Using mathematical modelling to estimate the impact of periodic presumptive treatment on the transmission of sexually transmitted infections and HIV among female sex workers
Bibliographic record
Abstract
BACKGROUND: In settings with poor sexually transmitted infection (STI) control in high-risk groups, periodic presumptive treatment (PPT) can quickly reduce the prevalence of genital ulcers, Neisseria gonorrhoeae (NG) and Chlamydia trachomatis (CT). However, few studies have assessed the impact on HIV. Mathematical modelling is used to quantify the likely HIV impact of different PPT interventions. METHODS: A mathematical model was developed to project the impact of PPT on STI/HIV transmission amongst a homogeneous population of female sex workers (FSWs) and their clients. Using data from Johannesburg, the impact of PPT interventions with different coverages and PPT frequencies was estimated. A sensitivity analysis explored how the projections were affected by different model parameters or if the intervention was undertaken elsewhere. RESULTS: Substantial decreases in NG/CT prevalence are achieved among FSWs receiving PPT. Although less impact is achieved among all FSWs, large decreases in NG/CT prevalence (>50%) are possible with >30% coverage and supplying PPT every month. Higher PPT frequencies achieve little additional impact, whereas improving coverage increases impact until NG/CT becomes negligible. The impact on HIV incidence is smaller, longer to achieve, and depends heavily on the assumed NG/CT cofactors, whether they are additive, the assumed STI/HIV transmission probabilities and STI durations. Greater HIV impact can be achieved in settings with lower sexual activity (except at high coverage), less STI treatment or high prevalences of Haemophilus ducreyi. CONCLUSIONS: Despite the model's assumption of homogeneous risk behaviour probably resulting in optimistic projections, and uncertainty in STI cofactors and transmission probabilities, projections suggest PPT interventions with sufficient coverage (> or = 40%) and follow-up (> or = 2 years) could noticeably decrease the HIV incidence (>20%) among FSW populations with inadequate STI treatment.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".