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Explaining variations in breast cancer screening across European countries

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  • Wuebker, A.

Abstract

This paper explores variations in the uptake of breast cancer screening and associated factors influencing utilisation of mammography screening among women aged 50 to 69 years in 13 European countries. We focus on the relative importance of individual (e.g. age, education, etc.) and institutional (e.g. public screening program) factors in explaining cross-country variation in the utilisation of mammograms. We take advantage of (a) newly available individual level data from the SHARE as well as (b) regional and country level data on institutional factors. We find that observed individual factors like age, education, health status, etc. are associated with screening uptake within countries but cannot statistically explain cross-country differences. In contrast, observed institutional factors like the availability of an organized screening program can statistically explain about 40 per cent of the between country differences in screening rates.

Suggested Citation

  • Wuebker, A., 2012. "Explaining variations in breast cancer screening across European countries," Health, Econometrics and Data Group (HEDG) Working Papers 12/26, HEDG, c/o Department of Economics, University of York.
  • Handle: RePEc:yor:hectdg:12/26
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    Cited by:

    1. Priaulx, Jennifer & de Koning, Harry J. & de Kok, Inge M.C.M. & Széles, György & McKee, Martin, 2018. "Identifying the barriers to effective breast, cervical and colorectal cancer screening in thirty one European countries using the Barriers to Effective Screening Tool (BEST)," Health Policy, Elsevier, vol. 122(11), pages 1190-1197.
    2. Magdalena Lagerlund & Juan Merlo & Raquel Pérez Vicente & Sophia Zackrisson, 2015. "Does the Neighborhood Area of Residence Influence Non-Attendance in an Urban Mammography Screening Program? A Multilevel Study in a Swedish City," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-21, October.

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    More about this item

    Keywords

    health economics; prevention; multilevel models; SHARE; cross country differences;
    All these keywords.

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • I - Health, Education, and Welfare
    • I - Health, Education, and Welfare

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