Sex differences in factors predicting post-treatment opioid use.

ABSTRACT:

Background and aims

Several reports have documented risk factors for opioid use following treatment discharge, yet few have assessed sex differences, and no study has assessed risk using contemporary machine learning approaches. The goal of the present paper was to inform treatments for opioid use disorder (OUD) by exploring individual factors for each sex that are most strongly associated with opioid use following treatment.

Design

Secondary analysis of Global Appraisal of Individual Needs (GAIN) database with follow-ups at 3, 6 and 12 months post-OUD treatment discharge, exploring demographic, psychological and behavioral variables that predict post-treatment opioid use.

Setting

One hundred and thity-seven treatment sites across the United States.

Participants

SUBMITTER: Davis JP 

PROVIDER: S-EPMC8254742 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature