Built Environment and Health Outcomes: Identification of Contextual Risk Factors for Mental Well-being of Older Adults

Firdaus, Ghuncha

doi:10.1007/s12126-016-9276-0

Cited by 41 publications

(20 citation statements)

References 41 publications

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“…We reviewed previous epidemiological literature and found that our results are more conclusive than the other studies [ 6 , 11 , 15 , 57 ]. Previous studies found that geriatric depression risk is associated with social vulnerability [ 6 , 11 , 15 , 57 , 58 ] but none of these studies used environmental variables as controlling factors, resulting in conflicting results in some studies [ 6 , 15 , 57 ]. For example, Roberts et al [ 6 ] found significant geriatric depression risk in population who received low education (OR: 1.62) but Forsell [ 57 ] and Østbye et al [ 15 ] suggested that there are no significant associations between educational status and geriatric depression.…”

Section: Discussionsupporting

confidence: 66%

“…For example, a previous study in Hong Kong indicated that approximately 8% of the senior population were affected by depression and there was a positive correlation between the prevalence of depression and the number of chronic medical conditions [ 10 ]. Among all the risk factors, social vulnerability such as older age, lower education, social isolation, and gender had the most significant influence on geriatric depression risk [ 6 , 10 , 11 , 12 , 13 , 14 , 15 ]. However, most cohort studies on the relationship between social vulnerability and geriatric depression did not consider environmental influence, which was found to affect mental health and depression by long-term deprivation of desirable life [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…Among all the risk factors, social vulnerability such as older age, lower education, social isolation, and gender had the most significant influence on geriatric depression risk [ 6 , 10 , 11 , 12 , 13 , 14 , 15 ]. However, most cohort studies on the relationship between social vulnerability and geriatric depression did not consider environmental influence, which was found to affect mental health and depression by long-term deprivation of desirable life [ 11 ]. In contrast, a number of community health studies found significant associations between urban environment, vegetation cover and mental health.…”

Section: Introductionmentioning

confidence: 99%

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

Lau

et al. 2017

IJERPH

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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.

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Section: Discussionsupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Lau

et al. 2017

IJERPH

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“…Recent years have also seen a growing number of empirical studies on the health effects of subjectively reported community or neighborhood noise [ 13 , 14 , 15 , 16 , 17 , 18 ]. Perceived neighborhood noise was found to be associated with poor self-rated health and some mental health symptoms (e.g., depression) in Delhi, India [ 44 , 45 ].…”

Section: Introductionmentioning

confidence: 99%

A Multilevel Analysis of Perceived Noise Pollution, Geographic Contexts and Mental Health in Beijing

Kwan

et al. 2018

IJERPH

100

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With rapid urbanization and increase in car ownership, ambient noise pollution resulting from diversified sources (e.g., road traffic, railway, commercial services) has become a severe environmental problem in the populated areas in China. However, research on the spatial variation of noise pollution and its potential effects on urban residents’ mental health has to date been quite scarce in developing countries like China. Using a health survey conducted in Beijing in 2017, we for the first time investigated the spatial distributions of multiple noise pollution perceived by residents in Beijing, including road traffic noise, railway (or subway) noise, commercial noise, and housing renovation (or construction) noise. Our results indicate that there is geographic variability in noise pollution at the neighborhood scale, and road traffic and housing renovation/construction are the principal sources of noise pollution in Beijing. We then employed Bayesian multilevel logistic models to examine the associations between diversified noise pollution and urban residents’ mental health symptoms, including anxiety, stress, fatigue, headache, and sleep disturbance, while controlling for a wide range of confounding factors such as socio-demographics, objective built environment characteristics, social environment and geographic context. The results show that perceived higher noise-pollution exposure is significantly associated with worse mental health, while physical environment variables seem to contribute little to variations in self-reported mental disorders, except for proximity to the main road. Social factors or socio-demographic attributes, such as age and income, are significant covariates of urban residents’ mental health, while the social environment (i.e., community attachment) and housing satisfaction are significantly correlated with anxiety and stress. This study provides empirical evidence on the noise-health relationships in the Chinese context and sheds light on the policy implications for environmental pollution mitigation and healthy city development in China.

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“…Furthermore, using a probability sample of 611 older people in Nanjing, China, Feng et al (2018) showed that the age of the building affected the subjective well-being of older people, with newer buildings having more positive effects. Based on a sample of 1,896 older people in Delhi, India, Firdaus (2017) studied the effects of the built environment on mental health. Results showed that older people living in buildings that were in better condition, defined as not requiring immediate repairs on the structure, demonstrated better mental health.…”

Section: Introductionmentioning

confidence: 99%

Built Environment and Self-Rated Health: Comparing Young, Middle-Aged, and Older People in Chengdu, China

Lyu

Forsyth

Worthington

2021

HERD

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Objectives: This article explores how the building-scale built environment is associated with self-rated health, examining differences in this association among younger, middle-aged, and older age groups. Features examined included building type, building condition, and sidewalk presence in front of dwellings. Background: Understanding how the relationships between built environments and health vary across age groups helps to build a healthy environment for all. However, most studies have concentrated on the neighborhood or indoor environment, rather than whole buildings, and few have compared age groups. Methods: This study analyzed survey data from 1,019 adults living in 40 neighborhoods in Chengdu, China, recruited through a clustered random sampling approach. It used a Bayesian logistic mixed-effects model with interaction terms between age-group indicators and other variables. Results: Significant differences exist in the relationships of self-rated health with some environmental and other indicators among age groups. For older people, living in multi-floor buildings, having a household smoker, and undertaking fewer hours of weekly exercise were associated with lower odds of reporting good, very good, or excellent health. These relationships were not identified among middle-aged and younger people. More education was associated with higher odds of reporting better health among older and middle-aged groups. Conclusions: Older people experience more health-related challenges compared to middle-aged and younger people. However, among the examined built environment factors, building type was the only significant factor related to self-rated health among older people. To promote health among older people, this study recommends adding elevators in the multi-floor buildings.

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Built Environment and Health Outcomes: Identification of Contextual Risk Factors for Mental Well-being of Older Adults

Cited by 41 publications

References 41 publications

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

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

A Multilevel Analysis of Perceived Noise Pollution, Geographic Contexts and Mental Health in Beijing

Built Environment and Self-Rated Health: Comparing Young, Middle-Aged, and Older People in Chengdu, China

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