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Suicide Risk Prediction Models Prove Cost-Effective in Healthcare


Suicide is the tenth leading cause of death in the US, researchers stated. While there are several effective interventions to reduce the risk of suicide, challenges in identifying people at risk of suicide and concerns about the potentially high costs of suicide-prevention strategies have hindered their widespread use.

Researchers from Massachusetts General Hospital (MGH) developed a mathematical model projecting suicide-related health economic outcomes over a lifetime for US adults treated by primary care physicians.

The model evaluated the practicality of predicting individuals’ risk of suicide and then offering either of two possible interventions for people at high risk. The first was active contact and follow-up, which consists of the patient at risk receiving an initial intensive evaluation and being contacted frequently thereafter by phone or mail.

The second intervention was cognitive behavioral therapy, a form of psychotherapy in which the therapist helps the patient identify and change self-destructive or disturbing thought patterns.

Using standard health economic measures, researchers found that both interventions could be cost-effective as long as the models used to predict suicide risk have a high degree of accuracy. When they examined risk prediction models developed by previous researchers, the team found that several of these models would be accurate enough […]

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