|Title||Comparison of methods for algorithmic classification of dementia status in the Health and Retirement Study.|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Gianattasio, KZ, Wu, Q, Glymour, MM, Power, MC|
|Keywords||Cognition & Reasoning, Dementia, Meta-analyses, Survey Methodology|
BACKGROUND: Dementia ascertainment is time-consuming and costly. Several algorithms use existing data from the U.S.-representative Health and Retirement Study (HRS) to algorithmically identify dementia. However, relative performance of these algorithms remains unknown.
METHODS: We compared performance across five algorithms (Herzog-Wallace, Langa-Kabeto-Weir, Crimmins, Hurd, Wu) overall and within sociodemographic subgroups in participants in HRS and Wave A of the Aging, Demographics, and Memory Study (ADAMS, 2000-2002), an HRS sub-study including in-person dementia ascertainment. We then compared algorithmic performance in an internal (time-split) validation dataset including participants of HRS and ADAMS Waves B, C, and/or D (2002-2009).
RESULTS: In the unweighted training data, sensitivity ranged from 53% to 90%, specificity ranged from 79% to 97%, and overall accuracy ranged from 81% to 87%. Though sensitivity was lower in the unweighted validation data (range: 18% to 62%), overall accuracy was similar (range: 79% to 88%) due to higher specificities (range: 82% to 98%). In analyses weighted to represent the age-eligible US population, accuracy ranged from 91% to 94% in the training data and 87% to 94% in the validation data. Using a 0.5 probability cutoff, Crimmins maximized sensitivity, Herzog-Wallace maximized specificity, and Wu and Hurd maximized accuracy. Accuracy was higher among younger, highly-educated, and non-Hispanic white participants versus their complements in both weighted and unweighted analyses.
CONCLUSIONS: Algorithmic diagnoses provide a cost-effective way to conduct dementia research. However, naïve use of existing algorithms in disparities or risk-factor research may induce non-conservative bias. Algorithms with more comparable performance across relevant subgroups are needed.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
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