title: Factors Associated with HIV Testing Among Participants from Substance Use Disorder Treatment Programs in the US: A Machine Learning Approach.
abstract: HIV testing is the foundation for consolidated HIV treatment and prevention. In this study, we aim to discover the most relevant variables for predicting HIV testing uptake among substance users in substance use disorder treatment programs by applying random forest (RF), a robust multivariate statistical learning method. We also provide a descriptive introduction to this method for those who are unfamiliar with it. We used data from the National Institute on Drug Abuse Clinical Trials Network HIV testing and counseling study (CTN-0032). A total of 1281 HIV-negative or status unknown participants from 12 US community-based substance use disorder treatment programs were included and were randomized into three HIV testing and counseling treatment groups. The a priori primary outcome was self-reported receipt of HIV test results. Classification accuracy of RF was compared to logistic regression, a standard statistical approach for binary outcomes. Variable importance measures for the RF model were used to select the most relevant variables. RF based models produced much higher classification accuracy than those based on logistic regression. Treatment group is the most important predictor among all covariates, with a variable importance index of 12.9%. RF variable importance revealed that several types of condomless sex behaviors, condom use self-efficacy and attitudes towards condom use, and level of depression are the most important predictors of receipt of HIV testing results. There is a non-linear negative relationship between count of condomless sex acts and the receipt of HIV testing. In conclusion, RF seems promising in discovering important factors related to HIV testing uptake among large numbers of predictors and should be encouraged in future HIV prevention and treatment research and intervention program evaluations.
authors.affiliation: Division of Epidemiology, Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 N.W. 14th ST, Miami, FL, 33136, USA. panyue@med.miami.edu.
authors.forename: Yue
authors.identifier: http://orcid.org/0000-0001-9586-0975
authors.initials: Y
authors.lastname: Pan
authors.affiliation: Division of Biostatistics, Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 N.W. 14th ST, Miami, FL, 33136, USA.
authors.forename: Hongmei
authors.initials: H
authors.lastname: Liu
authors.affiliation: Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, 722 W 168th ST, New York, NY, 10032, USA.
authors.forename: Lisa R
authors.initials: LR
authors.lastname: Metsch
authors.affiliation: Division of Biostatistics, Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 N.W. 14th ST, Miami, FL, 33136, USA.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Feaster
mesh_terms: D001294:Attitude to Health; D015897:Comorbidity; D017280:Condoms / Q000706:statistics & numerical data; D003376:Counseling; D055030:Drug Users; D005260:Female; D015658:HIV Infections / Q000175:diagnosis* / Q000453:epidemiology; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D008403:Mass Screening / Q000706:statistics & numerical data; D021841:Safe Sex; D020377:Self Efficacy; D012725:Sexual Behavior; D019966:Substance-Related Disorders / Q000453:epidemiology / Q000628:therapy*; D014481:United States / Q000453:epidemiology
keywords: HIV testing; Multivariate analysis; Random forest; Sexual risk behaviors; Substance use; Supervised learning