Risk and Protective Profile of Men Who Have Sex With Men Using Mobile Voluntary HIV Counseling and Testing: Latent Class Analysis
Bibliographic record
Abstract
BACKGROUND: Mobile voluntary counseling and testing (VCT) for HIV has been carried out to improve the targeting of at-risk populations and HIV case detection for men who have sex with men (MSM). However, the HIV-positive detection rate using this screening strategy has declined in recent years. This may imply unknown changes in risk-taking and protective features jointly influencing the testing results. These changing patterns in this key population remain unexplored. OBJECTIVE: The aim of this study was to identify the nuanced group classification of MSM who underwent mobile VCT using latent class analysis (LCA), and to compare the difference in characteristics and testing results between subgroups. METHODS: A cross-sectional research design and purposive sampling were applied between May 21, 2019, and December 31, 2019. Participants were recruited by a well-trained research assistant through social networking platforms, including the most popular instant messenger app Line, geosocial network apps dedicated to MSM, and online communities. Mobile VCT was provided to participants at an assigned time and place. Demographic characteristics and risk-taking and protective features of the MSM were collected via online questionnaires. LCA was used to identify discrete subgroups based on four risk-taking indicators-multiple sexual partners (MSP), unprotected anal intercourse (UAI), recreational drug use within the past 3 months, and history of sexually transmitted diseases-and three protective indicators-experience of postexposure prophylaxis, preexposure prophylaxis use, and regular HIV testing. RESULTS: Overall, 1018 participants (mean age 30.17, SD 7.29 years) were included. A three-class model provided the best fit. Classes 1, 2, and 3 corresponded to the highest risk (n=175, 17.19%), highest protection (n=121, 11.89%), and low risk and low protection (n=722, 70.92%), respectively. Compared to those of class 3, class 1 participants were more likely to have MSP and UAI within the past 3 months, to be ≥40 years of age (odds ratio [OR] 2.197, 95% CI 1.357-3.558; P=.001), to have HIV-positive results (OR 6.47, 95% CI 2.272-18.482; P<.001), and a CD4 count ≤349/μL (OR 17.50, 95% CI 1.223-250.357; P=.04). Class 2 participants were more likely to adopt biomedical preventions and have marital experience (OR 2.55, 95% CI 1.033-6.277; P=.04). CONCLUSIONS: LCA helped derive a classification of risk-taking and protection subgroups among MSM who underwent mobile VCT. These results may inform policies for simplifying the prescreening assessment and more precisely recognizing those who have higher probabilities of risk-taking features but remain undiagnosed targets, including MSM engaging in MSP and UAI within the past 3 months and those ≥40 years old. These results could be applied to tailor HIV prevention and testing programs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".