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Suicide Risk Prediction Tools Fail People of Color

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May 7, 2021 -- Current models used to predict suicide risk fall short for certain populations including Black, Indigenous and People of Color (BIPOC), new research shows.

Investigators developed two suicide prediction models to examine whether these types of tools are accurate.

They found both prediction models failed to identify high-risk BIPOC individuals. In the first model, nearly half of the visits followed by suicide were identified in white patients versus only 7% of visits followed by suicide in BIPOC patients. The second model had a sensitivity of 41% for white patients, but just 3% for Black patients and 7% for American Indian/Alaskan Native patients.

"You don't know whether a prediction model will be useful or harmful until it's evaluated. The take-home message of our study is this: You have to look," lead author Yates Coley, PhD, assistant investigator at the Kaiser Permanente Washington Health Research Institute in Seattle, told Medscape.

The study was published online April 28 in JAMA Psychiatry.

Racial Inequities

Suicide risk prediction models have been "developed and validated in several settings" and are now in regular use at the Veterans Health Administration, HealthPartners, and Kaiser Permanente, the authors write.

But the performance of suicide risk prediction models, while accurate in the overall population, "remains unexamined" among various groups, they note.

"Health records data reflect existing racial and ethnic inequities in health care access, quality, and outcomes; and prediction models using health records data may perpetuate these disparities by presuming that past health care patterns accurately reflect actual needs," Coley said.

Coley and her team "wanted to make sure that any suicide prediction model we implemented in clinical care reduced health disparities rather than exacerbated them."

To investigate, researchers examined all outpatient mental health visits to seven large health care systems by patients 13 years and older. In all they looked at records from 13.98 million visits. The visits spanned from Jan. 1, 2009 to Sept. 30, 2017 with follow-up through Dec. 31, 2017.

In particular, researchers looked at suicides that took place within 90 days of the outpatient visit.

'Unacceptable' Scenario

Within the total population, there were 768 deaths by suicide within 90 days of 3,143 visits. Suicide rates were highest for visits by patients with no recorded race or ethnicity, followed by visits by Asian, white, American Indian/Alaskan Native, Hispanic, and Black patients (Table 1).

Table 1. Suicide Rates by Ethnicity

Ethnicity

Outpatient visits followed by suicide*

Suicide rate per 10,000 visits

Not recorded

313

5.71

Asian

187

2.99

White

2134

2.65

American Indian/Alaskan Native

21

2.18

Hispanic

392

1.18

Black

65

.56

*Within 90 days.

"In our specific example of suicide prediction, BIPOC populations already face substantial barriers in accessing quality mental health care and, as a result, have poorer outcomes, and using either of the suicide prediction models examined in our study will provide less benefit to already underserved populations and widen existing care gaps" — a scenario Coley said is "unacceptable."

"We must insist that new technologies and methods be used to reduce racial and ethnic inequities in care, not exacerbate them," she added.

Biased Algorithms

Commenting on the study for Medscape, Jonathan Singer, PhD, associate professor in the School of Social Work at Loyola University in Chicago, described it as an "important contribution because it points to a systemic problem and also to the fact that the algorithms we create are biased, created by humans, and humans are biased."

Although the study focused on the health care system, Singer believes the findings have implications for individual doctors and other health care professionals.

"If clinicians may be biased against identifying suicide risk in Black and Native American patients, they may attribute suicidal risk to something else. For example, we know that in Black Americans, expressions of intense emotions are oftentimes interpreted as aggression or being threatening, as opposed to indicators of sadness or fear," noted Singer, who is also president of the American Academy of Suicidology.

"Clinicians who misinterpret these intense emotions are less likely to identify a Black client or patient who is suicidal," Singer said.

Medscape Medical News
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