{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T17:50:58Z","timestamp":1777917058236,"version":"3.51.4"},"reference-count":79,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"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":["Med Decis Making"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>This study introduces a simulation-optimization framework designed to optimize the services of opioid-focused harm reduction vending machines (HRVMs). Given the rising rates of overdose deaths and increased potential for infectious diseases among persons who inject drugs (PWID), HRVMs can become an important harm reduction (HR) strategy by providing essential supplies that mitigate health risks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed and validated an agent-based simulation\u2013optimization framework to model the impact of HRVM-item allocation on the burden of opioid-related harms, accounting for demand dynamics, item restocking, and regional characteristics. The model evaluated health outcomes\u2014cases of HIV, HCV, and fatal and nonfatal overdose\u2014using disability-adjusted life-years (DALYs). Scenario-based analyses were conducted for different HRVM configurations, considering current legal limits on safer-injection supplies, fentanyl\u2019s growing role as a drug of choice, and potential future policy changes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The base scenario estimated optimal HRVM capacity allocation at approximately 48.5% fentanyl test strips (FTS), 26.2% naloxone, and 25.3% safer injection kits. However, sensitivity analyses showed significant variations based on fentanyl prevalence and willingness to use FTS. In scenarios of intentional fentanyl use with high FTS utilization, allocation favored FTS, while scenarios with low FTS utilization prioritized naloxone and injection kits. Adding addiction treatment referral services to HRVMs further reduced DALYs and societal costs, primarily by preventing fatal overdoses. Safer injection kits consistently reduced blood-borne infections compared with scenarios without these kits.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The framework could aid in HRVM\u00e3related service planning and evaluation, highlighting the importance of strategic inventory management and linkages to addiction care for enhanced health outcomes. HRVMs show potential as scalable, cost-effective HR interventions, warranting further research on their impact on service accessibility and health outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Highlights<\/jats:title>\n                    <jats:list list-type=\"bullet\">\n                      <jats:list-item>\n                        <jats:p>A novel simulation-optimization framework for designing and evaluating harm reduction vending machines (HRVMs) is presented.<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:p>Optimal baseline allocation for products in the HRVMs included fentanyl test strips (48.5%), naloxone (26.2%), and safer injection kits (25.3%).<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:p>Sensitivity analysis indicated optimal allocations vary substantially by local fentanyl prevalence and by individual harm reduction behaviors surrounding the use of fentanyl test strips.<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:p>HRVM implementation reduces societal costs and disability-adjusted life-years associated with substance use\u2013related harms.<\/jats:p>\n                      <\/jats:list-item>\n                    <\/jats:list>\n                  <\/jats:sec>","DOI":"10.1177\/0272989x251367719","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T13:19:21Z","timestamp":1758719961000},"page":"1052-1069","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Reducing Substance Use-Related Harms: A Simulation-Optimization Framework for the Design and Evaluation of Harm Reduction Vending Machines"],"prefix":"10.1177","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8649-9468","authenticated-orcid":false,"given":"Reyhaneh","family":"Zafarnejad","sequence":"first","affiliation":[{"name":"Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul M.","family":"Griffin","sequence":"additional","affiliation":[{"name":"The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering; Consortium of Substance Use and Addiction, Social Science Research Institute; The Pennsylvania State University, University Park, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aleksandra E.","family":"Zgierska","sequence":"additional","affiliation":[{"name":"Penn State College of Medicine, Departments of Family &amp; Community Medicine, Public Health Sciences, and Anesthesiology and Perioperative Medicine, Hershey, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alice","family":"Zhang","sequence":"additional","affiliation":[{"name":"Penn State College of Medicine, Department of Family &amp; Community Medicine, Hershey, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,9,24]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Centers for Disease Control and Prevention. 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