The Use of Google Trends in Health Care Research: A Systematic Review

By S. Nuti, Brian Wayda, I. Ranasinghe, Sisi Wang, R. Dreyer, Serene I. Chen, K. Murugiah. PLoS ONE, 2014

960 citations recorded by the source corpus.

Background Google Trends is a novel, freely accessible tool that allows users to interact with Internet search data, which may provide deep insights into population behavior and health-related phenomena. However, there is limited knowledge about its potential uses and limitations. We therefore systematically reviewed health care literature using Google Trends to classify articles by topic and study aim; evaluate the methodology and validation of the tool; and address limitations for its use in research. Methods and Findings PRISMA guidelines were followed. Two independent reviewers systematically identified studies utilizing Google Trends for health care research from MEDLINE and PubMed. Seventy studies met our inclusion criteria. Google Trends publications increased seven-fold from 2009 to 2013. Studies were classified into four topic domains: infectious disease (27% of articles), mental health and substance use (24%), other non-communicable diseases (16%), and general population behavior (33%). By use, 27% of articles utilized Google Trends for casual inference, 39% for description, and 34% for surveillance. Among surveillance studies, 92% were validated against a reference standard data source, and 80% of studies using correlation had a correlation statistic ≥0.70. Overall, 67% of articles provided a rationale for their search input. However, only 7% of articles were reproducible based on complete documentation of search strategy. We present a checklist to facilitate appropriate methodological documentation for future studies. A limitation of the study is the challenge of classifying heterogeneous studies utilizing a novel data source. Conclusion Google Trends is being used to study health phenomena in a variety of topic domains in myriad ways. However, poor documentation of methods precludes the reproducibility of the findings. Such documentation would enable other researchers to determine the consistency of results provided by Google Trends for a well-specified query over time. Furthermore, greater transparency can improve its reliability as a research tool.

Subfields: Epidemiology, General Health Professions

View Paper: https://www.semanticscholar.org/paper/0f03dd995e5e0d5fb90f14e758bddc24dc23703e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

1. Karsten M. Borgwardt
2. Yoshua Bengio
3. Gabriela Ochoa
4. Amaryllis Mavragani
5. Konstantinos P. Tsagarakis

In General Health Professions

1. Yoshua Bengio
2. Gabriela Ochoa
3. Amaryllis Mavragani

Citations in the corpus

• Google Trends in Infodemiology and Infoveillance: Methodology Framework (2019, background): “Nevertheless, data from several online sources are being widely used to monitor disease outbreaks and occurrence, mainly from Google [3-7] and social media [8-12].”
• Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review (2018, background): “The current review differs from Nuti et al’s in two ways.”
• Tackling Climate Change with Machine Learning (2019, background): “rom web data and specially-designed apps, in addition to traditional surveys [626–628]. While non-survey proxies are observational and self-reported, current research attempts to address these issues [629, 630]. Beyond surveillance, point-of-care diagnostics have enjoyed a renaissance, thanks in par”
• Early prediction of circulatory failure in the intensive care unit using machine learning (2020, background): “The reported rates of monitoring alarms in ICUs vary from 6.5 to 53.1 per hour and patient in different studies 13,39–41 , and the rate is estimated at about 10 alarms per hour in our data.”