Tertiary Planning Units Research Articles

NEIGHBORHOOD SOCIOECONOMIC STATUS AND SARCOPENIA IN COMMUNITY-DWELLING OLDER ADULTS

Abstract Individual-level socioeconomic characteristics are known to predict sarcopenia, but less is known about how the neighborhood context shapes this debilitating problem among older adults. This study aims to examine the association between nSES and sarcopenia. We included 943 older adults aged over 60 years (56.0% female, mean age 72 ± 8.1). Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia 2019 (AWGS2019) algorithm covering skeletal muscle mass, muscle strength, and physical performance. nSES, the sum of the standardized values of the proportion of single-person households, never-married status, low education attainment, households with low income, unemployment, non-professional vocation, and non-owner occupiers, were obtained from the Census and Statistics Department and divided into distribution-based tertiles of all 292 Tertiary Planning Units in Hong Kong (T1 = highest nSES). There were 89 participants (8.4%) diagnosing sarcopenia. The sample was distributed across three tertiles of nSES (T1: 157 participants [16.6%]; T2: 193 participants [20.5%]; T3: 593 participants [62.9%]). Compared with T1, the lower nSES tertiles showed higher odds of sarcopenia, after adjusting for age, sex, body mass index, and individual-level socioeconomic characteristics (T2: OR = 1.38 [95% CI, 0.49 – 3.87], T3: OR = 3.53 [95% CI, 1.51 – 8.28], T2: aOR = 1.53 [0.51 – 4.54], T3: aOR = 3.12 [95% CI, 1.21 – 8.03]). This is the first study to identify the negative relationship between comprehensive nSES and the prevalence of sarcopenia. The findings urge the need to develop socio-environmental sensitive risk stratification and preventive care to manage later-age sarcopenia.

Confounding associations between green space and outdoor artificial light at night: Systematic investigations and implications for urban health

Confounding associations between green space and outdoor artificial light at night: Systematic investigations and implications for urban health

Planning scale flood risk assessment and prediction in ultra-high density urban environments: The case of Hong Kong

Planning scale flood risk assessment and prediction in ultra-high density urban environments: The case of Hong Kong

Spatial–temporal analysis of suicide clusters for suicide prevention in Hong Kong: a territory-wide study using 2014–2018 Hong Kong Coroner's Court reports

This study aimed to (i) identify high-risk suicide-methods clusters, based on location of residence and suicide incidence; and (ii) compare the characteristics of cases and spatial units inside and outside clusters. Suicide data of 4672 cases was obtained from the Coroner's Court reports in Hong Kong (2014-2018). Monthly aggregated suicide numbers based on location of residence, and suicide incidence, were obtained in small tertiary planning units (STPUs). Community-level characteristics and population of STPUs were retrieved from 2016 Census. Retrospective space-time analyses were performed to identify locations with elevated suicide rates over specific time periods, i.e., spatial-temporal clusters. Clusters were evaluated for overall suicide (any method), as well as jumping, hanging, and charcoal burning methods, in location of residence and suicide incidence. Bi-variate analysis was performed to compare the characteristics of cases, and spatial units, inside and outside the clusters. Suicide clusters involving jumping and charcoal burning were identified, but no hanging clusters were found. The within-cluster distribution of types of housing was different from that of outside. For most of the overall suicide and suicide by jumping clusters, spatial units within the clusters were more socially disadvantaged compared to those outside. Clusters varied by suicide methods, location of residence and location of incidence. The findings highlighted the need for consistent and concerted support from different stakeholders within suicide clusters, to ensure appropriate design, implementation and sustainability of effective suicide prevention programs. General Research Fund (37000320) and seed fund from the University of Hong Kong (104006710).

