دورية أكاديمية
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
| العنوان: | 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 |
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| المؤلفون: | Nichols, E, Abd-Allah, F, Abdoli, A, Abualhasan, A, Abu-Gharbieh, E, Afshin, A, Akinyemi, RO, Alanezi, FM, Alipour, V, Almasi-Hashiani, A, Arabloo, J, Ashraf-Ganjouei, A, Ayano, G, Ayuso-Mateos, JL, Baig, AA, Banach, M, Barboza, MA, Barker-Collo, SL, Baune, BT, Bhagavathula, AS, Bhattacharyya, K, Bijani, A, Biswas, A, Boloor, A, Brayne, C, Brenner, H, Burkart, K, Nagaraja, SB, Carvalho, F, Castro-de-Araujo, LFS, Catala-Lopez, F, Cerin, E, Cherbuin, N, Chu, D-T, Dai, X, De Sa-Junior, AR, Djalalinia, S, Douiri, A, Edvardsson, D, El-Jaafary, S, Eskandarieh, S, Faro, A, Farzadfar, F, Feigin, VL, Fereshtehnejad, S-M, Fernandes, E, Ferrara, P, Filip, I, Fischer, F, Gaidhane, S, Galluzzo, L, Gebremeskel, GG, Ghashghaee, A, Gialluisi, A, Gnedovskaya, E, Golechha, M, Gupta, R, Hachinski, V, Haider, MR, Haile, TG, Hamiduzzaman, M, Hankey, GJ, Hay, S, Heidari, G, Heidari-Soureshjani, R, Ho, HC, Househ, M, Hwang, B-F, Iacoviello, L, Ilesanmi, OS, Ilic, IM, Ilic, MD, Irvani, SSN, Iwagami, M, Iyamu, IO, Jha, RP, Kalani, R, Karch, A, Kasa, AS, Khader, YS, Khan, EA, Khatib, MN, Kim, YJ, Kisa, S, Kisa, A, Kivimaki, M, Koyanagi, A, Kumar, M, Landires, I, Lasrado, S, Li, B, Lim, SS, Liu, X, Kunjathur, SM, Majeed, A, Malik, P, Mehndiratta, MM, Menezes, RG, Mohammad, Y, Mohammed, S, Mokdad, AH, Moni, MA, Nagel, G, Naveed, M, Nayak, VC, Cuong, TN, Huong, LTN, Nunez-Samudio, V, Olagunju, AT, Ostroff, SM, Otstavnov, N, Owolabi, MO, Kan, FP, Patel, UK, Phillips, MR, Piradov, MA, Pond, CD, Pottoo, FH, Prada, S, Radfar, A, Rahim, F, Rana, J, Rashedi, V, Rawaf, S, Rawaf, DL, Reinig, N, Renzaho, AMN, Rezaei, N, Rezapour, A, Romoli, M, Roshandel, G, Sachdev, PS, Sahebkar, A, Sahraian, MA, Samaei, M, Saylan, M, Sha, F, Shaikh, MA, Shibuya, K, Shigematsu, M, Shin, JI, Shiri, R, Silva, DAS, Singh, JA, Singhal, D, Skryabin, VY, Skryabina, AA, Soheili, A, Sotoudeh, H, Spurlock, EE, Szoeke, CE, Tabares-Seisdedos, R, Taddele, BW, Tovani-Palone, MR, Tsegaye, GW, Vacante, M, Venketasubramanian, N, Vidale, S, Vlassov, V, Giang, TV, Wang, Y-P, Weiss, J, Weldemariam, AH, Westerman, R, Wimo, A, Winkler, AS, Wu, C, Yadollahpour, A, Yesiltepe, M, Yonemoto, N, Yu, C, Zastrozhin, MS, Zastrozhina, A, Zhang, Z-J, Murray, CJL, Vos, T |
| المصدر: | 10 ; 1 |
| بيانات النشر: | BioMed Central |
| سنة النشر: | 2021 |
| المجموعة: | Imperial College London: Spiral |
| مصطلحات موضوعية: | Science & Technology, Life Sciences & Biomedicine, Medical Informatics, Dementia, Prevalence, Algorithm, Validity, Global health, MINI-MENTAL-STATE, NEUROPSYCHOLOGICAL ASSESSMENT, DISEASE, Aged, 80 and over, Female, Humans, Longitudinal Studies, Male, Population Dynamics, Retirement, GBD 2019 Dementia Collaborators, 0806 Information Systems, 1103 Clinical Sciences |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 1472-6947 |
| العلاقة: | BMC Medical Informatics and Decision Making; http://hdl.handle.net/10044/1/92113Test |
| DOI: | 10.1186/s12911-021-01590-y |
| الإتاحة: | https://doi.org/10.1186/s12911-021-01590-yTest http://hdl.handle.net/10044/1/92113Test |
| حقوق: | © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0Test/. The Creative Commons Public Domain Dedication waiver (http://creativecoTest mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. ; http://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: | edsbas.3F59E640 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 14726947 |
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| DOI: | 10.1186/s12911-021-01590-y |