Improving prevalence estimation through data fusion: methods and validation

Estimation of health prevalences is usually performed with a single survey. Some attempts have been made to integrate more than one source of data. We propose here to validate this approach through data fusion. Data Fusion is the process of integrating two sources of data into one combined file. It...

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Detalhes bibliográficos
Autores: Aluja Banet, Tomàs|||0000-0003-3096-0339, Daunis Estadella, Josep, Brunsó, Núria, Mompart Penina, Anna
Tipo de documento: artigo
Data de publicação:2015
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositório:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglês
OAI Identifier:oai:upcommons.upc.edu:2117/80031
Acesso em linha:https://hdl.handle.net/2117/80031
https://dx.doi.org/10.1186/s12911-015-0169-z
Access Level:Acceso aberto
Palavra-chave:Combinatorial probabilities
Population surveys
Prevalences
Diabetes
Cardio vascular diseases
Multiple imputation
Sequential regression
Probabilitats
Classificació AMS::60 Probability theory and stochastic processes::60C05 Combinatorial probability
Àrees temàtiques de la UPC::Matemàtiques i estadística::Probabilitat
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spelling Improving prevalence estimation through data fusion: methods and validationAluja Banet, Tomàs|||0000-0003-3096-0339Daunis Estadella, JosepBrunsó, NúriaMompart Penina, AnnaCombinatorial probabilitiesPopulation surveysPrevalencesDiabetesCardio vascular diseasesMultiple imputationSequential regressionProbabilitatsClassificació AMS::60 Probability theory and stochastic processes::60C05 Combinatorial probabilityÀrees temàtiques de la UPC::Matemàtiques i estadística::ProbabilitatEstimation of health prevalences is usually performed with a single survey. Some attempts have been made to integrate more than one source of data. We propose here to validate this approach through data fusion. Data Fusion is the process of integrating two sources of data into one combined file. It allows us to take even greater advantage of existing information collected in databases. Here, we use data fusion to improve the estimation of health prevalences for two primary health factors: cardiovascular diseases and diabetes.Peer Reviewed20152015-06-2420152015-11-30journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/80031https://dx.doi.org/10.1186/s12911-015-0169-z26104747reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/800312026-05-27T15:37:01Z
dc.title.none.fl_str_mv Improving prevalence estimation through data fusion: methods and validation
title Improving prevalence estimation through data fusion: methods and validation
spellingShingle Improving prevalence estimation through data fusion: methods and validation
Aluja Banet, Tomàs|||0000-0003-3096-0339
Combinatorial probabilities
Population surveys
Prevalences
Diabetes
Cardio vascular diseases
Multiple imputation
Sequential regression
Probabilitats
Classificació AMS::60 Probability theory and stochastic processes::60C05 Combinatorial probability
Àrees temàtiques de la UPC::Matemàtiques i estadística::Probabilitat
title_short Improving prevalence estimation through data fusion: methods and validation
title_full Improving prevalence estimation through data fusion: methods and validation
title_fullStr Improving prevalence estimation through data fusion: methods and validation
title_full_unstemmed Improving prevalence estimation through data fusion: methods and validation
title_sort Improving prevalence estimation through data fusion: methods and validation
dc.creator.none.fl_str_mv Aluja Banet, Tomàs|||0000-0003-3096-0339
Daunis Estadella, Josep
Brunsó, Núria
Mompart Penina, Anna
author Aluja Banet, Tomàs|||0000-0003-3096-0339
author_facet Aluja Banet, Tomàs|||0000-0003-3096-0339
Daunis Estadella, Josep
Brunsó, Núria
Mompart Penina, Anna
author_role author
author2 Daunis Estadella, Josep
Brunsó, Núria
Mompart Penina, Anna
author2_role author
author
author
dc.subject.none.fl_str_mv Combinatorial probabilities
Population surveys
Prevalences
Diabetes
Cardio vascular diseases
Multiple imputation
Sequential regression
Probabilitats
Classificació AMS::60 Probability theory and stochastic processes::60C05 Combinatorial probability
Àrees temàtiques de la UPC::Matemàtiques i estadística::Probabilitat
topic Combinatorial probabilities
Population surveys
Prevalences
Diabetes
Cardio vascular diseases
Multiple imputation
Sequential regression
Probabilitats
Classificació AMS::60 Probability theory and stochastic processes::60C05 Combinatorial probability
Àrees temàtiques de la UPC::Matemàtiques i estadística::Probabilitat
description Estimation of health prevalences is usually performed with a single survey. Some attempts have been made to integrate more than one source of data. We propose here to validate this approach through data fusion. Data Fusion is the process of integrating two sources of data into one combined file. It allows us to take even greater advantage of existing information collected in databases. Here, we use data fusion to improve the estimation of health prevalences for two primary health factors: cardiovascular diseases and diabetes.
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-06-24
2015
2015-11-30
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/80031
https://dx.doi.org/10.1186/s12911-015-0169-z
26104747
url https://hdl.handle.net/2117/80031
https://dx.doi.org/10.1186/s12911-015-0169-z
identifier_str_mv 26104747
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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