Using decision tree models and comprehensive statewide data to predict opioid overdoses following prison release.
Ann Epidemiol
; 94: 81-90, 2024 Jun.
Article
em En
| MEDLINE
| ID: mdl-38710239
ABSTRACT
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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Árvores de Decisões
/
Overdose de Opiáceos
Limite:
Adult
/
Female
/
Humans
/
Male
/
Middle aged
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Ann Epidemiol
Assunto da revista:
EPIDEMIOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos