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Suicide Risk Prediction Models Could Be Cost-Effective

THURSDAY, March 25, 2021 -- With sufficient accuracy, statistical suicide risk prediction models could provide good health economic value in the United States, according to a study published online March 17 in JAMA Psychiatry.

Eric L. Ross, M.D., from McLean Hospital in Belmont, Massachusetts, and colleagues sought to identify the accuracy threshold that a suicide risk prediction method must attain to cost-effectively target a suicide risk reduction intervention to high-risk individuals. The model incorporated published data on suicide epidemiology, the health care and societal costs of suicide, and the costs and efficacy of suicide risk reduction interventions (active contact and follow-up [ACF], annual health care cost, $96; and cognitive behavioral therapy [CBT], annual health care cost, $1,088).

The researchers found that with a specificity of 95 percent and a sensitivity of 25 percent, primary care-based suicide risk prediction could reduce suicide death rates by 0.5 per 100,000 person-years (if used to target ACF) or 1.6 per 100,000 person-years (if used to target CBT). From the health care sector perspective, cost-effectiveness was achieved with a specificity of 95 percent and a sensitivity of ≥17.0 percent if used to target ACF or ≥35.7 percent if used to target CBT. Additionally, ACF required positive predictive (PP) values of 0.8 percent for predicting suicide attempt and 0.07 percent for predicting suicide death to achieve cost-effectiveness, and CBT required PP values of 1.7 percent for suicide attempt and 0.2 percent for suicide death.

"Several existing suicide risk prediction models exceed the accuracy thresholds identified in this analysis and thus may warrant pilot implementation in U.S. health care systems," the authors write.

One author disclosed financial ties to the pharmaceutical industry.

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