The perception of corruption in health: AutoCM methods for an international comparison

Paolo Massimo Buscema (), Lara Gitto, Simone Russo (), Andrea Marcellusi (), Federico Fiori (), Guido Maurelli (), Giulia Massini () and Francesco Saverio Mennini
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Paolo Massimo Buscema: SEMEION Research Centre of Sciences of Communication
Simone Russo: University of Rome Tor Vergata
Federico Fiori: University of Rome Tor Vergata
Guido Maurelli: SEMEION Research Centre of Sciences of Communication
Giulia Massini: SEMEION Research Centre of Sciences of Communication

Quality & Quantity: International Journal of Methodology, 2017, vol. 51, issue 1, No 26, 459-477

Abstract: Abstract According to an OCSE study, corruption in healthcare costs about €56 billion yearly, corresponding to €160 million daily (European Commission, Accompanying document on the draft Commission Decision on Establishing an EU Anti-corruption Reporting Mechanism (“EU Anti-Corruption Report”), 2011). In Italy the situation is even more alarming. A recent study (WHO, The World Health Report: Health Systems Financing: The Path to Universal Coverage, 2010) has estimated that waste, inefficiency and corruption cost our National Health Service about €20 billion a year, corresponding to about 20 % of the total health expense. By nature, corruption is a hidden, complex and difficult to quantify reality. The measurement of this phenomenon may only be approximate and risks not to include important elements such as social costs of corruption that cannot be quantified. For this reason, there is no mathematical formula able to supply a country’s level of corruption. However, in the course of time, some research demonstrated that people’s perception of corruption gives a rather reliable estimate of the nature and extent of some corruption phenomena in a given context. In this perspective, the objective of this study is to understand how the phenomenon of corruption is perceived across several world countries; in other words, is there a common view concerning corruption? Secondly, we aim at identifying any management “models” of this phenomenon, that may be applied to different social and economic contexts. These objectives are considered through the application and the comparison of results of two different methods of data mining, such as the principal component analysis and the auto-contractive map, based on the analysis of a dataset.

Keywords: Healthcare corruption; Data Mining; Artificial Neural Network; Auto-Contractive Map (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11135-016-0315-4

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