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Spatial Variability of Geriatric Depression Risk in a High-Density City: A Data-Driven Socio-Environmental Vulnerability Mapping Approach

Author

Listed:
  • Hung Chak Ho

    (Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China
    Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong, China)

  • Kevin Ka-Lun Lau

    (Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China
    Institute of Future Cities, The Chinese University of Hong Kong, Hong Kong, China
    CUHK Jockey Club Institute of Ageing, The Chinese University of Hong Kong, Hong Kong, China)

  • Ruby Yu

    (CUHK Jockey Club Institute of Ageing, The Chinese University of Hong Kong, Hong Kong, China
    Department of Medicine & Therapeutics, The Chinese University of Hong Kong, Hong Kong, China)

  • Dan Wang

    (CUHK Jockey Club Institute of Ageing, The Chinese University of Hong Kong, Hong Kong, China)

  • Jean Woo

    (CUHK Jockey Club Institute of Ageing, The Chinese University of Hong Kong, Hong Kong, China
    Department of Medicine & Therapeutics, The Chinese University of Hong Kong, Hong Kong, China)

  • Timothy Chi Yui Kwok

    (CUHK Jockey Club Institute of Ageing, The Chinese University of Hong Kong, Hong Kong, China
    Department of Medicine & Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
    Jockey Club Centre for Osteoporosis Care & Control, The Chinese University of Hong Kong, Hong Kong, China)

  • Edward Ng

    (Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China
    Institute of Future Cities, The Chinese University of Hong Kong, Hong Kong, China
    School of Architecture, The Chinese University of Hong Kong, Hong Kong, China)

Abstract

Previous studies found a relationship between geriatric depression and social deprivation. However, most studies did not include environmental factors in the statistical models, introducing a bias to estimate geriatric depression risk because the urban environment was found to have significant associations with mental health. We developed a cross-sectional study with a binomial logistic regression to examine the geriatric depression risk of a high-density city based on five social vulnerability factors and four environmental measures. We constructed a socio-environmental vulnerability index by including the significant variables to map the geriatric depression risk in Hong Kong, a high-density city characterized by compact urban environment and high-rise buildings. Crude and adjusted odds ratios (ORs) of the variables were significantly different, indicating that both social and environmental variables should be included as confounding factors. For the comprehensive model controlled by all confounding factors, older adults who were of lower education had the highest geriatric depression risks (OR: 1.60 (1.21, 2.12)). Higher percentage of residential area and greater variation in building height within the neighborhood also contributed to geriatric depression risk in Hong Kong, while average building height had negative association with geriatric depression risk. In addition, the socio-environmental vulnerability index showed that higher scores were associated with higher geriatric depression risk at neighborhood scale. The results of mapping and cross-section model suggested that geriatric depression risk was associated with a compact living environment with low socio-economic conditions in historical urban areas in Hong Kong. In conclusion, our study found a significant difference in geriatric depression risk between unadjusted and adjusted models, suggesting the importance of including environmental factors in estimating geriatric depression risk. We also developed a framework to map geriatric depression risk across a city, which can be used for identifying neighborhoods with higher risk for public health surveillance and sustainable urban planning.

Suggested Citation

  • Hung Chak Ho & Kevin Ka-Lun Lau & Ruby Yu & Dan Wang & Jean Woo & Timothy Chi Yui Kwok & Edward Ng, 2017. "Spatial Variability of Geriatric Depression Risk in a High-Density City: A Data-Driven Socio-Environmental Vulnerability Mapping Approach," IJERPH, MDPI, vol. 14(9), pages 1-16, August.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:9:p:994-:d:110477
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    References listed on IDEAS

    as
    1. Hung Chak Ho & Anders Knudby & Wei Huang, 2015. "A Spatial Framework to Map Heat Health Risks at Multiple Scales," IJERPH, MDPI, vol. 12(12), pages 1-14, December.
    2. Cindy M. Padilla & Wahida Kihal-Talantikit & Verónica M. Vieira & Séverine Deguen, 2016. "City-Specific Spatiotemporal Infant and Neonatal Mortality Clusters: Links with Socioeconomic and Air Pollution Spatial Patterns in France," IJERPH, MDPI, vol. 13(6), pages 1-21, June.
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    4. Junzhe Bao & Xudong Li & Chuanhua Yu, 2015. "The Construction and Validation of the Heat Vulnerability Index, a Review," IJERPH, MDPI, vol. 12(7), pages 1-15, June.
    5. Sabel, Clive Eric & Wilson, Jeff Gaines & Kingham, Simon & Tisch, Catherine & Epton, Mike, 2007. "Spatial implications of covariate adjustment on patterns of risk: Respiratory hospital admissions in Christchurch, New Zealand," Social Science & Medicine, Elsevier, vol. 65(1), pages 43-59, July.
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    Cited by:

    1. Ho, Hung Chak & Cheng, Wei & Song, Yimeng & Liu, Yuqi & Guo, Yingqi & Lu, Shiyu & Lum, Terry Yat Sang & Chiu, Rebecca & Webster, Chris, 2022. "Spatial uncertainty and environment-health association: An empirical study of osteoporosis among “old residents” in public housing estates across a hilly environment," Social Science & Medicine, Elsevier, vol. 306(C).
    2. Yue, Yafei & Yang, Dongfeng & Owen, Neville & Van Dyck, Delfien, 2022. "The built environment and mental health among older adults in Dalian: The mediating role of perceived environmental attributes," Social Science & Medicine, Elsevier, vol. 311(C).
    3. Dana M. Alhasan & Jana A. Hirsch & Chandra L. Jackson & Maggi C. Miller & Bo Cai & Matthew C. Lohman, 2021. "Neighborhood Characteristics and the Mental Health of Caregivers Cohabiting with Care Recipients Diagnosed with Alzheimer’s Disease," IJERPH, MDPI, vol. 18(3), pages 1-14, January.
    4. Fangfang Hou & Xiao Han & Qiong Wang & Shuai Zhou & Jingya Zhang & Guodong Shen & Yan Zhang, 2022. "Cross-Sectional Associations between Living and Built Environments and Depression Symptoms among Chinese Older Adults," IJERPH, MDPI, vol. 19(10), pages 1-12, May.
    5. John W. M. Yuen & Katherine K. P. Chang & Frances K. Y. Wong & Fiona Y. Wong & Judy Y. M. Siu & H. C. Ho & M. S. Wong & Janice Y. S. Ho & K. L. Chan & Lin Yang, 2019. "Influence of Urban Green Space and Facility Accessibility on Exercise and Healthy Diet in Hong Kong," IJERPH, MDPI, vol. 16(9), pages 1-15, April.
    6. Young-Shin Park & Barbara J. McMorris & Lisiane Pruinelli & Ying Song & Merrie J. Kaas & Jean F. Wyman, 2021. "Use of Geographic Information Systems to Explore Associations between Neighborhood Attributes and Mental Health Outcomes in Adults: A Systematic Review," IJERPH, MDPI, vol. 18(16), pages 1-28, August.
    7. Jean Woo, 2017. "How Can We Achieve Healthy Aging?," IJERPH, MDPI, vol. 14(12), pages 1-2, December.

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