ArticleAmerican journal of epidemiology2022
Identifying Predictors of Opioid Overdose Death at a Neighborhood Level With Machine Learning.
Article in American journal of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 33 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
33 citing papers in PubMed.
- Dynamic lysine acetylation and succinylation of platelet proteins regulates platelet storage lesion: mechanistic insights from multi-omics.Annals of medicine · 2026Article
- Methodological innovations to advance substance use disorder research: Proceedings of a NIDA workshop on target trial emulation and translational testing of digital health tools.Journal of substance use and addiction treatment · 2026Article
- Introduction to the Special Issue on Transforming Prevention Science: Advances and Applications of Artificial Intelligence and Related Methods.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Clinicians' and people who use drugs' perspectives on artificial intelligence in addiction medicine.Addiction (Abingdon, England) · 2026Article
- An Epidemic of Complexity: Polysubstance Use Fatal Overdose Typologies Across Cook County, Illinois, Neighborhoods, 2017‒2022.American journal of public health · 2026Article
- A Standardized Framework for Evaluating State Opioid Surveillance Dashboards: A Health Informatics Assessment.Journal of medical systems · 2026Article
- Area-specific autoencoder spatiotemporal graph neural networks for opioid overdose death prediction.JAMIA open · 2026Article
- Ethical challenges and opportunities for integrating predictive analytics in community-based overdose prevention.Lancet regional health. Americas · 2026Review
- A machine learning approach for opioid overdose risk prediction among Alabama Medicaid beneficiaries with opioid prescriptions.Scientific reports · 2026Article
- Trajectories of neighborhood-level overdose risk predictions for prioritization of harm reduction services: Results from the PROVIDENT study.Drug and alcohol dependence · 2025Article
- Work Disability and Fatal Opioid Overdose Risk in the United States: A Cohort Study.Journal of general internal medicine · 2025Article
- Population-level individualized prospective prediction of opioid overdose using machine learning.Molecular psychiatry · 2025Article
- Spatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learning.BMC public health · 2025Article
- Spatiotemporal forecasting of opioid-related fatal overdoses: towards best practices for modeling and evaluation.American journal of epidemiology · 2025Article
- An Overdose Forecasting Dashboard for Local Harm-Reduction Response.Health promotion practice · 2025Article
- Evaluating the predictive performance of different data sources to forecast overdose deaths at the neighborhood level with machine learning in Rhode Island.Preventive medicine · 2025Article
- Using decision tree models and comprehensive statewide data to predict opioid overdoses following prison release.Annals of epidemiology · 2024Article
- Characterizing opioid overdose hotspots for place-based overdose prevention and treatment interventions: A geo-spatial analysis of Rhode Island, USA.The International journal on drug policy · 2024Article
- A Systematic Review of Artificial Intelligence Used to Predict Loneliness, Social Isolation, and Drug Use During the COVID-19 Pandemic.Journal of multidisciplinary healthcare · 2024Review
- Implementation and Assessment of Machine Learning Models for Forecasting Suspected Opioid Overdoses in Emergency Medical Services Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
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 R2 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.
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