Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Mar 25, 2023
Date Accepted: Jun 12, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The characteristics, utilities and biases of studies related to malignancies using Google Trends: a systematic review
ABSTRACT
Background:
Google Trends (GT) is a freely-available tool presenting Google search statistics. GT is becoming a feasible tool to analyze the interest of Google users in different health phenomena. However, there are a few reviews on GT, and none has contributed specifically to oncology.
Objective:
We aimed to systematically characterize studies related to oncology using GT to describe its utilities and biases.
Methods:
We included all studies utilizing GT to analyze Google searches related to malignancies. We excluded studies written in non-English. The search was done on the PubMed engine on 1st August 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 the following sources of bias: 1) using search terms instead of topics, 2) lack of confrontation with real-world data, and 3) absence of a sensitivity analysis. We performed descriptive statistics.
Results:
A total of n=85 articles were included. We classified n=19 (22.4%) studies as related to prophylaxis, n=17 (20.0%) as an awareness event, n=9 (10.6%) as celebrity-related, n=11 (12.9%) related to COVID-19 and n=40 (47.1%) as others. The most frequently analyzed cancers were: breast (n=28), prostate (n=26), lung, and colorectal (both: n=18). n=79 (92.9%) of the studies provided all search input details to reproduce their results, and n=34 (40%.0) studies confronted GT statistics with real-world data. Authors of only n = 9 (10.6%) studies performed sensitivity analysis.
Conclusions:
The studies in this systematic review varied regarding topics, search strategy, and statistical methods. Most researchers provided reproducible search inputs, but many studies lacked sensitivity analysis.Scientists using GT for medical research should ensure the quality of studies by 1) providing a search strategy to reproduce results, 2) preferring using topics instead of search terms, and 3) performing robust statistical calculations and sensitivity analysis.
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