{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T15:42:53Z","timestamp":1789054973225,"version":"build-2803163510"},"reference-count":22,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"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":["Substance Abuse"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Opioid overdose is a leading cause of death among homeless individuals. Combining psychoactive substances with opioids increases overdose risk. This study aimed to describe intoxication patterns at a drop-in space offering medical monitoring and harm reduction services to individuals who arrive intoxicated and at risk of overdose.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We examined data from visits to the Supportive Place for Observation and Treatment at Boston Health Care for the Homeless Program between January 1, 2017 and December 31, 2017. We used k-means cluster analysis to characterize intoxication patterns based on clinically assessed sedation levels and vital sign parameters. Multinomial logistic regression analysis assessed demographic and substance consumption predictors of cluster membership. Linear and logistic regression models examined associations between cluster membership and care outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Across 305 care episodes involving 156 unique patients, cluster analysis revealed 3 distinct intoxication patterns. Cluster A (26.6%) had mild sedation and normal vital signs. Cluster B (44.5%) featured greater sedation with bradycardia and\/or hypotension. Cluster C (28.9%) was comparable to cluster B but with the addition of hypoxia. Self-reported consumption of non-opioid sedatives prior to arrival was common (63.3% of episodes) and predicted membership in cluster B (aOR 2.75, 95% CI 1.40, 5.40) and cluster C (aOR 3.38, 95% CI 1.48, 7.70). In comparison to cluster A episodes, cluster C episodes were longer (mean 4.8 vs. 2.3\u2009hours,\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      \u2009&lt;\u20090.001) and more likely to require supplemental oxygen (27.3% vs. 2.5%,\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      \u2009&lt;\u20090.001). Few episodes required hospital transfer (4.7%) or naloxone (1.0%). No deaths occurred.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In a medically supervised overdose monitoring program, reported use of non-opioid sedatives strongly predicted more complex clinical courses and should be factored into overdose prevention efforts. Low-threshold medical monitoring in an ambulatory setting was sufficient for most episodes, suggesting a role for such programs in reducing harm and averting costly emergency services.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1080\/08897077.2021.1876201","type":"journal-article","created":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T17:18:12Z","timestamp":1614014292000},"page":"851-857","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Substance consumption and intoxication patterns in a medically supervised overdose prevention program for people experiencing homelessness"],"prefix":"10.1177","volume":"42","author":[{"given":"Gabriel","family":"Wishik","sequence":"first","affiliation":[{"name":"Institute for Research, Quality, and Policy in Homeless Health Care, Boston Health Care for the Homeless Program, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessie M.","family":"Gaeta","sequence":"additional","affiliation":[{"name":"Institute for Research, Quality, and Policy in Homeless Health Care, Boston Health Care for the Homeless Program, Boston, Massachusetts, USA"},{"name":"Section of General Internal Medicine, Boston University School of Medicine, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melanie W.","family":"Racine","sequence":"additional","affiliation":[{"name":"Institute for Research, Quality, and Policy in Homeless Health Care, Boston Health Care for the Homeless Program, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James J.","family":"O'Connell","sequence":"additional","affiliation":[{"name":"Institute for Research, Quality, and Policy in Homeless Health Care, Boston Health Care for the Homeless Program, Boston, Massachusetts, USA"},{"name":"Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA"},{"name":"Harvard Medical School, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7032-2625","authenticated-orcid":false,"given":"Travis P.","family":"Baggett","sequence":"additional","affiliation":[{"name":"Institute for Research, Quality, and Policy in Homeless Health Care, Boston Health Care for the Homeless Program, Boston, Massachusetts, USA"},{"name":"Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA"},{"name":"Harvard Medical School, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2021,10,1]]},"reference":[{"issue":"2017","key":"e_1_3_4_2_2","first-page":"1419","article-title":"Drug and opioid-involved overdose deaths \u2014 United States, 2013","volume":"67","author":"Scholl L","year":"2019","unstructured":"Scholl L, , Seth P, , Kariisa M, , Wilson N, , Baldwin G. Drug and opioid-involved overdose deaths \u2014 United States, 2013. Morb Mortal Wkly Rep. 2019;67(2017):1419\u20131427.","journal-title":"Morb Mortal Wkly Rep"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13524-017-0565-3"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2016.1571"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.1080\/15563650.2018.1457792"},{"key":"e_1_3_4_6_2","unstructured":"Massachusetts Department of Public Health. Data brief: stimulants health disparities and the impact of the opioid epidemic on maternal health and high risk populations. Massachusetts Department of Public Health. Public Health Data Warehouse website. https:\/\/www.mass.gov\/files\/documents\/2019\/03\/13\/PHD-1.0-Combined-Data-Brief.pdf. Published 2019. 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