Wang Alex, McCarron Robert, Azzam Daniel, Stehli Annamarie, Xiong Glen, DeMartini Jeremy
Department of Psychiatry and Human Behavior, University of California, Irvine, Orange, CA, United States.
Department of Psychiatry & Behavioral Sciences, University of California, Davis, Sacramento, CA, United States.
JMIR Ment Health. 2022 Mar 31;9(3):e35253. doi: 10.2196/35253.
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.
This study aimed to map depression search intent in the United States based on internet-based mental health queries.
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.
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.
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.
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。近期大数据存储的增加以及分析工具的后续发展表明，挖掘搜索数据库可能会得出有关精神健康的重要趋势，可用于支持现有的人群健康研究。
本研究旨在基于互联网心理健康查询绘制美国抑郁症搜索意图图。
从谷歌趋势中提取了11年期间（2010 - 2021年）的每周心理健康搜索数据，并按美国各州对以下术语进行了分类：“感到悲伤”、“沮丧”、“抑郁症”、“空虚”、“失眠”、“疲劳”、“内疚”、“感到内疚”和“自杀”。基于地理和环境因素创建了多变量回归模型，并将其归一化到以下控制术语：“体育”、“新闻”、“谷歌”、“YouTube”、“脸书”和“网飞”。根据搜索意图生成了人群抑郁症热图。
从2010年1月到2021年3月，抑郁症搜索意图增长了67%。抑郁症搜索意图呈现出显著的季节性模式，相对于夏季月份，在冬季（调整后P <.001）和早春月份强度达到峰值（调整后P <.001）。地理位置与抑郁症搜索意图相关，东北部各州（调整后P =.01）的搜索意图高于南部各州。
从谷歌趋势推断出的趋势与抑郁症的已知风险因素（如季节性和纬度增加）成功相关。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，可用于绘制美国抑郁症患病率地图。
