Financial distress, feeling downhearted, and doing activities less carefully were identified through machine learning as risk factors for suicide, according to a study published in JAMA 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 colleagues. “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 reported by 222 study participants. The researchers then used an algorithmic approach using machine learning to develop a suicide attempt risk model. The first wave revealed 2,978 potential risk factors, which were used to classify suicide attempts in the second wave.
According to the model, the three top risk factors in determining whether participants had made a nonfatal suicide attempt were whether individuals felt at any point as though they wanted to die, whether they thought about attempting suicide, and whether they’d made a previous suicide attempt. Additionally, feeling downhearted or depressed in the past four weeks, doing activities less carefully as a result of emotional problems, and accomplishing less than usual due to emotional problems were strong risk factors of future suicide attempts.
Younger age, lower educational achievement, and experiencing a financial crisis in the last year were also important variables for suicide risk. The authors noted the emotional effects of financial crises are of particular relevance due to the economic stress caused by the COVID-19 pandemic.
“We hope that these results deepen our understanding of the etiology of suicide attempts in adults and improve suicidal behavior prediction by identifying new risk variables to guide clinical assessment and development of suicide risk scales,” the authors concluded.
For additional information, see the Psychiatric News article “Four R’s Believed to Be Protective Against Suicide.”
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