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dc.contributor.authorBoshuizen, Hendrike C
dc.contributor.authorPoos, Marinus J J C
dc.contributor.authorvan den Akker, Marjan
dc.contributor.authorvan Boven, Kees
dc.contributor.authorKorevaar, Joke C
dc.contributor.authorde Waal, Margot W M
dc.contributor.authorBiermans, Marion C J
dc.contributor.authorHoeymans, Nancy
dc.date.accessioned2018-02-07T08:11:03Z
dc.date.available2018-02-07T08:11:03Z
dc.date.issued2017-04-05
dc.identifier.citationEstimating incidence and prevalence rates of chronic diseases using disease modeling. 2017, 15 (1):13 Popul Health Metren
dc.identifier.issn1478-7954
dc.identifier.pmid28381229
dc.identifier.doi10.1186/s12963-017-0130-8
dc.identifier.urihttp://hdl.handle.net/10029/621348
dc.description.abstractMorbidity estimates between different GP registration networks show large, unexplained variations. This research explores the potential of modeling differences between networks in distinguishing new (incident) cases from existing (prevalent) cases in obtaining more reliable estimates.
dc.language.isoenen
dc.rightsArchived with thanks to Population health metricsen
dc.titleEstimating incidence and prevalence rates of chronic diseases using disease modeling.en
dc.typeArticleen
dc.identifier.journalPopul Health Metr 2017; 15(1):13en
html.description.abstractMorbidity estimates between different GP registration networks show large, unexplained variations. This research explores the potential of modeling differences between networks in distinguishing new (incident) cases from existing (prevalent) cases in obtaining more reliable estimates.


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