Accuracy Requirements for Cost-effective Suicide Risk Prediction Among Primary Care Patients in the US

Importance Several statistical models for predicting suicide risk have been developed, but how accurate such models must be to warrant implementation in clinical practice is not known. Objective To identify threshold values of sensitivity, specificity, and positive predictive value that a suicide risk prediction method must attain to cost-effectively target a suicide risk reduction intervention to high-risk individuals. Design, Setting, and Participants This economic evaluation incorporated published data on suicide epidemiology, the health care and societal costs of suicide, and the costs and efficacy of suicide risk reduction interventions into a novel decision analytic model. The model projected suicide-related health economic outcomes over a lifetime horizon among a population of US adults with a primary care physician. Data analysis was performed from September 19, 2019, to July 5, 2020. Interventions Two possible interventions were delivered to individuals at high predicted risk: active contact and follow-up (ACF; relative risk of suicide attempt, 0.83; annual health care cost, $96) and cognitive behavioral therapy (CBT; relative risk of suicide attempt, 0.47; annual health care cost, $1088). Main Outcomes and Measures Fatal and nonfatal suicide attempts, quality-adjusted life-years (QALYs), health care sector costs and societal costs (in 2016 US dollars), and incremental cost-effectiveness ratios (ICERs) (with ICERs <=$150 000 per QALY designated cost-effective). Results With a specificity of 95% and a sensitivity of 25%, 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 a baseline of 15.3 per 100 000 person-years. To be cost-effective from a health care sector perspective at a specificity of 95%, a risk prediction method would need to have a sensitivity of 17.0% or greater (95% CI, 7.4%-37.3%) if used to target ACF and 35.7% or greater (95% CI, 23.1%-60.3%) if used to target CBT. To achieve cost-effectiveness, ACF required positive predictive values of 0.8% for predicting suicide attempt and 0.07% for predicting suicide death; CBT required values of 1.7% for suicide attempt and 0.2% for suicide death. Conclusions and Relevance These findings suggest that with sufficient accuracy, statistical suicide risk prediction models can provide good health economic value in the US. Several existing suicide risk prediction models exceed the accuracy thresholds identified in this analysis and thus may warrant pilot implementation in US health care systems. This economic evaluation identifies threshold values of sensitivity, specificity, and positive predictive value that a suicide risk prediction model must attain for cost-effective use of a suicide risk reduction intervention for high-risk individuals. Question How accurate must suicide risk prediction models be to be cost-effective in targeting interventions to high-risk individuals in a US primary care population? Findings This economic evaluation found that suicide risk prediction could be cost-effective for targeting a safety planning and telephone call intervention if its specificity was 95% or higher and its sensitivity was 17% or higher, corresponding to a positive predictive value of 1% or greater. For a more expensive cognitive behavioral therapy intervention, the required positive predictive value was 2% or greater. Meaning Existing suicide risk prediction models may be accurate enough to be cost-effective in US health care settings.

Automated summary

This economic evaluation study explores the accuracy thresholds that suicide risk prediction models must achieve in order for suicide risk reduction interventions to be cost-effective for high-risk individuals.