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



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


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.


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.


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.


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.

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Fig. 1


  1. 1.

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

  2. 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. 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. 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].


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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.

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Correspondence to Paolo Mancuso.

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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.

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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).

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  • Data Envelopment Analysis
  • Ordinary Less Square
  • Data Envelopment Analysis Model
  • Decision Make Unit
  • Efficiency Change