ArticlePublic health reports (Washington, D.C. : 1974)
Characterizing Opioid Overdoses Using Emergency Medical Services Data : A Case Definition Algorithm Enhanced by Machine Learning.
Article in Public health reports (Washington, D.C. : 1974). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Article
- Using ICD Codes Alone May Misclassify Overdoses Among Perinatal People.American journal of preventive medicine · 2025Article
- EMS injury cause codes more accurate than emergency department visit ICD-10-CM codes for firearm injury intent in North Carolina.PloS one · 2024Article
- Artificial intelligence and machine learning in prehospital emergency care: A scoping review.iScience · 2023Article
- Emergency Medical Services Data: An Unexpected Source of Variation in Stroke Care Performance.Journal of the American Heart Association · 2023Article
- Sensitivity of health records for self-reported nonfatal drug and alcohol overdose.The American journal on addictions · 2022Article
- Goofballing of Opioid and Methamphetamine: The Science Behind the Deadly Cocktail.Frontiers in pharmacology · 2022Review
- Opioid Overdose Surveillance : Improving Data to Inform Action.Public health reports (Washington, D.C. : 1974)Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesTracking nonfatal overdoses in the escalating opioid overdose epidemic is important but challenging. The objective of this study was to create an innovative case definition of opioid overdose in North Carolina emergency medical services (EMS) data, with flexible methodology for application to other states' data.
methodsThis study used de-identified North Carolina EMS encounter data from 2010-2015 for patients aged >12 years to develop a case definition of opioid overdose using an expert knowledge, rule-based algorithm reflecting whether key variables identified drug use/poisoning or overdose or whether the patient received naloxone. We text mined EMS narratives and applied a machine-learning classification tree model to the text to predict cases of opioid overdose. We trained models on the basis of whether the chief concern identified opioid overdose.
resultsUsing a random sample from the data, we found the positive predictive value of this case definition to be 90.0%, as compared with 82.7% using a previously published case definition. Using our case definition, the number of unresponsive opioid overdoses increased from 3412 in 2010 to 7194 in 2015. The corresponding monthly rate increased by a factor of 1.7 from January 2010 (3.0 per 1000 encounters; n = 261 encounters) to December 2015 (5.1 per 1000 encounters; n = 622 encounters). Among EMS responses for unresponsive opioid overdose, the prevalence of naloxone use was 83%.
conclusionsThis study demonstrates the potential for using machine learning in combination with a more traditional substantive knowledge algorithm-based approach to create a case definition for opioid overdose in EMS data.
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Registered trials
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