Abstract: WITHDRAWN: Using Big Data and Geospatial Analysis to Target Suicide Prevention Efforts in Communities (Society for Prevention Research 27th Annual Meeting)

22 WITHDRAWN: Using Big Data and Geospatial Analysis to Target Suicide Prevention Efforts in Communities

Tuesday, May 28, 2019
Pacific D/L (Hyatt Regency San Francisco)
* noted as presenting author
Lauren Berny, M.Ed., Data Analyst, Centerstone Research Institute, Nashville, TN
Jennifer D. Lockman, PhD, Director of Clinical and Translational Research, Centerstone Research Institute, Nashville, TN
Bre P. Banks, PhD, Manager, Clinical Education & Dissemination Research, Centerstone Research Institute, Nashville, TN
Introduction: With suicide death rates in the United States rising, suicide prevention program development and implementation has become critical in order to save lives. Geospatial analysis can be used to identify high-risk geographic areas where suicide deaths are occurring. Further, descriptive characteristics of those cases can be leveraged to better inform localized suicide prevention efforts. The goal of the current study was to determine whether suicide death rates were spatially clustered at the Tennessee county-level and better identify to whom prevention efforts should be targeted.

Methods: 2012-2016 age-adjusted suicide death rates (AASDRs) and descriptive data on those deaths were obtained from the state health department. To analyze AASDR geographic patterns, incremental spatial autocorrelation was used to establish the distance to best conceptualize geospatial relationships using the zone of indifference method, and Moran’s I was calculated to assess the overall pattern of AASDRs across the state. Using those parameters, hotspot analysis was used to identify significant clusters of both high (hotspots) and low (coldspots) AASDRs at the county-level. Descriptive data from those suicide death cases was then used to understand who was dying by suicide in those clusters and how hotspots and coldspots may differ.

Results: Results of Moran’s I indicated that the spatial pattern of death by suicide is spatially clustered (Moran’s I = 0.18, z = 3.44, p <.001) at the county-level. Hot spot analysis identified localized clusters of high and low rates of suicide deaths (strength of each was determined by the Getis-Ord Gi statistic at 90% CI, 95% CI, and 99% CI) across the state. Significantly higher (p <.001) rates of White individuals died by suicide in hotspots (98.7%) than coldspots (79.6%). A consistent finding across clusters was that firearms accounted for the majority of suicide deaths in both coldspots (72%) and hotspots (65%).

Conclusions: Knowing who is dying by suicide and where those deaths are occurring is critical for suicide prevention. These findings help inform where to provide higher dosages of prevention programming and the populations to whom it should be tailored. For example, in counties with firearm deaths higher than the national average, tailored suicide prevention efforts could include: (a) providing suicide prevention education to gunshop owners and/or (b) supplying mental health providers with lock-boxes for clients at risk for suicide. Furthermore, future research could consider whether tailored public health interventions, based on geospatial analysis, have better outcomes than non-tailored approaches.