Individuals from older populations tend to have more than one health condition (multimorbidity). Current approaches to produce economic evidence for clinical guidelines using decision analytic models typically use a single-disease approach, which may not appropriately reflect the competing risks within a population with multimorbidity. This study aims to demonstrate a proof-of-concept method of modelling multiple conditions in a single decision-analytic model to estimate the impact of multimorbidity on the cost-effectiveness of interventions.
Multiple conditions were modelled within a single decision-analytic model by linking multiple singledisease models. Individual Discrete Event Simulation models were developed to evaluate the costeffectiveness of preventative interventions for a case study assuming a UK National Health Service perspective. The case study used three diseases (heart disease, Alzheimer’s disease, and osteoporosis) that were combined within a single ‘linked’ model. The linked model, with and without correlations between diseases incorporated, simulated the general population aged 45 years and older to compare results in terms of lifetime costs and quality-adjusted life years (QALYs).
The estimated incremental costs and QALYs for healthcare interventions differed when three diseases were modelled simultaneously (£840; 0.234QALYs) compared with aggregated results from three single-disease models (£408; 0.280QALYs). With correlations between diseases additionally incorporated, both absolute and incremental costs and QALYs estimates changed in different directions, suggesting that the inclusion of correlations can alter model results.
Linking multiple single-disease models provides a methodological option for decision-analysts who undertake research on populations with multimorbidity. It also has potential for wider applications in informing decisions on commissioning of healthcare services and long-term priority setting across diseases and healthcare programmes through providing potentially more accurate estimations of relative cost-effectiveness of interventions.