Evaluation of Medicare Claims Data as a Tool to Identify Dementia.

J Alzheimers Dis

Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Published: March 2020

Background: Medicare claims record linkage has been used to identify diagnosed dementia cases in order to estimate dementia prevalence and cost of care. Claims records in the 1990 s and early 2000 s have been found to provide 85% - ∼90% sensitivity and specificity.

Objective: Considering that dementia awareness has improved over time, we sought to examine sensitivity and specificity of more recent Medicare claims records against a standard criterion, clinical diagnosis of dementia.

Methods: For a sample of patients evaluated at the University of Southern California Alzheimer Disease Research Center (ADRC), we performed database linkage with Medicare claims files for a six-year period, 2007-2012. We used clinical diagnosis at the ADRC as the criterion diagnosis in order to calculate sensitivity and specificity.

Results: Medicare claims correctly identified 85% of dementia patients and 77% of individuals with normal cognition. About half of patients clinically diagnosed with mild cognitive impairment had dementia diagnoses in Medicare claims. Misclassified dementia patients (i.e., missed diagnosis by Medicare claims) had more favorable Mini-Mental State Examination and Clinical Dementia Rating scores and were less likely to present behavioral symptoms than correctly-classified dementia patients.

Conclusions: Database linkage to Medicare claims records is an efficient and reasonably accurate tool to identify dementia cases in a population-based cohort. However, possibilities of obtaining biased results due to misclassification of dementia status need to be carefully considered to use Medicare claims diagnosis for etiologic research studies. Additional confirmation of dementia diagnosis may also be considered. A larger study is warranted to confirm our findings.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7164318PMC
http://dx.doi.org/10.3233/JAD-181005DOI Listing

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