PERFORMANCE OF CLAIMS-BASED DEMENTIA ALGORITHMS IN MEDICARE FEE-FOR-SERVICE AND MEDICARE ADVANTAGE POPULATIONS

Sep 29, 2026

Claims-based dementia algorithms have not been evaluated in the Medicare Advantage (MA) population, despite MA’s rapid growth and concerns about data completeness. Validations used community-dwelling participants from the National Health and Aging Trends Study (NHATS) across Round 7 (2017; n=5,058), Round 9 (2019; n=4,048), and Round 11 (2021-2022; n=3,109). Bynum Standard and CCW algorithms were assessed against NHATS classification for probable dementia, showing similar prevalence and consistently high specificity and negative predictive value, with MA demonstrating lower sensitivity in selected rounds.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages1

Reading Time3 min

AccessFree to download

Abstract

Study design and data sources

Algorithm validation approach

Results: prevalence, sensitivity, PPV, specificity, NPV

Implications for algorithm selection

Innovation in Aging, 2025, Vol. 9, No. S2 1269 Abstract citation ID: igaf122.4238 PERFORMANCE OF CLAIMS-BASED DEMENTIA ALGORITHMS IN MEDICARE FEE-FOR-SERVICE AND MEDICARE ADVANTAGE POPULATIONS You Wu1, Chan Mi Park2, Lan Luo3, Xiecheng Chen4, Eunji G. Kim3, Sandra Shi5, Dae Hyun Kim5, and Ellen McCarthy5, 1. Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States, 2. Hebrew SeniorLife, Harvard Medical school, Boston, Massachusetts, United States, 3. Marcus Institute for Aging Research, Boston, Massachusetts, United States, 4. Hebrew Rehabilitation Center, Roslindale, Massachusetts, United States, 5. Hebrew SeniorLife, Boston, Massachusetts, United States Claims-based dementia algorithms have not been evaluated in the Medicare Advantage (MA) population, a critical gap given the rapid growth of MA and concerns about data completeness. Using community-dwelling participants from the National Health and Aging Trends Study (NHATS) Round 7 (2017; n=5,058), Round 9 (2019; n=4,048), and Round 11 (2021-2022; n = 3,109), we validated the Bynum Standard and Chronic Condition Warehouse (CCW) dementia algorithms against NHATS classification for probable dementia. The prevalence of probable dementia was similar between MA (8.2%-9.1%) and FFS (8.1%-8.5%) across rounds. Overall, both algorithms had comparable performance in MA vs FFS populations, demonstrating high specificity (>97.9%) and negative predictive value [NPV](>92.9%). The Bynum algorithm had similar sensitivity between MA and FFS in Round 7 (30 .2% vs. 29. 1%) and 11 (25 .6% vs. 23.9%), but statistically significant and clinically meaningful lower sensitivity in MA for Round 9 (25.0% vs. 36.4%) . However, positive predictive value [PPV] was similar between MA (62 .7%-70 .6%) and FFS (62 . 1%-70 .4%) in all rounds. Comparatively, the CCW algorithm had statistically significant and clinically meaningful lower sensitivity in MA than FFS for Round 9 (30.8% vs. 46.9%) and 11 (35.0% vs. 49.3%), yet similar sensitivity for Round 7 (40.4% vs. 42.2%) . The PPV in MA and FFS varied across rounds, from Round 7 (76 .3% vs. 71.2%) to 11 (68.5% vs. 76.4%) . Claims-based dementia algorithms have lower or comparable sensitivity in MA vs. FFS, with similarly moderate PPV and consistently high specificity and NPV. Researchers should consider these trade-offs when selecting algorithms for MA and FFS.

FAQs

What gap does the study address regarding dementia detection?

Claims-based dementia algorithms had not been evaluated in the Medicare Advantage (MA) population, a gap made important by MA’s growth and concerns about data completeness.

How were the Bynum Standard and CCW algorithms evaluated?

They were validated using NHATS community-dwelling participants across multiple rounds, comparing algorithm outputs to NHATS classification for probable dementia.

What were the key performance findings in MA versus fee-for-service?

Probable dementia prevalence was similar between MA and fee-for-service, and both algorithms showed comparable performance overall with consistently high specificity and negative predictive value; however, sensitivity was lower in MA for specific rounds for both algorithms.

Sep 29, 2026

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