TY - JOUR
T1 - 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
AU - AdolescentMedicine TrialsNetwork (ATN)CARES Team
AU - Comulada, W. Scott
AU - Rotheram-Borus, Mary Jane
AU - Arnold, Elizabeth Mayfield
AU - Norwood, Peter
AU - Lee, Sung Jae
AU - Ocasio, Manuel A.
AU - Flynn, Risa
AU - Nielsen-Saines, Karin
AU - Bolan, Robert
AU - Klausner, Jeffrey D.
AU - Swendeman, Dallas
AU - Abdalian, Sue Ellen
AU - Bryson, Yvonne
AU - Cortado, Ruth
AU - Fernandez, M. Isabel
AU - Kerin, Tara
AU - Lightfoot, Marguerita
AU - Milburn, Norweeta
AU - Ramos, Wilson
AU - Reback, Cathy
AU - Tang, Wenze
AU - Rezvan, Panteha Hayati
AU - Weiss, Robert E.
N1 - Publisher Copyright:
© Lippincott Williams & Wilkins.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - 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 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). Results At 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. Conclusions Sexually 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.
AB - 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 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). Results At 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. Conclusions Sexually 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.
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U2 - 10.1097/OLQ.0000000000001854
DO - 10.1097/OLQ.0000000000001854
M3 - Article
C2 - 37643402
AN - SCOPUS:85175292672
SN - 0148-5717
VL - 50
SP - 739
EP - 745
JO - Sexually Transmitted Diseases
JF - Sexually Transmitted Diseases
IS - 11
ER -