Individual and Poly-Substance use and Condomless Sex Among HIV-Uninfected Adults Reporting Heterosexual Sex in a Multi-Site Cohort
Bibliographic record
Abstract
Abstract BACKGROUND: We analyzed the association between substance use (SU) and condomless sex (CS) among HIV-negative adults reporting heterosexual sex in the Seek, Test, Treat, and Retain (STTR) consortium. We describe the impact of SU as well as person/partner and context-related factors on CS, identifying combinations of factors that indicate the highest likelihood of CS.METHODS: We analyzed data from four US-based STTR studies to examine the effect of SU on CS using two SU exposures: 1) current SU (within 3 months) and 2) SU before/during sex. Adjusted individual-study, multivariable relative risk regression was used to examine the relationship between CS and SU. We also examined interactions with type of sex and partner HIV status. Pooled effect estimates were calculated using traditional fixed-effects meta-analysis.RESULTS: We analyzed data for current SU (n=6781; 82% men, median age=33 years) and SU before/during sex (n=2915; 69% men, median age=40 years). For both exposure classifications, any SU other than cannabis increased the likelihood of CS relative to non-SU (8-16%, p-values<0.001). In the current SU group, however, polysubstance use did not increase the likelihood of CS compared to single-substance use. Cannabis use did not increase the likelihood of CS, regardless of frequency of use. Type of sex was associated with CS; those reporting vaginal and anal sex had a higher likelihood of CS compared to vaginal sex only for both exposure classifications (18-21%, p<0.001). Current SU increased likelihood of CS among those reporting vaginal sex only (9-10%, p<0.001); results were similar for those reporting vaginal and anal sex (5-8%, p <0.01). SU before/during sex increased the likelihood of CS among those reporting vaginal sex only (20%; p<0.001) and among those reporting vaginal and anal sex (7%; p=0.002). Single- and poly-SU before/during sex increased the likelihood of CS for those with exclusively HIV-negative partners (7-8%, p£0.02), and for those reporting HIV-negative and HIV-status unknown partners (9-13%, p£0.03).CONCLUSION: Except for cannabis, any SU increased the likelihood of CS. CS was associated with having perceived HIV-negative partners and with having had both anal/vaginal sex.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".