Estimating health system opportunity costs: the role of non-linearities and inefficiency

Karla Hernandez-Villafuerte; Bernarda Zamora; Yan Feng; David Parkin; Nancy Devlin; Adrian Towse

doi:10.1186/s12962-022-00391-y

Estimating health system opportunity costs: the role of non-linearities and inefficiency

Cost Eff Resour Alloc. 2022 Oct 29;20(1):56. doi: 10.1186/s12962-022-00391-y.

Authors

Karla Hernandez-Villafuerte¹, Bernarda Zamora², Yan Feng³, David Parkin⁴, Nancy Devlin⁵, Adrian Towse⁶

Affiliations

¹ German Cancer Research Center (DKFZ), Heidelberg, Germany.
² Imperial College London, London, UK.
³ Queen Mary University of London, London, UK.
⁴ Office of Health Economics, London, UK.
⁵ University of Melbourne, Melbourne, Australia.
⁶ Office of Health Economics, London, UK. atowse@ohe.org.

Abstract

Background: Empirical estimates of health system opportunity costs have been suggested as a basis for the cost-effectiveness threshold to use in Health Technology Assessment. Econometric methods have been used to estimate these in several countries based on data on spending and mortality. This study examines empirical evidence on four issues: non-linearity of the relationship between spending and mortality; the inclusion of outcomes other than mortality; variation in the efficiency with which expenditures generate health outcomes; and the relationship among efficiency, mortality rates and outcome elasticities.

Methods: Quantile Regression is used to examine non-linearities in the relationship between mortality and health expenditures along the mortality distribution. Data Envelopment Analysis extends the approach, using multiple measures of health outcomes to measure efficiency. These are applied to health expenditure data from 151 geographical units (Primary Care Trusts) of the National Health Service in England, across eight different clinical areas (Programme Budget Categories), for 3 fiscal years from 2010/11 to 2012/13.

Results: The results suggest differences in efficiency levels across geographical units and clinical areas as to how health resources generate outcomes, which indicates the capacity to adjust to a decrease in health expenditure without affecting health outcomes. Moreover, efficient units have lower absolute levels of mortality elasticity to health expenditure than inefficient ones.

Conclusions: The policy of adopting thresholds based on estimates of a single system-wide cost-effectiveness threshold assumes a relationship between expenditure and health outcomes that generates an opportunity cost estimate which applies to the whole system. Our evidence of variations in that relationship and therefore in opportunity costs suggests that adopting a single threshold may exacerbate the efficiency and equity concerns that such thresholds are designed to counter. In most health care systems, many decisions about provision are not made centrally. Our analytical approach to understanding variability in opportunity cost can help policy makers target efficiency improvements and set realistic targets for local and clinical area health improvements from increased expenditure.

Keywords: Cost-effectiveness threshold; DEA; English NHS; Opportunity cost; Outcome elasticities; Quantile regression.