Abstract
Background: Dust 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.
Methods: In 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.
Results: The 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.
Conclusion: Our 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.
© The Author(s) 2017.
| Original language | English |
|---|---|
| Article number | 26 |
| Journal | International Journal of Health Geographics |
| Volume | 16 |
| Online published | 24 Jul 2017 |
| DOIs | |
| Publication status | Published - 2017 |
| Externally published | Yes |
Funding
This research was supported in part by a grant from the Hong Kong Polytech‑ nic University (Grant PolyU 1‑ZE24). Dr. M.S. Wong acknowledges the support in part by a grant from General Research Fund (Project ID: 15205515); Grant PolyU 1‑ZVAJ from the Faculty of Construction and Environment, the Hong Kong Polytechnic University; and the Grants PolyU 1‑ZVFD from the Research Institute for Sustainable Urban Development, the Hong Kong Polytechnic University.
Research Keywords
- Community vulnerability
- Dust mortality
- Extreme weather event
- Geospatial modelling
- Spatial analytics
- Spatial variability
Publisher's Copyright Statement
- This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/