship between cost‐per‐diagnosis and the incremental cost‐effectiveness ratio (ICER) of the additional‐testing policy. The discount rate used in the base case was 3% per annum (costs in 2018 U.S. dollars).ResultsThere was a strong graded relationship between the cost‐per‐diagnosis and the ICER. Overall, the ICER was below $500 per‐DALY‐averted (the cost‐effectiveness threshold used in primary analysis) so long as the cost‐per‐diagnosis was below $315. This threshold cost‐per‐diagnosis was similar according to epidemic and programmatic features including the prevalence of undiagnosed HIV, the HIV incidence and a measure of HIV programme quality (the proportion of HIV diagnosed people having a viral load <1000 copies/mL). However, restricting to women, additional‐testing did not appear cost‐effective even at a cost‐per‐diagnosis of below $50, while restricting to men additional‐testing was cost‐effective up to a cost‐per‐diagnosis of $585. The threshold cost per diagnosis for testing in men to be cost‐effective fell to $256 when the cost‐effectiveness threshold was $300 instead of $500, and to $81 when considering a discount rate of 10% per annum.ConclusionsFor testing programmes in low‐income settings in southern African there is an extremely strong relationship between the cost‐per‐diagnosis and the cost‐per‐DALY averted, indicating that the cost‐per‐diagnosis can be used to monitor the cost‐effectiveness of testing programmes.

AbstractIntroductionHIV planning requires granular estimates for the number of people living with HIV (PLHIV), antiretroviral treatment (ART) coverage and unmet need, and new HIV infections by district, or equivalent subnational administrative level. We developed a Bayesian small‐area estimation model, called Naomi, to estimate these quantities stratified by subnational administrative units, sex, and five‐year age groups.MethodsSmall‐area regressions for HIV prevalence, ART coverage and HIV incidence were jointly calibrated using subnational household survey data on all three indicators, routine antenatal service delivery data on HIV prevalence and ART coverage among pregnant women, and service delivery data on the number of PLHIV receiving ART. Incidence was modelled by district‐level HIV prevalence and ART coverage. Model outputs of counts and rates for each indicator were aggregated to multiple geographic and demographic stratifications of interest. The model was estimated in an empirical Bayes framework, furnishing probabilistic uncertainty ranges for all output indicators. Example results were presented using data from Malawi during 2016–2018.ResultsAdult HIV prevalence in September 2018 ranged from 3.2% to 17.1% across Malawi's districts and was higher in southern districts and in metropolitan areas. ART coverage was more homogenous, ranging from 75% to 82%. The largest number of PLHIV was among ages 35 to 39 for both women and men, while the most untreated PLHIV were among ages 25 to 29 for women and 30 to 34 for men. Relative uncertainty was larger for the untreated PLHIV than the number on ART or total PLHIV. Among clients receiving ART at facilities in Lilongwe city, an estimated 71% (95% CI, 61% to 79%) resided in Lilongwe city, 20% (14% to 27%) in Lilongwe district outside the metropolis, and 9% (6% to 12%) in neighbouring Dowa district. Thirty‐eight percent (26% to 50%) of Lilongwe rural residents and 39% (27% to 50%) of Dowa residents received treatment at facilities in Lilongwe city.ConclusionsThe Naomi model synthesizes multiple subnational data sources to furnish estimates of key indicators for HIV programme planning, resource allocation, and target setting. Further model development to meet evolving HIV policy priorities and programme need should be accompanied by continued strengthening and understanding of routine health system data.

High-resolution estimates of HIV burden across space and time provide an important tool for tracking and monitoring the progress of prevention and control efforts and assist with improving the precision and efficiency of targeting efforts. We aimed to assess HIV incidence and HIV mortality for all second-level administrative units across sub-Saharan Africa. ; his work was primarily supported by the Bill & Melinda Gates Foundation (grant OPP1132415). Additionally, O Adetokunboh acknowledges the support of the Department of Science and Innovation, and National Research Foundation of South Africa. M Ausloos, A Pana, and C Herteliu are partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation, Executive Agency for Higher Education, Research, Development and Innovation Funding (Romania; project number PN-III-P4-ID-PCCF-2016-0084). T W Bärnighausen was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. M J Bockarie is supported by the European and Developing Countries Clinical Trials Partnership. F Carvalho and E Fernandes acknowledge support from Portuguese national funds (Fundação para a Ciência e Tecnologia and Ministério da Ciência, Tecnologia e Ensino Superior; UIDB/50006/2020, UIDB/04378/2020, and UIDP/04378/2020. K Deribe is supported by the Wellcome Trust (grant 201900/Z/16/Z) as part of his International Intermediate Fellowship. B-F Hwang was partially supported by China Medical University (CMU107-Z-04), Taichung, Taiwan. M Jakovljevic acknowledges support of the Serbia Ministry of Education Science and Technological Development (grant OI 175 014). M N Khan acknowledges the support of Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. Y J Kim was supported by the Research Management Centre, Xiamen University Malaysia, Malaysia, (XMUMRF/2020-C6/ITCM/0004). K Krishnan is supported by University Grants Commission Centre of Advanced Study, (CAS II), awarded to the Department of Anthropology, Panjab University, Chandigarh, India. M Kumar would like to acknowledge National Institutes of Health and Fogarty International Cente (K43TW010716). I Landires is a member of the Sistema Nacional de Investigación, which is supported by the Secretaría Nacional de Ciencia, Tecnología e Innovación, Panama. W Mendoza is a program analyst in population and development at the UN Population Fund Country Office in Peru, which does not necessarily endorse this study. M Phetole received institutional support from the Grants, Innovation and Product Development Unit, South African Medical Research Council. O Odukoya acknowledges support from the Fogarty International Center of the US National Institutes of Health (K43TW010704). The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health. O Oladimeji is grateful for the support from Walter Sisulu University, Eastern Cape, South Africa, the University of Botswana, Botswana, and the University of Technology of Durban, Durban, South Africa. J R Padubidri acknowledges support from Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, India. G C Patton is supported by an Australian Government National Health and Medical Research Council research fellowship. P Rathi acknowledges Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal India. A I Ribeiro was supported by National Funds through Fundação para a Ciência e Tecnologia, under the programme of Stimulus of Scientific Employment–Individual Support (CEECIND/02386/2018). A M Samy acknowledges the support of the Egyptian Fulbright Mission Program. F Sha was supported by the Shenzhen Social Science Fund (SZ2020C015) and the Shenzhen Science and Technology Program (KQTD20190929172835662). A Sheikh is supported by Health Data Research UK. N Taveira acknowledges partial funding by Fundação para a Ciência e Tecnologia, Portugal, and Aga Khan Development Network—Portugal Collaborative Research Network in Portuguese-speaking countries in Africa (332821690), and by the European and Developing Countries Clinical Trials Partnership (RIA2016MC-1615). C S Wiysonge is supported by the South African Medical Research Council. Y Zhang was supported by the Science and Technology Research Project of Hubei Provincial Department of Education (Q20201104) and Open Fund Project of Hubei Province Key Laboratory of Occupational Hazard Identification and Control (OHIC2020Y01).Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations

