1. Identifying Predictors of Opioid Overdose Death at a Neighborhood Level With Machine Learning.
- Author
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Schell, Robert C, Allen, Bennett, Goedel, William C, Hallowell, Benjamin D, Scagos, Rachel, Li, Yu, Krieger, Maxwell S, Neill, Daniel B, Marshall, Brandon D L, Cerda, Magdalena, and Ahern, Jennifer
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NARCOTICS , *ANALGESICS , *NEIGHBORHOODS , *DRUG overdose , *MACHINE learning , *RANDOM forest algorithms , *RACE , *INCOME , *SOCIAL isolation , *EMPLOYMENT , *PREDICTIVE validity , *ETHNIC groups , *ALGORITHMS , *EDUCATIONAL attainment ,MORTALITY risk factors - Abstract
Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable tool for identifying neighborhood-level predictors. We examined statewide data on opioid overdose death from Rhode Island (log-transformed rates for 2016–2019) and 203 covariates from the American Community Survey for 742 US Census block groups. The analysis included a least absolute shrinkage and selection operator (LASSO) algorithm followed by variable importance rankings from a random forest algorithm. We employed double cross-validation, with 10 folds in the inner loop to train the model and 4 outer folds to assess predictive performance. The ranked variables included a range of dimensions of socioeconomic status, including education, income and wealth, residential stability, race/ethnicity, social isolation, and occupational status. The R 2 value of the model on testing data was 0.17. While many predictors of overdose death were in established domains (education, income, occupation), we also identified novel domains (residential stability, racial/ethnic distribution, and social isolation). Predictive modeling with machine learning can identify new neighborhood-level predictors of overdose in the continually evolving opioid epidemic and anticipate the neighborhoods at high risk of overdose mortality. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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