Skip to main content
SpringerLink
Log in

Care Appropriateness and Health Productivity Evolution: A Non-Parametric Analysis of the Italian Regional Health Systems

  • Original Research Article
  • Published:
Applied Health Economics and Health Policy Aims and scope Submit manuscript

Abstract

Background

There has been increasing interest in measuring the productive performance of healthcare services since the mid-1980s.

Objective

By applying bootstrapped data envelopment analysis across the 20 Italian Regional Health Systems (RHSs) for the period 2008–2012, we employed a two-stage procedure to investigate the relationship between care appropriateness and productivity evolution in public hospital services.

Methods

In the first stage, we estimated the Malmquist index and decomposed this overall measure of productivity into efficiency and technological change. In the second stage, the two components of the Malmquist index were regressed on a set of variables measuring per capita health expenditure, care appropriateness, and clinical appropriateness.

Results

Malmquist analysis shows that no gains in productivity in the health industry have been achieved in Italy despite the sequence of reforms that took place during the 1990s, which were devoted to increasing efficiency and reducing costs. Analysis of the efficiency change index clearly indicates that the source of productivity gain relies on a rationalization of the employed inputs in the Italian RHSs. At the same time, the trend of the technological change index reveals that the health systems in the three macro-areas (North, Central, and South) are characterized by technological regress.

Conclusion

Overall, our results suggest that productivity increases could be achieved in the Italian health system by reducing the level of inputs, improving care and clinical appropriateness, and by counteracting the ‘DRG (diagnosis-related group) creep’ phenomenon.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. The number of discharges includes the number of patients discharged from day hospital.

  2. The cmx expresses the average complexity of a diagnosis-related group (DRG) treated in the hospital compared with the average complexity data from a set of reference hospitals (e.g., all Italian hospitals).

  3. 2000 replications have been employed. The bootstrap estimates of the indexes are significant at the 5 % values. The confidence intervals are available on request from the authors.

  4. As pointed out by the literature on DEA, an excessive number of inputs and/or outputs with respect to the number of observations causes in a large number of efficient units [16]. Therefore, since the three inputs are strongly correlated (>0.90) we reduced them to a single factor [43].

References

  1. Boni S, Malpelli V, De Stefano A, Compagnoni V, Gambino A, Ceccarelli A. I sistemi di governance dei Servizi Sanitari Regionali. Quaderno Formez n. 57. 2007; Formez.

  2. Censis. I modelli decisionali nella sanità locale. Roma: Censis; 2008.

    Google Scholar 

  3. Vainieri M, Nuti S. Performance measurement features of the Italian regional healthcare systems: differences and similarities. In: Smigorski K, editor. Health management-different approaches and solutions. Rijeka: InTech; 2011. http://www.intechopen.com/books/health-management-different-approaches-and-solutions/performance-measurement-features-of-the-italian-regional-healthcare-systems-differences-and-similari. Accessed 9 July 2016.

  4. Francese M, Romanelli M. Healthcare in Italy: expenditure determinants and regional differentials. Banca d’Italia working papers. 2011;828:1–46. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1880310. Accessed 9 Jul 2016.

  5. Tediosi F, Gabriele S, Longo F. Governing decentralization in health care under tough budget constraint: what can we learn from the Italian experience? Health Policy. 2009;90:303–12.

    Article  PubMed  Google Scholar 

  6. Italian Ministry of Health. Rapporto annuale sull’attività di ricovero ospedaliero, Dati SDO 2008–12, 2013. http://www.salute.gov.it/portale/documentazione/p6_2_2.jsp?lingua=italiano&area=ricoveriOspedalieri&btnCerca=. Accessed 13 July 2016.

  7. Lavis JN, Anderson GM. Health care delivery: definitions, measurements and policy implications. Can Med Assoc J. 1966;154:321–8.

    Google Scholar 

  8. Bevere F, Dotta F, Fava A, Gentile S, Lauro D, Lauro R, et al. Appropriatezza clinica, strutturale, tecnologica e operativa per la prevenzione, diagnosi e terapia dell’obesità e del diabete mellito. Quaderni del Ministero della Salute 2011;10. ISSN 2038-5293. http://www.salute.gov.it/imgs/C_17_pubblicazioni_1707_allegato.pdf. Accessed 9 Jul 2016.

  9. Siciliani L. Estimating technical efficiency in the hospital sector with panel data. Appl Health Econ Health Policy. 2006;2:99–116.

