Efficiency assessment of primary care providers: A conditional nonparametric approach

Cordero Ferrera, Jose Manuel and Alonso Morán, Edurne and Nuño Solís, Roberto and Orueta, Juan F. and Souto Arce, Regina (2013): Efficiency assessment of primary care providers: A conditional nonparametric approach.

Abstract

This paper uses a fully nonparametric approach to estimate efficiency measures for primary care units incorporating the effect of (exogenous) environmental factors. This methodology allows us to account for different types of variables (continuous and discrete) describing the main characteristics of patients served by those providers. In addition, we use an extension of this nonparametric approach to deal with the presence of undesirable outputs in data, represented by the rates of hospitalization for ambulatory care sensitive condition (ACSC) and of hospital readmissions. The empirical results show that all the exogenous variables considered have a significant and negative effect on efficiency estimates

• Item Type: MPRA Paper
• Language: English
• Keywords: OR in health services, Efficiency, Data Envelopment Analysis, Environmental factors, Nonparametric analysis
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General; I - Health, Education, and Welfare > I1 - Health > I12 - Health Behavior

References

Amado, C.A. and Dyson, R. (2008): On comparing the performance of primary care providers, European Journal of Operational Research, 185, 915-932.

Amado, C.A. and Santos, S. (2009): Challenges for performance assessment and improvement in primary health care: The case of the Portuguese health centres, Health Policy, 91, 43-56.

Badin, L., Daraio, C. and Simar, L. (2010): Optimal bandwidth selection for conditional efficiency measures: A data-driven approach, European Journal of Operational Research, 201, 633-640.

Badin, L., Daraio, C. and Simar, L. (2012): How to measure the impact of environmental factors in a nonparametric production model?, European Journal of Operational Research, 223, 818-833.

Banker, R, D., Charnes, A. and Cooper, W. W. (1984): Models for estimating technical and scale efficiencies in data envelopment analysis, Management Science, 30(9), 1078-92.

Banker, R. D. and Natarajan, R. (2008): Evaluating contextual variables affecting productivity using data envelopment analysis, Operations Research, 56(1), 48–58.

Bardsley, M., Steventon, A., Smith, J. and Dixon, J. (2013): Evaluating integrated and community-based care: How do we know what works? Research summary, London: Nuffield Trust.

Bates J, Baines D, and Whynes, D. (1996): Measuring the efficiency of prescribing by general practitioners, Journal of the Operational Research Society, 47, 1443–51.

Blass, R., Da Silva, G.and Miranda, B. (2008): A Probabilistic Approach for Assessingthe Significance of Contextual Variables in Nonparametric Frontier Models: An Application for Brazilian Banks, Brazilian Review of Econometrics, 28(1), 111-125.

Bonaccorsi, A., Daraioc C. and L. Simar (2006): Advanced Indicators of Productivity ofUniversities, An Application of Robust Nonparametric Methods to Italian Data, Scientometrics, 66 (2), 389-410.

Caminal, J., Starfield, B., Sánchez, E., Casanova, C. and Morales, M. (2004): The role of primary care in preventing ambulatory care sensitive conditions, European Journal of Public Health, 14(3), 246–51.

Campbell, S.M., Roland, M.O. and Buetow, S.A. (2000): Defining quality of care, Social Science & Medicine, 51, 1611-1625.

Carvalho, P. and Marques, R.C. (2011): The influence of the operational environment on the efficiency of water utilities, Journal of Environmental Management, 92 (10), 2698-2707.

Cazals, C., Florens, J.P. and Simar, L. (2002): Nonparametric frontier estimation: A robust approach, Journal of Econometrics, 106, 1–25.

Cazals, C., Dudley, P., Florens, J.P., Patel, S. and Rodriguez, F. (2008): Delivery OfficesCost Frontier: A Robust Non Parametric Approach with Exogenous Variables, TheReview of Network Economics, 7 (2), 294-308.

Charnes, A., Cooper, W.W. and Rhodes, E. (1978): Measuring the Efficiency of Decision Making Units, European Journal of Operational Research, 2 (6), 429-444.

Cheng, G., Zervopoulos, P. and Qian, Z. (2013): A variant of radial measure capable of dealing with negative inputs and outputs in data envelopment analysis, European Journal of Operational Research, 225, 100-105.

