1. Using Machine Learning to Identify Predictors of Sexually Transmitted Infections Over Time Among Young People Living With or at Risk for HIV Who Participated in ATN Protocols 147, 148, and 149
- Author
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Comulada, W Scott, Rotheram-Borus, Mary Jane, Arnold, Elizabeth Mayfield, Norwood, Peter, Lee, Sung-Jae, Ocasio, Manuel A, Flynn, Risa, Nielsen-Saines, Karin, Bolan, Robert, Klausner, Jeffrey D, Swendeman, Dallas, and Team, Adolescent Medicine Trials Network CARES
- Subjects
Biomedical and Clinical Sciences ,Public Health ,Clinical Sciences ,Health Sciences ,Paediatrics ,HIV/AIDS ,Health Disparities ,Clinical Research ,Sexually Transmitted Infections ,Prevention ,Infectious Diseases ,Machine Learning and Artificial Intelligence ,Women's Health ,Adolescent Sexual Activity ,Pediatric ,Minority Health ,Social Determinants of Health ,Behavioral and Social Science ,Sexual and Gender Minorities (SGM/LGBT*) ,Mental Health ,Infection ,Good Health and Well Being ,Humans ,Male ,Adolescent ,Sexually Transmitted Diseases ,Female ,Machine Learning ,HIV Infections ,Young Adult ,Child ,Sexual Behavior ,Los Angeles ,Sexual and Gender Minorities ,Risk Factors ,Adolescent Medicine Trials Network (ATN) CARES Team ,Biological Sciences ,Medical and Health Sciences ,Clinical sciences ,Epidemiology ,Public health - Abstract
BackgroundSexually transmitted infections (STIs) among youth aged 12 to 24 years have doubled in the last 13 years, accounting for 50% of STIs nationally. We need to identify predictors of STI among youth in urban HIV epicenters.MethodsSexual and gender minority (gay, bisexual, transgender, gender-diverse) and other youth with multiple life stressors (homelessness, incarceration, substance use, mental health disorders) were recruited from 13 sites in Los Angeles and New Orleans (N = 1482). Self-reports and rapid diagnostic tests for STI, HIV, and drug use were conducted at 4-month intervals for up to 24 months. Machine learning was used to identify predictors of time until new STI (including a new HIV diagnosis).ResultsAt recruitment, 23.9% of youth had a current or past STI. Over 24 months, 19.3% tested positive for a new STI. Heterosexual males had the lowest STI rate (12%); African American youth were 23% more likely to acquire an STI compared with peers of other ethnicities. Time to STI was best predicted by attending group sex venues or parties, moderate but not high dating app use, and past STI and HIV seropositive status.ConclusionsSexually transmitted infections are concentrated among a subset of young people at highest risk. The best predictors of youth's risk are their sexual environments and networks. Machine learning will allow the next generation of research on predictive patterns of risk to be more robust.
- Published
- 2023