Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study

Alex Wang; Robert McCarron; Daniel Azzam; Annamarie Stehli; Glen Xiong; Jeremy DeMartini

doi:10.2196/35253

Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study

JMIR Ment Health. 2022 Mar 31;9(3):e35253. doi: 10.2196/35253.

Authors

Alex Wang¹, Robert McCarron¹, Daniel Azzam¹, Annamarie Stehli¹, Glen Xiong², Jeremy DeMartini²

Affiliations

¹ Department of Psychiatry and Human Behavior, University of California, Irvine, Orange, CA, United States.
² Department of Psychiatry & Behavioral Sciences, University of California, Davis, Sacramento, CA, United States.

PMID: 35357320
PMCID: PMC9015761
DOI: 10.2196/35253

Abstract

Background: The epidemiology of mental health disorders has important theoretical and practical implications for health care service and planning. The recent increase in big data storage and subsequent development of analytical tools suggest that mining search databases may yield important trends on mental health, which can be used to support existing population health studies.

Objective: This study aimed to map depression search intent in the United States based on internet-based mental health queries.

Methods: Weekly data on mental health searches were extracted from Google Trends for an 11-year period (2010-2021) and separated by US state for the following terms: "feeling sad," "depressed," "depression," "empty," "insomnia," "fatigue," "guilty," "feeling guilty," and "suicide." Multivariable regression models were created based on geographic and environmental factors and normalized to the following control terms: "sports," "news," "google," "youtube," "facebook," and "netflix." Heat maps of population depression were generated based on search intent.

Results: Depression search intent grew 67% from January 2010 to March 2021. Depression search intent showed significant seasonal patterns with peak intensity during winter (adjusted P<.001) and early spring months (adjusted P<.001), relative to summer months. Geographic location correlated with depression search intent with states in the Northeast (adjusted P=.01) having higher search intent than states in the South.

Conclusions: The trends extrapolated from Google Trends successfully correlate with known risk factors for depression, such as seasonality and increasing latitude. These findings suggest that Google Trends may be a valid novel epidemiological tool to map depression prevalence in the United States.

Keywords: big data; depression; epidemiology; google trends; internet; mental health.

©Alex Wang, Robert McCarron, Daniel Azzam, Annamarie Stehli, Glen Xiong, Jeremy DeMartini. Originally published in JMIR Mental Health (https://mental.jmir.org), 31.03.2022.