ArticleAnnals of epidemiology2019
Identifying high-risk areas for nonfatal opioid overdose: a spatial case-control study using EMS run data.
Article in Annals of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Review
- Spatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learning.BMC public health · 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
- Are Treatment Services Ready for the Use of Big Data Analytics and AI in Managing Opioid Use Disorder?Journal of medical Internet research · 2025Article
- Overdose as a complex contagion: modelling the community spread of overdose events following law enforcement efforts to disrupt the drug market.Journal of epidemiology and community health · 2025Article
- Characteristics of Fatal, Pedestrian-Involved, Motor Vehicle Crashes in West Virginia: A Cross-Sectional and Spatial Analysis.International journal of environmental research and public health · 2023Article
- Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio.BMC public health · 2022Article
- Examining the spatial risk environment tied to the opioid crisis through a unique public health, EMS, and academic research collaborative: Lowell, Massachusetts, 2008-2018.Preventive medicine reports · 2021Article
- Methodological approaches for the prediction of opioid use-related epidemics in the United States: a narrative review and cross-disciplinary call to action.Translational research : the journal of laboratory and clinical medicine · 2021Review
- Evaluation of Policy Effectiveness by Mathematical Modeling for the Opioid Crisis with Spatial Study and Trend Analysis.Healthcare (Basel, Switzerland) · 2021Article
- Opioid Treatment Deserts: Concept development and application in a US Midwestern urban county.PloS one · 2021Article
- 311 service requests as indicators of neighborhood distress and opioid use disorder.Scientific reports · 2020Article
- The spatio-temporal distribution of naloxone administration events in rural Ohio 2010-16.Drug and alcohol dependence · 2020Article
- Suspected heroin-related overdoses incidents in Cincinnati, Ohio: A spatiotemporal analysis.PLoS medicine · 2019Article
- Using Emergency Medical Services Data to Monitor Nonfatal Opioid Overdoses in Real Time : Development, Validation, and Use of a Case Definition, Rhode Island, 2018.Public health reports (Washington, D.C. : 1974)Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeThe objective of our study was to incorporate stricter probable nonfatal opioid overdose case criteria, and advanced epidemiologic approaches to more reliably detect local clustering in nonfatal opioid overdose activity in EMS runs data.
methodsData were obtained using emsCharts for our study area in southwestern Pennsylvania from 2007 to 2018. Cases were identified as emergency medical service (EMS) responses where naloxone was administered, and improvement was noted in patient records between initial and final Glasgow Coma Score. A subsample of all-cause EMS responses sites were used as controls and exact matched to cases on sex and 10-year-age category. Clustering was assessed using difference in Ripley's K function for cases and controls and Kulldorff scan statistics.
resultsDifference in K functions indicated no significant difference in probable nonfatal overdose EMS runs across the study area compared to all-cause EMS runs. However, scan statistics did identify significant local clustering of probable nonfatal overdose EMS runs (maximum likelihood = 16.40, P = 0.0003).
conclusionsResults highlight relevance of EMS data to detect community-level overdose activity and promote reliable use through stricter case definition criteria and advanced methodological approaches. Techniques examined have the potential to improve targeted delivery of neighborhood-level public health response activities using a near real-time data source.
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