Hindcasts and forecasts of suicide mortality in US: A modeling study

Deaths by suicide, as well as suicidal ideations, plans and attempts, have been increasing in the US for the past two decades. Deployment of effective interventions would require timely, geographically well-resolved estimates of suicide activity. In this study, we evaluated the feasibility of a two-step process for predicting suicide mortality: a) generation of hindcasts, mortality estimates for past months for which observational data would not have been available if forecasts were generated in real-time; and b) generation of forecasts with observational data augmented with hindcasts. Calls to crisis hotline services and online queries to the Google search engine for suicide-related terms were used as proxy data sources to generate hindcasts. The primary hindcast model (auto) is an Autoregressive Integrated Moving average model (ARIMA), trained on suicide mortality rates alone. Three regression models augment hindcast estimates from auto with call rates (calls), GHT search rates (ght) and both datasets together (calls_ght). The 4 forecast models used are ARIMA models trained with corresponding hindcast estimates. All models were evaluated against a baseline random walk with drift model. Rolling monthly 6-month ahead forecasts for all 50 states between 2012 and 2020 were generated. Quantile score (QS) was used to assess the quality of the forecast distributions. Median QS for auto was better than baseline (0.114 vs. 0.21. Median QS of augmented models were lower than auto, but not significantly different from each other (Wilcoxon signed-rank test, p > .05). Forecasts from augmented models were also better calibrated. Together, these results provide evidence that proxy data can address delays in release of suicide mortality data and improve forecast quality. An operational forecast system of state-level suicide risk may be feasible with sustained engagement between modelers and public health departments to appraise data sources and methods as well as to continuously evaluate forecast accuracy.