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

• Main Author: Hoon Jang (Author)
• Format: Book
• Published: BMC, 2021-07-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12913-021-06654-3; 1472-6963
• Keywords: Access to health; Floating catchment area; Conditional logit; Realistic preference; Obstetric care service; Public aspects of medicine; RA1-1270
• Source: BMC Health Services Research, Vol 21, Iss 1, Pp 1-11 (2021)

Abstract 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.