    Article  Google Scholar 

  10. Liberati P. Fiscal federalism and national health standards in Italy: implication for redistribution. In: Il futuro dei sistemi di welfare nazionali tra integrazione europea e decentramento regionale. Conference of the Società Italiana di Economia Pubblica; 4–5 Oct 2002; Pavia.

  11. Francese M, Romanelli M. Is there room for containing healthcare costs? An analysis of regional spending differentials in Italy. Eur J Health Econ. 2014;15:117–32.

    Article  PubMed  Google Scholar 

  12. Francese M, Piacenza M, Romanelli M, Turati G. Understanding inappropriateness in health spending: the role of regional policies and institutions in caesarean deliveries. Reg Sci Urban Econ. 2014;43:262–77.

    Article  Google Scholar 

  13. De Nicola A, Gitto S, Mancuso P, Valdmanis VG. Healthcare reform in Italy: an analysis of efficiency based on nonparametric methods. Int J Health Plan Manag. 2014;29:e48–63.

    Article  Google Scholar 

  14. Caves DW, Christensen LR, Diewert WE. The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica. 1982;5:1393–400.

    Article  Google Scholar 

  15. Färe R, Grosskopf S, Lindgren B, Roos P. Productivity changes in Swedish pharmacies 1980–1989: a non parametric approach. J Prod Anal. 1992;3:85–101.

    Article  Google Scholar 

  16. Simar L, Wilson PW. Statistical inference in nonparametric frontier models: recent developments and perspectives. In: Fried H, Lovell K, Schmidt SS, editors. The measurement of productive efficiency and productivity growth. New York: Oxford University Press; 2008.

    Google Scholar 

  17. Thanassoulis E, Portela M, Despic O. Data envelopment analysis: the mathematical programming approach to efficiency analysis. In: Fried H, Lovell K, Schmidt SS, editors. The measurement of productive efficiency and productivity growth. New York: Oxford University Press; 2008.

    Google Scholar 

  18. Shepard RW. Theory of cost and production functions. Princeton: Princeton University Press; 1970.

    Google Scholar 

  19. Färe R, Grosskopf S, Roos P. Productivity and quality changes in Swedish pharmacies. Int J Prod Econ. 1995;39:137–47.

    Article  Google Scholar 

  20. Jacobs R, Smith PC, Street A. Measuring efficiency in health care. New York: Cambridge University Press; 2006.

    Book  Google Scholar 

  21. Simar L, Wilson PW. Estimating and bootstrapping Malmquist indices. Eur J Oper Res. 1999;115:459–71.

    Article  Google Scholar 

  22. Wilson PW. FEAR: a software package for frontier efficiency analysis with R. Socio Econ Plan Sci. 2007;42:247–54.

    Article  Google Scholar 

  23. Worthington A. Malmquist indices of productivity change in Australian financial services. J Int Financ Mark Inst Money. 1999;9:303–20.

    Article  Google Scholar 

  24. Nedelea IC, Fannin JM. Technical efficiency of critical access hospitals: an application of the two-stage approach with double bootstrap. Health Care Manag Sci. 2013;16:27–36.

    Article  PubMed  Google Scholar 

  25. Baltagi BH. Econometric analysis of panel data. 4th ed. Chichester: Wiley; 2008.

    Google Scholar 

  26. ISTAT (Istituto Nazionale di Statistica). Health for All database. http://www.istat.it/it/archivio/14562. Accessed 9 July 2016.

  27. Loghi M, D’Errico A. Health for All—Italia, an informative health system. Ital J Public Health. 2008;5(2):107–11.

    Google Scholar 

  28. Lo Scalzo A, Donatini A, Orzella L, Cicchetti A, Profili S, Maresso A. Health care systems in transition: Italy. Copenhagen: WHO regional office for Europe on behalf of the European Observatory on Health Systems and Policies; 2009.

  29. De Nicola A, Gitto S, Mancuso P. Evaluating Italian public hospital efficiency using bootstrap DEA and CART. Int J Appl Decis Sci. 2013;6:281–92.

    Google Scholar 

  30. Ferrier GD, Rosko MD, Valdmanis VG. Analysis of uncompensated hospital care using a DEA model of output congestion. Health Care Manag Sci. 2006;9:181–8.

    Article  PubMed  Google Scholar 

  31. Ozcan YA. Health care benchmarking and performance evaluation: an assessment using data envelopment analysis. New York: Springer; 2008.

    Book  Google Scholar 

  32. O’Neill L, Rauner M, Heidenberger K, Kraus K. A cross-national comparison and taxonomy of DEA-based hospital efficiency studies. Soc Econ Plan Sci. 2008;42:158–89.

