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A Bayesian approach to analysing the cost‐effectiveness of two primary care interventions aimed at improving attendance for breast screening

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  • J. Brown
  • N. J. Welton
  • C. Bankhead
  • S. H. Richards
  • L. Roberts
  • C. Tydeman
  • T. J. Peters

Abstract

Aims: To assess the cost‐effectiveness of two primary care interventions, a letter and a flag, aimed at improving attendance for breast screening among (i) all women invited for breast screening and (ii) non‐attenders. Methods: A probabilistic decision analytic model was developed using Markov chain Monte Carlo simulation implemented in WinBUGS. The model was populated using economic and effectiveness data collected alongside two randomised controlled trials. Results: For all women invited, the incremental cost‐effectiveness ratio (ICER) for the letter compared with no intervention is £27 per additional attendance, and the ICER for the combined letter and flag intervention compared to the letter alone is £171. The corresponding ICERs for non‐attenders are £41 and £90. The flag intervention is an inefficient option in both settings. A large proportion of the costs fall on the practices (25–67%), depending on the intervention and target population. The total costs incurred do not, however, seem prohibitive. Expected value of perfect information suggests that there is greater value in carrying out further research on the intervention implemented among all women invited for breast screening rather than on non‐attenders. Conclusions: The flag intervention alone does not appear to be an efficient option. The choice between the letter and both interventions combined is subjective, depending on the willingness to pay for an additional screening attendance. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • J. Brown & N. J. Welton & C. Bankhead & S. H. Richards & L. Roberts & C. Tydeman & T. J. Peters, 2006. "A Bayesian approach to analysing the cost‐effectiveness of two primary care interventions aimed at improving attendance for breast screening," Health Economics, John Wiley & Sons, Ltd., vol. 15(5), pages 435-445, May.
    • Handle: RePEc:wly:hlthec:v:15:y:2006:i:5:p:435-445
    DOI: 10.1002/hec.1077
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    References listed on IDEAS

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    1. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2001. "Representing uncertainty: the role of cost‐effectiveness acceptability curves," Health Economics, John Wiley & Sons, Ltd., vol. 10(8), pages 779-787, December.
    2. Johannesson, Magnus & Weinstein, Milton C., 1993. "On the decision rules of cost-effectiveness analysis," Journal of Health Economics, Elsevier, vol. 12(4), pages 459-467, December.
    3. Andrew H. Briggs & Ron Goeree & Gord Blackhouse & Bernie J. O’Brien, 2002. "Probabilistic Analysis of Cost-Effectiveness Models: Choosing between Treatment Strategies for Gastroesophageal Reflux Disease," Medical Decision Making, , vol. 22(4), pages 290-308, August.
    4. Karl Claxton & John Posnett, "undated". "An Economic Approach to Clinical Trial Design and Research Priority Setting," Discussion Papers 96/19, Department of Economics, University of York.
    5. Nicola J. Cooper & Alex J. Sutton & Keith R. Abrams & David Turner & Allan Wailoo, 2004. "Comprehensive decision analytical modelling in economic evaluation: a Bayesian approach," Health Economics, John Wiley & Sons, Ltd., vol. 13(3), pages 203-226, March.
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    Cited by:

    1. Sofia Dias & Nicky J. Welton & Alex J. Sutton & A. E. Ades, 2013. "Evidence Synthesis for Decision Making 5," Medical Decision Making, , vol. 33(5), pages 657-670, July.
    2. N. J. Welton & A. E. Ades & D. M. Caldwell & T. J. Peters, 2008. "Research prioritization based on expected value of partial perfect information: a case‐study on interventions to increase uptake of breast cancer screening," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(4), pages 807-841, October.
    3. C. Armero & G. García‐Donato & A. López‐Quílez, 2010. "Bayesian methods in cost–effectiveness studies: objectivity, computation and other relevant aspects," Health Economics, John Wiley & Sons, Ltd., vol. 19(6), pages 629-643, June.
    4. Olena Levenets & Tetiana Stepurko & Abel Polese & Milena Pavlova & Wim Groot, 2019. "Coping strategies of cancer patients in Ukraine," International Journal of Health Planning and Management, Wiley Blackwell, vol. 34(4), pages 1423-1438, October.
    5. Marion S. Rauner & Walter J. Gutjahr & Kurt Heidenberger & Joachim Wagner & Joseph Pasia, 2010. "Dynamic Policy Modeling for Chronic Diseases: Metaheuristic-Based Identification of Pareto-Optimal Screening Strategies," Operations Research, INFORMS, vol. 58(5), pages 1269-1286, October.
    6. Nicky J. Welton & Jason J. Madan & Deborah M. Caldwell & Tim J. Peters & Anthony E. Ades, 2014. "Expected Value of Sample Information for Multi-Arm Cluster Randomized Trials with Binary Outcomes," Medical Decision Making, , vol. 34(3), pages 352-365, April.

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