Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Journal of Medical Systems
Published in: Journal of Medical Systems 4/2011

01-08-2011 | Original Paper

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

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

Published in: Journal of Medical Systems | Issue 4/2011

Login to get access

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.
Appendix
Available only for authorised users
Footnotes
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.
 
Literature
1.
Nunamaker, T. R., Measuring routine nursing service efficiency: A comparison of cost per patient day and data envelopment analysis models. Health Serv. Res. 18:183–205, 1983.
2.
Hollingsworth, B., and Street, A., The market for efficiency analysis of health care organizations. Health Econ. 15:1055–1059, 2006.CrossRef
3.
Worthington, A. C., Frontier efficiency measurement in health care: a review of empirical techniques and selected applications. Med. Care Res. Rev. 61 (2)135–170, 2004.CrossRef
4.
Chilingerian, J. A., and Sherman, H., Health care applications: From hospitals to physicians, from productive efficiency to quality Frontiers. In: Cooper, W. W., Seiford, L. M., and Zhu, J. (Eds.), Handbook on Data Envelopment AnalysisKluwer, London, pp. 481–537, 2004.
5.
Seinmann, L., Dittrich, G., Karmann, A., and Zweifel, P., Measuring and comparing the (in)efficiency of German and Swiss hospitals. Eur. J. Health Econ. 5:216–226, 2004.CrossRef
6.
Puig-Junoy, J., Eficiencia en la Atención Primaria de Salud: Una revisión crítica de las medidas frontera. Rev. Esp. Salud Púb. 74:483–495, 2000.
7.
Amado, C. A., and Dyson, R. G., On comparing the performance of primary care providers. Eur. J. Oper. Res. 185:915–932, 2008.MATHCrossRef
8.
Murillo-Zamorano, L. R., Economic efficiency and frontier techniques. J. Econ. Surv. 18 (1)33–77, 2004.CrossRef
9.
Chilingerian, J. A., Evaluating physician efficiency in hospitals: a multivariate analysis of best practice. Eur. J. Oper. Res. 80:548–574, 1995.MATHCrossRef
10.
Rosko, M. D., Impact of internal and external environmental pressures on hospital inefficiency. Health Care Manag. Sci. 2:63–74, 1999.CrossRef
11.
Kontodimopoulos, N., Moschovakis, G., Aletras, V., and Niakas, D., The effect of environmental factors on technical and scale efficiency of primary health care providers in Greece. Cost Eff. Resour. Alloc. 5:14, 2007. doi:10.​1186/​1478-7547-5-14.CrossRef
12.
Fried, H. O., and Lovell, C. A. K., Searching for the Zeds, paper presented in the second Georgia Productivity Workshop, 1996.
13.
Muñiz, M., Separating managerial inefficiency and external conditions in data. Eur. J. Oper. Res. 149-3:625–643, 2002.CrossRef
14.
Huang, Y.-G. L., and McLaughlin, C. P., Relative efficiency in rural primary health care: An application of data envelopment analysis. Health Serv. Res. 24 (2)143–158, 1989.
15.
Szczepura, A., Davies, A., Fletcher, C., and Boussofiane, A., Efficiency and effectiveness in general practice. J. Manag. Med. 7 (5)36–47, 1993.CrossRef
16.
Bates, J., Baines, D., and Whynes, D., Measuring the efficiency of prescribing by general practitioners. J. Oper. Res. Soc. 47:1443–1451, 1996.MATH
17.
Giuffrida, A., Productivity, efficiency changes in primary care: a Malmquist index approach. Health Care Manage. Sci. 2:11–26, 1999.CrossRef
18.
Hollingsworth, B., Dawson, P. J., and Maniadakis, N., Efficiency measurement of health care: A review of non-parametric methods and applications. Health Care Manage. Sci. 2:161–172, 1999.CrossRef
19.
Pina, V., and Torres, L., Evaluating the efficiency of non-profit organizations: An application of data envelopment analysis to the public health services. Financ. Account. Manag. 8:213–225, 1992.CrossRef
