No Cover Image

Journal article 1014 views 335 downloads

Towards a threshold climate for emergency lower respiratory hospital admissions

Muhammad Saiful Islam, Thierry J. Chaussalet, Naoru Koizumi, Saiful Islam Orcid Logo

Environmental Research, Volume: 153, Pages: 41 - 47

Swansea University Author: Saiful Islam Orcid Logo

Check full text

DOI (Published version): 10.1016/j.envres.2016.11.011

Abstract

Identification of 'cut-points' or thresholds of climate factors would play a crucial role in alerting risks of climate change and providing guidance to policymakers. This study investigated a 'Climate Threshold' for emergency hospital admissions of chronic lower respiratory disea...

Full description

Published in: Environmental Research
ISSN: 00139351
Published: 2017
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa31260
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2016-11-25T20:13:40Z
last_indexed 2018-02-09T05:17:58Z
id cronfa31260
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2017-01-06T10:01:00.5817320</datestamp><bib-version>v2</bib-version><id>31260</id><entry>2016-11-25</entry><title>Towards a threshold climate for emergency lower respiratory hospital admissions</title><swanseaauthors><author><sid>4157d27b800a8357873bdfc9c71bd596</sid><ORCID>0000-0003-3182-8487</ORCID><firstname>Saiful</firstname><surname>Islam</surname><name>Saiful Islam</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2016-11-25</date><deptcode>HDAT</deptcode><abstract>Identification of 'cut-points' or thresholds of climate factors would play a crucial role in alerting risks of climate change and providing guidance to policymakers. This study investigated a 'Climate Threshold' for emergency hospital admissions of chronic lower respiratory diseases by using a distributed lag non-linear model (DLNM). We analyseda unique longitudinal dataset (10 years, 2000-2009) on emergency hospital admissions, climate, and pollution factors for the Greater London. Our study extends existing work on this topic by considering non-linearity, lag effects between climate factors and disease exposure within the DLNM model considering B-spline as smoothing technique. The final model also considered natural cubic splines of time since exposure and 'day of the week' as confounding factors. The results of DLNM indicated a significant improvement in model fitting compared to a typical GLM model. The final model identified the thresholds of several climate factors including: high temperature (&#x2265;270C), low relative humidity (&#x2264; 40%), high Pm10 level (&#x2265;70-&#x3BC;g/m3), low wind speed (&#x2264; 2 knots) and high rainfall (&#x2265;30mm). Beyond the threshold values, a significantly higher number of emergency admissions due to lower respiratory problems would be expected within the following 2-3 days after the climate shift in the Greater London. The approach will be useful to initiate 'region and disease specific' climate mitigation plans. It will help identify spatial hot spots and the most sensitive areas and population due to climate change, and will eventually lead towards a diversified health warning system tailored to specific climate zones and populations.</abstract><type>Journal Article</type><journal>Environmental Research</journal><volume>153</volume><paginationStart>41</paginationStart><paginationEnd>47</paginationEnd><publisher/><issnPrint>00139351</issnPrint><keywords>Climate Change; Threshold; Delayed model; Emergency hospital admissions; Hospital Episode Statistics; Health warning System.</keywords><publishedDay>1</publishedDay><publishedMonth>2</publishedMonth><publishedYear>2017</publishedYear><publishedDate>2017-02-01</publishedDate><doi>10.1016/j.envres.2016.11.011</doi><url>http://www.sciencedirect.com/science/article/pii/S0013935116310684</url><notes/><college>COLLEGE NANME</college><department>Health Data Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>HDAT</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2017-01-06T10:01:00.5817320</lastEdited><Created>2016-11-25T14:27:53.7406014</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Medicine</level></path><authors><author><firstname>Muhammad Saiful</firstname><surname>Islam</surname><order>1</order></author><author><firstname>Thierry J.</firstname><surname>Chaussalet</surname><order>2</order></author><author><firstname>Naoru</firstname><surname>Koizumi</surname><order>3</order></author><author><firstname>Saiful</firstname><surname>Islam</surname><orcid>0000-0003-3182-8487</orcid><order>4</order></author></authors><documents><document><filename>0031260-25112016141441.pdf</filename><originalFilename>FinalVersionPrior2Publication.pdf</originalFilename><uploaded>2016-11-25T14:14:41.0800000</uploaded><type>Output</type><contentLength>891222</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2017-11-25T00:00:00.0000000</embargoDate><documentNotes>Unable to open file</documentNotes><copyrightCorrect>true</copyrightCorrect></document></documents><OutputDurs/></rfc1807>
