HIV testing among women of reproductive age in 28 sub-Saharan African countries: a multilevel modelling
Bibliographic record
Abstract
BACKGROUND: Human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) remains one of the most significant public health challenges globally, particularly in sub-Saharan Africa (SSA). Although HIV testing is a vital step for both prevention and treatment, its uptake is still low in SSA. We therefore examined HIV testing in SSA and its individual/household and community factors among women of reproductive age groups (15-49 y). METHODS: Demographic and Health Survey data collected between 2010 and 2020 from 28 SSA countries were used for this analysis. We analysed the coverage of HIV testing and individual/household and community factors on 384 416 women in the reproductive age groups (15-49 y). Bivariate and multivariable multilevel binary logistic regression analysis were conducted to select candidate variables and to identify significant explanatory variables associated with HIV testing and the results were presented using adjusted odd ratios (AORs) at 95% confidence intervals (CIs). RESULTS: The pooled prevalence of HIV testing among women of reproductive age in SSA was 56.1% (95% CI 53.7 to 58.4), with the highest coverage found in Zambia (86.9%) and the lowest in Chad (6.1%). Age (45-49 y; AOR 0.30 [95% CI 0.15 to 0.62]), women's education level (secondary; AOR 1.97 [95% CI 1.36 to 2.84]) and economic status (richest; AOR 2.78 [95% CI 1.40 to 5.51]) were some of the individual/household factors associated with HIV testing. Similarly, religion (no religion; AOR 0.58 [95% CI 0.34 to 0.97]), marital status (married; AOR 0.69 [95% CI 0.50 to 0.95]) and comprehensive knowledge of HIV (yes; AOR 2.01 [95% CI 1.53 to 2.64]) were significantly associated individual/household factors for HIV testing. Meanwhile, place of residence (rural; AOR 0.65 [95% CI 0.45 to 0.94]) was found to be a significant community-level factor. CONCLUSION: More than half of married women in SSA have been tested for HIV, with between-country variations. Both individual/household factors were associated with HIV testing. Stakeholders should therefore consider all above-mentioned factors to plan an integrated approach to enhancing HIV testing through health education, sensitization, counselling and empowering older and married women, those with no formal education, those who do not have comprehensive HIV/AIDS knowledge and those in rural areas.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".