Spatial variability of excess mortality during prolonged dust events in a high-density city: a time-stratified spatial regression approach

Abstract

BackgroundDust events have long been recognized to be associated with a higher mortality risk. However, no study has investigated how prolonged dust events affect the spatial variability of mortality across districts in a downwind city.MethodsIn this study, we applied a spatial regression approach to estimate the district-level mortality during two extreme dust events in Hong Kong. We compared spatial and non-spatial models to evaluate the ability of each regression to estimate mortality. We also compared prolonged dust events with non-dust events to determine the influences of community factors on mortality across the city.ResultsThe density of a built environment (estimated by the sky view factor) had positive association with excess mortality in each district, while socioeconomic deprivation contributed by lower income and lower education induced higher mortality impact in each territory planning unit during a prolonged dust event. Based on the model comparison, spatial error modelling with the 1st order of queen contiguity consistently outperformed other models. The high-risk areas with higher increase in mortality were located in an urban high-density environment with higher socioeconomic deprivation.ConclusionOur model design shows the ability to predict spatial variability of mortality risk during an extreme weather event that is not able to be estimated based on traditional time-series analysis or ecological studies. Our spatial protocol can be used for public health surveillance, sustainable planning and disaster preparation when relevant data are available.

Spatial Accessibility to Primary Healthcare Services by Multimodal Means of Travel: Synthesis and Case Study in the City of Calgary

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Major Changes in Extreme Dust Events Dynamics Over the Arabian Peninsula During 2003–2017 Driven by Atmospheric Conditions

“…Long-term exposure to high PM10 concentrations can increase the risk of mortality through respiratory and cardiovascular diseases (e.g., Kotsyfakis et al, 2019). Multiple reports suggested that an increase in the nonaccidental mortality by up to more than 10% due to prolonged (≥3 days) high PM10 episodes (Kim et al, 2018a(Kim et al, , 2018bPizzo & Clerico, 2012;Wang et al, 2018;Wong et al, 2017;Zhang et al, 2017;Zhou et al, 2015), much higher than the mortality rate caused by shorter duration events (<3 days). (ii) Severe widespread prolonged dust events are triggered and maintained by persistent synoptic circulations (e.g., Beegum et al, 2018;Dumka et al, 2019;Hamidi et al, 2017Hamidi et al, , 2014Houssos et al, 2015;Knippertz & Fink, 2006;Knippertz & Stuut, 2014;Shao et al, 2010;Solomos et al, 2017).…”

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

“…Data-driven approaches to map local areas with higher vulnerability are commonly used for preventive healthcare and emergency planning [ 23 , 24 , 25 , 26 ]. These vulnerability maps can be used for identifying significant hotspots of different health risks, and were previously applied to government-based protocols for the purpose of: (1) targeting areas with higher general health risk of vulnerable population [ 27 , 28 , 29 , 30 , 31 , 32 , 33 ]; (2) locating higher risk areas during specific events such as violent trauma, heat waves and extreme pollution events [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ]; and (3) predicting spatial variability of non-communicable diseases such as cancer [ 46 , 47 , 48 ]. However, data-driven methods to predict the spatial variability of geriatric depression have rarely been investigated, leading to insufficient preventive measures to such an increasingly common disease among the senior population.…”