{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T10:04:30Z","timestamp":1753869870250,"version":"3.41.2"},"reference-count":34,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T00:00:00Z","timestamp":1696896000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmacoepidemiology and Drug"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The aim of this study is to use electronic opioid dispensing data to develop an individual segmented trajectory approach for identifying opioid use patterns. The resulting opioid use patterns can be used for examining the association between opioid use and drug overdose.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We retrospectively assembled a cohort of members on long\u2010term opioid therapy (LTOT) between January 1, 2006 and June 30, 2019 who were 18\u2009years and older and enrolled in one of three health care systems in the US. We have developed an individual segmented trajectory analysis for identifying various opioid use patterns by scanning over the follow\u2010up and finding distinct opioid use patterns based on variability measured with coefficient of variation and trends of milligram morphine equivalents levels.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among 31, 865 members who were on LTOT between January 1, 2006 and June 30, 2019, 58.3% were female, and the average age was 55.4\u2009years (STD\u2009=\u200915.4). The study population had 152\u2009557 person\u2010years of follow\u2010up, with an average follow\u2010up of 4.4\u2009years per enrollment per person (STD\u2009=\u20093.4). This novel approach identified up to 13 distinct patterns including 88\u2009756 episodes of \u201cstable\u201d pattern (42.1%) with an average follow\u2010up of 11.2\u2009months, 29\u2009140 episodes of \u201cincreasing\u201d pattern (13.8%) with an average follow\u2010up of 6.0\u2009months, 13\u2009201 episodes of \u226410% dose reduction (6.3%) with an average follow\u2010up of 10.4\u2009months, 7286 episodes of 11%\u201320% dose reduction (3.5%) with an average follow\u2010up of 5.3\u2009months, 4457 episodes of 21%\u201330% dose reduction (2.1%) with an average follow\u2010up of 4.0\u2009months, and 9903 episodes of &gt;30% dose reduction (4.7%) with an average follow\u2010up of 2.6\u2009months.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>A novel approach was developed to identify 13 distinct opioid use patterns using each individual's longitudinal dispensing data and these patterns can be used in examining overdose risk during the time that these patterns are ongoing.<\/jats:p><\/jats:sec>","DOI":"10.1002\/pds.5708","type":"journal-article","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T03:13:44Z","timestamp":1697080424000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An individual segmented trajectory approach for identifying opioid use patterns using longitudinal dispensing data"],"prefix":"10.1002","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4750-7672","authenticated-orcid":false,"given":"Stanley","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Research &amp; Evaluation Kaiser Permanente Southern California  Pasadena California USA"},{"name":"Department of Health Systems Science Kaiser Permanente Bernard J. Tyson School of Medicine  Pasadena California USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Komal J.","family":"Narwaney","sequence":"additional","affiliation":[{"name":"Institute for Health Research Kaiser Permanente Colorado  Aurora Colorado USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anh P.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Institute for Health Research Kaiser Permanente Colorado  Aurora Colorado USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ingrid A.","family":"Binswanger","sequence":"additional","affiliation":[{"name":"Department of Health Systems Science Kaiser Permanente Bernard J. Tyson School of Medicine  Pasadena California USA"},{"name":"Institute for Health Research Kaiser Permanente Colorado  Aurora Colorado USA"},{"name":"Division of General Internal Medicine, Department of Medicine University of Colorado School of Medicine  Aurora Colorado USA"},{"name":"Chemical Dependency Treatment Services Colorado Permanente Medical Group  Aurora Colorado USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David L.","family":"McClure","sequence":"additional","affiliation":[{"name":"Center for Clinical Epidemiology and Population Health Marshfield Clinic Research Institute  Marshfield Wisconsin USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason M.","family":"Glanz","sequence":"additional","affiliation":[{"name":"Institute for Health Research Kaiser Permanente Colorado  Aurora Colorado USA"},{"name":"Department of Epidemiology Colorado School of Public Health  Aurora Colorado USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,10,10]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev\u2010publhealth\u2010031914\u2010122957"},{"key":"e_1_2_8_3_1","unstructured":"CDC.Accessed on January 12.2022https:\/\/www.cdc.gov\/drugoverdose\/maps\/rxrate-maps.html"},{"volume-title":"National Vital Statistics System, Mortality. 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