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      Health information provision, health knowledge and health behaviours: Evidence from breast cancer screening


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          Many public health interventions aim to provide individuals with health information on the consequences of behaviours such as smoking, alcohol consumption or preventive care use, with the intention of changing health behaviour through better health knowledge. This paper examines whether the provision of health information in organised breast cancer screening programs affects mammography utilisation via changes in health knowledge. We use unique data on 10,610 European women from the Eurobarometer survey collected in 1997/1998, and we exploit variation in the availability and coverage of organised breast cancer screening programs for causal identification in a difference-in-differences design. We find that health information provision improves health knowledge. Yet, these changes in health knowledge had little to no effects on mammography utilisation in the overall population. Our findings imply that health information provision contributes little to health behaviour change. Although screening programs are effective at increasing preventive care use, their effect can be attributed almost entirely to factors other than health knowledge.


          • We estimate a difference-in-differences model and conduct a mediation analysis.

          • We exploit variation in screening program existence and eligibility ages in Europe.

          • Screening program eligibility improves health knowledge about breast cancer.

          • Changes in health knowledge contribute little to screening program uptake.

          • Health information provision has a stronger impact on less educated women.

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          Global cancer statistics, 2012.

          Cancer constitutes an enormous burden on society in more and less economically developed countries alike. The occurrence of cancer is increasing because of the growth and aging of the population, as well as an increasing prevalence of established risk factors such as smoking, overweight, physical inactivity, and changing reproductive patterns associated with urbanization and economic development. Based on GLOBOCAN estimates, about 14.1 million new cancer cases and 8.2 million deaths occurred in 2012 worldwide. Over the years, the burden has shifted to less developed countries, which currently account for about 57% of cases and 65% of cancer deaths worldwide. Lung cancer is the leading cause of cancer death among males in both more and less developed countries, and has surpassed breast cancer as the leading cause of cancer death among females in more developed countries; breast cancer remains the leading cause of cancer death among females in less developed countries. Other leading causes of cancer death in more developed countries include colorectal cancer among males and females and prostate cancer among males. In less developed countries, liver and stomach cancer among males and cervical cancer among females are also leading causes of cancer death. Although incidence rates for all cancers combined are nearly twice as high in more developed than in less developed countries in both males and females, mortality rates are only 8% to 15% higher in more developed countries. This disparity reflects regional differences in the mix of cancers, which is affected by risk factors and detection practices, and/or the availability of treatment. Risk factors associated with the leading causes of cancer death include tobacco use (lung, colorectal, stomach, and liver cancer), overweight/obesity and physical inactivity (breast and colorectal cancer), and infection (liver, stomach, and cervical cancer). A substantial portion of cancer cases and deaths could be prevented by broadly applying effective prevention measures, such as tobacco control, vaccination, and the use of early detection tests. © 2015 American Cancer Society.
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            The Transtheoretical Model of Health Behavior Change

            The transtheoretical model posits that health behavior change involves progress through six stages of change: precontemplation, contemplation, preparation, action, maintenance, and termination. Ten processes of change have been identified for producing progress along with decisional balance, self-efficacy, and temptations. Basic research has generated a rule of thumb for at-risk populations: 40% in precontemplation, 40% in contemplation, and 20% in preparation. Across 12 health behaviors, consistent patterns have been found between the pros and cons of changing and the stages of change. Applied research has demonstrated dramatic improvements in recruitment, retention, and progress using stage-matched interventions and proactive recruitment procedures. The most promising outcomes to data have been found with computer-based individualized and interactive interventions. The most promising enhancement to the computer-based programs are personalized counselors. One of the most striking results to date for stage-matched programs is the similarity between participants reactively recruited who reached us for help and those proactively recruited who we reached out to help. If results with stage-matched interventions continue to be replicated, health promotion programs will be able to produce unprecedented impacts on entire at-risk populations.
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              A general approach to causal mediation analysis.

              Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for 3 reasons: the lack of a general definition of causal mediation effects independent of a particular statistical model, the inability to specify the key identification assumption, and the difficulty of extending the framework to nonlinear models. In this article, we propose an alternative approach that overcomes these limitations. Our approach is general because it offers the definition, identification, estimation, and sensitivity analysis of causal mediation effects without reference to any specific statistical model. Further, our approach explicitly links these 4 elements closely together within a single framework. As a result, the proposed framework can accommodate linear and nonlinear relationships, parametric and nonparametric models, continuous and discrete mediators, and various types of outcome variables. The general definition and identification result also allow us to develop sensitivity analysis in the context of commonly used models, which enables applied researchers to formally assess the robustness of their empirical conclusions to violations of the key assumption. We illustrate our approach by applying it to the Job Search Intervention Study. We also offer easy-to-use software that implements all our proposed methods. PsycINFO Database Record (c) 2010 APA, all rights reserved.

                Author and article information

                Soc Sci Med
                Soc Sci Med
                Social Science & Medicine (1982)
                1 November 2020
                November 2020
                : 265
                [a ]Max Planck Institute for Demographic Research, Konrad-Zuse-Str. 1, 18057, Rostock, Germany
                [b ]Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, UK
                [c ]EDHEC Business School, France
                Author notes
                []Corresponding author. Max Planck Institute for Demographic Research, Konrad-Zuse-Str. 1, 18057, Rostock, Germany. eibich@ 123456demogr.mpg.de
                [∗∗ ]Corresponding author. leontine.goldzahl@ 123456edhec.edu
                S0277-9536(20)30724-3 113505
                © 2020 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).


                Health & Social care
                information,knowledge,health behaviour,mediation analysis,mammography,breast cancer,eurobarometer


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