背景:互联网是患者健康信息的主要来源,补充医生护理。谷歌趋势(GT),一个流行的工具,允许探索公众对健康相关现象的兴趣。尽管GT研究的数量越来越多,没有人明确关注肿瘤学,需要进行系统的审查来弥合这一差距。
目的:我们旨在使用GT系统表征与肿瘤学相关的研究,以描述其效用和偏见。
方法:我们纳入了所有使用GT分析与恶性肿瘤相关的Google搜索的研究。我们排除了用英语以外的语言编写的研究。搜索是在2022年8月1日使用PubMed引擎进行的。我们使用以下搜索输入:\"Googletrends\"AND(\"肿瘤学\"或\"癌症\"或\"恶性肿瘤\"或\"肿瘤\"或\"淋巴瘤\"或\"多发性骨髓瘤\"或\"白血病\")。我们分析了偏见的来源,包括使用搜索词而不是主题,缺乏GT统计数据与现实世界数据的对抗,缺乏敏感性分析。我们进行了描述性统计。
结果:共纳入85篇。第一项使用GT进行肿瘤学研究的研究发表于2013年,从那时起,出版物的数量逐年增加。这些研究分类如下:22%(19/85)与预防有关,20%(17/85)与意识事件有关,11%(9/85)与名人相关,13%(11/85)与COVID-19有关,47%(40/85)属于其他类别。最常分析的癌症是乳腺癌(n=28),前列腺(n=26),肺(n=18),结直肠癌(n=18)。我们发现在85项研究中,17(20%)承认使用GT主题而不是搜索词,79(93%)披露了复制结果所需的所有搜索输入细节,34人(40%)将GT统计数据与真实世界数据进行了比较。用于分析GT数据的最普遍的方法是相关性分析(55/85,65%)和峰分析(43/85,51%)。只有11%(9/85)的研究作者进行了敏感性分析。
结论:使用GT数据与肿瘤学相关的研究数量逐年增加。本系统综述中包含的研究展示了各种相关主题,搜索策略,和统计方法。最常分析的癌症是乳腺癌,前列腺,肺,结直肠,皮肤,和宫颈癌,可能反映了它们在人口或公共利益中的流行。尽管大多数研究人员提供了可重复的搜索输入,只有五分之一的人使用GT主题而不是搜索词,许多研究缺乏敏感性分析。使用GT进行医学研究的科学家应通过提供透明的搜索策略来复制结果来确保研究的质量,更喜欢使用主题而不是搜索词,并进行稳健的统计计算和敏感性分析。
The internet is a primary source of health information for patients, supplementing physician care. Google Trends (GT), a popular tool, allows the exploration of public interest in health-related phenomena. Despite the growing volume of GT studies, none have focused explicitly on oncology, creating a need for a systematic review to bridge this gap.
We aimed to systematically characterize studies related to oncology using GT to describe its utilities and biases.
We included all studies that used GT to analyze Google searches related to malignancies. We excluded studies written in languages other than English. The search was performed using the PubMed engine on August 1, 2022. We used the following search input: \"Google trends\" AND (\"oncology\" OR \"cancer\" or \"malignancy\" OR \"tumor\" OR \"lymphoma\" OR \"multiple myeloma\" OR \"leukemia\"). We analyzed sources of bias that included using search terms instead of topics, lack of confrontation of GT statistics with real-world data, and absence of sensitivity analysis. We performed descriptive statistics.
A total of 85 articles were included. The first study using GT for oncology research was published in 2013, and since then, the number of publications has increased annually. The studies were categorized as follows: 22% (19/85) were related to prophylaxis, 20% (17/85) pertained to awareness events, 11% (9/85) were
celebrity-related, 13% (11/85) were related to COVID-19, and 47% (40/85) fell into other categories. The most frequently analyzed cancers were breast (n=28), prostate (n=26), lung (n=18), and colorectal cancers (n=18). We discovered that of the 85 studies, 17 (20%) acknowledged using GT topics instead of search terms, 79 (93%) disclosed all search input details necessary for replicating their results, and 34 (40%) compared GT statistics with real-world data. The most prevalent methods for analyzing the GT data were correlation analysis (55/85, 65%) and peak analysis (43/85, 51%). The authors of only 11% (9/85) of the studies performed a sensitivity analysis.
The number of studies related to oncology using GT data has increased annually. The studies included in this systematic review demonstrate a variety of concerning topics, search strategies, and statistical methodologies. The most frequently analyzed cancers were breast, prostate, lung, colorectal, skin, and cervical cancers, potentially reflecting their prevalence in the population or public interest. Although most researchers provided reproducible search inputs, only one-fifth used GT topics instead of search terms, and many studies lacked a sensitivity analysis. Scientists using GT for medical research should ensure the quality of studies by providing a transparent search strategy to reproduce results, preferring to use topics over search terms, and performing robust statistical calculations coupled with sensitivity analysis.