Using decision tree models and comprehensive statewide data to predict opioid overdoses following prison release.
PURPOSE: Identifying predictors of opioid overdose following release from prison is critical for opioid overdose prevention. METHODS: We leveraged an individually linked, state-wide database from 2015-2020 to predict the risk of opioid overdose within 90 days of release from Massachusetts state prisons. We developed two decision tree modeling schemes: a model fit on all individuals with a single weight for those that experienced an opioid overdose and models stratified by race/ethnicity. We compared the performance of each model using several performance measures and identified factors that were most predictive of opioid overdose within racial/ethnic groups and across models. RESULTS: We found that out of 44,246 prison releases in Massachusetts between 2015-2020, 2237 (5.1%) resulted in opioid overdose in the 90 days following release. The performance of the two predictive models varied. The single weight model had high sensitivity (79%) and low specificity (56%) for predicting opioid overdose and was more sensitive for White non-Hispanic individuals (sensitivity = 84%) than for racial/ethnic minority individuals. CONCLUSIONS: Stratified models had better balanced performance metrics for both White non-Hispanic and racial/ethnic minority groups and identified different predictors of overdose between racial/ethnic groups. Across racial/ethnic groups and models, involuntary commitment (involuntary treatment for alcohol/substance use disorder) was an important predictor of opioid overdose.
Duke Scholars
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- Young Adult
- White
- Racial Groups
- Prisons
- Prisoners
- Opioid-Related Disorders
- Opiate Overdose
- Middle Aged
- Massachusetts
- Male
Citation
Published In
DOI
EISSN
Publication Date
Volume
Start / End Page
Location
Related Subject Headings
- Young Adult
- White
- Racial Groups
- Prisons
- Prisoners
- Opioid-Related Disorders
- Opiate Overdose
- Middle Aged
- Massachusetts
- Male