{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:20:57Z","timestamp":1789104057119,"version":"build-2803163510"},"reference-count":34,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2019,4,24]],"date-time":"2019-04-24T00:00:00Z","timestamp":1556064000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmacoepidemiology and Drug"],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The study aims to develop and validate algorithms to identify and classify opioid overdoses using claims and other coded data, and clinical text extracted from electronic health records using natural language processing (NLP).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Primary data were derived from Kaiser Permanente Northwest (2008\u20132014), an integrated health care system (~<jats:italic>n<\/jats:italic>\u00a0&gt;\u00a0475\u00a0000 unique individuals per year). Data included<jats:italic>International Classification of Diseases, Ninth Revision<\/jats:italic>(<jats:italic>ICD\u20109<\/jats:italic>) codes for nonfatal diagnoses,<jats:italic>International Classification of Diseases, Tenth Revision<\/jats:italic>(<jats:italic>ICD\u201010<\/jats:italic>) codes for fatal events, clinical notes, and prescription medication records. We assessed sensitivity, specificity, positive predictive value, and negative predictive value for algorithms relative to medical chart review and conducted assessments of algorithm portability in Kaiser Permanente Washington, Tennessee State Medicaid, and Optum.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Code\u2010based algorithm performance was excellent for opioid\u2010related overdoses (sensitivity\u00a0=\u00a097.2%, specificity\u00a0=\u00a084.6%) and classification of heroin\u2010involved overdoses (sensitivity\u00a0=\u00a091.8%, specificity\u00a0=\u00a099.0%). Performance was acceptable for code\u2010based suicide\/suicide attempt classifications (sensitivity\u00a0=\u00a070.7%, specificity\u00a0=\u00a090.5%); sensitivity improved with NLP (sensitivity\u00a0=\u00a078.7%, specificity\u00a0=\u00a091.0%). Performance was acceptable for the code\u2010based substance abuse\u2010involved classification (sensitivity\u00a0=\u00a075.3%, specificity\u00a0=\u00a079.5%); sensitivity improved with the NLP\u2010enhanced algorithm (sensitivity\u00a0=\u00a080.5%, specificity\u00a0=\u00a076.3%). The opioid\u2010related overdose algorithm performed well across portability assessment sites, with sensitivity greater than 96% and specificity greater than 84%. Cross\u2010site sensitivity for heroin\u2010involved overdose was greater than 87%, specificity greater than or equal to 99%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Code\u2010based algorithms developed to detect opioid\u2010related overdoses and classify them according to heroin involvement perform well. Algorithms for classifying suicides\/attempts and abuse\u2010related opioid overdoses perform adequately for use for research, particularly given the complexity of classifying such overdoses. The NLP\u2010enhanced algorithms for suicides\/suicide attempts and abuse\u2010related overdoses perform significantly better than code\u2010based algorithms and are appropriate for use in settings that have data and capacity to use NLP.<\/jats:p><\/jats:sec>","DOI":"10.1002\/pds.4772","type":"journal-article","created":{"date-parts":[[2019,4,25]],"date-time":"2019-04-25T02:29:28Z","timestamp":1556159368000},"page":"1127-1137","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Identifying and classifying opioid\u2010related overdoses: A validation study"],"prefix":"10.1002","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0000-4381","authenticated-orcid":false,"given":"Carla A.","family":"Green","sequence":"first","affiliation":[{"name":"Center for Health Research, Kaiser Permanente Northwest Portland Oregon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nancy A.","family":"Perrin","sequence":"additional","affiliation":[{"name":"Center for Health Research, Kaiser Permanente Northwest Portland Oregon"},{"name":"Johns Hopkins School of Nursing Johns Hopkins University Baltimore Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9365-3964","authenticated-orcid":false,"given":"Brian","family":"Hazlehurst","sequence":"additional","affiliation":[{"name":"Center for Health Research, Kaiser Permanente Northwest Portland Oregon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shannon L.","family":"Janoff","sequence":"additional","affiliation":[{"name":"Center for Health Research, Kaiser Permanente Northwest Portland Oregon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angela","family":"DeVeaugh\u2010Geiss","sequence":"additional","affiliation":[{"name":"Indivior, Inc. North Chesterfield Virginia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8471-0928","authenticated-orcid":false,"given":"David S.","family":"Carrell","sequence":"additional","affiliation":[{"name":"Health Research Institute, Kaiser Permanente Washington Seattle Washington"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos G.","family":"Grijalva","sequence":"additional","affiliation":[{"name":"Department of Health Policy Vanderbilt University Medical Center Nashville Tennessee"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6934-3587","authenticated-orcid":false,"given":"Caihua","family":"Liang","sequence":"additional","affiliation":[{"name":"Epidemiology Optum Boston Massachusetts"},{"name":"Epidemiology Optum Ann Arbor Michigan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheryl L.","family":"Enger","sequence":"additional","affiliation":[{"name":"Epidemiology Optum Boston Massachusetts"},{"name":"Epidemiology Optum Ann Arbor Michigan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul M.","family":"Coplan","sequence":"additional","affiliation":[{"name":"Epidemiology, Johnson &amp; Johnson New Brunswick New Jersey"},{"name":"Adjunct, Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,4,24]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"Substance Abuse and Mental Health Services Administration.Results from the 2012 National Survey on Drug Use and Health: mental health findings. 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