Analysis of the Spatial Distribution and Associated Factors of the Transmission Locations of COVID-19 in the First Four Waves in Hong Kong

Understanding the space–time pattern of the transmission locations of COVID-19, as well as the relationship between the pattern, socioeconomic status, and environmental factors, is important for pandemic prevention. Most existing research mainly analyzes the locations resided in or visited by COVID-19 cases, while few studies have been undertaken on the space–time pattern of the locations at which the transmissions took place and its associated influencing factors. To fill this gap, this study focuses on the space–time distribution patterns of COVID-19 transmission locations and the association between such patterns and urban factors. With Hong Kong as the study area, transmission chains of the four waves of COVID-19 outbreak in Hong Kong during the time period of January 2020 to June 2021 were reconstructed from the collected case information, and then the locations of COVID-19 transmission were inferred from the transmission chains. Statistically significant clusters of COVID-19 transmission locations at the level of tertiary planning units (TPUs) were detected and compared among different waves of COVID-19 outbreak. The high-risk areas and the associated influencing factors of different waves were also investigated. The results indicate that COVID-19 transmission began with the Hong Kong Island, further moved northward towards the New Territories, and finally shifted to the south Hong Kong Island, and the transmission population shows a difference between residential locations and non-residential locations. The research results can provide health authorities and policy-makers with useful information for pandemic prevention, as well as serve as a guide to the public in the avoidance of activities and places with a high risk of contagion.

A Spatiotemporal Solution to Control COVID-19 Transmission at the Community Scale for Returning to Normalcy: COVID-19 Symptom Onset Risk Spatiotemporal Analysis

Following the recent COVID-19 pandemic, returning to normalcy has become the primary goal of global cities. The key for returning to normalcy is to avoid affecting social and economic activities while supporting precise epidemic control. Estimation models for the spatiotemporal spread of the epidemic at the refined scale of cities that support precise epidemic control are limited. For most of 2021, Hong Kong has remained at the top of the "global normalcy index" because of its effective responses. The urban-community-scale spatiotemporal onset risk prediction model of COVID-19 symptom has been used to assist in the precise epidemic control of Hong Kong. Based on the spatiotemporal prediction models of COVID-19 symptom onset risk, the aim of this study was to develop a spatiotemporal solution to assist in precise prevention and control for returning to normalcy. Over the years 2020 and 2021, a spatiotemporal solution was proposed and applied to support the epidemic control in Hong Kong. An enhanced urban-community-scale geographic model was proposed to predict the risk of COVID-19 symptom onset by quantifying the impact of the transmission of SARS-CoV-2 variants, vaccination, and the imported case risk. The generated prediction results could be then applied to establish the onset risk predictions over the following days, the identification of high-onset-risk communities, the effectiveness analysis of response measures implemented, and the effectiveness simulation of upcoming response measures. The applications could be integrated into a web-based platform to assist the antiepidemic work. Daily predicted onset risk in 291 tertiary planning units (TPUs) of Hong Kong from January 18, 2020, to April 22, 2021, was obtained from the enhanced prediction model. The prediction accuracy in the following 7 days was over 80%. The prediction results were used to effectively assist the epidemic control of Hong Kong in the following application examples: identified communities within high-onset-risk always only accounted for 2%-25% in multiple epidemiological scenarios; effective COVID-19 response measures, such as prohibiting public gatherings of more than 4 people were found to reduce the onset risk by 16%-46%; through the effect simulation of the new compulsory testing measure, the onset risk was found to be reduced by more than 80% in 42 (14.43%) TPUs and by more than 60% in 96 (32.99%) TPUs. In summary, this solution can support sustainable and targeted pandemic responses for returning to normalcy. Faced with the situation that may coexist with SARS-CoV-2, this study can not only assist global cities in responding to the future epidemics effectively but also help to restore social and economic activities and people's normal lives.

