{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T23:01:55Z","timestamp":1792882915991,"version":"4.5.0"},"reference-count":28,"publisher":"Hogrefe Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Crisis"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:p>Abstract: Background: Accurate classification of intentional death as suicide is essential to target prevention measures appropriately. Unfortunately, manner of death (MOD) for many opioid-related fatalities are unclassified in the United States, and in Maryland, as many as 82% of overdose deaths are classified as undetermined manner. Aims: For opioid-related fatalities in Maryland, leverage death certificate data to develop a model for identifying suicide as MOD among those classified as undetermined by the medical examiner. Method: Demographic and toxicology data were used to develop a classification model for opioid-related deaths where MOD was known, and then applied to a cohort where MOD was undetermined to estimate the likelihood that the intent was suicide. Results: Antidepressants, neuroleptics, oxycodone, benzodiazepines, and acetaminophen were more common in deaths classified as suicide while fentanyl, cocaine, and morphine were more common among accidental deaths. Our classification model correctly identified suicide cases 82% of the time (PPV = 0.82; AUC = 0.90) and expanded the number of suicide cases by 43% when applied to undetermined deaths. Limitations: The accuracy and completeness of death records. Conclusions: Data from standard autopsies can be used to detect additional suicide deaths with good statistical precision. Incorporating clinical information could enhance predictive accuracy and improve classification.<\/jats:p>","DOI":"10.1027\/0227-5910\/a001033","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T11:08:19Z","timestamp":1763032099000},"page":"75-86","source":"Crossref","is-referenced-by-count":4,"title":["Use of Vital Records to Improve Identification of Suicide as Manner of Death for Opioid-Related Fatalities"],"prefix":"10.1027","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8850-1154","authenticated-orcid":true,"given":"Jean P.","family":"Flores","sequence":"first","affiliation":[{"name":"Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA"},{"name":"Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monica M.","family":"Desjardins","sequence":"additional","affiliation":[{"name":"Center for 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Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA"},{"name":"Biomedical Informatics and Data Science, Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA"},{"name":"Center for Suicide Prevention, Bloomberg School of Public Health, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Holly C.","family":"Wilcox","sequence":"additional","affiliation":[{"name":"Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA"},{"name":"Biomedical Informatics and Data Science, Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA"},{"name":"Center for Suicide Prevention, Bloomberg School of Public Health, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2479-703X","authenticated-orcid":true,"given":"Paul 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