Socio‐demographic and geographic disparities in HIV prevalence, HIV testing and treatment coverage: An analysis of 108 national household surveys in 33 African countries
Bibliographic record
Abstract
INTRODUCTION: Socio-demographic and geographic disparities in HIV prevalence, uptake of HIV testing and access to antiretroviral therapy (ART) persist in high HIV burden countries. Understanding demographic, spatial and temporal factors can guide interventions. METHODS: We analysed 108 geo-referenced population-based surveys conducted over 2000-2023 across 33 African countries, involving 2.3 million respondents. Multilevel Bayesian logistic regression models assessed associations between HIV outcomes (HIV prevalence, recent HIV testing and ART coverage) and socio-demographic characteristics (age, education, place of residence, relative wealth), geographic location (country, district) and time trends. Separate models were estimated for men and women in central, eastern, southern and western Africa. RESULTS: Inequalities in HIV risk and access to testing and treatment services were driven by differences in educational attainment and within-country variations. In southern Africa, women with tertiary education had a 12%-point lower HIV prevalence (95% Credible Interval [CrI]: -27% to -2%) than those with less than primary education. In eastern Africa, they had a 13%-points (95% CrI: 2-22%) higher probability of recent HIV testing. Associations with relative wealth were weaker and more heterogeneous: in southern Africa, HIV prevalence shifted over time from higher to lower wealth quintiles, and adolescent girls and young women became the most frequently tested age group. In central Africa, wealthier men maintained higher recent testing and ART coverage levels. District-level variations accounted for disparities in HIV outcomes. In western Africa, the expected difference in ART coverage between individuals with similar socio-demographic characteristics living in different districts was 14%-points (95% CrI: 3-32%) for men and 10%-points (95% CrI: 3-27%) for women. CONCLUSIONS: Disparities in HIV outcomes are strongly associated with differences in education, and across districts of the same country. Higher education levels are associated with lower HIV prevalence, greater testing and higher ART coverage, while districts with limited services sustain higher population viraemia. Despite the scale-up of HIV prevention and treatment programmes, important disparities remain, and renewed education-centred and geographically targeted efforts are needed to close gaps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".