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

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.

Socio-Environmental Vulnerability Assessment for Sustainable Management

“…The volume collects 20 different points of view, which cover environmental protection and development, urban planning, geography, public policymaking, participation processes, and other cross-disciplinary fields ( Figure 2). The interdisciplinary research conducted in this domain addresses such issues as evaluation of the quality of life in urban and suburban environments [5,6], issues related to public health protection [7,8], environmental injustice [9], engineering and infrastructure safety [10][11][12], energy security [13], income and environmental risk [14,15], hydrological and climate change risks [16][17][18], and mapping techniques [19][20][21][22]. All of them had been incorporated into socio-environmental vulnerability assessments, which present a broad perspective of this domain.…”

The associations between social, built and geophysical environment and age-specific dementia mortality in a high-density Asian city

“…Based on the perspective of human ecology, this population growth can also in uence the change of urban form and natural environment (Clement et al, 2015). Therefore, it is important to enhance the understanding of the human-environment nexus across a high-density city, in order to create a "healthy city" that can protect all ages and all vulnerable populations (Ho et al, 2017;Woo et al, 2017), including people with dementia at different ages.…”

Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets

“…[13] used the LCZ framework for monitoring sustainable urbanization and to assess the availability of adequate and safe housing, with a case study in the cities of Johannesburg and Pretoria in South Africa. Last but not least, as an environmental factor, LCZs are expected to enable evidence-based strategies for planning healthy and green cities worldwide [14].…”