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.

中文翻译：

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利用谷歌趋势中的大数据绘制美国人口萧条图：探索性信息流行病学研究

背景：精神健康障碍的流行病学对医疗保健服务和规划具有重要的理论和实践意义。最近大数据存储的增加和分析工具的后续发展表明，挖掘搜索数据库可能会产生心理健康的重要趋势，可用于支持现有的人口健康研究。目的：本研究旨在根据基于互联网的心理健康查询绘制美国抑郁症搜索意图。方法：每周从谷歌趋势（2010-2021）中提取心理健康搜索数据，并按美国各州划分为以下术语：“感到悲伤”、“沮丧”、“抑郁”、“空虚”、“失眠”、“疲劳”、“内疚”、“内疚”和“自杀”。多变量回归模型是基于地理和环境因素创建的，并归一化为以下控制项：“体育”、“新闻”、“谷歌”、“youtube”、“facebook”和“netflix”。人口抑郁的热图是根据搜索意图生成的。结果：从 2010 年 1 月到 2021 年 3 月，抑郁症搜索意图增长了 67%。抑郁症搜索意图显示出显着的季节性模式，在冬季（调整后的P <.001）和早春月份（调整后的P<.001)，相对于夏季月份。地理位置与抑郁症搜索意图相关，东北部各州（调整后的P =.01）的搜索意图高于南部各州。结论：从谷歌趋势推断的趋势成功地与已知的抑郁风险因素相关，例如季节性和纬度增加。这些发现表明，谷歌趋势可能是一种有效的新型流行病学工具，用于绘制美国抑郁症患病率图。