Jason Parker1, Courtney Cuthbertson2, Scott Loveridge3, Mark Skidmore4, Will Dyar5. 1. Agricultural, Food, and Resource Economics, Michigan State University, 446 W. Circle Dr., Suite 66, Morrill Hall of Agriculture, East Lansing, MI 48824-1039, USA. Electronic address: parke392@msu.edu. 2. Michigan State University, 446 W. Circle Dr., Suite 66, Morrill Hall of Agriculture, East Lansing, MI 48824-1039, USA. Electronic address: cuthbe16@msu.edu. 3. Agricultural, Food, and Resource Economics, Michigan State University, 446 W. Circle Dr., Suite 66, Morrill Hall of Agriculture, East Lansing, MI 48824-1039, USA. Electronic address: loverid2@msu.edu. 4. Agricultural, Food, and Resource Economics, Michigan State University, 446 W. Circle Dr., Suite 66, Morrill Hall of Agriculture, East Lansing, MI 48824-1039, USA. Electronic address: mskidmor@msu.edu. 5. Agricultural, Food, and Resource Economics, Michigan State University, 458 W Circle Dr., Suite 908, Cook Hall, East Lansing, MI 48824-1039, USA. Electronic address: dyarwill@msu.edu.
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.
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.
Authors: Joana M Barros; Ruth Melia; Kady Francis; John Bogue; Mary O'Sullivan; Karen Young; Rebecca A Bernert; Dietrich Rebholz-Schuhmann; Jim Duggan Journal: Int J Environ Res Public Health Date: 2019-09-02 Impact factor: 3.390
Authors: Damien Lekkas; Joseph A Gyorda; George D Price; Zoe Wortzman; Nicholas C Jacobson Journal: J Med Internet Res Date: 2022-01-27 Impact factor: 5.428