When context matters: Multilevel determinants of self-reported sexually transmitted infections symptoms among men engaged in transactional sex in 26 Sub-Saharan African countries
Bibliographic record
Abstract
BACKGROUND: Men engaged in transactional sex (METS) represent a neglected key population in sub-Saharan Africa (SSA), yet little is known about their burden of sexually transmitted infections (STIs) and associated factors. This study assessed the prevalence of self-reported STIs (SR-STI) and identified individual, community, and country-level determinants among this group. METHODS: We analyzed pooled recent nationally representative Demographic and Health Survey (DHS) data from 26 SSA countries. This study included 10,128 men who reported engagement in transactional sex within the past 12 months. Weighted prevalence estimates were calculated, and multilevel logistic regression models were applied to examine individual-, community-, and country-level determinants of SR-STI symptoms, adjusting for survey year. AIC and BIC were used as comparative model selection criteria to identify the best-fitting model among the sequential multilevel models built. RESULTS: The participants’ mean (± SD) age was 29.8 (± 10.2) years. The overall weighted prevalence of SR-STIs among METS was 19.5% (95%CI: 18.3–20.7%), nearly threefold higher than among men not reporting transactional sex (7.2%; 95%CI: 6.9–7.5%; p for difference < 0.001). Prevalence varied substantially across countries, from 5.6% in Niger to 36.9% in Liberia (p < 0.001). At the individual-level, younger age, lower education, employment, risky sexual behavior, middle household wealth, heard about STI, HIV testing, and media exposure were associated with higher odds of SR-STI, while circumcision, HIV knowledge, and Christian affiliation were protective. At the community-level, men from poorer communities were less likely to report SR-STI symptoms (aOR = 0.79; 95%CI: 0.66–0.87). At the country-level, participants from Southern Africa had lower odds (aOR = 0.49; 95%CI: 0.24–0.97) compared to those in West Africa. Significant between-country and -community heterogeneity was observed, but variance decreased with the inclusion of individual and contextual predictors. CONCLUSION: SR-STIs are highly prevalent among METS in SSA, with marked heterogeneity across countries and multiple individual and structural determinants. These findings underscore the need for targeted, context-specific interventions integrating biomedical, behavioral, and structural approaches to reduce STI burden in this population.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".