Publication
Which meteorological index is the best descriptor for winter mortality in elderly population in Lisbon district?
| dc.contributor.author | Silva, Susana Pereira | |
| dc.contributor.author | Mouriño, Helena | |
| dc.contributor.author | Antunes, Liliana | |
| dc.contributor.author | Marques, Jorge | |
| dc.contributor.author | Antunes, Sílvia | |
| dc.contributor.author | Dias, Carlos Matias | |
| dc.contributor.author | Nunes, Baltazar | |
| dc.date.accessioned | 2017-07-06T10:34:47Z | |
| dc.date.available | 2017-07-06T10:34:47Z | |
| dc.date.issued | 2017-06-20 | |
| dc.description.abstract | Background: As recognized by WHO, health is influenced climate change (WHO 2014). Several studies have already provided the association between ambient temperature and mortality, hospital admissions and affluence to urgency services, in elderly population, especially due to cardiovascular and circulatory diseases(Yang et al. 2012, Laaidi et al. 2013, Analitis et al. 2008, Hajat, Kovats, and Lachowycz 2007, Kysely et al. 2009). However, few studies have explored the association between elderly mortality in winter and extreme cold weather, considering as covariables different meteorological indices (Vaneckova et al. 2011, Kunst, Groenhof, and Mackenbach 1994). The present study aimed to assess which meteorological index is the best descriptor for winter mortality in elderly population living in Lisbon district. Methods: Mortality data was provided by Statistics Portugal (INE), meteorological data from The Portuguese Institute for Sea and Atmosphere (IPMA) and influenza-like-illness rates from Portuguese general practitioners (GP) sentinel network (Rede Médicos-Sentinela). Distributed lag linear and non-linear models (DLNM) (Gasparrini, Armstrong, and Kenward 2010, Gasparrini 2011) were applied to study the effect of cold on mortality by all causes of death, and, particularly, by circulatory and respiratory diseases, in the Lisbon district, in the winter season (from November to March) between 2002 and 2012. Based on different combinations of the meteorological variables (that is, mean temperature, mean temperature and wind speed, mean temperature and humidity, and windchill temperature), several models were fitted and their performance compared. All models were adjusted for trend and seasonality, and for the confounding effect of flu activity. As a reference for relative risk (RR) calculations, the 50th percentile of each temperature series was used. Results: The best fit was found from a linear relation between temperature (either mean or windchill) and both mortality causes under study (all causes, and circulatory and respiratory diseases). The results showed that the effect of cold appears with delay and persisted for about 23 to 30 days. The maximum effect occurs with the lowest temperature registered (Mean Temperature=-0.4ºC and windchill Temperature=-3.96ºC) and with a delay of 5 days. The highest cumulative relative risk for all causes of death was found using the windchill temperature [RR=1,8 (CI95%: 1,7; 2,0)]. For mortality by circulatory and respiratory diseases, the highest cumulative relative risk was also found using the windchill temperature [RR=2,0 (CI95%: 1,8; 2,3)]. Conclusions: Cold weather seems to be a strong predictor of mortality in Lisbon district, with the strongest association found out between cold temperature and both circulatory and respiratory mortality. Windchill temperature seems to be a better predictor of mortality than mean temperature. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.18/4743 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | Instituto Nacional de Saúde Doutor Ricardo Jorge, IP | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | pt_PT |
| dc.subject | Determinantes da Saúde e da Doença | pt_PT |
| dc.subject | Mortalidade | pt_PT |
| dc.subject | Frio | pt_PT |
| dc.subject | FRIESA | pt_PT |
| dc.subject | Winter Mortality | pt_PT |
| dc.subject | Elderly Population | pt_PT |
| dc.subject | Lisbon District | pt_PT |
| dc.subject | Portugal | pt_PT |
| dc.title | Which meteorological index is the best descriptor for winter mortality in elderly population in Lisbon district? | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/3599-PPCDT/EXPL%2FDTP-SAP%2F1373%2F2013/PT | |
| oaire.citation.conferencePlace | Lisboa, Portugal | pt_PT |
| oaire.citation.title | 26th Annual Conference/Meeting of the Society for Risk Analysis – Europe (SRA-E), 19-21 June 2017 | pt_PT |
| oaire.fundingStream | 3599-PPCDT | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
| relation.isProjectOfPublication | d26b94bb-1ead-4008-aa15-1a380ffa8159 | |
| relation.isProjectOfPublication.latestForDiscovery | d26b94bb-1ead-4008-aa15-1a380ffa8159 |