Confounding Effect of Undergraduate Semester-Driven "Academic" Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration.

IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES
JMIR infodemiology Pub Date : 2022-07-01 DOI:10.2196/34464
Timber Gillis, Scott Garrison
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引用次数: 1

Abstract

Background: Internet search volume for medical information, as tracked by Google Trends, has been used to demonstrate unexpected seasonality in the symptom burden of a variety of medical conditions. However, when more technical medical language is used (eg, diagnoses), we believe that this technique is confounded by the cyclic, school year-driven internet search patterns of health care students.

Objective: This study aimed to (1) demonstrate that artificial "academic cycling" of Google Trends' search volume is present in many health care terms, (2) demonstrate how signal processing techniques can be used to filter academic cycling out of Google Trends data, and (3) apply this filtering technique to some clinically relevant examples.

Methods: We obtained the Google Trends search volume data for a variety of academic terms demonstrating strong academic cycling and used a Fourier analysis technique to (1) identify the frequency domain fingerprint of this modulating pattern in one particularly strong example, and (2) filter that pattern out of the original data. After this illustrative example, we then applied the same filtering technique to internet searches for information on 3 medical conditions believed to have true seasonal modulation (myocardial infarction, hypertension, and depression), and all bacterial genus terms within a common medical microbiology textbook.

Results: Academic cycling explains much of the seasonal variation in internet search volume for many technically oriented search terms, including the bacterial genus term ["Staphylococcus"], for which academic cycling explained 73.8% of the variability in search volume (using the squared Spearman rank correlation coefficient, P<.001). Of the 56 bacterial genus terms examined, 6 displayed sufficiently strong seasonality to warrant further examination post filtering. This included (1) ["Aeromonas" + "Plesiomonas"] (nosocomial infections that were searched for more frequently during the summer), (2) ["Ehrlichia"] (a tick-borne pathogen that was searched for more frequently during late spring), (3) ["Moraxella"] and ["Haemophilus"] (respiratory infections that were searched for more frequently during late winter), (4) ["Legionella"] (searched for more frequently during midsummer), and (5) ["Vibrio"] (which spiked for 2 months during midsummer). The terms ["myocardial infarction"] and ["hypertension"] lacked any obvious seasonal cycling after filtering, whereas ["depression"] maintained an annual cycling pattern.

Conclusions: Although it is reasonable to search for seasonal modulation of medical conditions using Google Trends' internet search volume and lay-appropriate search terms, the variation in more technical search terms may be driven by health care students whose search frequency varies with the academic school year. When this is the case, using Fourier analysis to filter out academic cycling is a potential means to establish whether additional seasonality is present.

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本科学期驱动的“学术”互联网搜索对在Google趋势数据中检测真实疾病季节性的能力的混淆效应:傅立叶滤波方法的开发和演示。
背景:谷歌趋势(Google Trends)追踪的互联网医疗信息搜索量已被用于证明各种医疗状况的症状负担中意想不到的季节性。然而,当使用更多的专业医学语言(例如诊断)时,我们认为这种技术被循环的、学年驱动的医疗保健学生的互联网搜索模式所混淆。目的:本研究旨在(1)证明谷歌趋势搜索量的人为“学术循环”存在于许多医疗保健术语中,(2)证明如何使用信号处理技术从谷歌趋势数据中过滤学术循环,以及(3)将这种过滤技术应用于一些临床相关的例子。方法:我们获得了各种学术术语的Google Trends搜索量数据,显示出很强的学术循环,并使用傅里叶分析技术(1)在一个特别强的例子中识别出这种调制模式的频域指纹,(2)从原始数据中过滤出该模式。在这个说述性的例子之后,我们将同样的过滤技术应用于互联网搜索有关3种被认为具有真正季节性调节的医学病症(心肌梗死、高血压和抑郁症)的信息,以及一本普通医学微生物学教科书中的所有细菌属术语。结果:学术循环解释了许多以技术为导向的搜索词的互联网搜索量的季节性变化,包括细菌属术语[“葡萄球菌”],其中学术循环解释了搜索量变化的73.8%(使用平方Spearman秩相关系数,p)。虽然使用谷歌趋势的互联网搜索量和适合的搜索词来搜索医疗状况的季节性变化是合理的,但更多技术搜索词的变化可能是由医疗保健专业的学生驱动的,他们的搜索频率随着学年的变化而变化。在这种情况下,使用傅立叶分析来过滤掉学术周期是确定是否存在额外季节性的潜在手段。
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