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

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          Abstract

          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.

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          Most cited references49

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          Google trends: a web-based tool for real-time surveillance of disease outbreaks.

          Google Flu Trends can detect regional outbreaks of influenza 7-10 days before conventional Centers for Disease Control and Prevention surveillance systems. We describe the Google Trends tool, explain how the data are processed, present examples, and discuss its strengths and limitations. Google Trends shows great promise as a timely, robust, and sensitive surveillance system. It is best used for surveillance of epidemics and diseases with high prevalences and is currently better suited to track disease activity in developed countries, because to be most effective, it requires large populations of Web search users. Spikes in search volume are currently hard to interpret but have the benefit of increasing vigilance. Google should work with public health care practitioners to develop specialized tools, using Google Flu Trends as a blueprint, to track infectious diseases. Suitable Web search query proxies for diseases need to be established for specialized tools or syndromic surveillance. This unique and innovative technology takes us one step closer to true real-time outbreak surveillance.
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            Bias in analytic research.

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              More Diseases Tracked by Using Google Trends

              To the Editor: The idea that populations provide data on their influenza status through information-seeking behavior on the Web has been explored in the United States in recent years ( 1 , 2 ). Two reports showed that queries to the Internet search engines Yahoo and Google could be informative for influenza surveillance ( 2 , 3 ). Ginsberg et al. scanned the Google database and found that the sum of the results of 45 queries that most correlated with influenza incidences provided the best predictor of influenza trends ( 3 ). On the basis of trends of Google queries, these authors put their results into practice by creating a Web page dedicated to influenza surveillance. However, they did not develop the same approach for other diseases. To date, no studies have been published about the relationship of search engine query data with other diseases or in languages other than English. We compared search trends based on a list of Google queries related to 3 infectious diseases (influenza-like illness, gastroenteritis, and chickenpox) with clinical surveillance data from the French Sentinel Network ( 4 ). Queries were constructed through team brainstorming. Each participant listed queries likely to be used for searching information about these diseases on the Web. The query time series from January 2004 through February 2009 for France were downloaded from Google Insights for Search, 1 of the 2 websites with Google Trends that enables downloading search trends from the Google database ( 5 ). Correlations with weekly incidence rates (no. cases/100,000 inhabitants) of the 3 diseases provided by the Sentinel Network were calculated for different lag periods (Pearson coefficient ρ). The highest correlation with influenza-like illness was obtained with the query grippe –aviaire –vaccin, the French words for influenza, avian, and vaccine respectively (ρ = 0.82, p 1 of the terms. The second highest correlation was obtained when the keyword gastro (ρ = 0.88, p<0.001) (Appendix Figure, panel B) was used. The highest correlation with chickenpox was obtained with the French word for chickenpox (varicelle) (ρ = 0.78, p<0.001) (Appendix Figure, panel C). A time lag of 0 weeks gave the highest correlations between the best queries for influenza-like illness and acute diarrhea and the incidences of these diseases; the peak of the time series of Google queries occurred at the same time as that of the disease incidences. The best query for chickenpox had a 1-week lag, i.e., was 1 week behind the incidence time series. In conclusion, for each of 3 infectious diseases, 1 well-chosen query was sufficient to provide time series of searches highly correlated with incidence. We have shown the utility of an Internet search engine query data for surveillance of acute diarrhea and chickenpox in a non–English-speaking country. Thus, the ability of Internet search-engine query data to predict influenza in the United States presented by Ginsberg et al. ( 3 ) appears to have a broader application for surveillance of other infectious diseases in other countries. Supplementary Material Appendix Figure Time series of search queries plotted along the incidence of 3 diseases (influenza-like illness, gastroenteritis, and chickenpox), 2004-2008. Black lines show trends of search fractions containing the French words for influenza (A), gastroenteritis (B), and chickenpox (C). Red lines show incidence rates for the 3 corresponding diseases (influenza-like illness, acute diarrhea, and chickenpox). Search fractions are scaled between 0 and 100 by Google Insights for Search's internal processes ( 5 ). Incidence rates are expressed in no. cases for 100,000 inhabitants, as provided by the Sentinel Network ( 4 ).
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                22 October 2014
                30 October 2014
                : 9
                : 10
                : e109583
                Affiliations
                [1 ]Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut, United States of America
                [2 ]Yale School of Medicine, New Haven, Connecticut, United States of America
                [3 ]Yale School of Public Health, New Haven, Connecticut, United States of America
                University of Vienna, Austria
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SVN KM IR BW. Performed the experiments: SVN KM IR BW SW RPD SIC. Analyzed the data: SVN KM IR BW. Contributed reagents/materials/analysis tools: SVN KM IR BW SW RPD SIC. Contributed to the writing of the manuscript: SVN KM IR BW SW RPD SIC.

                Article
                PONE-D-14-22976
                10.1371/journal.pone.0109583
                4215636
                25337815
                bb92b200-bccb-40ef-9a83-bed117d3062c
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 May 2014
                : 3 September 2014
                Page count
                Pages: 49
                Funding
                The authors have no funding or support to report.
                Categories
                Research Article
                Biology and Life Sciences
                Plant Science
                Plant Pathology
                Disease Surveillance
                Infectious Disease Surveillance
                Psychology
                Collective Human Behavior
                Earth Sciences
                Geography
                Human Geography
                Behavioral Geography
                Computer and Information Sciences
                Computer Applications
                Web-Based Applications
                Computer Networks
                Internet
                Medicine and Health Sciences
                Epidemiology
                Cancer Epidemiology
                Cardiovascular Disease Epidemiology
                Social Epidemiology
                Research and Analysis Methods
                Research Assessment
                Systematic Reviews
                Research Design
                Custom metadata
                The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting Information files.

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