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Utilization of Norway’s Emergency Wards: The Second 5 Years after the Introduction of the Patient List System

Author

Listed:
  • Ursula S. Goth

    (Faculty of Education and International Studies, Oslo and Akershus University College of Applied Sciences, 0130 Oslo, Norway)

  • Hugo L. Hammer

    (Faculty of Technology, Art and Design, Oslo and Akerhus University College of Applied Sciences, 0130 Oslo, Norway)

  • Bjørgulf Claussen

    (Faculty of Medicine, University of Oslo, 0316 Oslo, Norway)

Abstract

Utilization of services is an important indicator for estimating access to healthcare. In Norway, the General Practitioner Scheme, a patient list system, was established in 2001 to enable a stable doctor-patient relationship. Although satisfaction with the system is generally high, people often choose a more accessible but inferior solution for routine care: emergency wards. The aim of the article is to investigate contact patterns in primary health care situations for the total population in urban and remote areas of Norway and for major immigrant groups in Oslo. The primary regression model had a cross-sectional study design analyzing 2,609,107 consultations in representative municipalities across Norway, estimating the probability of choosing the emergency ward in substitution to a general practitioner. In a second regression model comprising 625,590 consultations in Oslo, we calculated this likelihood for immigrants from the 14 largest groups. We noted substantial differences in emergency ward utilization between ethnic Norwegians both in rural and remote areas and among the various immigrant groups residing in Oslo. Oslo utilization of emergency ward services for the whole population declined, and so did this use among all immigrant groups after 2009. Other municipalities, while overwhelmingly ethnically Norwegian, showed diverse patterns including an increase in some and a decrease in others, results which we were unable to explain.

Suggested Citation

  • Ursula S. Goth & Hugo L. Hammer & Bjørgulf Claussen, 2014. "Utilization of Norway’s Emergency Wards: The Second 5 Years after the Introduction of the Patient List System," IJERPH, MDPI, vol. 11(3), pages 1-12, March.
    • Handle: RePEc:gam:jijerp:v:11:y:2014:i:3:p:3375-3386:d:34253
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    References listed on IDEAS

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    1. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258.
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
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