Who gets a mammogram amongst European women aged 50-69 years?
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On the basis of the Survey of Health, Ageing, and Retirement (SHARE), we analyse the determinants of who engages in mammography screening focusing on European women aged 50-69 years. A special emphasis is put on the measurement error of subjective life expectancy and on the measurement and impact of physician quality. Our main findings are that physician quality, better education, having a partner, younger age and better health are associated with higher rates of receipt. The impact of subjective life expectancy on screening decision substantially increases after taking measurement error into account.
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KeywordsMammogram Physician quality Life expectancy Instrumental variables
The author thanks Dirk Sauerland (Witten/Herdecke University), Hendrik Schmitz (University of Duisburg-Essen), Jens Harbecke (University Witten/Herdecke) and Patrick Bremer (Witten/Herdecke University) and all participants of the PhD seminar on health economics and policy in Grindelwald for valuable comments. Thanks also to two anonymous referees for providing most valuable comments and to the participants of the annual conference of the "Verein für Socialpolitik" 2011 in Frankfurt and the "DGGÖ-Jahrestagung" in Bayreuth 2011 where the paper was presented. The paper uses data from SHARELIFE release 1, as of November 24th 2010 or SHARE release 2.3.1, as of July 29th 2010. The SHARE data collection has been primarily funded by the European Commission through the 5th framework programme (project QLK6-CT-2001- 00360 in the thematic programme Quality of Life), through the 6th framework programme (projects SHARE-I3, RII-CT- 2006-062193, COMPARE, CIT5-CT-2005-028857, and SHARELIFE, CIT4-CT-2006-028812) and through the 7th framework programme (SHARE-PREP, 211909 and SHARE-LEAP, 227822). Additional funding from the U.S. National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, Y1-AG-4553-01 and OGHA 04-064, IAG BSR06-11, R21 AG025169) as well as from various national sources is gratefully acknowledged (see http://www.share-project.org for a full list of funding institutions)."
- 1.Commission of the European Communities - COM: Report from the commission to the council, The EuropeanPparliament, The European Economic and Social Committee and the Committee of the Regions. 2008. [http://ec.europa.eu/health/ph_determinants/genetics/documents/com_2008_882.en.pdf]Google Scholar
- 2.Organisation for Economic Co-operation and Development - OECD: Health at a Glance 2009 - OECD Indicators. OECD, Paris; 2009. [http://www.oecdilibrary.org/content/book/health_glance-2009-en]Google Scholar
- 4.Fang H, Wang J: Estimating dynamic discrete choice models with hyperbolic discounting, with an application to mammography decisions. NBER Working Paper 16438 2010.Google Scholar
- 6.IARC Working Group on the Evaluation of Cancer Preventive Strategies: Breast cancer screening. In IARC Handbooks of Cancer Prevention. Volume 7. Lyon: IARC Press; 2002.Google Scholar
- 7.European Commision: European guidelines for quality assurance in breast cancer screening and diagnosis. Fourth edition. Edited by: Perry N, et al. Luxembourg; 2006.Google Scholar
- 16.McGuire T: Physician agency. In Handbook of Health Economics. Volume 1. Edited by: Culyer A, Newhouse J. Amsterdam, Elsevier; 2000:461–536. Chapter 9Google Scholar
- 22.Khwaja A: Health insurance, habits and health outcomes: A dynamic stochastic model of investment in health. PhD thesis. University of Minnesota; 2001.Google Scholar
- 31.Gøtzsche PC, Nielsen M: Screening for breast cancer with mammography. Cochrane Database Syst Rev 2009,7(4):CD001877.Google Scholar
- 32.Avitabile C, Jappelli T, Padula M: Screening tests, information, and the health-education gradient. In CSEF Working Papers. 187, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy; 2008.Google Scholar
- 33.Cawley J, Ruhm CJ: The Economics of Risky Health Behaviors. In Handbook of Health Economics. Volume 2. Edited by: TG McGuire TG, Pauly MV, Barros PP. Amsterdam, Elsevier; 2012:95–199. Chapter 3Google Scholar
- 35.Koehler M, Kliegel M, Wiese B, Bickel H, Kaduszkiewicz H, van den Bussche H, Eifflaender-Gorfer S, Eisele M, Fuchs A, Koenig HH, Leicht H, Luck T, Maier W, Moesch E, Riedel-Heller S, Tebarth F, Wagner M, Weyerer S, Zimmermann T, Pentzek M: Malperformance in verbal fluency and delayed recall as cognitive risk factors for impairment in instrumental activities of daily living. Dement Geriatr Cogn Disord 2011,31(1):81–88. 10.1159/000323315CrossRefPubMedGoogle Scholar
- 44.Hurd MD, McFadden D, Gan L: Subjective survival curves and life cycle behavior. In Inquiries in the Economics of Aging. Edited by: Wise D. Chicago, University of Chicago Press; 1998.Google Scholar
- 45.Bloom DE, Canning D, Moore M, Song Y: The effect of subjective survival probabilities on retirement and wealth in the United States. NBER Working Paper 12688 2006.Google Scholar
- 46.Wooldridge JM: Econometric analysis of cross section and panel data. MIT Press; 2002.Google Scholar
- 51.Börsch-Supan A, Jürges H: The survey of health, aging and retirement in Europe - methodology. In Mannheim Research Institute for the Economics of Aging. Mannheim: Technical Report; 2005.Google Scholar
- 53.Santos-Eggimann B, Junod J, Cornaz S: Quality of health care delivered to older Europeans. In Health, Ageing and Retirement in Europe - First Results From the Survey of Health, Ageing and Retirement in Europe. Edited by: Börsch-Supan A. et al. MEA: Mannheim; 2005:141–149.Google Scholar
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