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Original Articles

Spatial and temporal variations of spatial population accessibility to public hospitals: a case study of rural–urban comparison

Pages 718-744
Received 23 Sep 2017
Accepted 26 Feb 2018
Accepted author version posted online: 09 Mar 2018
Published online: 15 Mar 2018
 
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Quantification and assessment of nationwide population access to health-care services is a critical undertaking for improving population health and optimizing the performance of national health systems. Rural–urban unbalance of population access to health-care services is widely involved in most of the nations. This unbalance is also potentially affected by varied weather and road conditions. This study investigates the rural and urban performances of public health system by quantifying the spatiotemporal variations of accessibility and assessing the impacts of potential factors. Australian health-care system is used as a case study for the rural–urban comparison of population accessibility. A nationwide travel time-based modified kernel density two-step floating catchment area (MKD2SFCA) model is utilized to compute accessibility of travel time within 30, 60, 120, and 240 min to all public hospitals, hospitals that provide emergency care, and hospitals that provide surgery service, respectively. Results show that accessibility is varied both temporally and spatially, and the rural–urban unbalance is distinct for different types of hospitals. In Australia, from the perspective of spatial distributions of health-care resources, spatial accessibility to all public hospitals in remote and very remote areas is not lower (and may even higher) than that in major cities, but the accessibility to hospitals that provide emergency and surgery services is much higher in major cities than other areas. From the angle of temporal variation of accessibility to public hospitals, reduction of traffic speed is 1.00–3.57% due to precipitation and heavy rain, but it leads to 18–23% and 31–50% of reduction of accessibility in hot-spot and cold-spot regions, respectively, and the impact is severe in New South Wales, Queensland, and Northern Territory during wet seasons. Spatiotemporal analysis for the variations of accessibility can provide quantitative and accurate evidence for geographically local and dynamic strategies of allocation decision-making of medical resources and optimizing health-care systems both locally and nationally.

Acknowledgments

We acknowledge the Australian Institute of Health and Welfare (AIHW) for their roles in making available the Australian hospital statistics 2012–2013 data set. Support of this data set is provided by the AIHW, Australian government. The authors would like to thank the anonymous reviewers for their careful reading of the manuscript and their many insightful comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Highlights

  1. MKD2SFCA model provides a reliable measure of spatial accessibility and makes sense in real-world nationwide health systems.

  2. MKD2SFCA-based performance investigation reveals that the accessibility is spatially and temporally varied in Australian public health system.

  3. Accessibility to all hospitals in remote areas is not lower (and even higher) than that in major cities, but the accessibility to hospitals that provide emergency and surgery services is higher in major cities.

  4. Precipitation has a significant negative impact on accessibility in hot-spot and cold-spot regions.

Abbreviations

2SCFA: two-step floating catchment area; 3SFCA: three-step floating catchment area; ABS: Australian Bureau of Statistics; ACT: Australian Capital Territory; AIHW: Australian Institute of Health and Welfare; ASGS: Australian Statistical Geographical Classification; BPR: bed-population ratio; CV: coefficient of variance; DPR: doctor-population ratio; E2SFCA: enhanced two-step floating catchment area; FCA: floating catchment area; GDP: gross domestic product; KD2SFCA: kernel density two-step floating catchment area; LGA: local government area; LISA: local indicators of spatial association; M2SFCA: modified two-step floating catchment area; MKD2SFCA: modified kernel density two-step floating catchment area; NSW: New South Wales; NT: Northern Territory; PWC: population weighted centroid; QLD: Queensland; SA: Southern Australia; SEDAC: Socioeconomic Data and Applications Centre; TAS: Tasmania; TRMM: Tropical Rainfall Measuring Mission; UHC: universal health care; VIC: Victoria; WA: Western Australia.

Authors’ contributions

YZS conceived the study and performed statistical analysis. XYW supervised the study. All authors jointly drafted and critically revised the paper. All authors read and approved the final manuscript.

Additional information

Funding

This research was funded by the Australia Research Council Discovery Early Career Researcher Award (Project No. [DE170101502]) by the Australian government.

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