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Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology
Alex Wang1, Robert McCarron1, Daniel Azzam1
1Department of Psychiatry and Human Behavior, University of California, Irvine, Orange, CA, United States.
JMIR Mental Health
|March 31, 2022
Summary
Internet search data reveals a 67% increase in depression-related queries from 2010-2021. This study shows search trends correlate with seasonality and geography, suggesting Google Trends is a valid tool for mapping depression prevalence.
Area of Science:
- Digital Epidemiology
- Public Health Informatics
- Mental Health Research
Background:
- Mental health epidemiology informs healthcare planning.
- Big data analytics can reveal population mental health trends.
- Internet search data offers novel insights into mental health.
Purpose of the Study:
- To map depression search intent across the United States.
- To analyze internet-based mental health queries for depression trends.
- To explore the utility of search data in understanding depression prevalence.
Main Methods:
- Extracted weekly Google Trends data (2010-2021) for depression-related terms.
- Analyzed search intent by US state, correlating with geographic and environmental factors.
- Normalized data against control search terms and generated heat maps.
Main Results:
- Depression search intent increased by 67% over the study period.
- Significant seasonal patterns observed, peaking in winter and early spring.
- Northeastern states exhibited higher depression search intent than Southern states.
Conclusions:
- Google Trends data correlates with known depression risk factors like seasonality and latitude.
- Search trends from Google Trends show potential as an epidemiological tool.
- This approach can effectively map depression prevalence in the United States.
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