LAUSR

BACKGROUND Vital statistics on the number of, alcohol-induced death (AICD) drug-induced death (DICD), and suicides at the local-level are only available after a substantial lag of up to two years after the events occur. We (1) investigate how well Google Trends search data explain variation in state-level rates in the US, and (2) use this method to forecast these rates of death for 2015 as official data are not yet available. METHODS We tested the degree to which Google Trends data on 27 terms can be fit to CDC data using L1-regularization on AICD, DICD, and suicide. Using Google Trends data, we forecast 2015 AICD, DICD, and suicide rates. RESULTS L1-regularization fit the pre-2015 data much better than the alternative model using state-level unemployment and income variables. Google Trends data account for substantial variation in growth of state-level rates of death: 30.9% for AICD, 23.9% for DICD, and 21.8% for suicide rates. Every state except Hawaii is forecasted to increase in all three of these rates in 2015. LIMITATIONS The model predicts state, not local or individual behavior, and is dependent on continued availability of Google Trends data. CONCLUSIONS The method predicts state-level AICD, DICD, and suicide rates better than the alternative model. The study findings suggest that this methodology can be developed into a public health surveillance system for behavioral health-related causes of death. State-level predictions could be used to inform state interventions aimed at reducing AICD, DICD, and suicide.