Effects of Condom Use on Human Immunodeficiency Virus Transmission Among Adolescent Sexual Minority Males in the United States: A Mixed Epidemiology and Epidemic Modeling Study.
Bibliographic record
Abstract
BACKGROUND: We examined condom use patterns and potential population-level effects of a hypothetical condom intervention on human immunodeficiency virus (HIV) transmission among adolescent sexual minority males (ASMM). METHODS: Using 3 data sets: national Youth Risk Behavior Survey 2015 to 2017 (YRBS-National), local YRBS data from 8 jurisdictions with sex of partner questions from 2011 to 2017 (YRBS-Trends), and American Men's Internet Survey (AMIS) 2014 to 2017, we assessed associations of condom use with year, age, and race/ethnicity among sexually active ASMM. Using a stochastic agent-based network epidemic model, structured and parameterized based on the above analyses, we calculated the percent of HIV infections averted over 10 years among ASMM ages 13 to 18 years by an intervention that increased condom use by 37% for 5 years and was delivered to 62% of ASMM at age 14 years. RESULTS: In YRBS, 51.8% (95% confidence interval [CI], 41.3-62.3%) and 37.9% (95% CI, 32.7-42.3%) reported condom use at last sexual intercourse in national and trend data sets, respectively. In AMIS, 47.3% (95% CI, 44.6-49.9%) reported condom use at last anal sex with a male partner. Temporal trends were not observed in any data set (P > 0.1). Condom use varied significantly by age in YRBS-National (P < 0.0001) and YRBS-Trends (P = 0.032) with 13- to 15-year-olds reporting the lowest use in both; age differences were not significant in AMIS (P = 0.919). Our hypothetical intervention averted a mean of 9.0% (95% simulation interval, -5.4% to 21.2%) of infections among ASMM. CONCLUSIONS: Condom use among ASMM is low and appears to have remained stable during 2011 to 2017. Modeling suggests that condom use increases, consistent with previous interventions, have potential to avert 1 in 11 new HIV infections among ASMM.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".