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Identifying Urban–Rural Disparities and Associated Factors in the Prevalence of Disabilities in Tianjin, China

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  • Yuxiao Jiang

    (School of Architecture and Urban Planning, Shandong Jianzhu University, Jinan 250100, China
    School of Architecture, Tianjin University, Tianjin 300072, China
    Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong, China)

  • Xinyu Han

    (School of Architecture and Urban Planning, Shandong Jianzhu University, Jinan 250100, China)

  • Ning Qiu

    (School of Architecture and Urban Planning, Shandong Jianzhu University, Jinan 250100, China)

  • Mengbing Du

    (School of Political Science & Public Administration, Wuhan University, Wuhan 4300722, China)

  • Liang Zhao

    (School of Architecture and Urban Planning, Shandong Jianzhu University, Jinan 250100, China)

Abstract

In the current context of aging and urbanization, the rapid increase in the prevalences of disabilities (PoDs) has become an important consideration in healthy urban planning. Previous studies have focused on the spatial prevalence of total disabilities based on large-scale survey data. However, few studies have examined different types of PoDs and the factors contributing to spatial disparities in micro-urban units at the municipal level. This study aims to fill this gap by exploring the spatial PoDs, related built environments, and socio-economic factors across the Tianjin municipality in 2020. The study employed Getis–Ord GI* analysis to identify urban-rural disparities and ordinary least square (OLS) and quantile regression (QR) analyses to model the heterogeneous effects of the spatial PoDs-associated factors across quantiles. The results reveal that the PoDs, especially of visual, hearing, and limb disabilities, in the urban center, are significantly higher than those in rural areas, which is inconsistent with previous studies conducted in China. The urbanization rate, medical facilities, and education facilities significantly reduced the total PoDs, while the elderly population, migrant population, bus route density, and road density significantly increased it. The built environments and socio-economic factors had heterogeneous impacts on different types of PoDs, which were summarized into three categories based on these dominant factors: (1) visual and hearing disabilities were medical facility-dominated; (2) intellectual and limb disabilities were urbanization- and aging-dominated; and (3) mental and speech disabilities were migrant-dominated. This study provides scientific advice to adapt to the expected increase in demand for disability-related medical and public health services and to expand the range of effective strategies and interventions aimed at preventing the deterioration of disability and improving disability management in the population.

Suggested Citation

  • Yuxiao Jiang & Xinyu Han & Ning Qiu & Mengbing Du & Liang Zhao, 2023. "Identifying Urban–Rural Disparities and Associated Factors in the Prevalence of Disabilities in Tianjin, China," Land, MDPI, vol. 12(8), pages 1-20, July.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:8:p:1480-:d:1202069
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    References listed on IDEAS

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    1. Bera Anil K. & Galvao Antonio F. & Montes-Rojas Gabriel V. & Park Sung Y., 2016. "Asymmetric Laplace Regression: Maximum Likelihood, Maximum Entropy and Quantile Regression," Journal of Econometric Methods, De Gruyter, vol. 5(1), pages 79-101, January.
    2. De Silva, P.N.K. & Simons, S.J.R. & Stevens, P., 2016. "Economic impact analysis of natural gas development and the policy implications," Energy Policy, Elsevier, vol. 88(C), pages 639-651.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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