Identification of Prevalent Dementia in Medicare Advantage Encounters, Compared with Rigorous Cohort Assessments

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2026
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Abstract
BACKGROUND: Rapid growth in Medicare Advantage (MA) enrollment has spurred dementia research using diagnoses in MA encounter data. However, the validity of MA-encounter-based dementia diagnoses is uncertain. METHODS: Using four cohort studies at the Rush Alzheimer's Disease Center linked to Medicare data from 2017-19, we validated prevalent dementia indicators in MA encounter data, based on the Bynum-Standard algorithm, against dementia status determined by rigorous cognitive assessment in cohort studies. We replicated analyses among participants in Traditional Medicare (TM). RESULTS: Of the 508 eligible MA enrollees, mean age was 82.8 (SD = 7.2) years, 81% were female, and 49% were non-Latino White; 62 participants (12%) were classified as having dementia by the cohort assessment. MA-encounter-based dementia indicators performed reasonably well in identifying participants with cohort-assessed dementia: using encounters alone, positive predictive value was 63% (95% CI: 52-75%), negative predictive value was 96% (95% CI: 94-98%), and accuracy was 92% (95% CI: 89-94%). Inclusion of chart reviews did not impact performance. Compared to TM-based indicators during the same period, MA-based indicators yielded similar validity metrics but identified a less functionally impaired group as having dementia. In secondary validation analysis of MCI, sensitivity was low. CONCLUSIONS: As one of the first studies to validate dementia diagnoses in MA encounter data, indicators based on the Bynum-Standard algorithm can be a valid tool for identifying prevalent dementia. Findings support the use of MA-encounter-based dementia indicators for research within MA. However, observed health differences between MA- and TM-identified dementia groups suggest potential for selection bias in MA plans.
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Authors Yi Chen, Slim Benloucif, Bryan D James, Mousumi Banerjee, Francine Grodstein, Julie P W Bynum
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Journal the journals of gerontology series a, biological sciences and medical sciences
Year 2026
DOI
10.1093/gerona/glag206
URL
https://doi.org/10.1093/gerona/glag206
Keywords Keywords not found

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