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      The utility of Google Trends data to examine interest in cancer screening

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Objectives

          We examined the utility of January 2004 to April 2014 Google Trends data from information searches for cancer screenings and preparations as a complement to population screening data, which are traditionally estimated through costly population-level surveys.

          Setting

          State-level data across the USA.

          Participants

          Persons who searched for terms related to cancer screening using Google, and persons who participated in the Behavioral Risk Factor Surveillance System (BRFSS).

          Primary and secondary outcome measures

          (1) State-level Google Trends data, providing relative search volume (RSV) data scaled to the highest search proportion per week (RSV100) for search terms over time since 2004 and across different geographical locations. (2) RSV of new screening tests, free/low-cost screening for breast and colorectal cancer, and new preparations for colonoscopy (Prepopik). (3) State-level breast, cervical, colorectal and prostate cancer screening rates.

          Results

          Correlations between Google Trends and BRFSS data ranged from 0.55 for ever having had a colonoscopy to 0.14 for having a Pap smear within the past 3 years. Free/low-cost mammography and colonoscopy showed higher RSV during their respective cancer awareness months. RSV for Miralax remained stable, while interest in Prepopik increased over time. RSV for lung cancer screening, virtual colonoscopy and three-dimensional mammography was low.

          Conclusions

          Google Trends data provides enormous scientific possibilities, but are not a suitable substitute for, but may complement, traditional data collection and analysis about cancer screening and related interests.

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          Most cited references 21

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          Permutation tests for joinpoint regression with applications to cancer rates.

          The identification of changes in the recent trend is an important issue in the analysis of cancer mortality and incidence data. We apply a joinpoint regression model to describe such continuous changes and use the grid-search method to fit the regression function with unknown joinpoints assuming constant variance and uncorrelated errors. We find the number of significant joinpoints by performing several permutation tests, each of which has a correct significance level asymptotically. Each p-value is found using Monte Carlo methods, and the overall asymptotic significance level is maintained through a Bonferroni correction. These tests are extended to the situation with non-constant variance to handle rates with Poisson variation and possibly autocorrelated errors. The performance of these tests are studied via simulations and the tests are applied to U.S. prostate cancer incidence and mortality rates. Copyright 2000 John Wiley & Sons, Ltd.
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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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              The role of theory in HIV prevention.

               M Fishbein (2000)
              There is growing evidence that well designed, targeted, theory-based behaviour change interventions can be effective in reducing the spread of HIV. Although each behaviour is unique, there are only a limited number of theoretical variables that serve as the determinants of any given behaviour. Understanding these variables and their role in behavioural prediction can guide the development of effective behaviour change interventions. This paper will describe and define these variables and show how they can be used in the development of behavioural interventions.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2015
                8 June 2015
                : 5
                : 6
                Affiliations
                [1 ]Department of Epidemiology, Saint Louis University College for Public Health and Social Justice , St. Louis, Missouri, USA
                [2 ]Alvin J Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine , St. Louis, Missouri, USA
                [3 ]Department of Psychiatry, Washington University School of Medicine , St. Louis, Missouri, USA
                [4 ]Division of General Medical Sciences, Department of Medicine, Washington University School of Medicine , St. Louis, Missouri, USA
                [5 ]Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina , Columbia, South Carolina, USA
                [6 ]Division of Gastroenterology, Department of Medicine, Washington University School of Medicine , St. Louis, Missouri, USA
                Author notes
                [Correspondence to ] Dr Mario Schootman; schootm@ 123456slu.edu
                Article
                bmjopen-2014-006678
                10.1136/bmjopen-2014-006678
                4466617
                26056120
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

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