Temporal Trends and Determinants of HIV Testing at Antenatal Care in Sub-Saharan Africa: A Pooled Analysis of Population-Based Surveys (2005–2021)
Bibliographic record
Abstract
BACKGROUND: In sub-Saharan Africa (SSA), integrating HIV testing into antenatal care (ANC) has been crucial toward reducing mother-to-child transmission of HIV. With the introduction of new testing modalities, we explored temporal trends in HIV testing within and outside of ANC and identified sociodemographic determinants of testing during ANC. METHODS: We analyzed data from 139 nationally representative household surveys conducted between 2005 and 2021, including more than 2.2 million women aged 15-49 years in 41 SSA countries. We extracted data on women's recent HIV testing history (<24 months), by modality (ie, at ANC versus outside of ANC) and sociodemographic variables (ie, age, socioeconomic status, education level, number of births, urban/rural). We used Bayesian generalized linear mixed models to estimate HIV testing coverage and the proportion of those that tested as part of ANC. RESULTS: HIV testing coverage (<24 months) increased substantially between 2005 and 2021 from 8% to 38%, with significant variations between countries and subregions. Two percent of women received an HIV test in the 24 months preceding the survey interview as part of ANC in 2005 and 11% in 2021. Among women who received an HIV test in the 24 months preceding the survey, the probability of testing at ANC was significantly greater for multiparous, adolescent girls, rural women, women in the poorest wealth quintile, and women in West and Central Africa. CONCLUSION: ANC testing remains an important component to achieving high levels of HIV testing coverage and benefits otherwise underserved women, which could prove instrumental to progress toward universal knowledge of HIV status in SSA.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".