Urban heat island effect-related mortality under extreme heat and non-extreme heat scenarios: A 2010–2019 case study in Hong Kong

The urban heat island (UHI) effect exacerbates the adverse impact of heat on human health. However, while the UHI effect is further intensified during extreme heat events, prior studies have rarely mapped the UHI effect during extreme heat events to assess its direct temperature impact on mortality. This study examined the UHI effect during extreme heat and non-extreme heat scenarios and compared their temperature-mortality associations in Hong Kong from 2010 to 2019. Four urban heat island degree hour (UHIdh) scenarios were mapped onto Hong Kong's tertiary planning units and classified into three levels (Low, Moderate, and High). We assessed the association between temperature and non-external mortality of populations living in each UHIdh level for the extreme heat/non-extreme heat scenarios during the 2010-2019 hot seasons. Our results showed substantial differences between the temperature-mortality associations in the three levels under the UHIdh extreme heat scenario (UHIdh_EH). While there was no evidence of increased mortality in Low UHIdh_EH areas, the mortality risk in Moderate and High UHIdh_EH areas were significantly increased during periods of hot temperature, with the High UHIdh_EH areas displaying almost double the risk (RR: 1.08, 95%CI: 1.03, 1.14 vs. RR: 1.05, 95 % CI: 1.01, 1.09). However, other non-extreme heat UHI scenarios did not demonstrate as prominent of a difference. When stratified by age, the heat effects were found in Moderate and High UHIdh_EH among the elderly aged 75 and above. Our study found a difference in the temperature-mortality associations based on UHI intensity and potential heat vulnerability of populations during extreme heat events. Preventive measures should be taken to mitigate heat especially in urban areas with high UHI intensity during extreme heat events, with particular attention and support for those prone to heat vulnerability, such as the elderly and poorer populations.

Identifying the High-Risk Population for COVID-19 Transmission in Hong Kong Leveraging Explainable Machine Learning.

The worldwide spread of COVID-19 has caused significant damage to people’s health and economics. Many works have leveraged machine learning models to facilitate the control and treatment of COVID-19. However, most of them focus on clinical medicine and few on understanding the spatial dynamics of the high-risk population for transmission of COVID-19 in real-world settings. This study aims to investigate the association between population features and COVID-19 transmission risk in Hong Kong, which can help guide the allocation of medical resources and the implementation of preventative measures to control the spread of the pandemic. First, we built machine learning models to predict the number of COVID-19 cases based on the population features of different tertiary planning units (TPUs). Then, we analyzed the distribution of cases and the prediction results to find specific characteristics of TPUs leading to large-scale outbreaks of COVID-19. We further evaluated the importance and influence of various population features on the prediction results using SHAP values to identify indicators for high-risk populations for COVID-19 transmission. The evaluation of COVID-19 cases and the TPU dataset in Hong Kong shows the effectiveness of the proposed methods. The top three most important indicators are identified as people in accommodation and food services, low income, and high population density.

Clustering of Socioeconomic Data in Hong Kong for Planning Better Community Health Protection.

The concept of socioeconomic vulnerability has made a substantial contribution to the understanding and conceptualization of health risk. To assess the spatial distribution of multi-dimensional socioeconomic vulnerability in an urban context, a vulnerability assessment scheme was proposed to guide decision-making in disaster resilience and sustainable urban development to reduce health risk. A two-stage approach was applied in Hong Kong to identify subgroups among Tertiary Planning Units (TPU) (i.e., the local geographic areas) with similar characteristics. In stage 1, principal components analysis was used for dimension reduction and to de-noise the socioeconomic data for each TPU based on the variables selected, while in stage 2, Gaussian mixture modeling was used to partition all the TPUs into different subgroups based on the results of stage 1. This study summarized socioeconomic-vulnerability-related data into five principal components, including indigenous degree, family resilience, individual productivity, populous grassroots, and young-age. According to these five principal components, all TPUs were clustered into five subgroups/clusters. Socioeconomic vulnerability is a concept that could be used to help identify areas susceptible to health risk, and even identify susceptible groups in affluent areas. More attention should be paid to areas with high populous grassroots scores and low young-age score since they were associated with a higher mortality rate.

