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A Rational Agent Model for the Spatial Accessibility of Primary Health Care

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  • James Saxon
  • Daniel Snow

Abstract

Accurate modeling of the spatial accessibility of health care is critical to measuring and responding to physician shortages. We develop a new model in which patients choose the primary care location that minimizes their combined accessibility and availability costs. This model offers several advantages with respect to existing access frameworks. It incorporates feedback between patient decisions and endogenizes the trade-off between travel times and congestion at the point of care. It allows for patients to seek care from their home or workplace and can account for multiple travel modes. Our open-sourced implementation scales efficiently to large areas and fine spatial granularity. Using distributed computing, we calculate travel times for this model at the census tract level for the entire United States, and we also make this resource available. We compare the results to those from existing primary care access models. Key Words: floating catchment areas, primary health care, rational agent models, spatial accessibility.

Suggested Citation

  • James Saxon & Daniel Snow, 2020. "A Rational Agent Model for the Spatial Accessibility of Primary Health Care," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 110(1), pages 205-222, January.
    • Handle: RePEc:taf:raagxx:v:110:y:2020:i:1:p:205-222
    DOI: 10.1080/24694452.2019.1629870
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    Citations

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    Cited by:

    1. Shao, Yaxiong & Luo, Wei, 2022. "Supply-demand adjusted two-steps floating catchment area (SDA-2SFCA) model for measuring spatial access to health care," Social Science & Medicine, Elsevier, vol. 296(C).
    2. Ali Khosravi Kazazi & Fariba Amiri & Yaser Rahmani & Raheleh Samouei & Hamidreza Rabiei-Dastjerdi, 2022. "A New Hybrid Model for Mapping Spatial Accessibility to Healthcare Services Using Machine Learning Methods," Sustainability, MDPI, vol. 14(21), pages 1-18, October.
    3. Gordon Cromley & Jie Lin, 2023. "Examining the interplay between racial segregation patterns and access to hospital care," Environment and Planning B, , vol. 50(1), pages 117-129, January.
    4. G. Arbia & V. Nardelli & N. Salvini & I. Valentini, 2024. "New accessibility measures based on unconventional big data sources," Papers 2401.13370, arXiv.org.
    5. James Saxon & Julia Koschinsky & Karina Acosta & Vidal Anguiano & Luc Anselin & Sergio Rey, 2022. "An open software environment to make spatial access metrics more accessible," Journal of Computational Social Science, Springer, vol. 5(1), pages 265-284, May.
    6. Hu, Yujie & Wang, Changzhen & Li, Ruiyang & Wang, Fahui, 2020. "Estimating a large drive time matrix between ZIP codes in the United States: A differential sampling approach," Journal of Transport Geography, Elsevier, vol. 86(C).

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