Trends in Serosorting and the Association With HIV/STI Risk Over Time Among Men Who Have Sex With Men
Bibliographic record
Abstract
BACKGROUND: Serosorting among men who have sex with men (MSM) is common, but recent data to describe trends in serosorting are limited. How serosorting affects population-level trends in HIV and other sexually transmitted infection (STI) risk is largely unknown. METHODS: We collected data as part of routine care from MSM attending a sexually transmitted disease clinic (2002-2013) and a community-based HIV/sexually transmitted disease testing center (2004-2013) in Seattle, WA. MSM were asked about condom use with HIV-positive, HIV-negative, and unknown-status partners in the prior 12 months. We classified behaviors into 4 mutually exclusive categories: no anal intercourse (AI); consistent condom use (always used condoms for AI); serosorting [condom-less anal intercourse (CAI) only with HIV-concordant partners]; and nonconcordant CAI (CAI with HIV-discordant/unknown-status partners; NCCAI). RESULTS: Behavioral data were complete for 49,912 clinic visits. Serosorting increased significantly among both HIV-positive and HIV-negative men over the study period. This increase in serosorting was concurrent with a decrease in NCCAI among HIV-negative MSM, but a decrease in consistent condom use among HIV-positive MSM. Adjusting for time since last negative HIV test, the risk of testing HIV positive during the study period decreased among MSM who reported NCCAI (7.1%-2.8%; P= 0.02), serosorting (2.4%-1.3%; P = 0.17), and no CAI (1.5%-0.7%; P = 0.01). Serosorting was associated with a 47% lower risk of testing HIV positive compared with NCCAI (adjusted prevalence ratio = 0.53; 95% confidence interval: 0.45 to 0.62). CONCLUSIONS: Between 2002 and 2013, serosorting increased and NCCAI decreased among Seattle MSM. These changes paralleled a decline in HIV test positivity among MSM.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".