Unpacking the inter- and intra-urban differences of the association between health and exposure to heat and air quality in Australia using global and local machine learning models.

Environmental stressors including high temperature and air pollution cause health problems. However, understanding how the combined exposure to heat and air pollution affects both physical and mental health remains insufficient due to the complexity of such effects mingling with human society, urban and natural environments. Our study roots in the Social Ecological Theory and employs a tri-environmental conceptual framework (i.e., across social, built and natural environment) to examine how the combined exposure to heat and air pollution affect self-reported physical and mental health via, for the first time, the fine-grained nationwide investigation in Australia and highlight how such effects vary across inter- and intra-urban areas. We conducted an ecological study to explore the importance of heat and air quality to physical and mental health by considering 48 tri-environmental confounders through the global and local random forest regression models, as advanced machine learning methods with the advantage of revealing the spatial heterogeneity of variables. Our key findings are threefold. First, the social and built environmental factors are important to physical and mental health in both urban and rural areas, and even more important than exposure to heat and air pollution. Second, the relationship between temperature and air quality and health follows a V-shape, reflecting people's different adaptation and tolerance to temperature and air quality. Third, the important roles that heat and air pollution play in physical and mental health are most obvious in the inner-city and near inner-city areas of the major capital cities, as well as in the industrial zones in peri-urban regions and in Darwin city with a low-latitude. We draw several policy implications to minimise the inter- and intra-urban differences in healthcare access and service distribution to populations with different sensitivity to heat and air quality across urban and rural areas. Our conceptual framework can also be applied to examine the relationship between other environmental problems and health outcomes in the era of a warming climate.

Nexus of heat-vulnerable chronic diseases and heatwave mediated through tri-environmental interactions: A nationwide fine-grained study in Australia.

The warming trend over recent decades has already contributed to the increased prevalence of heat-vulnerable chronic diseases in many regions of the world. However, understanding the relationship between heat-vulnerable chronic diseases and heatwaves remains incomplete due to the complexity of such a relationship mingling with human society, urban and natural environments. Our study extends the Social Ecological Theory by constructing a tri-environmental conceptual framework (i.e., across social, built, and natural environments) and contributes to the first nationwide study of the relationship between heat-vulnerable chronic diseases and heatwaves in Australia. We utilize the random forest regression model to explore the importance of heatwaves and 48 tri-environmental variables that contribute to the prevalence of six types of heat-vulnerable diseases. We further apply the local interpretable model-agnostic explanations and the accumulated local effects analysis to interpret how the heat-disease nexus is mediated through tri-environments and varied across urban and rural space. The overall effect of heatwaves on diseases varies across disease types and geographical contexts (latitudes; inland versus coast). The local heat-disease nexus follows a J-shape function-becoming sharply positive after a certain threshold of heatwaves-reflecting that people with the onset of different diseases have various sensitivity and tolerance to heatwaves. However, such effects are relatively marginal compared to tri-environmental variables. We propose a number of policy implications on reducing urban-rural disparity in healthcare access and service distribution, delineating areas, and identifying the variations of sensitivity to heatwaves across urban/rural space and disease types. Our conceptual framework can be further applied to examine the relationship between other environmental problems and health outcomes.

A spatiotemporal data mining study to identify high-risk neighborhoods for out-of-hospital cardiac arrest (OHCA) incidents.

Out-of-hospital cardiac arrest (OHCA) is a worldwide health problem. The aim of the study is to utilize the territorial-wide OHCA data of Hong Kong in 2012-2015 to examine its spatiotemporal pattern and high-risk neighborhoods. Three techniques for spatiotemporal data mining (SaTScan's spatial scan statistic, Local Moran's I, and Getis Ord Gi*) were used to extract high-risk neighborhoods of OHCA occurrence and identify local clusters/hotspots. By capitalizing on the strengths of these methods, the results were then triangulated to reveal "truly" high-risk OHCA clusters. The final clusters for all ages and the elderly 65+ groups exhibited relatively similar patterns. All ages groups were mainly distributed in the urbanized neighborhoods throughout Kowloon. More diverse distribution primarily in less accessible areas was observed among the elderly group. All outcomes were further converted into an index for easy interpretation by the general public. Noticing the spatial mismatches between hospitals and ambulance depots (representing supplies) and high-risk neighborhoods (representing demands), this setback should be addressed along with public education and strategic ambulance deployment plan to shorten response time and improve OHCA survival rate. This study offers policymakers and EMS providers essential spatial evidence to assist with emergency healthcare planning and informed decision-making.

The impact of geographic mobility on the spread of COVID-19 in Hong Kong.

The modern highly globalised economy is jeopardising human health as the increased mobility and interconnectedness has the potential to rapidly transmit pathogens across the globe. This was recently confirmed by the coronavirus disease 2019 outbreak, which quickly led to localised outbreaks in virtually every country. As the existing health systems were unprepared, the world has witnessed a critical shortage of life-supporting and health-sustaining resources. In the absence of effective non-pharmaceutical interventions to suppress the virus transmission, many governments imposed total or partial lockdowns, with devastating economic consequences. The case of Hong Kong in quickly suppressing the virus from spreading can thus be a lesson for all. In this study, open data sources of infected individuals are employed to compile maps of disease incidents at various geographic scales with the aim of better understanding the transmission dynamics and discern spatial variability. Our findings show that tracking human mobility patterns can improve awareness of spatiotemporal factors driving the risks of human exposure to viruses. Moreover, we have demonstrated that spatial tools can be successfully employed to explore connections between individuals and wider communities with the aim of informing adaptation of policies at different spatial scales and for different time periods. As was shown in the case of Hong Kong, disease control encompasses the interrelated tasks of reducing social interactions and encouraging adoption of protective behaviours.

Urban climate modified short-term association of air pollution with pneumonia mortality in Hong Kong.

BACKGROUND: City is becoming warmer, especially in the process of urbanization and climate change. However, it is largely unknown whether this warming urban climate may modify the short-term effects of air pollution. OBJECTIVES: To test whether warmer urban climates intensify the acute mortality effects of air pollution on pneumonia in Hong Kong. METHODS: Participants who died of pneumonia from a prospective Chinese elderly cohort between 1998 and 2011 were selected as cases. Urban climatic (UC) classes of cases were determined by an established Urban Climatic Map according to their residential addresses. UC classes were first dichotomized into cool and warm climates and case-crossover analysis was used to estimate the short-term association of pneumonia mortality with air pollution. We further classified UC classes into climate quartiles and used case-only analysis to test the trend of urban climate modification on the short-term association of pneumonia mortality with air pollution. RESULTS: Among 66,820 elders (≥65 years), 2208 pneumonia deaths (cases) were identified during the 11-14 years of follow-up. The effects of air pollution for cases residing in the warm climate were statistically significant (p < 0.05) higher than those living in the cool climate. There was an increasing linear trend of urban climate modification on the association of pneumonia mortality with NO2 (nitrogen dioxide) (p for trend = 0.035). Compared to climate Quartile 1 (the lowest), deaths resided in climate Quartile 2, 3, and 4 (the highest) were associated with an additional percent change of 9.07% (0.52%, 17.62%), 12.89% (4.34%, 21.43%), and 8.45% (-0.10%, 17.00%), respectively. CONCLUSIONS: Warmer urban climate worsened the acute mortality effects of pneumonia associated with air pollutants in Hong Kong. Our findings suggest that warmer urban climate introduced by climate change and urbanization may increase the risks of air pollution-related pneumonia.

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

AIMS: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.