Cherchye, L., De Witte, K. Ooghe, E. andNicaise, I. (2010): Efficiency and equity in private and public education: A nonparametric comparison, European Journal of Operational Research, 202, 563-573.

Chilingerian, J. A. (1995): Evaluating physician efficiency in hospitals: a multivariate analysis of best practices, European Journal of Operational Research, 80, 548–574.

Chilingerian, J. and H. Sherman (1996): Benchmarking physician practice patterns with DEA: A multi-stage approach for cost containment, Annals of Operations Research, 67, 83-116.

Chung, Y.H., Fare, R. and Grosskopf, S. (1997): Productivity and undesirable outputs: a directional distance function approach, Journal of Environmental Management, 51, 229-240.

Cordero, J.M., Pedraja, F. and Salinas, J. (2008): Measuring Efficiency in Education: an Analysis of Different Approaches for Incorporating Non-Discretionary Inputs, Applied Economics, 40 (10), 1323-1339.

Cordero, J.M., Crespo, E. and Murillo, L.R. (2010): Measuring Efficiency in Primary Health Care: The Effect of Exogenous Variables on Results, Journal of Medical Systems, 35, 545-554.

Cordero, J.M., Crespo, E. and Murillo, L.R. (2013): The effect of quality and socio-demographic variables on efficiency measures in primary health care, The European Journal of Health Economics, in press.

Curry, N. and Ham, C. (2010): Clinical and service integration: The route to improved outcomes, London: The King´s Fund.

Daouia, A. and Simar, L. (2007): Nonparametric Efficiency Analysis: A Multivariate Conditional Quantile Approach, Journal of Econometrics, 140, 375-400.

Daraio, C.andSimar, L. (2005): Introducing environmental variables in nonparametric frontier models: A probabilistic approach, Journal of Productivity Analysis, 24 (1), 93–121.

Daraio, C.andSimar, L. (2006): A robust nonparametric approach to evaluate and explain the performance of mutual funds, European Journal of Operational Research, 175 (1), 516–542.

Daraio, C. and Simar, L. (2007a): Advanced Robust and Nonparametric Methods in Efficiency Analysis. Methodology and Applications, Springer, New York.

Daraio, C. and Simar, L. (2007b): Conditional nonparametric Frontier models for convex and non convex technologies: A unifying approach, Journal of Productivity Analysis, 28, 13–32.

De Witte, K. and Geys, B. (2011): Evaluating efficient public good provision: Theory and evidence from generalized conditional efficiency model for public libraries, Journal of Urban Economics, 69 (3), 319-327.

De Witte, K. and Geys, B. (2013): Citizen co-production and efficient public good provision: Theory and evidence from local public libraries, European Journal of Operational Research, 224 (3), 592-602.

De Witte, K. and Kortelainen, M. (2013): What explains the performance of students in a heterogeneous environment? Conditional efficiency estimation with continuous and discrete environmental variables, Applied Economics, 45, 2401-2412.

De Witte, K. and Marques, R.C. (2010a): Incorporating heterogeneity in non-parametric models: a methodological comparison, International Journal of Operational Research, 9 (2), 188-204.

De Witte, K. and Marques, R.C. (2010b): Designing performance incentives, an international benchmark study in the water sector, Central European Journal of Operations Research, 18 (2), 189-220.

De Witte, K. and Rogge, N. (2011): Accounting for exogenous influences in performance evaluations of teachers, Economics of Education Review, 30, 641-653.

De Witte, K., Rogge, N., Cherchye, L. and van Puyenbroeck, T. (2013): Economies of scope in research and teaching: A non-parametric investigation, Omega, 41, 305-314.

Domínguez, M.F., Borrell, C., Cano, G., Esnaola, S., Nolasco, A., Pasarín, M.I., Ramis, R., Saurina, C. and Escolar, A. (2008): Construcción de un índice de privación a partir de datos censales en grandes ciudades españolas: (Proyecto MEDEA), Gaceta Sanitaria, 22:179–187.

Donabedian, A. (1980): The Definition of Quality and Approaches to its Assessment, vol. 1, Health Administration Press, Ann Arbor, Michigan.

Dyckhoff, H. and Allen, K. (2001): Measuring ecological efficiencywith data envelopment analysis (DEA), European Journal of Operational Research, 132,312–325. Dyson, R.G., Allen, R., Camanho, A.S., Podinovski, V.,

Sarrico, C.S. and Shale, E.A. (2001): Pitfalls and protocols in DEA, European Journal of Operational Research, 132,245-259.

