Population prevalence of sexually transmitted infections in a high HIV burden district in KwaZulu-Natal, South Africa: Implications for HIV epidemic control
Bibliographic record
Abstract
BACKGROUND: Sexually transmitted infections (STIs) and Human immunodeficiency virus (HIV) share a complex bidirectional relationship, however, population prevalence and the association between the presence of STIs and HIV in a high HIV burden district in KwaZulu-Natal, South Africa is not known. METHODS: A total of 9812 participants aged 15-49 years were enrolled in a cross-sectional population-based household survey. Participants completed a structured questionnaire and provided first-pass urine (males) or self-collected vulvo-vaginal swabs (females) for the detection of STIs. RESULTS: Prevalence of herpes simplex virus type-2 (HSV-2) was 57.8%, syphilis was 1.6%, Neisseria gonorrhoeae was 2.8%, Chlamydia trachomatis was 7.1%, Trichomonas vaginalis was 9.0%, Mycoplasma genitalium was 5.5% and HIV was 36.3%. HIV positive status was associated with an increased probability of having M. genitalium (aPR = 1.49, 95% CI 1.02-2.19) among males and syphilis (aPR = 2.54, 95% CI 1.32-4.86), N. gonorrhoeae (aPR = 2.39, 95% CI 1.62-3.52), T. vaginalis (aPR = 1.70, 95% CI 1.43-2.01) and M. genitalium (aPR = 1.60, 95% CI 1.15-2.22) among females. HIV viral load ≥400 copies per mL was associated with an increased probability of N. gonorrhoeae (aPR = 1.91, 95% CI 1.36-2.70), C. trachomatis (aPR = 1.52, 95% CI 1.12-2.05) and M. genitalium (aPR = 1.83, 95% CI 1.27-2.63). CONCLUSIONS: The high prevalence of STIs and the association between STIs and HIV, and HIV viral load underscores the public health implications of sustained transmission risk of STIs and HIV. These findings highlight the urgent need for expanding STI surveillance and implementing interventions to monitor and reduce the STI burden.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".