Statistical suicide risk prediction models could be cost-effective and life-saving

There are a number of effective interventions to reduce the risk of suicide, the tenth leading cause of death in the United States, but there are difficulties in identifying people at risk for suicide and concerns about the high costs that suicide prevention strategies may have hindered their wider use. .

But as researchers at Massachusetts General Hospital (MGH) point out, statistical suicide risk prevention models could be cost-effectively implemented in U.S. health care systems and could they help save many lives each year.

Evaluating data on suicide frequency and suicide attempts, the costs to society and the health care system of suicide, and the cost and effectiveness of suicide risk reduction interventions, Eric L. Ross , MD, a resident in the Department of Psychology at MGH and colleagues found that several suicide risk prediction models are accurate in identifying people at risk to allow costly implementation -effective in clinical practice.

They report on their findings in the magazine JAMA Psychology.

There are complex statistical models that researchers have come up with to predict who is at risk of suicide or suicide attempts, and our analysis suggests that these models -now that we could apply them in the real world. “

Eric L. Ross, MD, Resident, Department of Psychology, MGH

“And if we implement them, our analysis suggests that they would be cost-effective. That doesn’t suggest it would save the health care system money, but it does mean that it would be more cost-effective. good investment of resources to improve people ‘s quality of life and improve people’ s longevity, “he adds.

Ross and colleagues created a mathematical model designing the economic outcomes of suicide-related health over a lifetime for U.S. adults treated by primary care physicians. The model looked at the practicality of predicting suicide risk and then offered one of two possible interventions for high-risk people: communication. active and follow-up, in which the at-risk patient receives an initial intensive assessment, and is frequently contacted later by telephone or post; and behavioral therapy, a form of psychotherapy in which the therapist helps the patient to identify and change suicidal or anxious thought patterns.

Using conventional health economics measures, the researchers found that both interventions could be cost-effective while the models used to predict suicide risk were accurate. When they examined predictive models previously developed by researchers, they found that several of these models would be OK to be practical and cost-effective.

“[The results] suggest that conventional risk prediction models have achieved sufficient accuracy for health systems to move forward with pilot implementation projects, ”Ross and colleagues write in concluding the paper.

According to lead author Jordan W. Smoller, MD, ScD, of the Department of Psychology at MGH, “suicide rates have risen dramatically in the last 20 years, so it is clear that we need new tools to tackle this national problem.s who die by suicide are seen by healthcare providers in the months before they die, so healthcare settings have a vital opportunity to prevent Our results suggest that the tools are in place to enable cost-effective interventions. And that, I think, is encouraging. “