Emrouznejad, A., Parker, B.R. and Tavares, G. (2008): Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA, Socio-Economic Planning Sciences, 42 (3), 151-157.

European Commission (2010): Joint Report on Health Systems. European Economy, Occassional Papers 74.

Färe, R., Grosskopf, S., Lovell, C.A.K. and Pasurka, C. (1989): Multilateral productivity comparisons when some outputs are undesirable: a nonparametricapproach, The Review of Economics and Statistics, 71, 90–98.

Färe, R.andGrosskopf, S. (2004): Modeling undesirable factors inefficiency evaluation: Comment, European Journal of Operational Research, 157 (1), 242–245.

Farrell, M.J. (1957): The measurement of productive efficiency, Journal of the Royal Statistics Society, Serie A, 120 (3), 253-281.

Finegan, M. S., Gao, J., Pasquale, D. and Campbell, J. (2010): Trends and geographic variation of potentially avoidable hospitalizations in the veteran’s health-care system, Health Service Management Research, 23(2):66-75.

Fried, H., Lovell, C.A.K. and Schmidt, S. (2008): The measurement of Productive Efficiency and Productivity Growth, Oxford University Press.

Fried, H., Schmidt, S. and Yaisawarng, S. (1999): Incorporating the Operating Environment into a Nonparametric Measure of Technical Efficiency, Journal of Productivity Analysis, 12, 249-267.

Fried, H.O., Lovell, C.A.K., Schmidt, S.S. andYaisawarng, S. (2002): Accounting for environmental effectsand statistical noise in data envelopment analysis, Journal of Productivity Analysis, 17, 157–174.

García, F. Marcuello, C. Serrano, D. and O. Urbina (1999): Evaluation of efficiency in primary healt care centres: An application of data envelopment analysis, Financial Accountability and Management, 15(1), 67-83.

Gibson, O.R., Segal, L. and McDermott, R.A. (2013): A systematic review of evidence on the association between hospitalization for chronic disease related ambulatory care sensitive conditions and primary health care resourcing, BMC Health Services Research, 13(1):336.

Giuffrida, A., Gravelle, H. and Roland, M. (1999): Measuring quality of care with routine data: avoiding confusion between performance indicators and health outcomes, BMJ, 319, 94-98.

Goñi, S. (1999): An analysis of the effectiveness of the Spanish primary health care teams, Health Policy, 48, 107-117.

Haelermans, C. and De Witte, K. (2012): The role of innovations in secondary school performance: Evidence from a conditional efficiency model, European Journal of Operational Research, 223, 541-549.

Hall, P., J.S. Racine and Li, Q. (2004): Cross-validation and the estimation of conditional probability densities, Journal of the American Statistical Association, 99 (468), 1015-1026.

Halkos, G. and Tzeremes, N. (2011a): Modelling regional welfare efficiency applying conditional full frontiers, Spatial Economic Analysis, 6(4), 451-471

Halkos, G. and Tzeremes, N. (2011b): A conditional nonparametric analysis for measuring the efficiency of regional public healthcare delivery: An application to Greek prefectures, Health Policy, 103(1), 73-82.

Halkos, G. and Tzeremes, N. (2013): A conditional directional distance function approach for measuring regional environmental efficiency: Evidence from UK regions, European Journal of Operational Research, 227, 182-189.

Ham, C. (2010): The ten characteristics of the high-performing chronic care system, Health economics, policy, and law, 5(1), 71-90.

Hollingsworth, B. (2008): The measurement of efficiency and productivity of health care delivery, Health Economics, 17, 1107-1128.

Hu, H., Qi, Q. and Yang, C.H. (2012): Evaluation of China´s regional hospital efficiency: DEA approach with undesirable output, Journal of the Operational Research Society, 63, 715-725.

Hua, Z. and Bian, Y. (2007): DEA with undesirable factors, in Zhu, J. and Cook, W.D. (eds): Modeling Data Irregularities and StructuralComplexities in Data Envelopment Analysis, Springer Science, Boston, MA, 103–121.

Hua, Z., Bian, Y. and Liang, L. (2007): Eco-efficiency analysis of paper mills along the Huai River: An extended DEA approach, Omega, 35, 578-587.

