Fairness of National Health Service in Italy: a bivariate correlated random effects model
Fairness of National Health Service in Italy: a bivariate correlated random effects model
The primary purpose of this paper is to comprehensively assess households’ burden due to health payments. Starting from the fairness approach developed by the World Health Organization, we analyse the burden of healthcare payments on Italian households by modeling catastrophic payments and impoverishment due to healthcare expenditures. For this purpose, we propose to extend the analysis of fairness in financing contribution through a generalized linear mixed models by introducing a bivariate correlated random effects model, where association between the outcomes is modeled through individual- and outcome-specific latent effects which are assumed to be correlated. We discuss model parameter estimation in a finite mixture context. By using such model specification, the fairness of the Italian national health service is investigated.
fairness, healthcare, random effects models, binary data, non-parametric maximum likelihood
709-722
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e
July 2009
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e
Maruotti, Antonello
(2009)
Fairness of National Health Service in Italy: a bivariate correlated random effects model.
Journal of Applied Statistics, 36 (7), .
(doi:10.1080/02664760802499311).
Abstract
The primary purpose of this paper is to comprehensively assess households’ burden due to health payments. Starting from the fairness approach developed by the World Health Organization, we analyse the burden of healthcare payments on Italian households by modeling catastrophic payments and impoverishment due to healthcare expenditures. For this purpose, we propose to extend the analysis of fairness in financing contribution through a generalized linear mixed models by introducing a bivariate correlated random effects model, where association between the outcomes is modeled through individual- and outcome-specific latent effects which are assumed to be correlated. We discuss model parameter estimation in a finite mixture context. By using such model specification, the fairness of the Italian national health service is investigated.
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e-pub ahead of print date: 18 June 2009
Published date: July 2009
Keywords:
fairness, healthcare, random effects models, binary data, non-parametric maximum likelihood
Organisations:
Statistics, Statistical Sciences Research Institute
Identifiers
Local EPrints ID: 345967
URI: http://eprints.soton.ac.uk/id/eprint/345967
ISSN: 0266-4763
PURE UUID: 16158826-5603-4ad3-af36-97aab58b7a08
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Date deposited: 10 Dec 2012 12:26
Last modified: 14 Mar 2024 12:31
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Author:
Antonello Maruotti
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