Estimating the Population Sizes of Men Who Have Sex With Men in US States and Counties Using Data From the American Community Survey
Notice bibliographique
Résumé
BACKGROUND: In the United States, male-to-male sexual transmission accounts for the greatest number of new human immunodeficiency virus (HIV) diagnoses and a substantial number of sexually transmitted infections (STI) annually. However, the prevalence and annual incidence of HIV and other STIs among men who have sex with men (MSM) cannot be estimated in local contexts because demographic data on sexual behavior, particularly same-sex behavior, are not routinely collected by large-scale surveys that allow analysis at state, county, or finer levels, such as the US decennial census or the American Community Survey (ACS). Therefore, techniques for indirectly estimating population sizes of MSM are necessary to supply denominators for rates at various geographic levels. OBJECTIVE: Our objectives were to indirectly estimate MSM population sizes at the county level to incorporate recent data estimates and to aggregate county-level estimates to states and core-based statistical areas (CBSAs). METHODS: We used data from the ACS to calculate a weight for each county in the United States based on its relative proportion of households that were headed by a male who lived with a male partner, compared with the overall proportion among counties at the same level of urbanicity (ie, large central metropolitan county, large fringe metropolitan county, medium/small metropolitan county, or nonmetropolitan county). We then used this weight to adjust the urbanicity-stratified percentage of adult men who had sex with a man in the past year, according to estimates derived from the National Health and Nutrition Examination Survey (NHANES), for each county. We multiplied the weighted percentages by the number of adult men in each county to estimate its number of MSM, summing county-level estimates to create state- and CBSA-level estimates. Finally, we scaled our estimated MSM population sizes to a meta-analytic estimate of the percentage of US MSM in the past 5 years (3.9%). RESULTS: We found that the percentage of MSM among adult men ranged from 1.5% (Wyoming) to 6.0% (Rhode Island) among states. Over one-quarter of MSM in the United States resided in 1 of 13 counties. Among counties with over 300,000 residents, the five highest county-level percentages of MSM were San Francisco County, California at 18.5% (66,586/359,566); New York County, New York at 13.8% (87,556/635,847); Denver County, Colorado at 10.5% (25,465/243,002); Multnomah County, Oregon at 9.9% (28,949/292,450); and Suffolk County, Massachusetts at 9.1% (26,338/289,634). Although California (n=792,750) and Los Angeles County (n=251,521) had the largest MSM populations of states and counties, respectively, the New York City-Newark-Jersey City CBSA had the most MSM of all CBSAs (n=397,399). CONCLUSIONS: We used a new method to generate small-area estimates of MSM populations, incorporating prior work, recent data, and urbanicity-specific parameters. We also used an imputation approach to estimate MSM in rural areas, where same-sex sexual behavior may be underreported. Our approach yielded estimates of MSM population sizes within states, counties, and metropolitan areas in the United States, which provide denominators for calculation of HIV and STI prevalence and incidence at those geographic levels.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».