BACKGROUND
The epidemiology of mental health disorders has important theoretical and practical implications for healthcare service and planning. The recent increase in big data storage and subsequent development of analytical tools suggests that mining search databases may yield important trends on mental health, which can be used to replace or support existing population health studies.
OBJECTIVE
This study aimed to map out depression search intent in the United States based on internet 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 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 < 0.001) and early spring months (adjusted P < 0.001), relative to summer months. Geographic location correlated to depression search intent with states in the Northeast (adjusted P = 0.01) 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 out depression prevalence in the United States.
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