{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["GBD 2019 Dementia Collaborators"],"funding":["NIA NIH HHS","Medical Research Council","Bill and Melinda Gates Foundation","gates ventures","Wellcome Trust"],"pagination":["241"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8356410"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["21(1)"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Methods</h4>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 regressi"],"journal":["BMC medical informatics and decision making"],"pubmed_title":["Use of multidimensional item response theory methods for dementia prevalence prediction: an example using the Health and Retirement Survey and the Aging, Demographics, and Memory Study."],"pmcid":["PMC8356410"],"funding_grant_id":["OPP1152504","MR/R024227","221854/Z/20/Z","R01 AG025533","G0601022","R01 AG056477","MR/S011676"],"pubmed_authors":["Mohammed S","Edvardsson D","Baune BT","Bijani A","Kalani R","Akinyemi RO","Mokdad AH","Yesiltepe M","Pottoo FH","Rashedi V","Skryabina AA","Ilic MD","Singh JA","Banach M","El-Jaafary SI","Vacante M","Yadollahpour A","Hwang BF","Hankey GJ","Yonemoto N","Khader YS","Owolabi MO","Rezaei N","Shiri R","Szoeke CEI","Cerin E","Tsegaye GW","Burugina Nagaraja S","Biswas A","Alipour V","Ashraf-Ganjouei A","Heidari G","Khatib MN","Brayne C","Golechha M","Iacoviello L","Ghashghaee A","Wang YP","Vlassov V","Brenner H","Madhava Kunjathur S","Naveed M","Barboza MA","Fischer F","Vu GT","Roshandel G","Patel UK","Taddele BW","Irvani SSN","Saylan M","Wimo A","Abu-Gharbieh E","Ayano G","Nguyen HLT","Dai X","Skryabin VY","Weldemariam AH","de Sa-Junior AR","Ayuso-Mateos JL","Burkart K","Koyanagi A","Gebremeskel GG","Tabares-Seisdedos R","Abd-Allah F","Nunez-Samudio V","Li B","Nichols E","Djalalinia S","Gupta R","Ilic IM","Zastrozhina A","Chu DT","Hamiduzzaman M","Khan EA","Shibuya K","Cherbuin N","Karch A","Mehndiratta MM","Radfar A","Afshin A","Sha F","Castro-de-Araujo LFS","Zhang ZJ","Eskandarieh S","Yu C","Menezes RG","Sotoudeh H","Moni MA","Ostroff SM","Nagel G","Faro A","Kisa S","Boloor A","Wu C","Kumar M","Singhal D","Bhagavathula AS","Murray CJL","Iwagami M","Mohammad Y","Gialluisi A","Kisa A","Jha RP","Samaei M","Shigematsu M","Spurlock EE","Venketasubramanian N","Winkler AS","Rana J","Hay SI","Vos T","Barker-Collo SL","Prada SI","Farzadfar F","Abdoli A","Rawaf DL","Ferrara P","Tovani-Palone MR","Fernandes E","Otstavnov N","Rezapour A","Lasrado S","Sahraian MA","Renzaho AMN","Lim SS","Baig AA","Filip I","Ho HC","Vidale S","Soheili A","Galluzzo L","Nguyen CT","Hachinski V","Landires I","Sachdev PS","Househ M","Arabloo J","Kivimaki M","Nayak VC","Bhattacharyya K","Feigin VL","Alanezi FM","GBD 2019 Dementia Collaborators","Douiri A","Liu X","Weiss J","Shin JI","Haile TG","Pond CD","Westerman R","Kim YJ","Heidari-Soureshjani R","Silva DAS","Pashazadeh Kan F","Shaikh MA","Rawaf S","Sahebkar A","Gaidhane S","Phillips MR","Haider MR","Malik P","Olagunju AT","Reinig N","Zastrozhin MS","Romoli M","Rahim F","Carvalho F","Iyamu IO","Catala-Lopez F","Piradov MA","Gnedovskaya EV","Ilesanmi OS","Almasi-Hashiani A","Kasa AS","Fereshtehnejad SM","Majeed A","Abualhasan A"],"additional_accession":[]},"is_claimable":false,"name":"Use of multidimensional item response theory methods for dementia prevalence prediction: an example using the Health and Retirement Survey and the Aging, Demographics, and Memory Study.","description":"<h4>Background</h4>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.<h4>Methods</h4>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 regressi","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Aug","modification":"2025-05-18T13:26:58.143Z","creation":"2022-02-11T06:52:24.511Z"},"accession":"S-EPMC8356410","cross_references":{"pubmed":["34380485"],"doi":["10.1186/s12911-021-01590-y"]}}