Prevalence and incidence of HIV among female sex workers and their clients: modelling the potential effects of intervention in Rwanda
Bibliographic record
Abstract
BACKGROUND: Rwanda has identified several targeted HIV prevention strategies, such as promotion of condom use and provision of antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) for female sex workers (FSWs). Given this country's limited resources, understanding how the HIV epidemic will be affected by these strategies is crucial. METHODS: We developed a Markov model to estimate the effects of targeted strategies to FSWs on the HIV prevalence/incidence in Rwanda from 2017 to 2027. Our model consists of the six states: HIV-; HIV+ undiagnosed/diagnosed pre-ART; HIV+ diagnosed with/without ART; and death. We considered three populations: FSWs, sex clients and the general population. For the period 2017-2027, the HIV epidemic among each of these population was estimated using Rwanda's demographic, sexual risk behaviour and HIV-associated morbidity and mortality data. RESULTS: Between 2017 and 2027, with no changes in the current condom and ART use, the overall number of people living with HIV is expected to increase from 344,971 to 402,451. HIV incidence will also decrease from 1.36 to 1.20 100 person-years. By 2027, a 30% improvement in consistent condom use among FSWs will result in absolute reduction of HIV prevalence among FSWs, sex clients and the general population by 7.86%, 5.97% and 0.17%, respectively. While recurring HIV testing and improving the ART coverage mildly reduced the prevalence/incidence among FSWs and sex clients, worsening the two (shown by our worst-case scenario) will result in an increase in the HIV prevalence/incidence among FSWs and sex clients. Introduction of PrEP to FSWs in 2019 will reduce the HIV incidence among FSWs by 1.28%. CONCLUSIONS: Continued efforts toward improving condom and ART use will be critical for Rwanda to continue their HIV epidemic control. Implementing a targeted intervention strategy in PrEP for FSWs will reduce the HIV epidemic in this high-risk population.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".