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A model for measuring healthcare accessibility using the behavior of demand: a conditional logit model-based floating catchment area method

BACKGROUND: Estimating realistic access to health services is essential for designing support policies for healthcare delivery systems. Many studies have proposed a metric to calculate accessibility. However, patients’ realistic willingness to use a hospital was not explicitly considered. This study...

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Detalles Bibliográficos
Autor principal: Jang, Hoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259122/
https://www.ncbi.nlm.nih.gov/pubmed/34225720
http://dx.doi.org/10.1186/s12913-021-06654-3
Descripción
Sumario:BACKGROUND: Estimating realistic access to health services is essential for designing support policies for healthcare delivery systems. Many studies have proposed a metric to calculate accessibility. However, patients’ realistic willingness to use a hospital was not explicitly considered. This study aims to derive a new type of potential accessibility that incorporates a patient’s realistic preference in selecting a hospital. METHODS: This study proposes a floating catchment area (FCA)-type metric combined with a discrete choice model. Specifically, a new FCA-type metric (clmFCA) was proposed using a conditional logit model. Such a model estimates patients’ realistic willingness to use health services. The proposed metric was then applied to calculate the accessibility of obstetric care services in Korea. RESULTS: The clmFCA takes advantage of patients’ realistic preferences. Specifically, it can represent each patient’s heterogeneous characteristics regarding hospital choice. Such characteristics include bypassing behavior, which could not be considered using prior FCA metrics. Empirical analysis reveals that the clmFCA avoids the misestimation of accessibility to health services to an extent. CONCLUSIONS: The clmFCA offers a new framework that more realistically estimates patients’ accessibility to health services. This is achieved by accurately estimating the potential demand for a service. The proposed method’s effectiveness was verified through a case study using nationwide data. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-021-06654-3.