Sex differences in factors predicting post-treatment opioid use

Area of Science:

• Addiction research
• Clinical psychology
• Machine learning in healthcare

Background:

• Opioid use disorder (OUD) treatment effectiveness is well-documented, but risk factors for relapse post-treatment require further investigation.
• Few studies have explored sex-specific risk factors for post-treatment opioid use.
• Contemporary machine learning approaches have not been widely applied to identify these risks.

Purpose of the Study:

• To identify individual factors associated with opioid use after treatment discharge, specifically examining sex differences.
• To inform the development of tailored treatments for opioid use disorder (OUD).
• To leverage machine learning for a deeper understanding of OUD relapse predictors.

Main Methods:

• Secondary analysis of the Global Appraisal of Individual Needs (GAIN) database.
• Follow-up assessments at 3, 6, and 12 months post-OUD treatment discharge.
• Exploration of demographic, psychological, and behavioral variables using regularized Cox regression and random survival forests.

Main Results:

• For women, key predictors of post-treatment opioid use included withdrawal symptoms, younger age, prior substance use disorder (SUD) treatment, and treatment resistance.
• For men, significant risk factors were conduct disorder symptoms, younger age, and multiple SUDs.
• Machine learning identified substance use problems, criminal justice involvement, and younger age as critical risk factors for women, and younger age, conduct disorder, multiple SUDs, and criminal justice involvement for men.

Conclusions:

• Relapse risk factors following OUD treatment are sex-dependent.
• Women's relapse is associated with substance use problems and withdrawal severity.
• Men's relapse is linked to younger age, conduct disorder, and multiple SUDs, highlighting the need for personalized interventions.