@article{ref1,
title="Forecasting state-level premature deaths from alcohol, drugs, and suicides using Google Trends data",
journal="Journal of affective disorders",
year="2016",
author="Parker, Jason and Cuthbertson, Courtney and Loveridge, Scott and Skidmore, Mark and Dyar, Will",
volume="213",
number="",
pages="9-15",
abstract="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. <br><br>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. <br><br>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. <br><br>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.<br><br>Copyright © 2017. Published by Elsevier B.V.<p /> <p>Language: en</p>",
language="en",
issn="0165-0327",
doi="10.1016/j.jad.2016.10.038",
url="http://dx.doi.org/10.1016/j.jad.2016.10.038"
}