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. 2018 Sep 19;15(9):2051.
doi: 10.3390/ijerph15092051.

Evaluating the Accessibility of Healthcare Facilities Using an Integrated Catchment Area Approach

Xiaofang Pan  1   2 , Mei-Po Kwan  3   4 , Lin Yang  5   6 , Shunping Zhou  7 , Zejun Zuo  8 , Bo Wan  9
Affiliations

Evaluating the Accessibility of Healthcare Facilities Using an Integrated Catchment Area Approach

Xiaofang Pan et al. Int J Environ Res Public Health. .

Abstract

Accessibility is a major method for evaluating the distribution of service facilities and identifying areas in shortage of service. Traditional accessibility methods, however, are largely model-based and do not consider the actual utilization of services, which may lead to results that are different from those obtained when people's actual behaviors are taken into account. Based on taxi GPS trajectory data, this paper proposed a novel integrated catchment area (ICA) that integrates actual human travel behavior to evaluate the accessibility to healthcare facilities in Shenzhen, China, using the enhanced two-step floating catchment area (E2SFCA) method. This method is called the E2SFCA-ICA method. First, access probability is proposed to depict the probability of visiting a healthcare facility. Then, integrated access probability (IAP), which integrates model-based access probability (MAP) and data-based access probability (DAP), is presented. Under the constraint of IAP, ICA is generated and divided into distinct subzones. Finally, the ICA and subzones are incorporated into the E2SFCA method to evaluate the accessibility of the top-tier hospitals in Shenzhen, China. The results show that the ICA not only reduces the differences between model-based catchment areas and data-based catchment areas, but also distinguishes the core catchment area, stable catchment area, uncertain catchment area and remote catchment area of healthcare facilities. The study also found that the accessibility of Shenzhen's top-tier hospitals obtained with traditional catchment areas tends to be overestimated and more unequally distributed in space when compared to the accessibility obtained with integrated catchment areas.

Keywords: E2SFCA; access probability; catchment areas; healthcare accessibility; taxi GPS trajectories.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The relationship among model-based catchment area (MCA), data-based catchment area (DCA), integrated catchment area (ICA) and actual catchment area.
Figure 2
Figure 2
A scenario of model-based and data-based visiting probability to healthcare facilities.
Figure 3
Figure 3
Taxi travel statistics of Shenzhen from 2010 to 2017.
Figure 4
Figure 4
The top-tier hospitals and the population density of Shenzhen.
Figure 5
Figure 5
The flow of the accessibility evaluation.
Figure 6
Figure 6
MCA and DCA of H1 under different thresholds (a) MCA (b) DCA.
Figure 7
Figure 7
(a) Area differences of MCA and DCA at low level; (b) Area differences of MCA and DCA at middle level; (c) Area differences of MCA and DCA at high level; (d) The area of ICA at low, middle and high level.
Figure 8
Figure 8
The boundary of the MCA, DCA and ICA at a high, middle and low level of probability threshold. (black line represents MCA boundary, blue line represents DCA boundary, red line represents ICA boundary).
Figure 9
Figure 9
The ICA of the top-tier hospitals under different probability thresholds.
Figure 10
Figure 10
The population and area percentage of ICA under different access probabilities.
Figure 11
Figure 11
The accessibility of the top-tier hospitals (E2SFCA-ICA).
Figure 12
Figure 12
The accessibility of the top-tier hospitals (E2SFCA).
Figure 13
Figure 13
The accessibility of the top-tier hospitals (2SFCA).
Figure 14
Figure 14
E2SFCA-ICA and E2SFCA comparison.
See this image and copyright information in PMC

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