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Forecasting state-level premature deaths from alcohol, drugs, and suicides using Google Trends data.

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. 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.

journal_name

J Affect Disord

journal_title

Journal of affective disorders

authors

Parker J,Cuthbertson C,Loveridge S,Skidmore M,Dyar W

doi

10.1016/j.jad.2016.10.038

subject

Has Abstract

pub_date

2017-04-15 00:00:00

pages

9-15

eissn

0165-0327

issn

1573-2517

pii

S0165-0327(16)31110-7

journal_volume

213

pub_type

杂志文章

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