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A spatio-temporal auto-regressive model for generating savings calls to a health line

dc.contributor.authorPaula Simões
dc.contributor.authorM. Lucília Carvalho
dc.contributor.authorAleixo, Sandra
dc.contributor.authorGomes, Sérgio
dc.contributor.authorNatário, Isabel
dc.date.accessioned2019-07-23T08:58:09Z
dc.date.available2019-07-23T08:58:09Z
dc.date.issued2019-07
dc.description.abstractUrgency admission is one of the most important factors regarding hospital costs, which can possibly be mitigated by the use of national health lines such as the Portuguese Saúde24 line (S24). Aiming future development of decision support indicators in a hospital savings context, based on the economic impact of the use of S24 rather than hospital urgency services, this study investigates spatio-temporal dependencies of the number of S24 calls generating savings in each Portuguese municipality, over the period 2010–2016, under an autoregressive approach. An econometric analysis of the savings obtained by the use of S24 is also carried out considering a savings index. Combining insights from classical spatial econometrics and from the analysis of spatio-temporal data, novel Bayesian Poisson spatio-temporal lag models are presented and applied in this paper. This extends to time the ideas of a Bayesian Poisson spatial lag model, considering both a parametric and a non-parametic structure for time and space-time effects. The results obtained for the savings index reveal that, over the last seven years, there has been a more comprehensive spatial effectiveness of the S24 line in solving the non-urgent emergency situations, that could be handled by primary health care services or in a self care basis.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSIMÕES, Paula; [et al] – A spatio-temporal auto-regressive model for generating savings calls to a health line. In 19th International Conference on Computational Science and Its Applications – ICCSA 2019 (Lecture Notes in Computer Science). Saint Petersburg, Russia: Springer, 2019. ISBN 978-3-030-24301-2. Vol. 11621, pp. 81-96pt_PT
dc.identifier.doi10.1007/978-3-030-24302-9_7pt_PT
dc.identifier.isbn978-3-030-24301-2
dc.identifier.isbn978-3-030-24302-9
dc.identifier.urihttp://hdl.handle.net/10400.21/10357
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relationUID/MAT/00297/2019 - FCTpt_PT
dc.subjectBayesian analysispt_PT
dc.subjectSpatial econometricspt_PT
dc.subjectAutoregressive modelspt_PT
dc.subjectSpace-time correlationpt_PT
dc.subjectPoissonpt_PT
dc.titleA spatio-temporal auto-regressive model for generating savings calls to a health linept_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FMAT%2F00006%2F2013/PT
oaire.citation.conferencePlaceJuly 1–4, 2019 - Saint Petersburg, Russiapt_PT
oaire.citation.endPage96pt_PT
oaire.citation.startPage81pt_PT
oaire.citation.title19th International Conference on Computational Science and Its Applications – ICCSA 2019pt_PT
oaire.citation.volume11621pt_PT
oaire.fundingStream5876
person.familyNameSimões
person.familyNameCarvalho
person.familyNameAleixo
person.familyNameNatário
person.givenNamePaula
person.givenNameMaria Lucília
person.givenNameSandra
person.givenNameIsabel
person.identifierR-000-67W
person.identifier.ciencia-id5A18-8430-431E
person.identifier.ciencia-idFA1D-DACF-6CAE
person.identifier.orcid0000-0002-1074-1297
person.identifier.orcid0000-0003-3307-1844
person.identifier.orcid0000-0003-1740-8371
person.identifier.orcid0000-0001-6020-9373
person.identifier.scopus-author-id57197481451
person.identifier.scopus-author-id55767020700
person.identifier.scopus-author-id24829443600
person.identifier.scopus-author-id6508376082
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsclosedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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