As healthcare organizations continue to grapple with COVID-19, there has been rising concern about myriad additional health impacts stemming from the pandemic, including rising rates of depression, opioid abuse and suicide.

That’s the unintended backdrop for a recently published study which used new Machine Learning techniques to identify suicide attempt risk factors beyond what is considered the usual “high-risk” population.  

As researchers from Columbia University and the National Institute on Drug Abuse, among other institutions, put it in their report, “Because more than one-third of people making nonfatal suicide attempts do not receive mental health treatment, it is essential to extend suicide attempt risk factors beyond high-risk clinical populations to the general adult population.”

Drawing on data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), which is conducted with a nationally representative sample of U.S. adults 18 years and older, the team used an algorithmic approach to analyze survey data and identify new risk factors, thus  offering new avenues to guide future clinical assessment and development of suicide risk scales in the general population.

Financial distress, feeling downhearted and doing activities less carefully, along with lower educational achievement, were identified in the study as risk factors for suicide.

“[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.”

Data for the study came from wave 1 (2001 to 2002) and wave 2 (2004 to 2005) of the NESARC. 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.

“After searching through more than 2500 survey questions, several well-known risk factors of suicide attempt were confirmed,” the researchers wrote, “such as previous suicidal behaviors and ideation, and new risks were identified, including functional impairment resulting from mental disorders and socioeconomic disadvantage. 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.”