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

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  • Paula Simões

    (Centro de Matemática e Aplicações (CMA), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
    Área Departamental de Matemática, ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, 1959-007 Lisboa, Portugal)

  • M. Lucília Carvalho

    (Centro de Estatística e Aplicações (CEAUL), Universidade de Lisboa, 1749-016 Lisboa, Portugal
    Faculdade de Ciências, 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
    Centro de Estatística e Aplicações (CEAUL), Universidade de Lisboa, 1749-016 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
    Departamento de Matemática, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal)

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.

Suggested Citation

  • Paula Simões & M. Lucília Carvalho & Sandra Aleixo & Sérgio Gomes & Isabel Natário, 2017. "A Spatial Econometric Analysis of the Calls to the Portuguese National Health Line," Econometrics, MDPI, vol. 5(2), pages 1-23, June.
  • Handle: RePEc:gam:jecnmx:v:5:y:2017:i:2:p:24-:d:101618
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    References listed on IDEAS

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    Cited by:

    1. Yu Lin & Wenhui Chen & Junchang Liu, 2021. "Research on the Temporal and Spatial Distribution and Influencing Factors of Forestry Output Efficiency in China," Sustainability, MDPI, vol. 13(9), pages 1-17, April.
    2. Paula Simões & Sérgio Gomes & Isabel Natário, 2021. "Hospital Emergency Room Savings via Health Line S24 in Portugal," Econometrics, MDPI, vol. 9(1), pages 1-10, February.

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