Bayesian Inference for Comorbid Disease Risks Using Marginal Disease Risks and Correlation Information From a Separate Source

Background: Public health interventions are increasingly being evaluated for their cost-effectiveness. Such interventions act ‘upstream’ on the determinants of ill health and commonly reduce the incidence of several diseases. Diseases that share determinants are usually correlated at an individual level, which we observe as comorbidity. This paper is motivated by the problem of estimating comorbid disease state risks when only single disease risk estimates are available. Methods: 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 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 incremental benefit due to the intervention’s impact on reducing or increasing the disease risk correlations is explored in a sensitivity analysis. Results: If comorbidity is not taken into account, incremental benefit is overestimated compared with all scenarios in which the comorbidity is included in the model. Overestimation is greatest when physical activity is assumed to reduce disease state co-occurrence as well as disease risk. Conclusions: The proposed method reduces overestimation of benefit and allows the sensitivity to different assumptions about the correlation between disease risks to be determined.