학술논문
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
Document Type
Report
Author
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; Burugina Nagaraja , Sharath; 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; Galluzzo, 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; Kivimäki, Mika; Koyanagi, Ai; Kumar, Manasi; Landires, Iván; Lasrado, Savita; Li, Bingyu; Lim, Stephen S.; Liu, Xuefeng; Madhava Kunjathur , Shilpashree; 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.; Pashazadeh Kan , Fatemeh; 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
Source
BMC Medical Informatics and Decision Making. August 11, 2021, Vol. 21 Issue 1
Subject
Language
English
ISSN
1472-6947
Abstract
Author(s): Emma Nichols[sup.1,1] , Foad Abd-Allah[sup.] , Amir Abdoli[sup.] , Ahmed Abualhasan[sup.] , Eman Abu-Gharbieh[sup.] , Ashkan Afshin[sup.] , Rufus Olusola Akinyemi[sup.] , Fahad Mashhour Alanezi[sup.] , Vahid Alipour[sup.] , [...]
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
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