Sexually Transmitted Infection Prevalence Among Women at Risk for HIV Exposure Initiating Safer Conception Care in Rural, Southwestern Uganda
Bibliographic record
Abstract
BACKGROUND: Knowledge of sexually transmitted infection (STI) prevalence and risk factors is important to the development of tenofovir-based preexposure prophylaxis (PrEP) and safer conception programming. We introduced STI screening among women at risk for HIV exposure who were participating in a safer conception study in southwestern Uganda. METHODS: We enrolled 131 HIV-uninfected women, planning for pregnancy with a partner living with HIV or of unknown HIV serostatus (2018-2019). Women were offered comprehensive safer conception counseling, including PrEP. Participants completed interviewer-administered questionnaires detailing sociodemographics and sexual history. We integrated laboratory screening for chlamydia, gonorrhea, trichomoniasis, and syphilis as a substudy to assess STI prevalence. Multivariable logistic regression was used to determine correlates. RESULTS: Ninety-four women completed STI screening (72% of enrolled). Median age was 30 (interquartile range, 26-34) years, and 94% chose PrEP as part of safer conception care. Overall, 24% had STIs: 13% chlamydia, 2% gonorrhea, 6% trichomoniasis, 6% syphilis, and 3% ≥2 STI. Sexually transmitted infection prevalence was associated with younger age (adjusted odds ratio [AOR], 0.87; 95% confidence interval [CI], 0.77-0.99), prior stillbirth (AOR, 5.04; 95% CI, 1.12-22.54), and not feeling vulnerable to HIV (AOR, 16.33; 95% CI, 1.12-237.94). CONCLUSIONS: We describe a 24% curable STI prevalence among women at risk for HIV exposure who were planning for pregnancy. These data highlight the importance of integrating laboratory-based STI screening into safer conception programs to maximize the health of HIV-affected women, children, and families.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".