Multicohort models in cost-effectiveness analysis: Why aggregating estimates over multiple cohorts can hide useful information

Background. Models used in cost-effectiveness analysis (CEA) of screening programs may include 1 or many birth cohorts of patients. As many screening programs involve multiple screens over many years for each birth cohort, the actual implementation of screening often involves multiple concurrent recipient cohorts. Consequently, some advocate modeling all recipient cohorts rather than 1 birth cohort, arguing it more accurately represents actual implementation. However, reporting the cost-effectiveness estimates for multiple cohorts on aggregate rather than per cohort will fail to account for any heterogeneity in cost-effectiveness between cohorts. Such heterogeneity may be policy relevant where there is considerable variation in cost-effectiveness between cohorts, as in the case of cancer screening programs with multiple concurrent recipient birth cohorts, each at different stages of screening at any one point in time. Objective. The purpose of this study is to illustrate the potential disadvantages of aggregating cost-effectiveness estimates over multiple cohorts, without first considering the disaggregate estimates. Analysis. We estimate the cost-effectiveness of 2 alternative cervical screening tests in a multicohort model and compare the aggregated and per-cohort estimates. We find instances in which the policy choices suggested by the aggregate and per-cohort results differ. We use this example to illustrate a series of potential disadvantages of aggregating CEA estimates over cohorts. Conclusions. Recent recommendations that CEAs should consider the cost-effectiveness of more than just a single cohort appear justified, but the aggregation of estimates across multiple cohorts into a single estimate does not.

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