Population Percentage and Population Size of Men Who Have Sex With Men in the United States, 2017-2021: Meta-Analysis of 5 Population-Based Surveys
Bibliographic record
Abstract
BACKGROUND: Male-to-male sexual transmission continues to account for the greatest proportion of new HIV diagnoses in the United States. However, calculating population-specific surveillance metrics for HIV and other sexually transmitted infections requires regularly updated estimates of the number and proportion of men who have sex with men (MSM) in the United States, which are not collected by census surveys. OBJECTIVE: The purpose of this analysis was to estimate the number and percentage of MSM in the United States from population-based surveys. METHODS: We used data from 5 population-based surveys to calculate weighted estimates of the proportion of MSM in the United States and pooled these estimates using meta-analytic procedures. We estimated the proportion of MSM using sexual behavior-based questions (encompassing anal or oral sex) for 3 recall periods-past 12 months, past 5 years, and lifetime. In addition, we estimated the proportion of MSM using self-reported identity and attraction survey responses. The total number of MSM and non-MSM in the United States were calculated from estimates of the percentage of MSM who reported sex with another man in the past 12 months. RESULTS: The percentage of MSM varied by recall period: 3.3% (95% CI 1.7%-4.9%) indicated sex with another male in the past 12 months, 4.7% (95% CI 0.0%-33.8%) in the past 5 years, and 6.2% (95% CI 2.9%-9.5%) in their lifetime. There were comparable percentages of men who identified as gay or bisexual (3.4%, 95% CI 2.2%-4.6%) or who indicated that they are attracted to other men (4.9%, 95% CI 3.1%-6.7%) based on pooled estimates. Our estimate of the total number of MSM in the United States is 4,230,000 (95% CI 2,179,000-6,281,000) based on the history of recent sexual behavior (sex with another man in the past 12 months). CONCLUSIONS: We calculated the pooled percentage and number of MSM in the United States from a meta-analysis of population-based surveys collected from 2017 to 2021. These estimates update and expand upon those derived from the Centers for Disease Control and Prevention in 2012 by including estimates of the percentage of MSM based on sexual identity and sexual attraction. The percentage and number of MSM in the United States is an important indicator for calculating population-specific disease rates and eligibility for preventive interventions such as pre-exposure prophylaxis.
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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 | 0.025 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.082 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".