![]() This study fills major gaps by leveraging standard EHR and claims data to detect indicators of suicide risk and protection in the largest case-control study of suicide mortality among individuals receiving care in US health systems to date. While two-thirds of healthcare visits before suicide occur in outpatient primary care, medical specialty settings, or the emergency department without a documented MH diagnosis, there is limited information on non-MH risk factors that may support risk detection. More information is needed to identify and understand suicide risk among those without known suicide risk factors. Thus, suicide risk detection and prevention directed primarily towards individuals with a known MH diagnosis can only reach half of all individuals before their death. However, the other half of all US suicides occur among the 85% of the population without a MH diagnosis. Furthermore, approximately 15% of the total US population has a MH diagnosis, and half of all suicides occur among these individuals. Nonetheless, recent data show that while over 83% of individuals make a healthcare visit before suicide, 50% do not have a MH diagnosis. Second, most of the known risk factors associated with suicide are mental health (MH) diagnoses (including MH, substance use disorders, and prior suicide attempts). First, most US-based studies have examined suicide ideation or attempt outcomes, and there remains an unmet need to understand risk of suicide death. Several innovative statistical models leveraging electronic health records (EHR) or claims data have been developed to better detect suicide risk in health systems, but there remain important gaps. The first step in the US suicide research strategy is to determine how to best detect individuals at-risk to strategically target suicide prevention. According to the most recent US National Strategy for Suicide Prevention, healthcare settings are important for suicide prevention, where they often target interventions to patients ‘known’ to be at-risk. There is an urgent need to develop and implement effective strategies to prevent suicide using timely and accurate data to detect individuals at risk and to inform clinical outreach. ![]() In the United States (US), > 47,000 individuals died by suicide in 2019 – a 25% increase since 2000. Suicide is a major public health concern. Healthcare data include many indicators of suicide risk for those with and without MH diagnoses, which may be used to support the identification and understanding of risk as well as targeting of prevention in health systems. The lowest risk groups were characterized as predominantly young, female, and high utilizers of preventive services. The highest risk groups were characterized via high utilization with multiple healthcare concerns in both groups. MH-stratified latent class models validated five subgroups with distinct patterns of indicators in both those with and without MH. Protective effects across MH-stratified models included diagnoses of benign neoplasms, respiratory infections, and utilization of reproductive services. Malignant cancer diagnoses were risk factors for suicide in those without MH diagnoses, and multiple individual psychiatric-related indicators were unique to the MH subgroup. Of the 202 indicators studied, 170 (84%) were associated with suicide in the discovery cohort, with 148 (86%) of those in the validation cohort. The study included 3,195 individuals who died by suicide from 2000 to 2015 and 249,092 randomly selected matched controls, who were age 18+ and affiliated with nine Mental Health Research Network affiliated health systems. This case-control study used statistical modeling with health record data on diagnoses, procedures, and encounters. This study seeks to discover and validate health indicators of suicide death among those with, and without, MH diagnoses. ![]() ![]() Most efforts target people with mental health (MH) diagnoses, but this only represents half of the people who die by suicide. Health systems are essential for suicide risk detection.
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