Strong, M. and Oakley, J. (2011) Bayesian inference for comorbid disease state risks using marginal disease risks and correlation information from a separate source. Medical Decision Making, 31. pp. 571-581. ISSN 0272-989XFull text available as:
Background. Public health interventions are increasingly being evaluated for their cost-effectiveness. Such interventions act `upstream' on the determinants of ill health and therefore may reduce the incidence of several diseases. In this case the risks of the separate diseases are likely to be correlated at the individual level, and considerable comorbidity may be present. An economic evaluation should take this comorbidity into account, but estimates of the risks and intervention effects may only be available separately for each disease. This paper proposes a method for combining marginal disease risks and treatment effects with correlation information from a separate source in order to estimate comorbid disease risks and treatment effects.
Method. A case study is presented based on a physical activity cost-effectiveness model. The correlation between the risk of coronary heart disease, stroke and diabetes is estimated from cross sectional data using a Bayesian multivariate probit model. This information is then combined with disease specific marginal baseline risks and intervention effects to give comorbid disease state risks. The expected numbers of QALYs gained through avoiding the comorbid states is estimated from disease specific utility data under a range of assumptions. Finally, the incremental benefit of physical activity is calculated under these utility assumptions. The difference in effectiveness of the intervention due to its impact on reducing or increasing the disease risk correlations is explored in a sensitivity analysis.
Results. If comorbidity is not taken into account, total benefit is overestimated compared with all scenarios in which the comorbidity is included in the model. The overestimation is greatest when physical activity is assumed to reduce disease state co-occurrence as well as overall disease incidence.
|Copyright, Publisher and Additional Information:||This is the pre-publication, author-produced version of a manuscript accepted for publication in Medical Decision Making. This version does not include post-acceptance editing and formatting. Medical Decision Making is not responsible for the quality of the content or presentation of the author-produced accepted version of the manuscript or of any version that a third party derives from it. Readers who wish to access the definitive published version of this manuscript and any ancillary material related to this manuscript (correspondence, corrections, editorials, linked articles, etc.) should go to http://mdm.sagepub.com/. Those who cite this manuscript should cite the published version as it is the official version of record. Both the prepublication version and the published manuscript are protected by Medical Decision Making copyright at the time of publication and thereafter.|
|Keywords:||Public health; Cost-effectiveness analysis; Bayesian; Multivariate probit model; Correlated binary data; Evidence synthesis.|
|Institution:||The University of Sheffield|
|Academic Units:||The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > Section of Public Health (Sheffield)
The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield)
The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield)
|Depositing User:||Dr Mark Strong|
|Date Deposited:||07 Mar 2012 15:43|
|Last Modified:||08 Feb 2013 17:37|