Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/32740
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dc.contributor.authorMartins, Anapt_PT
dc.contributor.authorScotto, Manuelpt_PT
dc.contributor.authorDeus, Ricardopt_PT
dc.contributor.authorMonteiro, Alexandrapt_PT
dc.contributor.authorGouveia, Sóniapt_PT
dc.date.accessioned2021-12-14T15:19:50Z-
dc.date.available2021-12-14T15:19:50Z-
dc.date.issued2021-07-09-
dc.identifier.urihttp://hdl.handle.net/10773/32740-
dc.description.abstractAlthough regulatory improvements for air quality in the European Union have been made, air pollution is still a pressing problem and, its impact on health, both mortality and morbidity, is a topic of intense research nowadays. The main goal of this work is to assess the impact of the exposure to air pollutants on the number of daily hospital admissions due to respiratory causes in 58 spatial locations of Portugal mainland, during the period 2005-2017. To this end, INteger Generalised AutoRegressive Conditional Heteroskedastic (INGARCH)-based models are extensively used. This family of models has proven to be very useful in the analysis of serially dependent count data. Such models include information on the past history of the time series, as well as the effect of external covariates. In particular, daily hospitalisation counts, air quality and temperature data are endowed within INGARCH models of optimal orders, where the automatic inclusion of the most significant covariates is carried out through a new block-forward procedure. The INGARCH approach is adequate to model the outcome variable (respiratory hospital admissions) and the covariates, which advocates for the use of count time series approaches in this setting. Results show that the past history of the count process carries very relevant information and that temperature is the most determinant covariate, among the analysed, for daily hospital respiratory admissions. It is important to stress that, despite the small variability explained by air quality, all models include on average, approximately two air pollutants covariates besides temperature. Further analysis shows that the one-step-ahead forecasts distributions are well separated into two clusters: one cluster includes locations exclusively in the Lisbon area (exhibiting higher number of one-step-ahead hospital admissions forecasts), while the other contains the remaining locations. This results highlights that special attention must be given to air quality in Lisbon metropolitan area in order to decrease the number of hospital admissions.pt_PT
dc.language.isoengpt_PT
dc.publisherPublic Library of Sciencept_PT
dc.relationIEETA/UA (UIDB/00127/2020pt_PT
dc.relationUIDB/04621/2020pt_PT
dc.relationUIDP/50017/2020pt_PT
dc.relationUIDB/50017/2020pt_PT
dc.relationSFRH/BD/143973/2019pt_PT
dc.rightsopenAccesspt_PT
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAir Pollutantspt_PT
dc.subjectAir Pollutionpt_PT
dc.subjectHospitalizationpt_PT
dc.subjectHospitalspt_PT
dc.subjectHumanspt_PT
dc.subjectParticulate Matterpt_PT
dc.subjectPortugalpt_PT
dc.subjectRespiratory Tract Diseasespt_PT
dc.subjectSeasonspt_PT
dc.titleAssociation between respiratory hospital admissions and air quality in Portugal: a count time series approachpt_PT
dc.typearticlept_PT
dc.description.versionpublishedpt_PT
dc.peerreviewedyespt_PT
degois.publication.issue7pt_PT
degois.publication.titlePLoS ONEpt_PT
degois.publication.volume16pt_PT
dc.relation.publisherversionhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0253455pt_PT
dc.identifier.doi10.1371/journal.pone.0253455pt_PT
dc.identifier.essn1932-6203pt_PT
dc.identifier.articlenumbere0253455pt_PT
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