Using machine learning technology, a new study has identified three distinct profiles describing social and economic factors that are associated with a higher risk of suicide. Scientists at Weill Cornell Medicine and Columbia University Vagelos College of Physicians and Surgeons led the research that showed suicide rates vary significantly across the three clusters and that the patterns differ geographically across the United States.
The findings, published May 12 in Nature Mental Health, could facilitate more effective prevention strategies and thereby help counter the substantial rise in suicide rates over the past two decades in the U.S.
This is the first study to use unsupervised machine learning to analyze a comprehensive set of social determinants of health such as poverty, poor housing, lack of access to health care, harmful environmental exposures and social factors like high family stress, which can all contribute to suicide risk. While prior prevention efforts largely targeted individual or clinical risk factors, this research emphasizes the importance of broader, community-level social conditions.
