The Prevalence of Syphilis Infection and Its Associated Factors in the General Population of Rwanda: A National Household-Based Survey
Bibliographic record
Abstract
Background. The prevalence of syphilis among HIV-infected people is a public health concern, but there is limited literature to describe the true burden of syphilis in resource-limited settings. We conducted this survey in 2013 to estimate the prevalence of syphilis. Methods. A cross-sectional survey. Participants were tested for syphilis and HIV. Factors associated with syphilis infection were identified. Results. The prevalence of syphilis was 0.9% (95% CI: 0.7-1.1). This prevalence was higher in the 25-49-year-old age (1.1% [95% CI: 0.8-1.3]) than in the 15-24-year-old age (0.6% (95% CI: 0.4-0.9)). Women with lower education had a higher prevalence of syphilis (1.2% (95% CI: 0.9-1.5)) compared to others (0.4% (95% CI: 0.2-0.8)). This prevalence among HIV-infected people was six times higher: 4.8% (95% CI: 2.9-7.9) compared to HIV-negative people (0.8% (95% CI: 0.6-1.0)). The prevalence of syphilis among HIV-infected females was 5.9% (95% CI: 3.4-10.0). HIV-infected or concurrent sexual partners was associated with increased syphilis prevalence with aOR = 4.2 (95% CI: 2.5-7.2) and aOR = 4.2 (95% CI: 2.8-6.5), respectively. Conclusions. The prevalence of syphilis was significantly higher among HIV-infected patients. HIV infection and concurrent sexual partners are associated with an increased prevalence of syphilis. Preventing HIV might help in preventing syphilis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".