{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T23:13:01Z","timestamp":1777936381644,"version":"3.51.4"},"reference-count":53,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Subst\ufffdAbuse"],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Opioid addiction and overdose rates are reaching unprecedented levels in the U.S., with around 47,736 overdose deaths in 2017. Many stakeholders affect the opioid epidemic, including government entities, healthcare providers and policymakers, and opioid users. Simulation and conceptual modeling can help us understand the dynamics of the opioid epidemic by simplifying the real world and informing policymakers about different health interventions that could reduce the deaths caused by opioid overdose in the United States every year.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives:<\/jats:title>\n                    <jats:p>To conduct a scoping review of simulation and conceptual models that propose policies capable of controlling the opioid epidemic. We demonstrate the strengths and limitations of these models and provide a framework for further improvement of future decision support tools.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Using the methodology of a scoping review, we identified articles published after 2000 from eight electronic databases to map the literature that uses simulation and conceptual modeling in developing public health policies to address the opioid epidemic.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>We reviewed 472 papers of which 14 were appropriate for inclusion. Each used either system dynamics simulation modeling, mathematical modeling, conceptual modeling, or agent-based modeling. All included studies tested and proposed strategies to improve health outcomes related to the opioid epidemic. Factors considered in the models included physicians prescribing opioids, trafficking, users recruiting new users, and doctor shopping; no model investigated the impact of age and spatial factors on the dynamics of the epidemic. Key findings from these studies were (1) prevention of opioid initiation is better than treatment of opioid addiction, (2) the analysis of an intervention\u2019s impact should include both benefits and harms, and (3) interventions with short-term benefits might have a counterproductive impact on the epidemic in long run.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>While most studies examined the role of prescription opioids and trafficking on this epidemic, the transition of patients from prescription opioid use to nonprescription use including heroin and synthetic opioids such as fentanyl impacts the system significantly and results in an epidemic with quite different characteristics than what it had a decade ago. We recommend including the impact of age and geographic location on the opioid epidemic using modeling methods.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/1178221819866211","type":"journal-article","created":{"date-parts":[[2019,8,9]],"date-time":"2019-08-09T05:01:18Z","timestamp":1565326878000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":9,"title":["Evidence of Modeling Impact in Development of Policies for Controlling the Opioid Epidemic and Improving Public Health: A Scoping Review"],"prefix":"10.1177","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9552-2028","authenticated-orcid":false,"given":"Nasser","family":"Sharareh","sequence":"first","affiliation":[{"name":"Health System Innovation and Research Division, Population Health Sciences Department, School of Medicine, University of Utah, Salt Lake City, UT, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shabnam S","family":"Sabounchi","sequence":"additional","affiliation":[{"name":"College of Community and Public Affairs, the State University of New York at Binghamton, Binghamton, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mary","family":"McFarland","sequence":"additional","affiliation":[{"name":"Eccles Health Science Library, University of Utah, Salt Lake City, UT, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2545-8504","authenticated-orcid":false,"given":"Rachel","family":"Hess","sequence":"additional","affiliation":[{"name":"Health System Innovation and Research Division, Population Health Sciences Department, School of Medicine, University of Utah, Salt Lake City, UT, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,8,9]]},"reference":[{"key":"bibr1-1178221819866211","unstructured":"Ahmad FB, Escobedo LA, Rossen LM, Spencer MR, Warner M, Sutton P. 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