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Health Disparity Resulting from the Effect of Built Environment on Temperature-Related Mortality in a Subtropical Urban Setting

Author

Listed:
  • Zhe Huang

    (Collaborating Centre for Oxford University and CUHK for Disaster and Medical Humanitarian Response (CCOUC), The Chinese University of Hong Kong, Hong Kong SAR, China)

  • Emily Ying-Yang Chan

    (Collaborating Centre for Oxford University and CUHK for Disaster and Medical Humanitarian Response (CCOUC), The Chinese University of Hong Kong, Hong Kong SAR, China
    GX Foundation, Hong Kong SAR, China)

  • Chi-Shing Wong

    (Collaborating Centre for Oxford University and CUHK for Disaster and Medical Humanitarian Response (CCOUC), The Chinese University of Hong Kong, Hong Kong SAR, China)

  • Sida Liu

    (Collaborating Centre for Oxford University and CUHK for Disaster and Medical Humanitarian Response (CCOUC), The Chinese University of Hong Kong, Hong Kong SAR, China
    GX Foundation, Hong Kong SAR, China)

  • Benny Chung-Ying Zee

    (Centre for Clinical Research and Biostatistics (CCRB), The Chinese University of Hong Kong, Hong Kong SAR, China
    Office of Research and Knowledge Transfer Services (ORKTS), The Chinese University of Hong Kong, Hong Kong SAR, China)

Abstract

Whereas previous studies have assessed the overall health impact of temperature in Hong Kong, the aim of this study was to investigate whether the health impact is modified by local temperature of small geographic units, which may be related to the diverse socioeconomic characteristics of these units. The effects of local temperature on non-accidental and cause-specific mortality were analyzed using Bayesian spatial models at a small-area level, adjusting for potential confounders, i.e., area-level air pollutants, socioeconomic status, and green space, as well as spatial dependency. We found that a 10% increase in green space density was associated with an estimated 4.80% decrease in non-accidental mortality risk and a 5.75% decrease in cardiovascular disease mortality risk in Hong Kong, whereas variation in local annual temperature did not significantly contribute to mortality. We also found that the spatial variation of mortality within this city could be explained by the geographic distribution of green space and socioeconomic factors rather than local temperature or air pollution. The findings and methodology of this study may help to further understanding and investigation of social and structural determinants of health disparities, particularly place-based built environment across class-based small geographic units in a city, taking into account the intersection of multiple factors from individual to population levels.

Suggested Citation

  • Zhe Huang & Emily Ying-Yang Chan & Chi-Shing Wong & Sida Liu & Benny Chung-Ying Zee, 2022. "Health Disparity Resulting from the Effect of Built Environment on Temperature-Related Mortality in a Subtropical Urban Setting," IJERPH, MDPI, vol. 19(14), pages 1-17, July.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:14:p:8506-:d:860866
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    References listed on IDEAS

    as
    1. Zhe Huang & Emily Ying Yang Chan & Chi Shing Wong & Benny Chung Ying Zee, 2021. "Clustering of Socioeconomic Data in Hong Kong for Planning Better Community Health Protection," IJERPH, MDPI, vol. 18(23), pages 1-21, November.
    2. Wilkinson, R.G. & Pickett, K.E., 2008. "Income inequality and socioeconomic gradients in mortality," American Journal of Public Health, American Public Health Association, vol. 98(4), pages 699-704.
    3. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    4. Dominici F. & Daniels M. & Zeger S. L. & Samet J. M., 2002. "Air Pollution and Mortality: Estimating Regional and National Dose-Response Relationships," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 100-111, March.
    5. Wilkinson, R.G., 1992. "National mortality rates: The impact of inequality?," American Journal of Public Health, American Public Health Association, vol. 82(8), pages 1082-1084.
    6. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    7. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    8. Sida Liu & Emily Yang Ying Chan & William Bernard Goggins & Zhe Huang, 2020. "The Mortality Risk and Socioeconomic Vulnerability Associated with High and Low Temperature in Hong Kong," IJERPH, MDPI, vol. 17(19), pages 1-14, October.
    9. Jens Kandt & Shu-Sen Chang & Paul Yip & Ricky Burdett, 2017. "The spatial pattern of premature mortality in Hong Kong: How does it relate to public housing?," Urban Studies, Urban Studies Journal Limited, vol. 54(5), pages 1211-1234, April.
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