Use of Geosocial Networking Apps and HIV Risk Behavior Among Men Who Have Sex With Men: Case-Crossover Study
Bibliographic record
Abstract
BACKGROUND: HIV disproportionately affects men who have sex with men (MSM) in China. The HIV epidemic is largely driven by unprotected anal sex (ie, sex not protected by condoms or HIV pre-exposure prophylaxis [PrEP]). The possible association between unprotected anal sex and the use of geospatial networking apps has been the subject of scientific debate. OBJECTIVE: This study assessed whether users of a gay geospatial networking app in China were more likely to use condoms when they met their partners online versus offline. A case-crossover analysis, with each person serving as his own control, was employed to address the potential bias that men looking for sex partners through an online dating medium might have inherently different (and riskier) patterns of sexual behavior than men who do not use online dating media. METHODS: A cross-sectional survey was administered in 2018 to adult male users of Blued-a gay geospatial networking app-in Beijing, Tianjin, Sichuan, and Yunnan, China. A case-crossover analysis was conducted among 1311 MSM not taking PrEP who reported engaging in both unprotected and protected anal sex in the previous 6 months. Multivariable conditional logistic regression was used to quantify the association between where the partnership was initiated (offline or online) and the act of unprotected anal sex, controlling for other interval-level covariates. Four sensitivity analyses were conducted to assess other potential sources of bias. RESULTS: We identified 1311 matched instances where a person reported having both an unprotected anal sex act and a protected anal sex act in the previous 6 months. Of the most recent unprotected anal sex acts, 22.3% (292/1311), were initiated offline. Of the most recent protected anal sex acts, 16.3% (214/1311), were initiated offline. In multivariable analyses, initiating a partnership offline was positively associated with unprotected anal sex (odds ratio 2.66, 95% CI 1.84 to 3.85, P<.001) compared with initiating a partnership online. These results were robust to each of the different sensitivity analyses we conducted. CONCLUSIONS: Among Blued users in 4 Chinese cities, men were less likely to have unprotected anal sex in partnerships that they initiated online compared with those that they initiated offline. The relationship was strong, with over 2.5 times the likelihood of engaging in unprotected anal sex in partnerships initiated offline compared with those initiated online. These findings suggest that geospatial networking apps are a proxy for, and not a cause of, high-risk behaviors for HIV infection; these platforms should be viewed as a useful venue to identify individuals at risk for HIV transmission to allow for targeted service provision.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".