Predicting Opioid Overdose Deaths Using Prescription Drug Monitoring Program Data.

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Bibliographic Details
Title: Predicting Opioid Overdose Deaths Using Prescription Drug Monitoring Program Data.
Authors: Ferris LM; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Chesapeake Regional Information System for our Patients, Baltimore, Maryland., Saloner B; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. Electronic address: bsaloner@jhu.edu., Krawczyk N; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland., Schneider KE; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland., Jarman MP; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Center for Surgery and Public Health, Brigham and Women's Hospital, Boston, Massachusetts., Jackson K; Maryland Department of Health, Behavioral Health Administration, Office of PDMP and Overdose Prevention Applied Data Programs, Baltimore, Maryland., Lyons BC; Maryland Department of Health, Behavioral Health Administration, Office of PDMP and Overdose Prevention Applied Data Programs, Baltimore, Maryland., Eisenberg MD; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland., Richards TM; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Johns Hopkins Center for Population Health Information Technology, Baltimore, Maryland., Lemke KW; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Johns Hopkins Center for Population Health Information Technology, Baltimore, Maryland., Weiner JP; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Johns Hopkins Center for Population Health Information Technology, Baltimore, Maryland.
Source: American journal of preventive medicine [Am J Prev Med] 2019 Dec; Vol. 57 (6), pp. e211-e217.
Publication Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.; Validation Study
Journal Info: Publisher: Elsevier Science Country of Publication: Netherlands NLM ID: 8704773 Publication Model: Print Cited Medium: Internet ISSN: 1873-2607 (Electronic) Linking ISSN: 07493797 NLM ISO Abbreviation: Am J Prev Med Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1873-2607
DOI:10.1016/j.amepre.2019.07.026