    Article  Google Scholar 

  33. Mitropoulos P, Kounetas K, Mitropoulos I. Factors affecting primary health care centers’ economic and production efficiency. Ann Oper Res. doi:10.1007/s10479-015-2056-5 (Epub 12 Nov 2015).

  34. De Nicola A, Gitto S, Mancuso P. Uncover the predictive structure of healthcare efficiency applying a bootstrapped data envelopment analysis. Expert Syst Appl. 2012;39:10495–9.

    Article  Google Scholar 

  35. Grosskopf S, Margaritis D, Valdaminis VG. Competitive effects on teaching hospitals. Eur J Oper Res. 2004;154:515–25.

    Article  Google Scholar 

  36. Mobley LR, Magnussen J. An international comparison of hospital efficiency: does institutional environment matter? Appl Econ. 1998;30:1089–100.

    Article  Google Scholar 

  37. Chowdhury H, Zelenyuk V, Laporte A, Wodchis WP. Analysis of productivity, efficiency and technological changes in hospital services in Ontario: how does case-mix matter? Int J Prod Econ. 2014;150:74–82.

    Article  Google Scholar 

  38. O’Malley KJ, Cook KF, Price MD, Wildes KR, Hurdle JF, Ashton CM. Measuring diagnoses: ICD code accuracy. Health Serv Res. 2005;40:1620–39.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kahur K. Error DRGs—what are they for? BMC Health Serv Res. 2010;10(Suppl 2):A5.

    Article  PubMed Central  Google Scholar 

  40. Simborg DW. DRG creep: a new hospital acquire disease. N Engl J Med. 1981;304:1062–4.

    Article  Google Scholar 

  41. Gibbons L, Belizán JM, Lauer JA, Betrán AP, Merialdi M, Althabe F. The global numbers and costs of additionally needed and unnecessary caesarean sections performed per year: overuse as a barrier to universal coverage. World Health Report (2010), Background Paper 30. Geneva: WHO; 2010.

  42. ISTAT (Istituto Nazionale di Statistica). Ageing index. 2012. http://noi-italia2012en.istat.it/index.php?id=7&user_100ind_pi1%5Bid_pagina%5D=15&cHash=3af4f87fc1eba1af87e862f57aa97d91. Accessed 9 July 2016.

  43. Daraio C, Simar L. Advanced robust and nonparametric methods in efficiency analysis: methodology and applications. New York: Springer; 2007.

    Google Scholar 

  44. Everitt BS, Torsten H. A handbook of statistical analyses using R. Boca Raton: Chapman and Hall/CRC; 2006.

    Book  Google Scholar 

  45. Hollingsworth B. The measurement of efficiency and productivity of health care delivery. Health Econ. 2008;17:1107–28.

    Article  PubMed  Google Scholar 

  46. Ferré F, de Belvis AG, Valerio L, Longhi S, Lazzari A, Fattore G, et al. European Observatory on Health System and Policy. Italy: health system review. Health Syst Transit. 2014;16(4):1–168.

    PubMed  Google Scholar 

Download references

Acknowledgments

The authors would like to thank to Tim Wrightson, Editor-in-Chief of Applied Health Economics and Health Policy, and two anonymous referees for their comments and critical remarks, which were very helpful in crafting earlier drafts of this manuscript.

Authors’ contributions

Paolo Mancuso and Vivian Grace Valdmanis evaluated the literature and were equally involved in the model design. Paolo Mancuso built the model and conducted the model analyses. Both authors reviewed and approved the final submitted version of the manuscript. Paolo Mancuso is the guarantor for the overall content.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133, Roma, Italy

    Paolo Mancuso

  2. School of Interdisciplinary Health, Program of Public Health, Western Michigan University, 200 Ionia Avenue SW, Grand Rapids, MI, 49503, USA

    Vivian Grace Valdmanis

  3. IESEG School of Management, Lille, France

    Vivian Grace Valdmanis

  4. IESEG School of Management, Paris, France

    Vivian Grace Valdmanis

Authors
  1. Paolo Mancuso
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vivian Grace Valdmanis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paolo Mancuso.

Ethics declarations

No sources of funding were used to prepare this paper. Paolo Mancuso and Vivian Grace Valdmanis have no conflicts of interest that are directly relevant to the content of this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mancuso, P., Valdmanis, V.G. Care Appropriateness and Health Productivity Evolution: A Non-Parametric Analysis of the Italian Regional Health Systems. Appl Health Econ Health Policy 14, 595–607 (2016). https://doi.org/10.1007/s40258-016-0257-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40258-016-0257-y

Keywords

Search

Navigation