spelling 2017-01-06T10:01:00.5817320 v2 31260 2016-11-25 Towards a threshold climate for emergency lower respiratory hospital admissions 4157d27b800a8357873bdfc9c71bd596 0000-0003-3182-8487 Saiful Islam Saiful Islam true false 2016-11-25 HDAT Identification of 'cut-points' or thresholds of climate factors would play a crucial role in alerting risks of climate change and providing guidance to policymakers. This study investigated a 'Climate Threshold' for emergency hospital admissions of chronic lower respiratory diseases by using a distributed lag non-linear model (DLNM). We analyseda unique longitudinal dataset (10 years, 2000-2009) on emergency hospital admissions, climate, and pollution factors for the Greater London. Our study extends existing work on this topic by considering non-linearity, lag effects between climate factors and disease exposure within the DLNM model considering B-spline as smoothing technique. The final model also considered natural cubic splines of time since exposure and 'day of the week' as confounding factors. The results of DLNM indicated a significant improvement in model fitting compared to a typical GLM model. The final model identified the thresholds of several climate factors including: high temperature (≥270C), low relative humidity (≤ 40%), high Pm10 level (≥70-μg/m3), low wind speed (≤ 2 knots) and high rainfall (≥30mm). Beyond the threshold values, a significantly higher number of emergency admissions due to lower respiratory problems would be expected within the following 2-3 days after the climate shift in the Greater London. The approach will be useful to initiate 'region and disease specific' climate mitigation plans. It will help identify spatial hot spots and the most sensitive areas and population due to climate change, and will eventually lead towards a diversified health warning system tailored to specific climate zones and populations. Journal Article Environmental Research 153 41 47 00139351 Climate Change; Threshold; Delayed model; Emergency hospital admissions; Hospital Episode Statistics; Health warning System. 1 2 2017 2017-02-01 10.1016/j.envres.2016.11.011 http://www.sciencedirect.com/science/article/pii/S0013935116310684 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University 2017-01-06T10:01:00.5817320 2016-11-25T14:27:53.7406014 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Muhammad Saiful Islam 1 Thierry J. Chaussalet 2 Naoru Koizumi 3 Saiful Islam 0000-0003-3182-8487 4 0031260-25112016141441.pdf FinalVersionPrior2Publication.pdf 2016-11-25T14:14:41.0800000 Output 891222 application/pdf Accepted Manuscript true 2017-11-25T00:00:00.0000000 Unable to open file true
title Towards a threshold climate for emergency lower respiratory hospital admissions
spellingShingle Towards a threshold climate for emergency lower respiratory hospital admissions
Saiful Islam
title_short Towards a threshold climate for emergency lower respiratory hospital admissions
title_full Towards a threshold climate for emergency lower respiratory hospital admissions
title_fullStr Towards a threshold climate for emergency lower respiratory hospital admissions
title_full_unstemmed Towards a threshold climate for emergency lower respiratory hospital admissions
title_sort Towards a threshold climate for emergency lower respiratory hospital admissions
author_id_str_mv 4157d27b800a8357873bdfc9c71bd596
author_id_fullname_str_mv 4157d27b800a8357873bdfc9c71bd596_***_Saiful Islam
author Saiful Islam
author2 Muhammad Saiful Islam
Thierry J. Chaussalet
Naoru Koizumi
Saiful Islam
format Journal article
container_title Environmental Research
container_volume 153
container_start_page 41
publishDate 2017
institution Swansea University
issn 00139351
doi_str_mv 10.1016/j.envres.2016.11.011
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
url http://www.sciencedirect.com/science/article/pii/S0013935116310684
document_store_str 1
active_str 0
description Identification of 'cut-points' or thresholds of climate factors would play a crucial role in alerting risks of climate change and providing guidance to policymakers. This study investigated a 'Climate Threshold' for emergency hospital admissions of chronic lower respiratory diseases by using a distributed lag non-linear model (DLNM). We analyseda unique longitudinal dataset (10 years, 2000-2009) on emergency hospital admissions, climate, and pollution factors for the Greater London. Our study extends existing work on this topic by considering non-linearity, lag effects between climate factors and disease exposure within the DLNM model considering B-spline as smoothing technique. The final model also considered natural cubic splines of time since exposure and 'day of the week' as confounding factors. The results of DLNM indicated a significant improvement in model fitting compared to a typical GLM model. The final model identified the thresholds of several climate factors including: high temperature (≥270C), low relative humidity (≤ 40%), high Pm10 level (≥70-μg/m3), low wind speed (≤ 2 knots) and high rainfall (≥30mm). Beyond the threshold values, a significantly higher number of emergency admissions due to lower respiratory problems would be expected within the following 2-3 days after the climate shift in the Greater London. The approach will be useful to initiate 'region and disease specific' climate mitigation plans. It will help identify spatial hot spots and the most sensitive areas and population due to climate change, and will eventually lead towards a diversified health warning system tailored to specific climate zones and populations.
published_date 2017-02-01T03:38:10Z
_version_ 1763751693161332736
score 11.036531

Similar Items