A spatial and dynamic solution for allocation of COVID-19 vaccines when supply is limited

BackgroundSince most of the global population needs to be vaccinated to reduce COVID-19 transmission and mortality, a shortage of COVID-19 vaccine supply is inevitable. We propose a spatial and dynamic vaccine allocation solution to assist in the allocation of limited vaccines to people who need them most.MethodsWe developed a weighted kernel density estimation (WKDE) model to predict daily COVID-19 symptom onset risk in 291 Tertiary Planning Units in Hong Kong from 18 January 2020 to 22 December 2020. Data of 5,409 COVID-19 onset cases were used. We then obtained spatial distributions of accumulated onset risk under three epidemic scenarios, and computed the vaccine demands to form the vaccine allocation plan. We also compared the vaccine demand under different real-time effective reproductive number (Rt) levels.ResultsThe estimated vaccine usages in three epidemiologic scenarios are 30.86% - 45.78% of the Hong Kong population, which is within the total vaccine availability limit. In the sporadic cases or clusters of onset cases scenario, when 6.26% of the total population with travel history to high-risk areas can be vaccinated, the COVID-19 transmission between higher- and lower-risk areas can be reduced. Furthermore, if the current Rt is increased to double, the vaccine usages needed will be increased by more than 7%.ConclusionsThe proposed solution can be used to dynamically allocate limited vaccines in different epidemic scenarios, thereby enabling more effective protection. The increased vaccine usages associated with increased Rt indicates the necessity to maintain appropriate control measures even with vaccines available.

The neighborhood socioeconomic inequalities in urban parks in a High-density City: An environmental justice perspective

• Park facilities varied by neighborhood socioeconomic deprivation and ethnic minorities. • Park amenities varied by neighborhood socioeconomic deprivation and ethnic minorities. • Around one-third of the Tertiary Planning Units contained at least one urban park. • No socioeconomic or ethnic differences were revealed in park safety or aesthetics. A disparity in the distribution and quality of urban parks has been widely recognized as an important environmental justice issue. There is currently inconsistent evidence focusing on park distribution in high-density cities that can shed light on socioeconomic inequalities in park quality. To address this issue from an environmental justice perspective, the current study aimed to examine the associations between the quantity and quality of urban parks and neighborhood-level socioeconomic deprivation and ethnic minorities in Hong Kong, as an example of a high-density city. A total of 102 urban parks from 209 Tertiary Planning Units (TPUs) were included in this study. Quantity and quality of the 102 urban parks were objectively measured using Geographic Information Systems and the Community Park Audit Tool from April to June 2018. Findings of the current study demonstrated that the quantity of parks, quality of amenities, park safety, and park aesthetics were not associated with socioeconomic deprivation or ethnic minorities. However, compared to the TPUs with low deprivation values, a greater quantity and diversity of active facilities were observed in the moderate and high deprived TPUs. Moreover, the TPUs with a higher percentage of ethnic minorities were associated with parks with fewer active facilities, as well as less diversity of active facilities and supporting amenities. The current study provides key evidence of distributional injustice for urban parks in high-density cities and contributes to the development of urban planning strategies to address these issues.

Built environment and the metropolitan pandemic: Analysis of the COVID-19 spread in Hong Kong

The COVID-19 reported initially in December 2019 led to thousands and millions of people infections, deaths at a rapid scale, and a global scale. Metropolitans suffered serious pandemic problems as the built environments of metropolitans contain a large number of people in a relatively small area and allow frequent contacts to let virus spread through people's contacting with each other. The spread inside a metropolitan is heterogeneous, and we propose that the spatial variation of built environments has a measurable association with the spread of COVID-19. This paper is the pioneering work to investigate the missing link between the built environment and the spread of the COVID-19. In particular, we intend to examine two research questions: (1) What are the association of the built environment with the risk of being infected by the COVID-19? (2) What are the association of the built environment with the duration of suffering from COVID-19? Using the Hong Kong census data, confirmed cases of COVID-19 between January to August 2020 and large size of built environment sample data from the Hong Kong government, our analysis are carried out. The data is divided into two phases before (Phase 1) and during the social distancing measure was relaxed (Phase 2). Through survival analysis, ordinary least squares analysis, and count data analysis, we find that (1) In Phase 1, clinics and restaurants are more likely to influence the prevalence of COVID-19. In Phase 2, public transportation (i.e. MTR), public market, and the clinics influence the prevalence of COVID-19. (2) In Phase 1, the areas of tertiary planning units (i.e., TPU) with more restaurants are found to be positively associated with the period of the prevalence of COVID-19. In Phase 2, restaurants and public markets induce long time occurrence of the COVID-19. (3) In Phase 1, restaurant and public markets are the two built environments that influence the number of COVID-19 confirmed cases. In Phase 2, the number of restaurants is positively related to the number of COVID-19 reported cases. It is suggested that governments should not be too optimistic to relax the necessary measures. In other words, the social distancing measure should remain in force until the signals of the COVID-19 dies out.

The Impact of the Environment on the Quality of Life and the Mediating Effects of Sleep and Stress

(1) Background: Environment is an independent factor that affects one’s quality of life (QoL), where studies suggest that health behaviours also affect one’s quality of life. The purpose of the present study was to examine the association between environmental conditions and QoL and how individual health behaviours affect this association. (2) Methods: Participants aged 20 or above were recruited from 11 tertiary planning units in the central part of Kowloon. These tertiary planning units were selected as they represented the overall living environment in Hong Kong, with a mix of the poorer urban areas alongside relatively affluent districts. A mediation analysis was implemented using multiple linear regressions to examine the effects of environmental conditions on QoL. (3) Results: Of the 607 eligible participants included for analysis, 390 were female and 217 were male, with a mean age of 47.4 years. Living within 500 m of a green space area had benefits on the physical aspect of QoL and physical activity but no effect on the psychological aspect of QoL. Moderate satisfaction with public spaces affected QoL positively. In contrast, less satisfaction with public spaces affected QoL negatively in both physical and psychological aspects through the mediating effect of stress. Poor environmental quality affected all domains of QoL negatively through the mediating effects of increased stress and poor sleep. (4) Conclusions: Environment is an important factor that affects individuals’ overall well-being. The interaction between environmental conditions and individual variables, especially perceived stress and sleep, is extremely important when assessing its impact on QoL. The findings of this study support the importance of individual stress and sleep in mediating the relationship between the environment and QoL for health. Further studies should be conducted to include objective measurements, such as those of cortisol levels for stress and physical fitness tests.

Long-term effects of traffic exposures on mortality in a Chinese cohort

Abstract A large proportion of the population living in close proximity to roads are continuously exposed to vehicle traffic and vulnerable to its ill effect in everyday life. However, only a limited number of epidemiological studies have examined effects of long-term road vehicle traffic exposures on mortality. We investigated long-term associations between road vehicle traffic exposures measured by vehicle-kilometres travelled (VKT) and mortality in a cohort of Chinese elderly in Hong Kong, where road vehicle traffic is generally high. The Elderly Health Service cohort consisted of 66,820 adults aged 65 years or older enrolled at 18 Elderly Health Centres of the Department of Health from 1998 to 2001. Deaths were followed-up until 2011. All cohort members had medical, socio-demographic, lifestyle, anthropometric data and place of residence recorded at baseline. The residential addresses of cohort members were geo-coded and aggregated by geographic areas, Tertiary Planning Units (TPU). The annual total VKT was used as traffic exposure at the TPU level. We used Cox proportional hazards regression to estimate hazard ratios (HR) for one million km increase in the annual total VKT for natural-cause and cardiopulmonary mortality, adjusted for individual demographic and socio-economic variables, lifestyle, and medication, as well as environmental covariates. There were 17,422 deaths in 197 TPUs, with the median age at death of 75.3 years. The mean annual total VKT was 26 million kilometres. A 1 million increase in the annual total VKT was associated with excess risk [100 × (HR - 1)] of 0.45% (95% confidence interval [CI]: 0.17, 0.74) for natural-cause mortality and 0.81% (95% CI: 0.30, 1.32) for cardiovascular mortality. No significant association was found for respiratory mortality. These findings suggest that vehicle traffic exposure is associated with raised risks of natural-cause and cardiovascular mortality in the older population.

Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics.

Recent studies have suggested that some disadvantaged socio-demographic groups face serious environmental-related inequities in Hong Kong due to the rising ambient urban temperatures. Identifying heat-vulnerable groups and locating areas of Surface Urban Heat Island (SUHI) inequities is thus important for prioritizing interventions to mitigate death/illness rates from heat. This study addresses this problem by integrating methods of remote sensing retrieval, logistic regression modelling, and spatial autocorrelation. In this process, the SUHI effect was first estimated from the Land Surface Temperature (LST) derived from a Landsat image. With the scale assimilated to the SUHI and socio-demographic data, a logistic regression model was consequently adopted to ascertain their relationships based on Hong Kong Tertiary Planning Units (TPUs). Lastly, inequity “hotspots” were derived using spatial autocorrelation methods. Results show that disadvantaged socio-demographic groups were significantly more prone to be exposed to an intense SUHI effect: over half of 287 TPUs characterized by age groups of 60+ years, secondary and matriculation education attainment, widowed, divorced and separated, low and middle incomes, and certain occupation groups of workers, have significant Odds Ratios (ORs) larger than 1.2. It can be concluded that a clustering analysis stratified by age, income, educational attainment, marital status, and occupation is an effective way to detect the inequity hotspots of SUHI exposure. Additionally, inequities explored using income, marital status and occupation factors were more significant than the age and educational attainment in these areas. The derived maps and model can be further analyzed in urban/city planning, in order to mitigate the physical and social causes of the SUHI effect.

A spatial–temporal analysis of hotels in urban tourism destination

This study investigates the spatial associations of urban tourism phenomena by using GIS and statistical methods to examine the relationships between hotels and land use types, attractions, transportation facilities, and the economic variables of the tertiary planning units in which the hotels are located. Hong Kong is used as an example. The study first introduces the spatial characteristics of hotels and attractions development in Hong Kong. A geographical information system is then used to map hotels and investigate the characteristics of the land use, attractions, and transport facilities around hotels. The spatial relationships are then analyzed with a set of logistic regression models. The results reveal that commercial land type and the number of attractions around hotels are significantly related to the distribution of upper-grade hotels in Hong Kong. The determinants vary over time and the spatial structure changes accordingly. The analysis is important theoretically as it enriches the methodologies for analyzing the relationships between hotels and urban structure, and for conceptualizing and identifying tourism functional zones. It is important for practitioners as it provides useful information for selecting sites for hotels.

Assessing spatial associations between thermal stress and mortality in Hong Kong: A small-area ecological study

Physiological equivalent temperature (PET) is a widely used index to assess thermal comfort of the human body. Evidence on how thermal stress-related health effects vary with small geographical areas is limited. The objectives of this study are (i) to explore whether there were significant patterns of geographical clustering of thermal stress as measured by PET and mortality and (ii) to assess the association between PET and mortality in small geographical areas. A small area ecological cross-sectional study was conducted at tertiary planning units (TPUs) level. Age-standardized mortality rates (ASMR) and monthly deaths at TPUs level for 2006 were calculated for cause-specific diseases. A PET map with 100 m × 100 m resolution for the same period was derived from Hong Kong Urban Climatic Analysis Map data and the annual and monthly averages of PET for each TPU were computed. Global Moran's I and local indicator of spatial association (LISA) analyses were performed. A generalized linear mixed model was used to model monthly deaths against PET adjusted for socio-economic deprivation. We found positive spatial autocorrelation between PET and ASMR. There were spatial correlations between PET and ASMR, particularly in the north of Hong Kong Island, most parts of Kowloon, and across New Territories. A 1°C change in PET was associated with an excess risk (%) of 2.99 (95% CI: 0.50-5.48) for all natural causes, 4.75 (1.14-8.36) for cardiovascular, 7.39 (4.64-10.10) for respiratory diseases in the cool season, and 4.31 (0.12 to 8.50) for cardiovascular diseases in the warm season. Variations between TPUs in PET had an important influence on cause-specific mortality, especially in the cool season. PET may have an impact on the health of socio-economically deprived population groups. Our results suggest that targeting policy interventions at high-risk areas may be a feasible option for reducing PET-related mortality.

Effect modification of the association between short-term meteorological factors and mortality by urban heat islands in Hong Kong.