20.
Chilingerian, J., and Sherman, H., Benchmarking physician practice patterns with DEA: A multi-stage approach for cost containment. Ann. Oper. Res. 67:83–116, 1996.CrossRef
21.
Chilingerian, J., and Sherman, H., DEA and primary care physicians report cards: Deriving preferred practice cones from managed care service concepts and operating strategies. Ann. Oper. Res. 73:35–66, 1997.MATHCrossRef
22.
Ozcan, Y. A., Physician benchmarking: Measuring variation in practice behavior in treatment of otitis media. Health Care Manage. Sci. 1:5–17, 1998.CrossRef
23.
Goñi, S., An analysis of the effectiveness of the Spanish primary health care teams. Health Policy. 48:107–117, 1999.CrossRef
24.
Salinas-Jiménez, J., and Smith, P. C., Data envelopment analysis applied to quality in primary health care. Ann. Oper. Res. 67:141–161, 1996.CrossRef
25.
García, F., Marcuello, C., Serrano, D., and Urbina, O., Evaluation of efficiency in primary healt care centres: an application of data envelopment analysis. Financ. Account. Manag. 15 (1)67–83, 1999.CrossRef
26.
Rosenman, R., and Friesner, D., Scope and scale efficiencies in physician practices. Health Econ. 13:1091–1116, 2004.CrossRef
27.
Puig-Junoy, J., and Ortún, V., Cost efficiency in primary care contracting: a stochastic frontier cost function approach. Health Econ. 13:1149–1165, 2004.CrossRef
28.
Schmacker, E. R., and McKay, N. L., Factors affecting productive efficiency in primary care clinics. Health Serv. Manage. Res. 21:60–70, 2008.CrossRef
29.
Murillo, L. R., and Petraglia, C., Technical efficiency in primary health care: does quality matter?”, Working paper #10725, MPRA, University Library of Munich, 2008.
30.
Morita, H., Analysis of economies of scope by data envelopment analysis: Comparison of efficient frontiers. Int. Trans. Oper. Res. 10:393–402, 2003.MathSciNetMATHCrossRef
31.
Charnes, A., Cooper, W. W., and Rhodes, E., Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2 (6)429–444, 1978.MathSciNetMATHCrossRef
32.
Banker, R. D., Charnes, A., and Cooper, W. W., Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science. 30 (9)1078–1092, 1984.MATHCrossRef
33.
Seiford, L. M., and Thrall, R. M., Recent Developments in DEA: the mathematical programming approach to Frontier analysis. J. Econom. 46 (1/2)7–38, 1990.MathSciNetMATHCrossRef
34.
Charnes, A., Cooper, W. W., Lewin, A. Y., and Seiford, L. M., Data envelopment analysis: theory, methodology and applications. Kluwer, New York, 1994.MATH
35.
Cooper, W. W., Seiford, L. M. and Tone, K., Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software. Kluwer: New York.
36.
Thanassoulis, E., Introduction to the theory and application of data envelopment analysis. Kluwer, Dordrecht, 2001.
37.
Emrouznejad, A., Parker, B. R., and Tavares, G., Evaluation of research on efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Econ. Plann. Sci. 42 (3)151–157, 2008.CrossRef
38.
Hoff, A., Second stage DEA: Comparison of approaches for modelling the DEA score. Eur. J. Oper. Res. 181:425–435, 2007.MATHCrossRef
39.
Banker, R., and Natarajan, R., Evaluating contextual variables affecting productivity using data envelopment analysis. Oper. Res. 56 (1)48–58, 2008.MathSciNetMATHCrossRef
40.
McDonald, J., Using least squares and tobit in second stage DEA analyses. Eur. J. Oper. Res. 197:792–798, 2009.MATHCrossRef
41.
Banker, R. D., and Morey, R. C., Efficiency analysis for exogenously fixed inputs and outputs. Oper. Res. 34 (4)513–521, 2008.CrossRef
42.
Golany, B., and Roll, Y., Some extensions of techniques to handle non-discretionary factors in data envelopment analysis. J. Prod. Anal. 4:419–432, 1993.CrossRef
43.
Tofallis, C., Combining two approaches to efficiency assessment. J. Oper. Res. Soc. 52:1225–1231, 2001.MATHCrossRef
