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Latent Risk Subtypes Based on Injection and Sexual Behavior Among People Who Inject Drugs in Rural Puerto Rico
Author(s) -
Hautala Dane,
Abadie Roberto,
Thrash Courtney,
Reyes Juan Carlos,
Dombrowski Kirk
Publication year - 2017
Publication title -
the journal of rural health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.439
H-Index - 57
eISSN - 1748-0361
pISSN - 0890-765X
DOI - 10.1111/jrh.12262
Subject(s) - latent class model , demography , psychological intervention , medicine , environmental health , respondent , relative risk , rural area , gerontology , psychiatry , confidence interval , sociology , statistics , mathematics , pathology , political science , law
Background People who inject drugs (PWID) in Puerto Rico engage in high levels of injection and sexual risk behavior, and they are at high risk for HIV and hepatitis C (HCV) infection, relative to their US counterparts. Less is known, however, about the clustering of risk behavior conducive to HIV and HCV infection among rural Puerto Rican communities. Objectives The purpose of this study was to examine concurrent injection and sexual risk subtypes among a rural sample of PWID in Puerto Rico. Methods Data were drawn from a respondent‐driven sample collected in 2015 of 315 PWID in 4 rural communities approximately 30‐40 miles from San Juan. Latent class analysis (LCA) was used to examine risk subtypes using 3 injection and 3 sexual risk indicators. In addition, demographic and other PWID characteristics were examined as possible predictors of latent class membership. Results Four LCA subtypes were identified: low risk (36%), high injection/low sexual risk (22%), low injection/high sexual risk (20%), and high risk (22%). Younger age and past year homelessness predicted high risk latent class membership, relative to the other classes. In addition, daily speedball use predicted membership in the high injection/low sexual risk class, relative to the low risk and low injection/high sexual risk classes. Conclusion/Importance The findings suggest ways in which PWID risk clusters can be identified for targeted interventions.