IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v7y2019i3p81-d249184.html
   My bibliography  Save this article

Hospital Proximity and Mortality in Australia

Author

Listed:
  • Andrew Leung

    (Independent Researcher, Richmond 3121, Australia)

Abstract

It is intuitive that proximity to hospitals can only improve the chances of survival from a range of medical conditions. This study examines the empirical evidence for this assertion, based on Australian data. While hospital proximity might serve as a proxy for other factors, such as indigenity, income, wealth or geography, the evidence suggests that proximity provides the most direct link to these factors. In addition, as it turns out, a very statistically significant one that transcends economies.

Suggested Citation

  • Andrew Leung, 2019. "Hospital Proximity and Mortality in Australia," Risks, MDPI, vol. 7(3), pages 1-24, July.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:3:p:81-:d:249184
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/7/3/81/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/7/3/81/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fung, Man Chung & Peters, Gareth W. & Shevchenko, Pavel V., 2017. "A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting," Annals of Actuarial Science, Cambridge University Press, vol. 11(2), pages 343-389, September.
    2. Bentham, Graham, 1986. "Proximity to hospital and mortality from motor vehicle traffic accidents," Social Science & Medicine, Elsevier, vol. 23(10), pages 1021-1026, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jonas Hirz & Uwe Schmock & Pavel V. Shevchenko, 2017. "Actuarial Applications and Estimation of Extended CreditRisk+," Risks, MDPI, vol. 5(2), pages 1-29, March.
    2. de Jong, Piet & Tickle, Leonie & Xu, Jianhui, 2020. "A more meaningful parameterization of the Lee–Carter model," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 1-8.
    3. Paola Bertoli & Veronica Grembi, 2017. "The life‐saving effect of hospital proximity," Health Economics, John Wiley & Sons, Ltd., vol. 26(S2), pages 78-91, September.
    4. Leung, Melvern & Li, Youwei & Pantelous, Athanasios A. & Vigne, Samuel A., 2021. "Bayesian Value-at-Risk backtesting: The case of annuity pricing," European Journal of Operational Research, Elsevier, vol. 293(2), pages 786-801.
    5. Rokas Gylys & Jonas Šiaulys, 2020. "Estimation of Uncertainty in Mortality Projections Using State-Space Lee-Carter Model," Mathematics, MDPI, vol. 8(7), pages 1-23, June.
    6. Ivana Benzaquén & Nicolás Epele & Mariana Marchionni, 2020. "Impacto de la implementación del Servicio de Atención Médica de Emergencias (SAME) sobre las muertes por accidentes de tránsito en la provincia de Buenos Aires," Asociación Argentina de Economía Política: Working Papers 4311, Asociación Argentina de Economía Política.
    7. Cheng, Yung-Hsiang & Liang, Zheng-Xian, 2014. "A strategic planning model for the railway system accident rescue problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 75-96.
    8. José Castillo-Manzano & Mercedes Castro-Nuño & Xavier Fageda, 2014. "Can health public expenditure reduce the tragic consequences of road traffic accidents? The EU-27 experience," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(6), pages 645-652, July.
    9. Leung, Melvern & Fung, Man Chung & O’Hare, Colin, 2018. "A comparative study of pricing approaches for longevity instruments," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 95-116.
    10. Qian Lu & Katja Hanewald & Xiaojun Wang, 2021. "Subnational Mortality Modelling: A Bayesian Hierarchical Model with Common Factors," Risks, MDPI, vol. 9(11), pages 1-21, November.
    11. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2017. "Cohort effects in mortality modelling: a Bayesian state-space approach," Papers 1703.08282, arXiv.org.
    12. Li, Johnny Siu-Hang & Liu, Yanxin & Chan, Wai-Sum, 2023. "Hedging longevity risk under non-Gaussian state-space stochastic mortality models: A mean-variance-skewness-kurtosis approach," Insurance: Mathematics and Economics, Elsevier, vol. 113(C), pages 96-121.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:7:y:2019:i:3:p:81-:d:249184. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.