{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T05:10:11Z","timestamp":1790745011867,"version":"4.1.0"},"reference-count":30,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T00:00:00Z","timestamp":1677715200000},"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":[[2023,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Drug utilization researchers are often interested in evaluating prescribing and medication use patterns and trends over a specified period of time. Joinpoint regression is a useful methodology to identify any deviations in secular trends without a preconceived notion of where these break points might occur. This article provides a tutorial on the use of joinpoint regression, within Joinpoint software, for the analysis of drug utilization data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The statistical considerations for whether a joinpoint regression analytical technique is a suitable approach are discussed. Then, we offer a tutorial as an introduction on conducting joinpoint regression (within Joinpoint software) through a step\u2010by\u2010step application, which is a case study developed using opioid prescribing data from the United States. Data were obtained from public files available through the Centers for Disease Control and Prevention from 2006 to 2018. The tutorial provides parameters and sample data needed to replicate the case study and it concludes with general considerations for the reporting of results using joinpoint regression in drug utilization research.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The case study evaluated the trend of opioid prescribing in the United States from 2006 to 2018, where time points of significant variation (one in 2012 and another in 2016) are detected and interpreted.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Joinpoint regression is a helpful methodology for drug utilization for the purposes of conducting descriptive analyses. This tool also assists with corroborating assumptions and identifying parameters for fitting other models such as interrupted time series. The technique and accompanying software are user\u2010friendly; however, researchers interested in using joinpoint regression should exercise caution and follow best practices for correct measurement of drug utilization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pds.5606","type":"journal-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T22:09:26Z","timestamp":1677103766000},"page":"509-516","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Using Joinpoint regression for drug utilization research: Tutorial and case study of prescription opioid use in the United States"],"prefix":"10.1002","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6945-2713","authenticated-orcid":false,"given":"Juan M.","family":"Hincapie\u2010Castillo","sequence":"first","affiliation":[{"name":"Department of Epidemiology University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"},{"name":"Injury Prevention Research Center University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0020-8720","authenticated-orcid":false,"given":"Amie","family":"Goodin","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Outcomes &amp; Policy University of Florida  Gainesville Florida USA"},{"name":"Center for Drug Evaluation and Safety University of Florida  Gainesville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,3,2]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1176\/appi.ps.201700260"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/lary.23554"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781118949740.ch9"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2014.12.018"},{"key":"e_1_2_12_6_1","first-page":"171","volume-title":"Experimental and Quasi\u2010Experimental Designs for Generalized Causal Inference","author":"Shadish WR","year":"2002"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2020.0234"},{"key":"e_1_2_12_8_1","unstructured":"National Cancer Institute.Joinpoint regression program \u2010 surveillance research program.2018.https:\/\/surveillance.cancer.gov\/joinpoint\/."},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(20000215)19:3<335::AID-SIM336>3.0.CO;2-Z"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.1365\u20102710.2002.00430.x"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/pm\/pnaa211"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/pds.3894"},{"key":"e_1_2_12_13_1","first-page":"250","article-title":"Use of Joinpoint regression in drug utilization research: post\u2010tonsillectomy codeine use in pediatrics, United States (2005\u20102016)","volume":"28","author":"Hincapie\u2010Castillo JM","year":"2019","journal-title":"Pharmacoepidemiol Drug Saf"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.2307\/2346413"},{"issue":"2","key":"e_1_2_12_15_1","first-page":"597","article-title":"Selecting the number of change\u2010points in segmented line regression","volume":"19","author":"Kim H\u2010J","year":"2009","journal-title":"Stat Sin"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006\u2010341X.2004.00256.x"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.2478\/jos\u20102020\u20100003"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.15585\/mmwr.mm6911a4"},{"key":"e_1_2_12_19_1","unstructured":"Centers for Disease Control and Prevention. 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