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

by Hung Chak Ho 1,2,*, Kevin Ka-Lun Lau 1,3,4,*, Ruby Yu 4,5, Dan Wang 4, Jean Woo 4,5, Timothy Chi Yui Kwok 4,5,6 and Edward Ng 1,3,7
1
Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China
2
Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong, China
3
Institute of Future Cities, The Chinese University of Hong Kong, Hong Kong, China
4
CUHK Jockey Club Institute of Ageing, The Chinese University of Hong Kong, Hong Kong, China
5
Department of Medicine & Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
6
Jockey Club Centre for Osteoporosis Care & Control, The Chinese University of Hong Kong, Hong Kong, China
7
School of Architecture, The Chinese University of Hong Kong, Hong Kong, China
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2017, 14(9), 994; https://doi.org/10.3390/ijerph14090994
Received: 8 July 2017 / Revised: 29 August 2017 / Accepted: 30 August 2017 / Published: 31 August 2017
(This article belongs to the Special Issue Ageing Well: The Role of Age-Friendly Environments)
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. View Full-Text
Keywords: geriatric depression; high-density living; socio-environmental vulnerability; urban environment; spatial analytics; urban wellbeing geriatric depression; high-density living; socio-environmental vulnerability; urban environment; spatial analytics; urban wellbeing
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MDPI and ACS Style

Ho, H.C.; Lau, K.K.-L.; Yu, R.; Wang, D.; Woo, J.; Kwok, T.C.Y.; Ng, E. Spatial Variability of Geriatric Depression Risk in a High-Density City: A Data-Driven Socio-Environmental Vulnerability Mapping Approach. Int. J. Environ. Res. Public Health 2017, 14, 994.

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