HIV Pre-Exposure Prophylaxis Cascade Stages Among Men Who Have Sex With Men With Sexually Transmitted Infections in China: Multicenter Cross-Sectional Survey Study
Bibliographic record
Abstract
Background: There is limited literature available regarding the knowledge and use of HIV pre-exposure prophylaxis (PrEP) among men who have sex with men (MSM) with sexually transmitted infections (STIs). Objective: This study's objective was to explore the HIV PrEP cascade stages (knowledge, willingness to use, and use) among MSM with STIs in China, in order to promote the implementation of PrEP in this population. Methods: A cross-sectional study was conducted using a respondent-driven sampling method in 19 cities in China, from January to August 2022. The study collected data on demographics, behaviors, and PrEP cascade stages from participants who were not infected with HIV and who self-reported being recently infected with STIs. After using chi-square tests or Fisher exact tests to analyze differences in the knowledge of PrEP, willingness to use PrEP, and PrEP use across different variables, multivariate logistic regression was used to analyze the influences of the different variables on PrEP cascade stages. Results: By August 2022, following screening and exclusion, a total of 1329 MSM were included in the study. Among them, 85.55% (n=1137) had heard of PrEP, 81.57% (n=1084) expressed their willingness to use PrEP if engaging in high-risk HIV behaviors, 70.58% (n=938) had consulted a health care professional about PrEP, 62.98% (n=837) reported having used PrEP, and 46.35% (n=616) possessed a basic understanding of PrEP. The results of multivariate logistic regression analyses showed that the same factors significantly influenced both knowledge of PrEP and willingness to take PrEP, including age, education, marital status, income, condom usage, participation in group sex, HIV status of the most recent male sexual partner, and postexposure prophylaxis (PEP) usage. The factors significantly related to the PrEP use included income, engagement in commercial sex, participation in group sex, HIV status of the most recent male sexual partner, new drug usage, and PEP usage. Conclusions: MSM with STIs were engaged with the PrEP cascade stages at a relatively high rate, but they lacked an understanding of PrEP and underestimated HIV risk. The prevalence of having a basic understanding of PrEP was lower than PrEP usage, and this suboptimal awareness could impede PrEP efficacy and lead to risk compensation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".