ArticleJMIR mental health2022
Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study.
Article in JMIR mental health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Public interest in biodiversity and climate change: A comparative culturomics study of China and the UK.PloS one · 2026Article
- Online search interest in major depressive disorder: Infodemiology study using the most visited search engine in Japan.PloS one · 2026Observational
- Monitoring Public Health During the 2023 Maui Wildfire Using Google Search Trends.Hawai'i journal of health & social welfare · 2026Article
- Age-stratified comparison of psychopathological characteristics between psychiatric and psychological outpatients.BMC psychiatry · 2025Article
- Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review.JMIR mental health · 2025Article
- Assessing the impact of small firm dynamics on public mental health amid the pandemic in Latin America.BMC public health · 2024Article
- Online searches for hepatocellular carcinoma drugs mirror prescription trends across specialties and changes in guideline recommendations.Frontiers in oncology · 2024Article
- The 2022 Summer record-breaking heatwave and health information-seeking behaviours: an infodemiology study in Mainland China.BMJ global health · 2023Article
- Did online information seeking for depression increase during COVID-19 lockdown times? A google trend analysis on data from Germany and the UK.Journal of affective disorders reports · 2023Article
- Tracking Population-Level Anxiety Using Search Engine Data: Ecological Study.JMIR formative research · 2023Article
- Psychological Impact of COVID-19 Lockdown and Its Evolution: A Case Study Based on Internet Searching Data during the Lockdown of Wuhan 2020 and Shanghai 2022.Healthcare (Basel, Switzerland) · 2023Article
- Google Trends for health research: Its advantages, application, methodological considerations, and limitations in psychiatric and mental health infodemiology.Frontiers in big data · 2023Review
- Heterogeneous responses in Google Trends measures of well-being to the COVID-19 dynamic quarantines in Chile.Scientific reports · 2022Article
- A Google Trends Approach to Identify Distinct Diurnal and Day-of-Week Web-Based Search Patterns Related to Conjunctivitis and Other Common Eye Conditions: Infodemiology Study.Journal of medical Internet research · 2022Observational
- Online searches for SGLT-2 inhibitors and GLP-1 receptor agonists correlate with prescription rates in the United States: An infodemiological study.Frontiers in cardiovascular medicine · 2022Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
objectiveThis study aimed to map depression search intent in the United States based on internet-based mental health queries.
methodsWeekly 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.
resultsDepression 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.
conclusionsThe 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.
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