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Statistical models of the demand for emergency medical services in an urban area

Author

Listed:
  • Kvalseth, T.O.
  • Deems, J.M.

Abstract

First- and second-order statistical regression models are presented for the Emergency Medical services (EMS) demand in an urban area as it relates to various socioeconomic, demographic, and other characteristics of the areas. Individual models are formulated for different types of medical emergencies with the city of Atlanta, GA, serving as the data base. These models are generally shown to provide excellent fits to the empirical data.

Suggested Citation

  • Kvalseth, T.O. & Deems, J.M., 1979. "Statistical models of the demand for emergency medical services in an urban area," American Journal of Public Health, American Public Health Association, vol. 69(3), pages 250-255.
    • Handle: RePEc:aph:ajpbhl:1979:69:3:250-255_6
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    Cited by:

    1. Lambert, Thomas & Meyer, Peter, 2008. "New and fringe residential development and emergency medical services response times in the United States," MPRA Paper 38891, University Library of Munich, Germany.
    2. Thomas E. Lambert & Peter B. Meyer, 2006. "Ex-Urban Sprawl as a Factor in Traffic Fatalities and EMS Response Times in the Southeastern United States," Journal of Economic Issues, Taylor & Francis Journals, vol. 40(4), pages 941-953, December.
    3. McArthur, David Philip & Gregersen, Fredrik A. & Hagen, Terje P., 2014. "Modelling the cost of providing ambulance services," Journal of Transport Geography, Elsevier, vol. 34(C), pages 175-184.
    4. Mielczarek, Bożena, 2014. "Simulation modelling for contracting hospital emergency services at the regional level," European Journal of Operational Research, Elsevier, vol. 235(1), pages 287-299.
    5. Abreu, Paulo & Santos, Daniel & Barbosa-Povoa, Ana, 2023. "Data-driven forecasting for operational planning of emergency medical services," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    6. Ranon Jientrakul & Chumpol Yuangyai & Klongkwan Boonkul & Pakinai Chaicharoenwut & Suriyaphong Nilsang & Sittiporn Pimsakul, 2022. "Integrating Spatial Risk Factors with Social Media Data Analysis for an Ambulance Allocation Strategy: A Case Study in Bangkok," Sustainability, MDPI, vol. 14(16), pages 1-15, August.

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