44.
Ray, S. C., Resource use efficiency in public schools: A study of Connecticut data. Manag. Sci. 37 (12)1620–1628, 1991.MATHCrossRef
45.
Afonso, A., and St. Aubyn, M., Cross-country efficiency of secondary education provision: a semi-parametric analysis with non-discretionary inputs. Econ. Model. 23(3):476–491, 2006.
46.
Prado, J. M., and García-Sánchez, I., Efficiency evaluation in municipal services. an application to the street lighting service in Spain. J. Prod. Anal. 27:149–162, 2007.CrossRef
47.
Ruggiero, J., Non-discretionary inputs in data envelopment analysis. Eur. J. Oper. Res. 111:461–469, 1998.MATHCrossRef
48.
Fried, H., Schmidt, S., and Yaisawarng, S., Incorporating the operating environment into a nonparametric measure of technical efficiency. J. Prod. Anal. 12:249–267, 1999.CrossRef
49.
Fried, H., Lovell, C. A. K., Schmidt, S., and Yaisawarng, S., Accounting for environmental effects and statistical noise in data envelopment analysis. J. Prod. Anal. 17:157–174, 2002.CrossRef
50.
Muñiz, M., Paradi, J., Ruggiero, J., and Yang, Z., Evaluating alternative DEA models used to control for non-discretionary inputs. Comput. Oper. Res. 33:1173–1183, 2006.MATHCrossRef
51.
Cordero, J. M., Pedraja, F., and Santin, D., Alternative approaches to include exogenous variables in DEA measures: A comparison using Monte Carlo. Comput. Oper. Res. 36:2699–2706, 2009.MATHCrossRef
52.
Cordero, J. M., Pedraja, F., and Salinas, J., Measuring efficiency in education: An analysis of different approaches for incorporating non-discretionary inputs. Appl. Econ. 40 (10)1323–1339, 2008.CrossRef
53.
Simar, L., and Wilson, P., Estimation and inference in two-stage, semi parametric models of production processes. J. Econ. 136:31–64, 2007.MathSciNet
54.
Vega-Cervera, J. A., Murillo-Zamorano, L. R., DeMiguel, F., Morillo, J., and Rivero, M., Un sistema integrado de información para la atención primaria en Extremadura. Consejería de Sanidad y Consumo, Junta de Extremadura, 2007.
55.
Hollingsworth, B., and Smith, P., Use of ratios in data envelopment analysis. Appl. Econ. Lett. 10:733–735, 2003.CrossRef
56.
Torgersen, A. M., Forsund, F. R., and Kittelsen, S. A. C., Slack-adjusted efficiency measures and ranking of efficient unit. J. Prod. Anal. 7 (4)379–398, 1996.CrossRef
57.
Emrouznejad, A., and Amin, G. R., DEA models for ratio data: Convexity consideration. Appl. Math. Model. 33 (1)486–498, 2009.MATHCrossRef
58.
Coelli, T., A guide to DEAP Version 2.1: A data envelopment analysis (computer) program”, CEPA Working Paper 96/08, Australia: University of New England, 1996.
59.
Metadata
Title
Measuring Technical Efficiency in Primary Health Care: The Effect of Exogenous Variables on Results
Authors
José Manuel Cordero-Ferrera
Eva Crespo-Cebada
Luis R. Murillo-Zamorano
Publication date
01-08-2011
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 4/2011
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-009-9390-6

Other articles of this Issue 4/2011

Journal of Medical Systems 4/2011 Go to the issue

Original Paper

Singular Spectrum Analysis of Sleep EEG in Insomnia

Original Paper

Using Health Smart Cards to Check Drug Allergy History: The Perspective from Taiwan’s Experiences

Original Paper

Classifying Epilepsy Diseases Using Artificial Neural Networks and Genetic Algorithm

Original Paper

HIT Implementation in Critical Access Hospitals: Extent of Implementation and Business Strategies Supporting IT Use

Original Paper

Time Frequency Analysis for Automated Sleep Stage Identification in Fullterm and Preterm Neonates

Original Paper

Electronic Medical Archives: A Different Approach to Applying Re-signing Mechanisms to Digital Signatures