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dc.contributor.authorStopka, Thomas J.
dc.contributor.authorAmaravadi, Harsha
dc.contributor.authorKaplan, Anna R.
dc.contributor.authorHoh, Rachel
dc.contributor.authorBernson, Dana
dc.contributor.authorChui, Kenneth K.H.
dc.contributor.authorLand, Thomas
dc.contributor.authorWalley, Alexander Y.
dc.contributor.authorLaRochelle, Marc R.
dc.contributor.authorRose, Adam J.
dc.date2022-08-11T08:09:53.000
dc.date.accessioned2022-08-23T16:47:12Z
dc.date.available2022-08-23T16:47:12Z
dc.date.issued2019-06-01
dc.date.submitted2019-06-05
dc.identifier.citation<p>Int J Drug Policy. 2019 Jun;68:37-45. doi: 10.1016/j.drugpo.2019.03.024. Epub 2019 Apr 11. <a href="https://doi.org/10.1016/j.drugpo.2019.03.024">Link to article on publisher's site</a></p>
dc.identifier.issn0955-3959 (Linking)
dc.identifier.doi10.1016/j.drugpo.2019.03.024
dc.identifier.pmid30981166
dc.identifier.urihttp://hdl.handle.net/20.500.14038/41034
dc.description.abstractINTRODUCTION: Opioid overdose deaths quintupled in Massachusetts between 2000 and 2016. Potentially inappropriate opioid prescribing practices (PIP) are associated with increases in overdoses. The purpose of this study was to conduct spatial epidemiological analyses of novel comprehensively linked data to identify overdose and PIP hotspots. METHODS: Sixteen administrative datasets, including prescription monitoring, medical claims, vital statistics, and medical examiner data, covering >98% of Massachusetts residents between 2011-2015, were linked in 2017 to better investigate the opioid epidemic. PIP was defined by six measures: > /=100 morphine milligram equivalents (MMEs), co-prescription of benzodiazepines and opioids, cash purchases of opioid prescriptions, opioid prescriptions without a recorded pain diagnosis, and opioid prescriptions through multiple prescribers or pharmacies. Using spatial autocorrelation and cluster analyses, overdose and PIP hotspots were identified among 538 ZIP codes. RESULTS: More than half of the adult population (n = 3,143,817, ages 18 and older) were prescribed opioids. Nearly all ZIP codes showed increasing rates of overdose over time. Overdose clusters were identified in Worcester, Northampton, Lee/Tyringham, Wareham/Bourne, Lynn, and Revere/Chelsea (Getis-Ord Gi*; p < 0.05). Large PIP clusters for > /=100 MMEs and prescription without pain diagnosis were identified in Western Massachusetts; and smaller clusters for multiple prescribers in Nantucket, Berkshire, and Hampden Counties (p < 0.05). Co-prescriptions and cash payment clusters were localized and nearly identical (p < 0.05). Overlap in PIP and overdose clusters was identified in Cape Cod and Berkshire County. However, we also found contradictory patterns in overdose and PIP hotspots. CONCLUSIONS: Overdose and PIP hotspots were identified, as well as regions where the two overlapped, and where they diverged. Results indicate that PIP clustering alone does not explain overdose clustering patterns. Our findings can inform public health policy decisions at the local level, which include a focus on PIP and misuse of heroin and fentanyl that aim to curb opioid overdoses.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=30981166&dopt=Abstract">Link to Article in PubMed</a></p>
dc.rights© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectClusters
dc.subjectGeographic information systems (GIS)
dc.subjectGetis-Ord Gi*
dc.subjectHotspots
dc.subjectMassachusetts
dc.subjectOpioid overdose
dc.subjectPrescription opioids
dc.subjectClinical Epidemiology
dc.subjectCommunity Health and Preventive Medicine
dc.subjectEpidemiology
dc.subjectGeographic Information Sciences
dc.subjectHealth Policy
dc.subjectHealth Services Administration
dc.subjectHealth Services Research
dc.subjectPharmaceutical Preparations
dc.subjectSubstance Abuse and Addiction
dc.subjectTherapeutics
dc.titleOpioid overdose deaths and potentially inappropriate opioid prescribing practices (PIP): A spatial epidemiological study
dc.typeJournal Article
dc.source.journaltitleThe International journal on drug policy
dc.source.volume68
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=4842&amp;context=oapubs&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/oapubs/3828
dc.identifier.contextkey14670826
refterms.dateFOA2022-08-23T16:47:12Z
html.description.abstract<p>INTRODUCTION: Opioid overdose deaths quintupled in Massachusetts between 2000 and 2016. Potentially inappropriate opioid prescribing practices (PIP) are associated with increases in overdoses. The purpose of this study was to conduct spatial epidemiological analyses of novel comprehensively linked data to identify overdose and PIP hotspots.</p> <p>METHODS: Sixteen administrative datasets, including prescription monitoring, medical claims, vital statistics, and medical examiner data, covering >98% of Massachusetts residents between 2011-2015, were linked in 2017 to better investigate the opioid epidemic. PIP was defined by six measures: > /=100 morphine milligram equivalents (MMEs), co-prescription of benzodiazepines and opioids, cash purchases of opioid prescriptions, opioid prescriptions without a recorded pain diagnosis, and opioid prescriptions through multiple prescribers or pharmacies. Using spatial autocorrelation and cluster analyses, overdose and PIP hotspots were identified among 538 ZIP codes.</p> <p>RESULTS: More than half of the adult population (n = 3,143,817, ages 18 and older) were prescribed opioids. Nearly all ZIP codes showed increasing rates of overdose over time. Overdose clusters were identified in Worcester, Northampton, Lee/Tyringham, Wareham/Bourne, Lynn, and Revere/Chelsea (Getis-Ord Gi*; p < 0.05). Large PIP clusters for > /=100 MMEs and prescription without pain diagnosis were identified in Western Massachusetts; and smaller clusters for multiple prescribers in Nantucket, Berkshire, and Hampden Counties (p < 0.05). Co-prescriptions and cash payment clusters were localized and nearly identical (p < 0.05). Overlap in PIP and overdose clusters was identified in Cape Cod and Berkshire County. However, we also found contradictory patterns in overdose and PIP hotspots.</p> <p>CONCLUSIONS: Overdose and PIP hotspots were identified, as well as regions where the two overlapped, and where they diverged. Results indicate that PIP clustering alone does not explain overdose clustering patterns. Our findings can inform public health policy decisions at the local level, which include a focus on PIP and misuse of heroin and fentanyl that aim to curb opioid overdoses.</p>
dc.identifier.submissionpathoapubs/3828
dc.contributor.departmentUMass Worcester Prevention Research Center
dc.contributor.departmentDepartment of Medicine, Division of Preventive and Behavioral Medicine
dc.contributor.departmentDivision of Clinical Informatics
dc.source.pages37-45


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© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/BY-NC-ND/4.0/).
Except where otherwise noted, this item's license is described as © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).