Mortality by cause of death in Colombia: a local analysis using spatial econometrics

dc.contributor.authorSpijker, Jeroen
dc.contributor.authorRecaño, Joaquín
dc.contributor.authorMartínez, Sandra
dc.contributor.authorCarioli, Alessandra
dc.date.accessioned2020-09-11T20:54:14Z
dc.date.available2020-09-11T20:54:14Z
dc.description.abstractenglishColombia is undergoing major changes in mortality patterns. National- and depart-ment-level cause-specific analyses have previously been carried out, but very little is known about municipal-level trends, despite their epidemiological interest. We first analyze standardized mortality rates for seven cause-of-death groups to obtain high and low mortality clusters based on the spatial autocorrelation indicators Global Moran’s I and Local Moran’s I. The Mann–Whitney nonparametric test is then used to ascertain statistical associations between the high and low mortality clus-ters and known health determinants. We subsequently apply spatial lag and Durbin (when spatial autocorrelation was present) and OLS models (when not) to explain overall spatial patterns in cause-specific mortality. Age- and sex-specific cause-of-death mortality and population data were obtained from the National Administrative Department of Statistics (DANE). Deaths were corrected for each municipality due to under-registration. Results show that spatial autocorrelation declined over time for all cause-of-death categories, except male circulatory system diseases and peri-natal mortality. It is highest in external causes, especially among men, with mortal-ity hotspots moving from the central Andean area to Orinoquia and the Amazon rainforest. Male mortality is also more spatially clustered than female mortality and especially neoplasms, and external-cause mortality is also indirectly affected by the conditions of neighboring municipalities. Municipal surface area, ethnicity and pub-lic expenditure on health and education are the most frequent contextual variables explaining territorial differences in mortality. The identification of geographical mortality clusters in Colombia will allow decision makers to prioritize those regions with higher mortality.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1007/s10109-020-00335-1
dc.identifier.instnameinstname:Universidad El Bosquespa
dc.identifier.issn1435-5949
dc.identifier.reponamereponame:Repositorio Institucional Universidad El Bosquespa
dc.identifier.repourlhttps://repositorio.unbosque.edu.co
dc.identifier.urihttps://hdl.handle.net/20.500.12495/4066
dc.language.isoeng
dc.publisherSpringer Naturespa
dc.publisher.journalJournal of geographical systemsspa
dc.relation.ispartofseriesJournal of geographical systems, 1435-5949, 2020spa
dc.relation.urihttps://link.springer.com/article/10.1007%2Fs10109-020-00335-1
dc.rights.accessrightshttps://purl.org/coar/access_right/c_abf2
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.accessrightsAcceso abierto
dc.rights.creativecommons2020-08-13
dc.rights.localAcceso abiertospa
dc.subject.keywordsSpatial cluster analysisspa
dc.subject.keywordsSpatial Durbin modelspa
dc.subject.keywordsMortalityspa
dc.subject.keywordsCauses of deathspa
dc.subject.keywordsEpidemiologyspa
dc.subject.keywordsColombiaspa
dc.titleMortality by cause of death in Colombia: a local analysis using spatial econometricsspa
dc.title.translatedMortality by cause of death in Colombia: a local analysis using spatial econometricsspa
dc.type.coarhttps://purl.org/coar/resource_type/c_6501
dc.type.driverinfo:eu-repo/semantics/article
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.type.localArtículo de revista

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