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dc.contributor.authorAyma D.
dc.contributor.authorDurbán M.
dc.contributor.authorLee D.-J.
dc.contributor.authorEilers P.H.C.
dc.date.accessioned2016-06-19T18:43:07Z
dc.date.available2016-06-19T18:43:07Z
dc.date.issued2016-01-01
dc.identifier.issn2211-6753
dc.identifier.issn2211-6753
dc.identifier.urihttp://hdl.handle.net/20.500.11824/286
dc.description.abstractMortality data provide valuable information for the study of the spatial distri- bution of mortality risk, in disciplines such as spatial epidemiology and public health. However, they are frequently available in an aggregated form over irreg- ular geographical units, hindering the visualization of the underlying mortality risk. Also, it can be of interest to obtain mortality risk estimates on a finer spatial resolution, such that they can be linked to potential risk factors that are usually measured in a different spatial resolution. In this paper, we propose the use of the penalized composite link model and its mixed model representation. This model considers the nature of mortality rates by incorporating the population size at the finest resolution, and allows the creation of mortality maps at a finer scale, thus reducing the visual bias resulting from the spatial aggrega- tion within original units. We also extend the model by considering individual random effects at the aggregated scale, in order to take into account the overdis- persion. We illustrate our novel proposal using two datasets: female deaths by lung cancer in Indiana, USA, and male lip cancer incidence in Scotland counties. We also compare the performance of our proposal with the area-to-point Poisson kriging approach.
dc.formatapplication/pdf
dc.languageeng
dc.publisherSpatial Statistics
dc.relationES/1PE/SEV-2013-0323
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/
dc.subjectPenalized composite link models
dc.subjectMixed models
dc.subjectMortality rates
dc.subjectDisease mapping
dc.titlePenalized composite link models for aggregated spatial count data: a mixed model approach
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.provenancebcamath
dc.identifier.doi10.1016/j.spasta.2016.06.007


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