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Measuring Technical Efficiency in Primary Health Care: The Effect of Exogenous Variables on Results

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An Erratum to this article was published on 13 October 2011

Abstract

The aim of this paper is to extend the existing literature about efficiency measurement in primary health care with the application of a recently developed method to deal with exogenous variables. In this context, these variables are represented by the main characteristics of the covered population. The use of this technique allows calculating more accurate efficiency scores that can reflect the performance of units more properly. Our results show that the inclusion of these variables in the evaluation has a great impact on both the values of efficiency scores and the rank of units. This analysis has been carried out using a great amount of data available about primary health care centers in the Spainsh region of Extremadura.

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Notes

  1. The reader is referred to [4] and [5] for a comprehensive review on the measurement of economic efficiency of hospitals. For a survey of applications on primary care services, see [6] and [7]. A theoretical overview of main frontier techniques for the measurement of economic efficiency can be found in [8].

  2. [18] provide an excellent literature review about efficiency measurement in health care using DEA. An extended review that also includes parametric methods can be found in [7].

  3. DEA is considered preferable to any other approaches when the aim of the study is to measure the efficiency of a group of units producing several outputs [33].

  4. For an overview, see [34–36].

  5. See [43] for an illustrated explanation of slacks.

  6. Scores calculated in [44] using a regression model with a one-sided disturbance term are biased upward, while in [45] and [46] efficiency scores are biased downwards since all the units are classified as inefficient.

  7. See [52] for a detailed review on different methodological options available to incorporate the effect of exogenous variables into efficiency analysis through non-parametric techniques,

  8. See [53] for details.

  9. See Muñiz [13], p. 631 for a detailed explanation of alternative correction mechanisms.

  10. We also considered the inclusion of a proxy variable for the capital structure of the care centre area measured in squared feet, but we finally refused to use it on the basis of statistical criteria (the value of the correlation coefficient between this variable and output variables is always lower than 0.2) and according to results obtained in previous studies [49]. In addition, it is worth mentioning that, on average, capital accounted for only 6% of overall costs in our sample.

  11. For instance, the Pearson correlation coefficients (in absolute values) between these variables and FREQUENCYGP are: GNR (0.544), DR (0.659), LR (0.710), MR (0.545), RR (0.604), DENSITY (0.508), AGRIEMP (0.447) and SERVIEMP (0.546). Data on other output variables are available upon request.

  12. The use of this model when input variables are in the form of ratios can result incorrect efficiency scores according to the demonstration provided in [57]. However, the modified DEA model proposed in that paper to avoid this problem cannot be used in our analysis since it requires knowing the values of the nominator and denominator of the ratio and our database does not provide that information.

  13. See [58, 59] for details.

  14. Some of the exogenous variables are positively correlated with total slacks, i.e., negatively correlated with efficiency scores (DR, LR and AGRIEMP). Thus, they have been transformed by using their inverse values for a correct application of the three-stage model.

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Acknowledgments

The authors are most grateful to the Consejeria de Sanidad y Dependencia of the Junta de Extremadura for its financial and data availability support. We also thank Carmelo Petraglia for his helpful assistance in preparing the data set used in the research and to three anonymous referes por for their comments and suggestions.

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  1. University of Extremadura, Badajoz, Spain

    José Manuel Cordero-Ferrera, Eva Crespo-Cebada & Luis R. Murillo-Zamorano

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  1. José Manuel Cordero-Ferrera
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  2. Eva Crespo-Cebada
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  3. Luis R. Murillo-Zamorano
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Correspondence to José Manuel Cordero-Ferrera.

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An erratum to this article can be found at http://dx.doi.org/10.1007/s10916-011-9782-2

Appendix

Appendix

Table 7 Efficiency scores in BHZ without and with exogenous variables
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Cordero-Ferrera, J.M., Crespo-Cebada, E. & Murillo-Zamorano, L.R. Measuring Technical Efficiency in Primary Health Care: The Effect of Exogenous Variables on Results. J Med Syst 35, 545–554 (2011). https://doi.org/10.1007/s10916-009-9390-6

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