Huang, Y-G. L. and McLaughlin, C. P. (1989): Relative efficiency in rural primary health care: an application of data envelopment analysis, Health Services Research, 24, 143-158.

Koopmans, T.C. (1951): An analysis of production as an efficient combination of activities, in Koopmans, T.C. (Ed.): Activity Analysis of Production and Allocation, Cowles Commission for Research in Economics, Wiley, New York Monograph, 13.

Kontodimopoulos, N., Papathanasiou, N., Tountas, Y. and Niakas, D. (2010): Separating Managerial Inefficiency from Influences of the Operating Environment: An Application in Dialysis, Journal of Medical Systems, 34 (3), 397-405.

Korhonen, P. and Luptacik, M. (2004): Eco-efficiency analysis of power plants: Anextension of data envelopment analysis, European Journal of Operational Research, 154, 437–446.

Kringos, D.S., Boerma, W., Van der Zee, J. and Groenewegen, P (2013): Europe's Strong Primary Care Systems Are Linked To Better Population Health But Also To Higher Health Spending, Health Affairs, 32 (4), 686-694.

Lezzoni, L. (1994): Risk Adjustment for Measuring Health Care Outcomes, Ann Arbor, Michigan: Heath Administration Press.

Li, Q. and Racine, J.S. (2004): Cross-Validated Local Linear Nonparametric Regression, Statistica Sinica, 14(2), 485-512.

Li, Q. and Racine, J. (2007): Nonparametric econometrics: theory and practice. Princeton: Princeton UniversityPress.

Lovell, C.A.K. and Pastor, J. (1995): Units invariant and translation invariant DEA models, Operations Research Letters, 18, 147–151.

Lovell, C. A. K., Pastor, J. and Turner, J. (1995): Measuring Macroeconomic Performance in the OECD: A Comparison of European and Non-European Countries, European Journal of Operational Research, 87, 507-518.

Lu, W.M. and Lo, S.F. (2007): A closer look at the economic environmental disparities for regional development in China. European Journal of Operational Research, 183, 882–894.

McDonald, J. (2008): Using least squares and tobit in second stage DEA efficiency analyses, European Journal of Operational Research, 197 (2), 792-798.

Muecke, S. (2010): Avoiding hospitalisation: ambulatory care sensitive conditions, Research Roundup, 12.

Muñiz, M. (2002): Separating Managerial Inefficiency and External Conditions in Data, European Journal of Operational Research, 143-3, 625-643.

Nunamaker, T.R. (1983): Measuring routine nursing service efficiency: A comparison of cost per patient day and data envelopment analysis models, Health Services Research, 18: 183-205.

Nuño, R., Coleman K., Bengoa R. and Sauto R (2012): Integrated care for chronic conditions: the contribution of the ICCC Framework, Health Policy, 105(1):55-64.

Orueta, J.F., Urraca, J., Berraondo, I., Darpón, J. and Aurrekoetxea, J.J. (2006): Adjusted Clinical Groups (ACGs) explain the utilization of primary care in Spain based on information registered in the medical records: a cross-sectional study, Health Policy, 76:38-48.

Pelone, F., Kringos, D. S., Valerio, L., Romaniello, A., Lazzari, A., Ricciardi, W. and Giulio de Belvis, A. (2012): The measurement of relative efficiency of general practice and the implications for policy makers, Health policy, 107(2-3), 258–68.

Pina, V. and L. Torres (1992): Evaluating the efficiency of non-profit organizations: An application of data envelopment analysis to the public health services, Financial Accountability and Management, 8, 213-225.

Podinovski, V. and Kuosmanen, T. (2011): Modelling weak disposability in data envelopment analysis under relaxedconvexity assumptions, European Journal of Operational Research, 211, 577-585.

Racine, J.S. (1997): Consistent significance testing for nonparametric regression, Journal of Business and Economic Statistics, 15, 369–379.

Racine, J.S. and Li, Q. (2004): Nonparametric Estimation of Regression Functions with both categorical and continuous Data, Journal of Econometrics, 119 (1), 99-130.

Racine, J.S., Hart, J.D. and Li, Q. (2006): Testing the significance of categorical predictor variables in nonparametric regression models, Econometric Reviews, 25, 523–544.

Rogge, N. and De Jaeger, S. (2012): Measuring and explaining the cost efficiency of municipal solid waste collection and processing services, Omega, 41 (4), 653-664.

