Wednesday, October 26, 2011: 10:15 AM
Columbus Hall C-F (Hyatt Regency Chicago)
(ESP) Applied Health Economics, Services, and Policy Research

William Witteman, MISt1, Holly O. Witteman, PhD2 and Mike Paulden, MA., MSc.1, (1)University of Toronto, Toronto, ON, Canada, (2)University of Michigan, Ann Arbor, MI

Purpose: When using health care costs, it is common practice to apply costing data from one time point in one country to another time point in another country. This requires converting across currencies, health care systems, and time. The conventional recommendation is to first convert to the desired currency using purchasing power parity, then adjust for inflation using the local context to determine the rate of adjustment [1]. However, this recommendation was based on untested assumptions that may not consistently hold. This study aims to demonstrate the implications of using different methods for converting health care costs between countries and across time. [1] Drummond et al. Issues in the cross-national assessment of health technology. Int J Technol Assess Health Care. 1992;8(4):671-82.

Methods: Using a preliminary convenience sample of nine common drugs, we extracted costing data for 2006 and 2009 from the drug formularies for the Ontario Drug Benefit Program and the United Kingdom National Health Service. We examined differences in accuracy (defined as percent error between calculated and actual cost) for two different possible conversion routes: 1) convert currency, then inflate or 2) inflate, then convert currency, crossed with two different currency exchange mechanisms: a) purchasing power parity or b) exchange on currency markets. This yields four different possible conversion methods: 1a (recommended method as per [1]), 1b, 2a and 2b.

Results: Even in this very small sample, there were significant differences in accuracy for the four different conversion methods, whether calculating Ontario costs from NHS data (F(1,8)=14.16, p=.006) or NHS costs from Ontario data (F(1,8)=75.94, p<.001). Across drugs and methods, Ontario costs were underestimated by up to 47% and overestimated by up to 249%. UK costs were never underestimated and were overestimated by as much as 578%. Best accuracy for Ontario came from methods 2b (2 drugs) and 1b (7 drugs). Best accuracy for calculating UK costs was achieved with method 2b for all drugs. The recommended method (1a) yielded results that differed from the most accurate method for a given drug by up to 73%.

Conclusions: Differences in methods for cost conversion lead to vastly different results. Within this sample, the currently recommended method never yielded the most accurate results.