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A Spatial Econometric Analysis of the Calls to the Portuguese National Health Line

Paula Simões (), M. Lucília Carvalho (), Sandra Aleixo (), Sérgio Gomes () and Isabel Natário ()
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Paula Simões: Centro de Matemática e Aplicações (CMA), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
M. Lucília Carvalho: Centro de Estatística e Aplicações (CEAUL), Universidade de Lisboa, 1749-016 Lisboa, Portugal
Sandra Aleixo: Área Departamental de Matemática, ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, 1959-007 Lisboa, Portugal
Sérgio Gomes: Direção Geral de Saúde (DGS), 1049-005 Lisboa, Portugal
Isabel Natário: Centro de Matemática e Aplicações (CMA), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal

Econometrics, 2017, vol. 5, issue 2, 1-23

Abstract: The Portuguese National Health Line, LS24, is an initiative of the Portuguese Health Ministry which seeks to improve accessibility to health care and to rationalize the use of existing resources by directing users to the most appropriate institutions of the national public health services. This study aims to describe and evaluate the use of LS24. Since for LS24 data, the location attribute is an important source of information to describe its use, this study analyses the number of calls received, at a municipal level, under two different spatial econometric approaches. This analysis is important for future development of decision support indicators in a hospital context, based on the economic impact of the use of this health line. Considering the discrete nature of data, the number of calls to LS24 in each municipality is better modelled by a Poisson model, with some possible covariates: demographic, socio-economic information, characteristics of the Portuguese health system and development indicators. In order to explain model spatial variability, the data autocorrelation can be explained in a Bayesian setting through different hierarchical log-Poisson regression models. A different approach uses an autoregressive methodology, also for count data. A log-Poisson model with a spatial lag autocorrelation component is further considered, better framed under a Bayesian paradigm. With this empirical study we find strong evidence for a spatial structure in the data and obtain similar conclusions with both perspectives of the analysis. This supports the view that the addition of a spatial structure to the model improves estimation, even in the case where some relevant covariates have been included.

Keywords: Bayesian spatial econometric models; hierarchical models; autoregressive models; Poisson (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2017
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