Technical efficiency in the use of health care resources: a comparison of OECD countries
Introduction
The process of globalization of health care has increased substantially in recent years, expedited by several broad factors. The USA exported increasing amounts of both healthcare technology and pharmaceutical products throughout the industrialized world. Further, the Internet has facilitated the dissemination of medical information to both physicians and patients [1], [2]. Meanwhile, countries face the critical issue of determining whether the gains from increased medical expenditure, driven primarily by advanced technology, accrue adequate returns. Despite dramatic increases in levels of health care expenditures in industrialized countries, insufficient research has been directed to the issues of technical efficiency in resource use and of measuring relative efficiency among countries.
This paper examines technical efficiency in healthcare resource use by comparing health outputs achieved, given the level of healthcare resources consumed and the health challenges of each country. The prime research question is: how efficiently do different countries use their resources to achieve their health outcomes? Technical efficiency is obtained when output is maximized for a given level of inputs, or alternately, when input is minimized for a given amount of output.
Applying the linear programming technique of data envelopment analysis (DEA) to 2000 Organization for Economic Cooperation and Development (OECD) health data, we shed light on the issue of whether it is relatively more beneficial for a country to pursue enhanced health status or resource and cost containment. We find that the efficient OECD countries include both countries with good health outcomes (Japan, Sweden, Norway, and Canada) and countries with modest or relatively poor health outcomes (Mexico and Turkey). The findings motivate examination of the policy implications of this comparative analysis of efficiency in the production of health care. We conclude that the USA may have something to learn from countries that are more economical in their allocation of resources to health care.
Section snippets
Background
The World Health Organization’s (WHO) World Health Report 2000 emphasized the widespread concern with measuring health system performance [3]. The WHO ranked the USA behind many less wealthy nations on indicators of health system performance [4]. On an index of overall performance of health systems, the USA was ranked 37th out of 191 WHO member countries [4], despite the fact that the USA spends more per capita on health care than any other country [5], [6], [7], [8]. In 2000, the USA spent
Data
The data used are from the OECD health data 2000, an annual database developed by the OECD Health Policy Unit in Paris. The data were contributed by the member countries and verified by the OECD to insure accuracy and consistency to allow accurate comparisons across healthcare systems [9]. This database provides a rich spectrum of cross-country data for industrialized countries [18]. However, previous analyses of OECD health data note the measurement problems of using aggregate cross-country
Model and variables
We model health status outputs as dependent upon the inputs of healthcare resources and the social environments of the 27 countries. The outputs of our model are infant mortality and life expectancy at birth, which are two of the three indicators on health (the third is the child mortality rate) published by the United Nations [21]. We also tested a third output variable, premature mortality from all causes below age 70. The findings were consistent with those of the two outputs reported, and,
Data envelopment analysis
Data envelopment analysis is a method of measuring relative efficiency for a group of operating units where the relative values of variables are unknown [31]. It accommodates multiple inputs and outputs and can also include exogenously fixed environmental variables [32]. DEA utilizes the fundamental concept of a production function, and by using linear programming it is a non-parametric technique that does not require assumptions about the statistical properties of the variables.
There are
Results
Both models were run individually for each of the two outputs and for both input and output orientations, so that four linear programs were solved for each country. The results for the output oriented model are shown in Table 2 and the results for the input oriented model are shown in Table 3. A detailed example of an input oriented solution is shown in the Appendix A to provide an in-depth illustration of the DEA model. The first two columns of Table 2, Table 3 show the DEA score from the
Discussion
Our results have at least two important policy implications. The first is which path to the frontier offers greater potential improvement for technically inefficient countries, via either input reduction or output enhancement. The second is which output provides the greater improvement potential for a given country.
Our findings for output improvement (Table 2) show that the technically inefficient OECD countries on average can reduce infant mortality by 14.5% without using more resources, but
Conclusions
Access to the OECD health data has generated an increasing quantity of empirical research on international comparisons of health spending, health systems, and outcomes. Many of the studies have focused on the relative performance of healthcare systems, various health status outputs, and the determinants of health and healthcare expenditures of the different OECD countries. However, less attention has been paid to the issue of how technically efficient individual countries are in the production
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