Rosano, A., Abo Loha, C., Falvo, R., Van der Zee, J., Ricciardi, W., Guasticchi, G. and De Belvis, A.G. (2012): The relationship between avoidable hospitalization and accessibility to primary care: a systematic review, European Journal of Public Health, 23(3):356-60.

Rosenman, R. and D. Friesner (2004): Scope and scale efficiencies in physician practices, Health Economics, 13: 1091-1116.

Sahoo, B.K., Luptacik, M. and Mahlberg, B. (2011): Alternative measures of environmental technology structure in DEA: An application, European Journal of Operational Research, 215, 750-762.

Salinas-Jiménez, J. and Smith, P.C. (1996): Data envelopment analysis applied to quality in primary health care, Annals of Operations Research, 67, 141-161.

Scheel, H. (2001): Undesirable outputs in efficiency valuations, European Journal of Operational Research, 132, 400-410.

Schiøtz, M., Price, M., Frølich, A., Søgaard, J,.Kristensen, J.K., Krasnik, A., Ross, M.N., Diderichsen, F. and Hsu, J. (2011): Something is amiss in Denmark: a comparison of preventable hospitalisations and readmissions for chronic medical conditions in the Danish Healthcare system and Kaiser Permanente, BMC Health Services Research, 11:347.

Seiford, L. M., and Zhu, J. (2002): Modeling undesirable factors in efficiency evaluation, European Journal of Operational Research, 142, 16–20.

Silva Portela, M.C.A., Thanassoulis, E. and Simpson, G. (2004): Negative data in DEA: a directional distance function approach applied to bank branches, Journal of the Operational Research Society, 55, 1111-1121.

Simar, L. and Wilson, P. W. (2007): Estimation and inference in two-stage, semi-parametric models of productionprocesses, Journal of Econometrics, 136(1), 31–64.

Simar, L. and Wilson, P. W. (2011): Two-stage DEA: caveat emptor, Journal of Productivity Analysis, 36,205–218. Starfield B., Weiner J., Mumford, L. and Steinwachs, D. (1991): Ambulatory care groups: a categorization of diagnoses for research and management, Health Services Research, 26:53-74.

Steinmann, L., Dittrich, G., Karmann, A. and Zweifel, P. (2004): Measuring and comparing the (in)efficiency of German and Swiss hospitals, The European Journal of Health Economics, 5 (3), 216-226.

Szczepura, A., Davies, C. and Fletcher, J. (1993): Efficiency and Effectiveness in General Practice, Journal of Management in Medicine, 7 (5), 36-47.

Tucker, A.M., Weiner, J.P., Honigfeld, S. and Parton, R.A. (1996): Profiling primary care physician resource use: examining the application of case mix adjustment, Journal of Ambulatory Care Manage, 19 (1), 60-80.

Verschelde, M. and Rogge, N. (2012): An environment-adjusted evaluation of citizen satisfaction with local police effectiveness: Evidence from a conditional Data Envelopment Analysis approach, European Journal of Operational Research, 223, 214-215.

Vidoli, F. (2011): Evaluating the water sector in Italy through a two-stage method using the conditional robust nonparametric frontier and multivariate adaptive regression splines, European Journal of Operational Research, 212 (3), 583-595.

Worthington, A.C. (2004): Frontier Efficiency Measurement in Health Care: A Review of Empirical Techniques and Selected Applications, Medical Care Research and Review, 61 (2), 135-170.

Yang, C., Hsiao, C. and Yu, M. (2008): Technical efficiency and impact of environmental regulations in farrow-to-finish swine production in Taiwan, Agricultural Economics, 39, 51–61.

Yang, H. and Pollitt, M. (2009): Incorporating both undesirable outputs and uncontrollable variables into DEA: The performance of Chinese coal-fired power plants, European Journal of Operational Research, 197, 1095-1105.

You, S. and Yan, H. (2011): A new approach in modelling undesirable outputin DEA model, Journal of the Operational Research Society, 62, 2146-2156.

Zavras, A., Tsakos, G. Economou, C. and Kyriopoulos, J. (2002): Using DEA to Evaluate Efficiency and Formulate Policy Within a Greek National Primary Health Care Network, Journal of Medical Systems, 26 (4), 285-292.

Zhou, P., Ang, B.W. and Poh, K.L. (2008): A survey of data envelopment analysis in energy and environmental studies, European Journal of Operational Research, 189 (1), 1-18.