BackgroundPrior studies from around the world have indicated that very high temperatures tend to increase summertime mortality. However possible effect modification by urban micro heat islands has only been examined by a few studies in North America and Europe. This study examined whether daily mortality in micro heat island areas of Hong Kong was more sensitive to short term changes in meteorological conditions than in other areas.MethodAn urban heat island index (UHII) was calculated for each of Hong Kong’s 248 geographical tertiary planning units (TPU). Daily counts of all natural deaths among Hong Kong residents were stratified according to whether the place of residence of the decedent was in a TPU with high (above the median) or low UHII. Poisson Generalized Additive Models (GAMs) were used to estimate the association between meteorological variables and mortality while adjusting for trend, seasonality, pollutants and flu epidemics. Analyses were restricted to the hot season (June-September).ResultsMean temperatures (lags 0–4) above 29°C and low mean wind speeds (lags 0–4) were significantly associated with higher daily mortality and these associations were stronger in areas with high UHII. A 1°C rise above 29°C was associated with a 4.1% (95% confidence interval (CI): 0.7%, 7.6%) increase in natural mortality in areas with high UHII but only a 0.7% (95% CI: −2.4%, 3.9%) increase in low UHII areas. Lower mean wind speeds (5th percentile vs. 95th percentile) were associated with a 5.7% (95% CI: 2.7, 8.9) mortality increase in high UHII areas vs. a −0.3% (95% CI: −3.2%, 2.6%) change in low UHII areas.ConclusionThe results suggest that urban micro heat islands exacerbate the negative health consequences of high temperatures and low wind speeds. Urban planning measures designed to mitigate heat island effects may lessen the health effects of unfavorable summertime meteorological conditions.

A multilevel analysis of the effects of neighbourhood income inequality on individual self-rated health in Hong Kong

We examined the effect on self-rated health of neighbourhood-level income inequality in Hong Kong, which has a high and growing Gini coefficient. Data were derived from two population household surveys in 2002 and 2005 of 25,623 and 24,610 non-institutional residents aged 15 or over. We estimated neighbourhood-level Gini coefficients in each of 287 Government Planning Department Tertiary Planning Units. We used multilevel regression analysis to assess the association of neighbourhood income inequality with individual self-perceived health status. After adjustment for both individual- and household-level predictors, there was no association between neighbourhood income inequality, median household income or household-level income and self-rated health. We tested for but did not find any statistical interaction between these three income-related exposures. These findings suggest that neighbourhood income inequality is not an important predictor of individual health status in Hong Kong.

The Effects of Air Pollution on Mortality in Socially Deprived Urban Areas in Hong Kong, China

BackgroundPoverty is a major determinant of population health, but little is known about its role in modifying air pollution effects.ObjectivesWe set out to examine whether people residing in socially deprived communities are at higher mortality risk from ambient air pollution.MethodsThis study included 209 tertiary planning units (TPUs), the smallest units for town planning in the Special Administrative Region of Hong Kong, China. The socioeconomic status of each TPU was measured by a social deprivation index (SDI) derived from the proportions of the population with a) unemployment, b) monthly household income < US$250, c) no schooling at all, d) one-person household, e) never-married status, and f ) subtenancy, from the 2001 Population Census. TPUs were classified into three levels of SDI: low, middle, and high. We performed time-series analysis with Poisson regression to examine the association between changes in daily concentrations of ambient air pollution and daily number of deaths in each SDI group for the period from January 1996 to December 2002. We evaluated the differences in pollution effects between different SDI groups using a case-only approach with logistic regression.ResultsWe found significant associations of nitrogen dioxide, sulfur dioxide, particulate matter with aerodynamic diameter < 10 μm, and ozone with all nonaccidental and cardiovascular mortality in areas of middle or high SDI (p < 0.05). Health outcomes, measured as all nonaccidental, cardiovascular, and respiratory mortality, in people residing in high SDI areas were more strongly associated with SO2 and NO2 compared with those in middle or low SDI areas.ConclusionsNeighborhood socioeconomic deprivation increases mortality risks associated with air pollution.