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

The perception of corruption in health: AutoCM methods for an international comparison 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

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

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Publisher
Springer Netherlands
Copyright
Copyright © 2016 by Springer Science+Business Media Dordrecht
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-016-0315-4
Publisher site
See Article on Publisher Site

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.

Journal

Quality & QuantitySpringer Journals

Published: Feb 20, 2016

References

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