A comparison of dementia diagnoses and cognitive function measures in Medicare claims and the Minimum Data Set

Author:

Niznik Joshua D.1234ORCID,Lund Jennifer L.5,Hanson Laura C.12ORCID,Colón‐Emeric Cathleen67,Kelley Casey J.2ORCID,Gilliam Meredith12,Thorpe Carolyn T.34

Affiliation:

1. Division of Geriatric Medicine, School of Medicine University of North Carolina at Chapel Hill Chapel Hill North Carolina USA

2. Center for Aging and Health, School of Medicine University of North Carolina at Chapel Hill Chapel Hill North Carolina USA

3. Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy University of North Carolina at Chapel Hill Chapel Hill North Carolina USA

4. Center for Health Equity Research and Promotion Veterans Affairs (VA) Pittsburgh Healthcare System Pittsburgh Pennsylvania USA

5. Department of Epidemiology University of North Carolina at Chapel Hill Gillings School of Global Public Health Chapel Hill North Carolina USA

6. Division of Geriatrics Duke University School of Medicine Durham North Carolina USA

7. Durham VA Geriatric Research Education and Clinical Center Durham North Carolina USA

Abstract

AbstractBackgroundGold standard dementia assessments are rarely available in large real‐world datasets, leaving researchers to choose among methods with imperfect but acceptable accuracy to identify nursing home (NH) residents with dementia. In healthcare claims, options include claims‐based diagnosis algorithms, diagnosis indicators, and cognitive function measures in the Minimum Data Set (MDS), but few studies have compared these. We evaluated the proportion of NH residents identified with possible dementia and concordance of these three.MethodsUsing a 20% random sample of 2018–2019 Medicare beneficiaries, we identified MDS admission assessments for non‐skilled NH stays among individuals with continuous enrollment in Medicare Parts A, B, and D. Dementia was identified using: (1) Chronic Conditions Warehouse (CCW) claims‐based algorithm for Alzheimer's disease and non‐Alzheimer's dementia; (2) MDS active diagnosis indicators for Alzheimer's disease and non‐Alzheimer's dementias; and (3) the MDS Cognitive Function Scale (CFS) (at least mild cognitive impairment). We compared the proportion of admissions with evidence of possible dementia using each criterion and calculated the sensitivity, specificity, and agreement of the CCW claims definition and MDS indicators for identifying any impairment on the CFS.ResultsAmong 346,013 non‐SNF NH admissions between 2018 and 2019, 57.2% met criteria for at least one definition (44.7% CFS, 40.7% CCW algorithm, 26.0% MDS indicators). The MDS CFS uniquely identified the greatest proportion with evidence of dementia. The CCW claims algorithm had 63.7% sensitivity and 78.1% specificity for identifying any cognitive impairment on the CFS. Active diagnosis indicators from the MDS had lower sensitivity (47.0%), but higher specificity (91.0%).ConclusionsClaims‐ and MDS‐based methods for identifying NH residents with possible dementia have only partial overlap in the cohorts they identify, and neither is an obvious gold standard. Future studies should seek to determine whether additional functional assessments from the MDS or prescriptions can improve identification of possible dementia in this population.

Funder

National Institute on Aging

Publisher

Wiley

Reference28 articles.

1. 2020 Alzheimer's disease facts and figures

2. Centers for Medicare and Medicaid Services Chronic Conditions Data Warehouse.30 CCW chronic conditions algorithms.2023.https://www2.ccwdata.org/web/guest/condition‐categories‐chronic#cc30

3. Centers for Medicare and Medicaid Services Chronic Conditions Data Warehouse.27 CCW chronic conditions algorithms.2023.https://www2.ccwdata.org/documents/10280/19139421/ccw-chronic-condition-algorithms.pdf

4. The accuracy of medicare claims data in identifying Alzheimer's disease

5. The Accuracy of Medicare Claims as an Epidemiological Tool: The Case of Dementia Revisited

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