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
W. Scott Comulada,
Mary Jane Rotheram-Borus,
Elizabeth Mayfield Arnold
et al.
Abstract:Background
Sexually 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.
Methods
Sexual 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… Show more
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