{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T08:13:44Z","timestamp":1770970424529,"version":"3.50.1"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"8-9","license":[{"start":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T00:00:00Z","timestamp":1556496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"NIH National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health","award":["K08 EB022631"],"award-info":[{"award-number":["K08 EB022631"]}]},{"DOI":"10.13039\/100000052","name":"NIH Office of the Director","doi-asserted-by":"publisher","award":["5DP 5OD021338"],"award-info":[{"award-number":["5DP 5OD021338"]}],"id":[{"id":"10.13039\/100000052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Examine whether individual, geographic, and economic phenotypes predict missing data on specific drug involvement in overdose deaths, manifesting inequities in overdose mortality data, which is a key data source used in measuring the opioid epidemic.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>We combined national data sources (mortality, demographic, economic, and geographic) from 2014\u20132016 in a multi-method analysis of missing drug classification in the overdose mortality records (as defined by the use of ICD-10 T50.9 on death certificates). We examined individual disparities in decedent-level multivariate logistic regression models, geographic disparities in spatial analysis (heat maps), and economic disparities in a combination of temporal trend analyses (descriptive statistics) and both decedent- and county-level multivariate logistic regression models.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Our analyses consistently found higher rates of unclassified overdoses in decedents of female gender, White race, non-Hispanic ethnicity, with college education, aged 30\u201359 and those from poorer counties. Despite the fact that unclassified drug overdose death rates have reduced over time, gaps persist between the richest and poorest counties. There are also striking geographic differences both across and within states.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>Given the essential role of mortality data in measuring the scale of the opioid epidemic, it is important to understand the individual and community inequities underlying the missing data on specific drug involvements. Knowledge of these inequities could enhance our understanding of the opioid crisis and inform data-driven interventions and policies with more equitable resource allocations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Multiple individual, geographic, and economic disparities underlie unclassified overdose deaths, with important implications for public health informatics and addressing the opioid crisis.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocz050","type":"journal-article","created":{"date-parts":[[2019,3,28]],"date-time":"2019-03-28T20:13:07Z","timestamp":1553803987000},"page":"767-777","source":"Crossref","is-referenced-by-count":37,"title":["Unclassified drug overdose deaths in the opioid crisis: emerging patterns of inequity"],"prefix":"10.1093","volume":"26","author":[{"given":"Andrew J","family":"Boslett","sequence":"first","affiliation":[{"name":"Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA"}]},{"given":"Alina","family":"Denham","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA"}]},{"given":"Elaine L","family":"Hill","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3969-4279","authenticated-orcid":false,"given":"Meredith C B","family":"Adams","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Wake Forest Baptist Health, Winston-Salem, North Carolina, 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