HIV Risk Score and Prediction Model in the United States: A Scoping Review.
Human immunodeficiency virus (HIV) remains a public health issue in the U.S., affecting approximately 1.2 million individuals, many of whom are unaware of their infection status. This study reviews predictors and the performance of HIV risk prediction models. We analyzed 18 studies published since 2...
| Publicado en: | AIDS & Behavior Vol. 29; no. 8; pp. 2388 - 2408 |
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| Autores principales: | , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187534074&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187534074 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10907165 G1T jtl: AIDS & Behavior issn: 10907165 maglogo: N pubinfo: dt: Aug2025 vid: 29 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187534074 184223194 187534074 187534074 10.1007/s10461-025-04702-1 187534074 ppf: 2388 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: HIV Risk Score and Prediction Model in the United States: A Scoping Review. aug: au: Albernas, Adrian Patel, Maitri D. Cook, Robert L. Vaddiparti, Krishna Prosperi, Mattia Liu, Yiyang affil: https://ror.org/02y3ad647 Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, PO Box 100231, 2004 Mowry Road, 32610-0231, Gainesville, FL, USA sug: subj: HIV Infections Risk Factors Risk Assessment Prediction Models Human Scoping Review United States PubMed Embase CINAHL Database Logistic Regression Survival Analysis Men Who Have Sex With Men Age Factors Sex Factors Race Factors Unsafe Sex Sexually Transmitted Diseases Sexual Partners ab: Human immunodeficiency virus (HIV) remains a public health issue in the U.S., affecting approximately 1.2 million individuals, many of whom are unaware of their infection status. This study reviews predictors and the performance of HIV risk prediction models. We analyzed 18 studies published since 2010, which featured logistic regression, survival analysis, and machine learning techniques. These studies focused on diverse populations, including men who have sex with men, emergency department visitors, and the general population. Key predictors of HIV risk included demographics (age, sex, race) and behavioral factors (sexual practices, drug use). Electronic health records (EHR) documenting diagnoses of sexually transmitted infection (STI) were significant in all models. Behaviors like condomless sex, multiple sexual partners, and drug use were also strongly linked to increased risk scores. However, we noted a lack of social determinants of health in risk models, and a gap in studies focusing on cis female and transgender populations. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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