Mind the Gap! - Geographic transferability of economic evaluation in health

Abstract

Background: Transferring cost-effectiveness information between geographic domains offers the potential for more efficient use of analytical resources. However, it is difficult for decision-makers to know when they can rely on costeffectiveness evidence produced for another context. Objectives: This thesis explores the transferability of economic evaluation results produced for one geographic area to another location of interest, and develops an approach to identify factors to predict when this is appropriate. Methods: Multilevel statistical models were developed for the integration of published international costeffectiveness data to assess the impact of contextual effects on country-level; whilst controlling for baseline characteristics within, and across, a set of economic evaluation studies. Explanatory variables were derived from a list of factors suggested in the literature as possible constraints on the transferability of costeffectiveness evidence. The approach was illustrated using published estimates of the cost-effectiveness of statins for the primary and secondary prevention of cardiovascular disease from 67 studies and related to 23 geographic domains, together with covariates on data, study and country-level. Results: The proportion of variation at the country-level observed depends on the appropriate multilevel model structure and never exceeds 15% for incremental effects and 21% for incremental cost. Key sources of variability are patient and disease characteristics, intervention cost and a number of methodological characteristics defined on the data-level. There were fewer significant covariates on the study and country-levels. Conclusions: Analysis suggests that variability in cost-effectiveness data is primarily due to differences between studies, not countries. Further, comparing different models suggests that data from multinational studies severely underestimates country-level variability. Additional research is needed to test the robustness of these conclusions on other sets of cost-effectiveness data, to further explore the appropriate set of covariates, and to foster the development of multilevel statistical modelling for economic evaluation data in health.