Identifying Predictors of Opioid Overdose Death at a Neighborhood Level With Machine Learning.

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. T

SUBMITTER: Schell RC 

PROVIDER: S-EPMC9214774 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature