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New Research May Help Prevent Suicide

     

On average, more than 100 people die by suicide every day in the U.S. New research is leading to a better understanding of who is at greater risk.

suicide-prevention-graphic

One group of researchers identified a clear link between being hospitalized for infection and risk of suicide. The study, reported in JAMA Psychiatry, looked records for about seven million Danish citizens over 30 years to examine the relationship between infections and suicide. They found that individuals who were hospitalized for infections had a more than 40 percent increased risk of death by suicide compared to those not hospitalized for infection. And the risk increased with more infections: individuals with more than seven infections had an increased risk for suicide of nearly 300 percent.

Even among people with no history of mental disorder or substance abuse (strong predictors of suicide), the risk for suicide was still higher for people hospitalized for infection. The study did not determine cause and effect, but previous studies have found that inflammation can trigger depressive symptoms and infections are triggers of inflammation.

Another study used data from electronic health records (EHRs) to identify people at increased risk of suicide. The growing use of EHRs is generating vast data resources that are being tapped for many uses. Researchers analyzed EHR data for more than two million patients at two Boston Hospitals over a 15-year period. More than 16,000 individuals met the criteria for suicidal behavior. Data-mining (discovering patterns in large data sets) allowed researchers to develop a model which could predict nearly half of all subsequent suicides and suicidal behavior an average of 3 to 4 years in advance. These predictions were significantly better than predictions based on a history of substance abuse, depression or other mental health condition.

The authors note that “although a statistical model is never a substitute for a clinical evaluation, an early warning system based on our approach may provide a mechanism for identifying patients who are at elevated statistical risk of future suicidal behavior and therefore require screening.”

Other researchers are working to better predict suicidal behavior using real-time monitoring of thoughts and behaviors via smartphone apps, and monitoring of physical data (such as skin temperature and heart rate) though bracelets. Others are working to use implicit association tests to analyze subconscious thoughts associated with suicide. Implicit association tests involve rapidly categorizing images and words. They measure attitudes, identities and beliefs that people are either unwilling or unable to report.

While experts generally agree that many different factors contribute to suicidal behavior, new research may give health care providers and others information and tools to identify those at risk. This would provide an opportunity to reach out and help individuals before they get to the point of seriously considering suicide.

References

  • Barak-Corren Y, et al. Predicting suicidal behavior from longitudinal electronic health records. Am J Psychiatry 2016 Sep 9. E-pub.
  • Lund-Sorensen, BM, et al. A Nationwide Cohort Study of the Association Between Hospitalization with Infection and Risk of Death by Suicide. JAMA Psychiatry. Published online August 10, 2016.

     

AnxietyDissociative DisordersBipolar DisordersDepressionPersonality DisordersSchizophreniaAddictionPTSD

 

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