Identifying spatial and temporal suicide clusters in a Californian county

Abstract

Abstract Barriers to suicide cluster detection and monitoring include requiring advanced software and statistical knowledge. We tested face validity of a simple method using readily accessible household software, Excel 3D Maps, to identify suicide clusters in this county, years 2014–2019. For spatial and temporal clusters, respectively, we defined meaningful thresholds of suicide density as 1.39/km2 and 33.9/yearly quarter, defined as the 95th percentile of normal logarithmic and normal scale distributions of suicide density per area in each ZIP Code Tabulated Area and 24 yearly quarters from all years. We generated heat maps showing suicide densities per 2.5 km viewing diameter. We generated a one-dimensional temporal map of 3-month meaningful cluster(s). We identified 21 total population spatial clusters and one temporal cluster. For greater accessibility, we propose an alternative method to traditional scan statistics using Excel 3D Maps potentially broadly advantageous in detecting, monitoring, and intervening at suicide clusters.