Using Real-Time Coronial Data to Detect Spatiotemporal Suicide Clusters.

Abstract

Background: Real-time suicide registers are being established in many countries and enable regular monitoring of suspected suicides over time. The use of these data to monitor for suicide clusters is in its infancy. Aims: We sought to test the feasibility of using real-time suicide register data to detect spatiotemporal suicide clusters. Method: Using the Victorian Suicide Register and SaTScan's spatiotemporal scan statistic, we simulated a monthly search for clusters from January 2015 to June 2022 using rolling 2-year windows of data in each search. Monthly scans were performed at three different levels of geographic granularity and for all-ages and under-25 populations. Results: Our results indicated the rapid identification of possible suicide clusters and demonstrated a practical approach to combining real-time suicide data and scanning algorithms. We developed new model outputs that showed cluster timelines. Limitations: The main limitations are that the computational burden of fitting multiple models meant we were unable to scan for ellipses and other irregular shapes and we were unable to consider space-time permutation models. Conclusions: Using data from a real-time suicide register, we were able to scan for space-time suicide clusters simulating the situation where the data are updated monthly with new updates.