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dc.contributor.authorNichols, Emma
dc.contributor.authorAbd-Allah, Foad
dc.contributor.authorHay, Simon I.
dc.contributor.authorKisa, Adnan
dc.contributor.authorMurray, Christopher J L
dc.contributor.authorVos, Theo
dc.contributor.authorKisa, Sezer
dc.contributor.authorMokdad, Ali H.
dc.contributor.authorDementia Collaborators, GBD 2019
dc.date.accessioned2021-12-13T13:54:05Z
dc.date.available2021-12-13T13:54:05Z
dc.date.created2021-08-15T20:43:03Z
dc.date.issued2021
dc.identifier.citationBMC Medical Informatics and Decision Making. 2021, 21, 1-10 .en_US
dc.identifier.issn1472-6947
dc.identifier.urihttps://hdl.handle.net/11250/2834023
dc.description.abstractBackground Data sparsity is a major limitation to estimating national and global dementia burden. Surveys with full diagnostic evaluations of dementia prevalence are prohibitively resource-intensive in many settings. However, validation samples from nationally representative surveys allow for the development of algorithms for the prediction of dementia prevalence nationally. Methods Using cognitive testing data and data on functional limitations from Wave A (2001–2003) of the ADAMS study (n = 744) and the 2000 wave of the HRS study (n = 6358) we estimated a two-dimensional item response theory model to calculate cognition and function scores for all individuals over 70. Based on diagnostic information from the formal clinical adjudication in ADAMS, we fit a logistic regression model for the classification of dementia status using cognition and function scores and applied this algorithm to the full HRS sample to calculate dementia prevalence by age and sex. Results Our algorithm had a cross-validated predictive accuracy of 88% (86–90), and an area under the curve of 0.97 (0.97–0.98) in ADAMS. Prevalence was higher in females than males and increased over age, with a prevalence of 4% (3–4) in individuals 70–79, 11% (9–12) in individuals 80–89 years old, and 28% (22–35) in those 90 and older. Conclusions Our model had similar or better accuracy as compared to previously reviewed algorithms for the prediction of dementia prevalence in HRS, while utilizing more flexible methods. These methods could be more easily generalized and utilized to estimate dementia prevalence in other national surveys.en_US
dc.language.isoengen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleUse of multidimensional item response theory methods for dementia prevalence prediction: an example using the Health and Retirement Survey and the Aging, Demographics, and Memory Studyen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber1-10en_US
dc.source.volume21en_US
dc.source.journalBMC Medical Informatics and Decision Makingen_US
dc.identifier.doi10.1186/s12911-021-01590-y
dc.identifier.cristin1926093
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal