IDEAS home Printed from https://ideas.repec.org/a/wly/hlthec/v15y2006i5p435-445.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/hec.1077
    Download Restriction: no
    File URL: https://libkey.io/10.1002/hec.1077?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Karl Claxton & Mark Sculpher & Chris McCabe & Andrew Briggs & Ron Akehurst & Martin Buxton & John Brazier & Tony O'Hagan, 2005. "Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 339-347, April.
    2. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2005. "The value of implementation and the value of information: combined and uneven development," Working Papers 005cherp, Centre for Health Economics, University of York.
    3. Rachael DiSantostefano & Andrea Biddle & John Lavelle, 2006. "The Long-Term Cost Effectiveness of Treatments for Benign Prostatic Hyperplasia," PharmacoEconomics, Springer, vol. 24(2), pages 171-191, February.
    4. Neil Hawkins & Mark Sculpher & David Epstein, 2005. "Cost-Effectiveness Analysis of Treatments for Chronic Disease: Using R to Incorporate Time Dependency of Treatment Response," Medical Decision Making, , vol. 25(5), pages 511-519, September.
    5. McKenna, Claire & Chalabi, Zaid & Epstein, David & Claxton, Karl, 2010. "Budgetary policies and available actions: A generalisation of decision rules for allocation and research decisions," Journal of Health Economics, Elsevier, vol. 29(1), pages 170-181, January.
    6. Martin Henriksson & Fredrik Lundgren & Per Carlsson, 2006. "Informing the efficient use of health care and health care research resources ‐ the case of screening for abdominal aortic aneurysm in Sweden," Health Economics, John Wiley & Sons, Ltd., vol. 15(12), pages 1311-1322, December.
    7. Rachael L. Fleurence, 2007. "Setting priorities for research: a practical application of 'payback' and expected value of information," Health Economics, John Wiley & Sons, Ltd., vol. 16(12), pages 1345-1357.
    8. Stefano Conti & Karl Claxton, 2008. "Dimensions of design space: a decision-theoretic approach to optimal research design," Working Papers 038cherp, Centre for Health Economics, University of York.
    9. Simon Eckermann & Andrew Briggs & Andrew R. Willan, 2008. "Health Technology Assessment in the Cost-Disutility Plane," Medical Decision Making, , vol. 28(2), pages 172-181, March.
    10. Richard M. Nixon & David Wonderling & Richard D. Grieve, 2010. "Non‐parametric methods for cost‐effectiveness analysis: the central limit theorem and the bootstrap compared," Health Economics, John Wiley & Sons, Ltd., vol. 19(3), pages 316-333, March.
    11. Karl Claxton & Elisabeth Fenwick & Mark J. Sculpher, 2012. "Decision-making with Uncertainty: The Value of Information," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 51, Edward Elgar Publishing.
    12. Fleurence, Rachael L. & Torgerson, David J., 2004. "Setting priorities for research," Health Policy, Elsevier, vol. 69(1), pages 1-10, July.
    13. Elamin H. Elbasha, 2005. "Risk aversion and uncertainty in cost‐effectiveness analysis: the expected‐utility, moment‐generating function approach," Health Economics, John Wiley & Sons, Ltd., vol. 14(5), pages 457-470, May.
    14. Simon Eckermann & Andrew Willan, 2011. "Presenting Evidence and Summary Measures to Best Inform Societal Decisions When Comparing Multiple Strategies," PharmacoEconomics, Springer, vol. 29(7), pages 563-577, July.
    15. Haitham Tuffaha & Shelley Roberts & Wendy Chaboyer & Louisa Gordon & Paul Scuffham, 2015. "Cost-Effectiveness and Value of Information Analysis of Nutritional Support for Preventing Pressure Ulcers in High-risk Patients: Implement Now, Research Later," Applied Health Economics and Health Policy, Springer, vol. 13(2), pages 167-179, April.
    16. Elizabeth Fenwick & Karl Claxton & Mark Sculpher & Andrew Briggs, 2000. "Improving the efficiency and relevance of health technology assessent: the role of iterative decision analytic modelling," Working Papers 179chedp, Centre for Health Economics, University of York.
    17. Marta O Soares & L Canto e Castro, 2010. "Simulation or cohort models? Continuous time simulation and discretized Markov models to estimate cost-effectiveness," Working Papers 056cherp, Centre for Health Economics, University of York.
    18. Douglas Coyle, 2003. "Determining the optimal combinations of mutually exclusive interventions: a response to Hutubessy and colleagues," Health Economics, John Wiley & Sons, Ltd., vol. 12(2), pages 159-162, February.
    19. Andrea Manca & Nigel Rice & Mark J. Sculpher & Andrew H. Briggs, 2005. "Assessing generalisability by location in trial‐based cost‐effectiveness analysis: the use of multilevel models," Health Economics, John Wiley & Sons, Ltd., vol. 14(5), pages 471-485, May.
    20. Manca, A & Austin, P. C, 2008. "Using propensity score methods to analyse individual patient-level cost-effectiveness data from observational studies," Health, Econometrics and Data Group (HEDG) Working Papers 08/20, HEDG, c/o Department of Economics, University of York.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:hlthec:v:15:y:2006:i:5:p:435-445. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/5749 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.