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The leading neighborhood-level predictors of drug overdose: A mixed machine learning and spatial approach
Journal article   Peer reviewed

The leading neighborhood-level predictors of drug overdose: A mixed machine learning and spatial approach

Parisa Bozorgi, Dwayne E. Porter, Jan M. Eberth, Jeannie P. Eidson and Amir Karami
Drug and alcohol dependence, v 229(Pt B), 109143
01 Dec 2021
DOI: https://doi.org/10.1016/j.drugalcdep.2021.109143
PMID: 34794060
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Drug overdose Geographic information system (GIS) Geographically weighted regression (GWR) Machine learning Spatial prediction
Drug overdose is a leading cause of unintentional death in the United States and has contributed significantly to a decline in life expectancy during recent years. To combat this health issue, this study aims to identify the leading neighborhood-level predictors of drug overdose and develop a model to predict areas at the highest risk of drug overdose using geographic information systems and machine learning (ML) techniques. Neighborhood-level (block group) predictors were grouped into three domains: socio-demographic factors, drug use variables, and protective resources. We explored different ML algorithms, accounting for spatial dependency, to identify leading predictors in each domain. Using geographically weighted regression and the best-performing ML algorithm, we combined the output prediction of three domains to produce a final ensemble model. The model performance was validated using classification evaluation metrics, spatial cross-validation, and spatial autocorrelation testing. The variables contributing most to the predictive model included the proportion of households with food stamps, households with an annual income below $35,000, opioid prescription rate, smoking accessories expenditures, and accessibility to opioid treatment programs and hospitals. Compared to the error estimated from normal cross-validation, the generalized error of the model did not increase considerably in spatial cross-validation. The ensemble model using ML outperformed the GWR method. This study identified strong neighborhood-level predictors that place a community at risk of experiencing drug overdoses, as well as protective factors. Our findings may shed light on several specific avenues for targeted intervention in neighborhoods at risk for high drug overdose burdens. •Used machine learning to predict the location and risk of drug overdose using neighborhood-level predictors.•Among sociodemographic factors, households with food stamps and income below $35,000 were strongest predictors.•Among drug-related risk factors, the opioid prescription rate was the most important predictor.•Among protective resources, low access to opioid treatment programs was the strongest predictor.•The ensemble model by XGBoost explained 73% of the variation and outperformed the GWR.

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12 Record Views
24 citations in Web of Science
24 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

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Web of Science research areas
Psychiatry
Substance Abuse
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