High-resolution estimates of HIV burden across space and time provide an important tool for tracking and monitoring the progress of prevention and control efforts and assist with improving the precision and efficiency of targeting efforts. We aimed to assess HIV incidence and HIV mortality for all second-level administrative units across sub-Saharan Africa. ; his work was primarily supported by the Bill & Melinda Gates Foundation (grant OPP1132415). Additionally, O Adetokunboh acknowledges the support of the Department of Science and Innovation, and National Research Foundation of South Africa. M Ausloos, A Pana, and C Herteliu are partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation, Executive Agency for Higher Education, Research, Development and Innovation Funding (Romania; project number PN-III-P4-ID-PCCF-2016-0084). T W Bärnighausen was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. M J Bockarie is supported by the European and Developing Countries Clinical Trials Partnership. F Carvalho and E Fernandes acknowledge support from Portuguese national funds (Fundação para a Ciência e Tecnologia and Ministério da Ciência, Tecnologia e Ensino Superior; UIDB/50006/2020, UIDB/04378/2020, and UIDP/04378/2020. K Deribe is supported by the Wellcome Trust (grant 201900/Z/16/Z) as part of his International Intermediate Fellowship. B-F Hwang was partially supported by China Medical University (CMU107-Z-04), Taichung, Taiwan. M Jakovljevic acknowledges support of the Serbia Ministry of Education Science and Technological Development (grant OI 175 014). M N Khan acknowledges the support of Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. Y J Kim was supported by the Research Management Centre, Xiamen University Malaysia, Malaysia, (XMUMRF/2020-C6/ITCM/0004). K Krishnan is supported by University Grants Commission Centre of Advanced Study, (CAS II), awarded to the Department of Anthropology, Panjab University, Chandigarh, India. M Kumar would like to acknowledge National Institutes of Health and Fogarty International Cente (K43TW010716). I Landires is a member of the Sistema Nacional de Investigación, which is supported by the Secretaría Nacional de Ciencia, Tecnología e Innovación, Panama. W Mendoza is a program analyst in population and development at the UN Population Fund Country Office in Peru, which does not necessarily endorse this study. M Phetole received institutional support from the Grants, Innovation and Product Development Unit, South African Medical Research Council. O Odukoya acknowledges support from the Fogarty International Center of the US National Institutes of Health (K43TW010704). The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health. O Oladimeji is grateful for the support from Walter Sisulu University, Eastern Cape, South Africa, the University of Botswana, Botswana, and the University of Technology of Durban, Durban, South Africa. J R Padubidri acknowledges support from Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, India. G C Patton is supported by an Australian Government National Health and Medical Research Council research fellowship. P Rathi acknowledges Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal India. A I Ribeiro was supported by National Funds through Fundação para a Ciência e Tecnologia, under the programme of Stimulus of Scientific Employment–Individual Support (CEECIND/02386/2018). A M Samy acknowledges the support of the Egyptian Fulbright Mission Program. F Sha was supported by the Shenzhen Social Science Fund (SZ2020C015) and the Shenzhen Science and Technology Program (KQTD20190929172835662). A Sheikh is supported by Health Data Research UK. N Taveira acknowledges partial funding by Fundação para a Ciência e Tecnologia, Portugal, and Aga Khan Development Network—Portugal Collaborative Research Network in Portuguese-speaking countries in Africa (332821690), and by the European and Developing Countries Clinical Trials Partnership (RIA2016MC-1615). C S Wiysonge is supported by the South African Medical Research Council. Y Zhang was supported by the Science and Technology Research Project of Hubei Provincial Department of Education (Q20201104) and Open Fund Project of Hubei Province Key Laboratory of Occupational Hazard Identification and Control (OHIC2020Y01).Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations