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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

Nichols, Emma
; 
Abd-Allah, Foad
; 
Abdoli, Amir
; 
Abualhasan, Ahmed
; 
Abu-Gharbieh, Eman
; 
Afshin, Ashkan
; 
Akinyemi, Rufus Olusola
; 
Alanezi, Fahad Mashhour
; 
Alipour, Vahid
; 
Almasi-Hashiani, Amir
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Nichols, Emma
Abd-Allah, Foad
Abdoli, Amir
Abualhasan, Ahmed
Abu-Gharbieh, Eman
Afshin, Ashkan
Akinyemi, Rufus Olusola
Alanezi, Fahad Mashhour
Alipour, Vahid
Almasi-Hashiani, Amir
Arabloo, Jalal
Ashraf-Ganjouei, Amir
Ayano, Getinet
Ayuso-Mateos, Jose L.
Baig, Atif Amin
Banach, Maciej
Barboza, Miguel A.
Barker-Collo, Suzanne Lyn
Baune, Bernhard T.
Bhagavathula, Akshaya Srikanth
Bhattacharyya, Krittika
Bijani, Ali
Biswas, Atanu
Boloor, Archith
Brayne, Carol
Brenner, Hermann
Burkart, Katrin
Nagaraja, Sharath Burugina
Carvalho, Felix
Castro-de-Araujo, Luis F. S.
Catalá-López, Ferrán
Cerin, Ester
Cherbuin, Nicolas
Chu, Dinh-Toi
Dai, Xiaochen
de Sá-Junior, Antonio Reis
Djalalinia, Shirin
Douiri, Abdel
Edvardsson, David
El-Jaafary, Shaimaa I.
Eskandarieh, Sharareh
Faro, Andre
Farzadfar, Farshad
Feigin, Valery L.
Fereshtehnejad, Seyed-Mohammad
Fernandes, Eduarda
Ferrara, Pietro
Filip, Irina
Fischer, Florian
Gaidhane, Shilpa
Galluzo, Lucia
Gebremeskel, Gebreamlak Gebremedhn
Ghashghaee, Ahmad
Gialluisi, Alessandro
Gnedovskaya, Elena V.
Golechha, Mahaveer
Gupta, Rajeev
Hachinski, Vladimir
Haider, Mohammad Rifat
Haile, Teklehaimanot Gereziher
Hamiduzzaman, Mohammad
Hankey, Graeme J.
Hay, Simon I.
Heidari, Golnaz
Heidari-Soureshjani, Reza
Ho, Hung Chak
Househ, Mowafa
Hwang, Bing-Fang
Iacoviello, Licia
Ilesanmi, Olayinka Stephen
Ilic, Irena M.
Ilic, Milena D.
Irvani, Seyed Sina Naghibi
Iwagami, Masao
Iyamu, Ihoghosa Osamuyi
Jha, Ravi Prakash
Kalani, Rizwan
Karch, André
Kasa, Ayele Semachew
Khader, Yousef Saleh
Khan, Ejaz Ahmad
Khatib, Mahalaqua Nazli
Kim, Yun Jin
Kisa, Sezer
Kisa, Adnan
Kivimaki, Mika
Koyanagi, Ai
Kumar, Manasi
Landires, Iván
Lasrado, Savita
Li, Bingyu
Lim, Stephen S.
Liu, Xuefeng
Kunjathur, Shilpashree Madhava
Majeed, Azeem
Malik, Preeti
Mehndiratta, Man Mohan
Menezes, Ritesh G.
Mohammad, Yousef
Mohammed, Salahuddin
Mokdad, Ali H.
Moni, Mohammad Ali
Nagel, Gabriele
Naveed, Muhammad
Nayak, Vinod C.
Nguyen, Cuong Tat
Nguyen, Huong Lan Thi
Nunez-Samudio, Virginia
Olagunju, Andrew T.
Ostroff, Samuel M.
Otstavnov, Nikita
Owolabi, Mayowa O.
Kan, Fatemeh Pashazadeh
Patel, Urvish K.
Phillips, Michael R.
Piradov, Michael A.
Pond, Constance Dimity
Pottoo, Faheem Hyder
Prada, Sergio I.
Radfar, Amir
Rahim, Fakher
Rana, Juwel
Rashedi, Vahid
Rawaf, Salman
Rawaf, David Laith
Reinig, Nickolas
Renzaho, Andre M. N.
Rezaei, Nima
Rezapour, Aziz
Romoli, Michele
Roshandel, Gholamreza
Sachdev, Perminder S.
Sahebkar, Amirhossein
Sahraian, Mohammad Ali
Samaei, Mehrnoosh
Saylan, Mete
Sha, Feng
Shaikh, Masood Ali
Shibuya, Kenji
Shigematsu, Mika
Shin, Jae Il
Shiri, Rahman
Silva, Diego Augusto Santos
Singh, Jasvinder A.
Singhal, Deepika
Skryabin, Valentin Yurievich
Skryabina, Anna Aleksandrovna
Soheili, Amin
Sotoudeh, Houman
Spurlock, Emma Elizabeth
Szoeke, Cassandra E. I.
Tabarés-Seisdedos, Rafael
Taddele, Biruk Wogayehu
Tovani-Palone, Marcos Roberto
Tsegaye, Gebiyaw Wudie
Vacante, Marco
Venketasubramanian, Narayanaswamy
Vidale, Simone
Vlassov, Vasily
Vu, Giang Thu
Wang, Yuan-Pang
Weiss, Jordan
Weldemariam, Abrha Hailay
Westerman, Ronny
Wimo, Anders
Winkler, Andrea Sylvia
Wu, Chenkai
Yadollahpour, Ali
Yesiltepe, Metin
Yonemoto, Naohiro
Yu, Chuanhua
Zastrozhin, Mikhail Sergeevich
Zastrozhina, Anasthasia
Zhang, Zhi-Jiang
Murray, Christopher J. L.
Vos, Theo
Abstract
Background 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.
Keywords
dementia, prevalence, algorithm, validity, global health
Date
2021
Type
Journal article
Journal
BMC Medical Informatics & Decision Making
Book
Volume
21
Issue
241
Page Range
1-10
Article Number
Article 241
ACU Department
Mary MacKillop Institute for Health Research
Faculty of Health Sciences
Collections
Mary MacKillop Institute for Health Research
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URI
https://hdl.handle.net/20.500.14802/23092
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DOI
10.1186/s12911-021-01590-y
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Published as ‘gold’ (paid) open access
License
CC BY 4.0
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