Suicide prediction models exacerbate racial disparities in health care

Suicide prediction models exacerbate racial disparities in health care

When someone is at risk for suicide, there’s a chance they’ll turn to healthcare systems for help. Clinicians can then use statistical prediction models to determine who’s most at risk, working quickly to intervene and provide care.

However, a new study suggests that some of these models exacerbate racial and ethnic disparities by more accurately predicting suicide deaths in some groups compared to others.

Specifically, suicide death prediction rates for Black, American Indian/Alaska Native (AI/AN), and patients without a recorded race or ethnicity were less accurate than those for White, Hispanic, and Asian patients. The study was published in JAMA Psychiatry in late April.
Statistical Modeling for Suicide Prediction

“AI/AN rates of suicide are remarkably high and have remained so for several decades,” Lisa Wexler, PhD, MSW, professor at the University of Michigan School of Social Work who researches American Indian/Alaska Native suicide prevention and Indigenous youth resilience, but who was not involved with the study, tells Verywell. “For Black youth, particularly younger girls, suicidal behavior is growing at a fast pace. The difficulties of identifying risk in our models within these two populations signal an important reflection point to address.”

Of the more than 1.4 million patients included in the data, 768 suicide deaths were recorded within 90 days after 3,143 mental health visits. In running the analyses, researchers focused on the number of visits of those who died by suicide, finding that suicide rates were highest for patients:

In 2018, suicide was the 10th leading cause of death in the United States, having increased 35% in the last 20 years. In the same year, suicide rates among AI/AN males were highest (34.8 per 100,000), followed by those among White, Hispanic, Black, and Asian males. Rates were overall lower for women, but AI/AN women and girls were most affected (10.5 per 100,000) followed by White, Asian, Black, and Hispanic women.

“Clinical implementation of these models would exacerbate existing disparities in mental health access, treatment, and outcomes for Black, American Indian, and Alaska Native populations,” lead study author Yates Coley, PhD, biostatistician and investigator at Kaiser Permanente Washington Health Research Institute, tells Verywell. “We must test for disparities in accuracy and consider the possible negative consequences, including harm.”

Regardless of suicide rate or the number of healthcare visits, additional statistical tests found that prediction models were most sensitive to White, Hispanic, and Asian patients, and least sensitive to Black and AI/AN patients, and patients without race/ethnicity recorded.
With no race/ethnicity recorded (313 visits)
Asian (187 visits)
White (2,134 visits)
American Indian/Alaskan Native (21 visits)
Hispanic (392 visits)
Black (65 visits)

The models used the following parameters to predict suicide:
Demographic characteristics
Comorbidities
Prior suicide attempts
Mental health and substance use diagnoses
Psychiatric medications
Prior mental health encounters
Responses to Patient Health Questionnaire 9
This means that predictive models developed to assist healthcare systems in judging who’s most at risk for suicide may be better at predicting for some groups rather than others, with Black and AI/AN patients at the biggest disadvantage.

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