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
Authors:   GBD 2019 Dementia Collaborators, 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 
Published: August 2021

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.