Suicide is one of the leading causes of death in the United States. A recent study of Veterans Administration (VA) patients could help reduce the risk of patients taking their own lives.
Suicide Rate & Demographics
The most recent figures from the American Foundation for Suicide Prevention show https://www.afsp.org/understanding-suicide/facts-and-figures that there were 41,149 suicides in 2013. That amounts to 12.6 deaths out of every 100,000 people, compared to 10.4 suicide deaths per 100,000 in the year 2000.
Suicide rates are very different based on demographics. The CDC calculates rates of four different demographic characteristics:
- Age – The two most at-risk age groups are 45-64 years (19.1) and 85+ years (18.6).
- Sex – Suicide is approximately four times more common in men (20.2) than women (5.5).
- Race/ethnicity – The two highest racial or ethnic groups were whites (14.2) and Native Americans (11.7).
- Location – The highest states for suicide were Montana (23.7), Alaska (23.1), and Utah (21.4).
Particularly High for Veterans
Shockingly, the number for veterans is much higher: 35.9 per 100,000 in 2009, according to the National Death Index. It makes sense that the Veterans Administration wanted to find a way to prevent suicide by building a big-data algorithm using healthcare cloud technology.
The VA collaborated with the National Institute of Mental Health to more accurately pinpoint patients whose suicide risk was particularly high. Their study, published in the American Journal of Public Health, used half of the VA suicide data to create a predictive model and the other half for comparison purposes. Each collection of data included 3180 suicides and 1,056,004 controls.
The predictive model built by the scientists was better at identifying vulnerable patients than was the VA’s current observational method. Only one in three patients that were identified by the predictive algorithm had information in their notes suggesting that they were at-risk.
“This is valuable, because it gives the VA more extensive information about suicide risk,” explained http://www.healthdatamanagement.com/news/EHR-Data-Model-Better-Predict-Patient-Suicide-Risk-50713-1.html study co-author Michael Schoenbaum of the NIMH. “If the VA can identify small groups of people with a particularly high-risk of suicide, then they can target enhanced prevention and treatment services to these highest-risk individuals.”
Schoenbaum added that the research is also important because it used information that many healthcare systems already have at their disposal.
Using Big Data To Your Advantage
Implementing predictive models to make better use of big data seems to have been incredibly helpful in identifying veterans who are potentially suicidal. How might your big data be of use? ClearDATA’s open cloud infrastructure delivers advanced interoperability to optimize time-to-market with new big-data projects.