Journal Article

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

Majeed, Azeem, Baig, Atif Amin, Abualhasan, Ahmed, Hachinski, Vladimir, Irvani, Seyed Sina Naghibi, Galluzzo, Lucia, Haile, Teklehaimanot Gereziher, Hamiduzzaman, Mohammad, Nichols, Emma, Kisa, Adnan, Alanezi, Fahad Mashhour, Kisa, Sezer, Landires, Iván, Boloor, Archith, El-Jaafary, Shaimaa I., Akinyemi, Rufus Olusola, Iwagami, Masao, Karch, André, Biswas, Atanu, Fischer, Florian, Iyamu, Ihoghosa Osamuyi, Ho, Hung Chak, Ashraf-Ganjouei, Amir, Baune, Bernhard T., Khatib, Mahalaqua Nazli, Bijani, Ali, Farzadfar, Farshad, Gnedovskaya, Elena V., Madhava Kunjathur, Shilpashree, Mohammed, Salahuddin, Fernandes, Eduarda, Abdoli, Amir, Gaidhane, Shilpa, Malik, Preeti, Kim, Yun Jin, Bhattacharyya, Krittika, Lasrado, Savita, Catalá-López, Ferrán, Chu, Dinh-Toi, Barker-Collo, Suzanne Lyn, Gebremeskel, Gebreamlak Gebremedhn, Ilic, Irena M., Ayuso-Mateos, Jose L., Filip, Irina, Li, Bingyu, Khan, Ejaz Ahmad, Banach, Maciej, Ilic, Milena D., Edvardsson, David, Barboza, Miguel A., Feigin, Valery L., Dai, Xiaochen, Ghashghaee, Ahmad, Afshin, Ashkan, Kalani, Rizwan, Burugina Nagaraja, Sharath, Khader, Yousef Sale

2021

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Activities of daily living / Adjudication / Aged, 80 and over / Aging / Algorithm / Algorithms / Alzheimer's disease / Analysis / Cognition / Cognition & reasoning / Cognitive ability / Dementia / Dementia - diagnosis / Dementia - epidemiology / Dementia disorders / Demographic aspects / Demographics / Demography / Diagnosis / Diagnostic systems / Estimates / Evaluation / Female / Global health / Health Informatics / Humans / Information Systems and Communication Service / Item response theory / Longitudinal Studies / machine learning / Male / Management of Computing and Information Systems / Medicine / Medicine & Public Health / Memory / modeling / Multidimensional methods / Nursing homes / Older people / Polls & surveys / Population Dynamics / Predictions / Prevalence / Prevalence studies (Epidemiology) / Regression models / Research Article / Retirement / Sample size / Sex differences / technology / Two dimensional models / Validity