Study Confirms Well-Known Suicide Risk Factors, Identifies New Risks

Financial distress, feeling downhearted, and doing activities less carefully were identified through machine learning as risk factors for suicide, according to astudy published inJAMA Psychiatry.“[M]ost of the published literature on nonfatal suicide attempt prediction has focused on high-risk patients who have received mental health treatment,” wrote Ángel García de la Garza, B.A., of Columbia University, Carlos Blanco, M.D., Ph.D., of the National Institute on Drug Abuse, and collea gues. “These findings underscore the importance of extending suicide attempt prediction models beyond high-risk populations to the general adult population.”The authors drew on data from the National Epidemiologic Survey on Alcohol and Related Conditions, which is conducted with a nationally representative sample of U.S. adults 18 years and older. The first wave of the survey took place from 2001 to 2002, during which participants were interviewed using the Alcohol Use Disorder and Associated Disabilities Interview Schedule. This interview assesses alcohol use, drug use, and mental illness according to DSM-IV criteria. During the second survey wave —from 2004 to 2005—the participants were assessed using a similar face-to-face structured interview and were asked whether they had attempted suicide in the three years prior. A total of 34,653 participants completed the second wave of the survey.Attempted suicide during the three years between the wave 1 and wave 2 interviews was report...
Source: Psychiatr News - Category: Psychiatry Tags: algorithm JAMA Psychiatry machine learning risk factors suicidal thoughts suicide suicide attempts Source Type: research