Center for Behavioral Epidemiology and Community Health, San Diego, CA 92123, USA.
Am J Prev Med. 2013 May;44(5):520-5. doi: 10.1016/j.amepre.2013.01.012.
BACKGROUND: Population mental health surveillance is an important challenge limited by resource constraints, long time lags in data collection, and stigma. One promising approach to bridge similar gaps elsewhere has been the use of passively generated digital data. PURPOSE: This article assesses the viability of aggregate Internet search queries for real-time monitoring of several mental health problems, specifically in regard to seasonal patterns of seeking out mental health information. METHODS: All Google mental health queries were monitored in the U.S. and Australia from 2006 to 2010. Additionally, queries were subdivided among those including the terms ADHD (attention deficit-hyperactivity disorder); anxiety; bipolar; depression; anorexia or bulimia (eating disorders); OCD (obsessive-compulsive disorder); schizophrenia; and suicide. A wavelet phase analysis was used to isolate seasonal components in the trends, and based on this model, the mean search volume in winter was compared with that in summer, as performed in 2012. RESULTS: All mental health queries followed seasonal patterns with winter peaks and summer troughs amounting to a 14% (95% CI=11%, 16%) difference in volume for the U.S. and 11% (95% CI=7%, 15%) for Australia. These patterns also were evident for all specific subcategories of illness or problem. For instance, seasonal differences ranged from 7% (95% CI=5%, 10%) for anxiety (followed by OCD, bipolar, depression, suicide, ADHD, schizophrenia) to 37% (95% CI=31%, 44%) for eating disorder queries in the U.S. Several nonclinical motivators for query seasonality (such as media trends or academic interest) were explored and rejected. CONCLUSIONS: Information seeking on Google across all major mental illnesses and/or problems followed seasonal patterns similar to those found for seasonal affective disorder. These are the first data published on patterns of seasonality in information seeking encompassing all the major mental illnesses, notable also because they likely would have gone undetected using traditional surveillance.
背景：人口心理健康监测是一个重要的挑战，受到资源限制、数据收集时间滞后和污名化的限制。在其他地方弥合类似差距的一种有前途的方法是使用被动生成的数字数据。
目的：本文评估了汇总互联网搜索查询在实时监测几种心理健康问题方面的可行性，特别是在寻求心理健康信息的季节性模式方面。
方法：从 2006 年到 2010 年，在美国和澳大利亚监测了所有谷歌心理健康查询。此外，根据是否包含 ADHD（注意力缺陷多动障碍）；焦虑症；双相情感障碍；抑郁症；厌食症或贪食症（饮食障碍）；强迫症（强迫症）；精神分裂症和自杀等术语，将查询细分为不同的类别。使用小波相位分析来分离趋势中的季节性成分，并且根据该模型，与 2012 年相比，冬季的平均搜索量与夏季进行了比较。
结果：所有心理健康查询都遵循季节性模式，冬季高峰期和夏季低谷期的量相差 14%（95%置信区间=11%，16%），美国为 11%（95%置信区间=7%，15%）。这些模式也适用于所有特定的疾病或问题类别。例如，季节性差异范围从焦虑症的 7%（95%置信区间=5%，10%）（其次是强迫症、双相情感障碍、抑郁症、自杀、ADHD、精神分裂症）到美国饮食障碍查询的 37%（95%置信区间=31%，44%）。还探讨并拒绝了查询季节性的几种非临床动机（例如媒体趋势或学术兴趣）。
结论：在美国，所有主要精神疾病和/或问题的谷歌信息搜索都遵循与季节性情感障碍相似的季节性模式。这是第一批发布的涵盖所有主要精神疾病的信息搜索季节性模式的数据，值得注意的是，因为使用传统监测方法可能无法发现这些数据。
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