Using Data Envelopment Analysis to Address the Challenges of Comparing Health System Efficiency

Efficiency is one of the most potent measures of health system performance and is of particular interest to policy makers because it seeks to assess the valued outcomes of a health system in relation to the resources that are sacrificed to achieve those outcomes. However, the production process of the health care system is a complex sequence, and most indicators are only able to capture part of that process; these indicators offer limited scope for analysis. While researchers have previously constructed composite indicators which combine partial measures into a single number, the weights used for aggregating data can be contentious and may not be universally applicable across systems. Data envelopment analysis (DEA) is most often used to compare the productivity of different producing entities, including health systems. In this article, we instead propose a method that relies on DEA to construct composite health system efficiency indicators from several partial efficiency measures. Among other noted benefits, this enables the construction of composite indicators where different weights are attached to partial indicators for each country, allowing countries to be viewed according to the weights that cast each in the best light. Our application of this method suggests that there is reasonable consistency among the countries that are found to be efficient.

[1]  B. W. Ang,et al.  A mathematical programming approach to constructing composite indicators , 2007 .

[2]  Rodney H. Green,et al.  Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses , 1994 .

[3]  Willem Moesen,et al.  Towards a synthetic indicator of macroeconomic performance: Unequal weighting when limited information is available , 1991 .

[4]  M. Farrell The Measurement of Productive Efficiency , 1957 .

[5]  A. Sagar,et al.  The human development index: a critical review , 1998 .

[6]  Nardo Michela,et al.  Constructing Consistent Composite Indicators: the Issue of Weights , 2005 .

[7]  Gerard F Anderson,et al.  Cross-national comparisons of health systems using OECD data, 1999. , 2002, Health affairs.

[8]  P. Dolan,et al.  Valuing health states: a comparison of methods. , 1996, Journal of health economics.

[9]  Bruce Hollingsworth,et al.  Use of ratios in data envelopment analysis , 2003 .

[10]  Mervyn Stone,et al.  How not to measure the efficiency of public services (and how one might) , 2002 .

[11]  Laurens Cherchye,et al.  An Introduction to ‘Benefit of the Doubt’ Composite Indicators , 2007 .

[12]  Emmanuel Thanassoulis,et al.  Introduction to the theory and application of data envelopment analysis , 2001 .

[13]  Laurens Cherchye,et al.  Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index , 2006, J. Oper. Res. Soc..

[14]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[15]  Bruce Hollingsworth,et al.  The efficiency of health production: re-estimating the WHO panel data using parametric and non-parametric approaches to provide additional information. , 2003, Health economics.

[16]  Peter Smith,et al.  Model misspecification in Data Envelopment Analysis , 1997, Ann. Oper. Res..

[17]  Koen Decancq,et al.  Weights in Multidimensional Indices of Well-Being: An Overview , 2010 .