File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

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

TitleSpatial variability of excess mortality during prolonged dust events in a high-density city: A time-stratified spatial regression approach
Authors
KeywordsExtreme weather event
Dust mortality
Spatial variability
Spatial analytics
Community vulnerability
Geospatial modelling
Issue Date2017
Citation
International Journal of Health Geographics, 2017, v. 16, n. 1, article no. 26 How to Cite?
Abstract© 2017 The Author(s). 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.
Persistent Identifierhttp://hdl.handle.net/10722/265711
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWong, Man Sing-
dc.contributor.authorHo, Hung Chak-
dc.contributor.authorYang, Lin-
dc.contributor.authorShi, Wenzhong-
dc.contributor.authorYang, Jinxin-
dc.contributor.authorChan, Ta Chien-
dc.date.accessioned2018-12-03T01:21:28Z-
dc.date.available2018-12-03T01:21:28Z-
dc.date.issued2017-
dc.identifier.citationInternational Journal of Health Geographics, 2017, v. 16, n. 1, article no. 26-
dc.identifier.urihttp://hdl.handle.net/10722/265711-
dc.description.abstract© 2017 The Author(s). 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.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Health Geographics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectExtreme weather event-
dc.subjectDust mortality-
dc.subjectSpatial variability-
dc.subjectSpatial analytics-
dc.subjectCommunity vulnerability-
dc.subjectGeospatial modelling-
dc.titleSpatial variability of excess mortality during prolonged dust events in a high-density city: A time-stratified spatial regression approach-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/s12942-017-0099-3-
dc.identifier.pmid28738805-
dc.identifier.scopuseid_2-s2.0-85025446773-
dc.identifier.volume16-
dc.identifier.issue1-
dc.identifier.spagearticle no. 26-
dc.identifier.epagearticle no. 26-
dc.identifier.eissn1476-072X-
dc.identifier.isiWOS:000407007400001-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats