The geographic identification of elevated suicide risk model: evaluating a method for examining suicide-related behaviors at the neighborhood level in Harris County, Texas - Ryan M. Hill; Sam C. Cacace; Austin Starkey; Tyler Hendley; Jackson Bolner; Angela Cummings; Angie Hayes; Robert J. Cramer (BMC Public Health)
Abstract Background The 2024 National Strategy for Suicide Prevention has called for the development of community-based suicide prevention resources, and improved existing prevention efforts. In line with such efforts, Hill and colleagues developed the Geospatial Identification of Elevated Suicide Risk model that estimates the relative prevalence of adolescent suicide risk within specific geographical areas. The current study seeks to further evaluate and refine the model for use as a tool to evaluate risk and protective factors at the neighborhood level. Method Drawing from multiple sources, data was collected detailing adolescent suicidal ideation, suicide attempts, suicide fatalities, and census tract characteristics. Utilizing data resulting from an initial pool of 74,883 suicidal ideation and attempt screens found in electronic health records, suicidal ideation and attempt rates were calculated, described, and mapped onto relevant census tracts via the Census Geocoder. Once mapped, a total of 1,098 census tracts were examined for criterion validity and minimum data evaluations. Results Data indicate that rates of positive suicide risk screens are relatively normally distributed when using a minimum cell size of at least n = 5, with additional improvements at n = 10 screens per census tract. Of 48,928 records with completed screens and patient address data listed in the electronic health record, 44,776 addresses (91.5%) were matched to U.S. census tracts via the Census Geocoder database. When evaluating criterion validity, the simultaneous multivariate logistic regression revealed that the model did not fit well to the data, and suicide attempts and suicidal ideation only predicted 0.02% of the variance in the probability of suicide fatality. Finally, a classification tree revealed that a minimum of 10 data points were required to delineate between high and low-risk census tracts. Conclusion The refined model may act as a helpful tool to evaluate neighborhood level risk and protective factors. Findings suggest a prevention-oriented, as opposed to risk prediction, approach to suicide risk management at the community level may be needed; such an approach would prioritize community connectedness, adequate mental health support services, and reduction of community-level risk factors (e.g., substance misuse), among others.