ArticleBMC health services research2021
A model for measuring healthcare accessibility using the behavior of demand: a conditional logit model-based floating catchment area method.
Article in BMC health services research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Gravity models for potential spatial healthcare access measurement: a systematic methodological review.International journal of health geographics · 2023Pooled it
- Spatial accessibility and outpatient utilization among children aged 1-5 years in South Korea: a nationwide claims-based cross-sectional study, 2023.BMC health services research · 2026Article
- Measuring and optimizing spatial accessibility to emergency medical services with a focus on age-based spatial variations in EMS risk.Frontiers in public health · 2026Article
- Does the reduction in obstetric hospitals result in an unintended decreased in-hospital delivery utilisation? A causal multilevel analysis in China.BMJ public health · 2025Article
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1 author.
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Abstract
backgroundEstimating 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.
methodsThis 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.
resultsThe 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.
conclusionsThe 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.
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