Validation of Claims Algorithms to Identify Alzheimer's Disease and Related Dementias

Ellen P McCarthy, Chiang-Hua Chang, Nicholas Tilton, Mohammed U Kabeto, Kenneth M Langa, Julie P W Bynum, Ellen P McCarthy, Chiang-Hua Chang, Nicholas Tilton, Mohammed U Kabeto, Kenneth M Langa, Julie P W Bynum

Abstract

Background: Using billing data generated through health care delivery to identify individuals with dementia has become important in research. To inform tradeoffs between approaches, we tested the validity of different Medicare claims-based algorithms.

Methods: We included 5 784 Medicare-enrolled, Health and Retirement Study participants aged older than 65 years in 2012 clinically assessed for cognitive status over multiple waves and determined performance characteristics of different claims-based algorithms.

Results: Positive predictive value (PPV) of claims ranged from 53.8% to 70.3% and was highest using a revised algorithm and 1 year of observation. The tradeoff of greater PPV was lower sensitivity; sensitivity could be maximized using 3 years of observation. All algorithms had low sensitivity (31.3%-56.8%) and high specificity (92.3%-98.0%). Algorithm test performance varied by participant characteristics, including age and race.

Conclusion: Revised algorithms for dementia diagnosis using Medicare administrative data have reasonable accuracy for research purposes, but investigators should be cognizant of the tradeoffs in accuracy among the approaches they consider.

Keywords: Accuracy; Algorithm; Dementia; Diagnosis; Medicare.

© The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Figures

Figure 1.
Figure 1.
Predictive value positive and predictive value negative of the Bynum-standard 1-year algorithm by characteristics. PPV = positive predictive value; NPV = negative predictive value.

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