Suicide rates in much of the world have been stubbornly consistent for decades, accounting for about 1.4 percent of all deaths globally. Unlike most public health issues, where researchers have been able to identify risk factors for effective interventions, suicide is still incredibly difficult to predict and prevent, says Dr. Jaimie Gradus, associate professor of epidemiology at the Boston University School of Public Health.
“Every suicide death is the result of multiple interacting risk factors in one’s life,” she says. “One of the reasons that the suicide rate has not really improved despite decades of research is because studies have been hampered by what traditional statistics will allow us to look at at once.”
So, Dr. Gradus and her colleagues at Boston University and at Aarhus University in Denmark tried a new approach to look at as many factors as possible.
Their study, published in JAMA Psychiatry, is the first to use data on thousands of different factors from the population of an entire country — Denmark — and parse it with a machine-learning system to identify new suicide risk factors and interactions. The study takes advantage of the Danish national healthcare system’s thorough and standardized health records, looking at all 14,103 individuals who died from suicide in the country from 1995 through 2015, and the health histories of 265,183 other Danes in the same period.Friday Letter Submission, Publish on January 10