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Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9015761/ https://www.ncbi.nlm.nih.gov/pubmed/35357320 http://dx.doi.org/10.2196/35253 |
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author | Wang, Alex McCarron, Robert Azzam, Daniel Stehli, Annamarie Xiong, Glen DeMartini, Jeremy |
author_facet | Wang, Alex McCarron, Robert Azzam, Daniel Stehli, Annamarie Xiong, Glen DeMartini, Jeremy |
author_sort | Wang, Alex |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9015761 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-90157612022-04-19 Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study Wang, Alex McCarron, Robert Azzam, Daniel Stehli, Annamarie Xiong, Glen DeMartini, Jeremy JMIR Ment Health Original Paper 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. JMIR Publications 2022-03-31 /pmc/articles/PMC9015761/ /pubmed/35357320 http://dx.doi.org/10.2196/35253 Text en ©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. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on https://mental.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Wang, Alex McCarron, Robert Azzam, Daniel Stehli, Annamarie Xiong, Glen DeMartini, Jeremy Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study |
title | Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study |
title_full | Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study |
title_fullStr | Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study |
title_full_unstemmed | Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study |
title_short | Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study |
title_sort | utilizing big data from google trends to map population depression in the united states: exploratory infodemiology study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9015761/ https://www.ncbi.nlm.nih.gov/pubmed/35357320 http://dx.doi.org/10.2196/35253 |
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