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Dependence Clusters in Alzheimer Disease and Medicare Expenditures: A Longitudinal Analysis From the Predictors Study

Zhu, Carolyn W.; Lee, Seonjoo; Ornstein, Katherine A.; Cosentino, Stephanie; Gu, Yian; Andrews, Howard F.; Stern, Yaakov

Introduction: Dependence in Alzheimer disease has been proposed as a holistic, transparent, and meaningful representation of disease severity. Modeling clusters in dependence trajectories can help understand changes in disease course and care cost over time.

Methods: Sample consisted of 199 initially community-living patients with probable Alzheimer disease recruited from 3 academic medical centers in the United States followed for up to 10 years and had ≥2 Dependence Scale recorded. Nonparametric K-means cluster analysis for longitudinal data (KmL) was used to identify dependence clusters. Medicare expenditures data (1999-2010) were compared between clusters.

Results: KmL identified 2 distinct Dependence Scale clusters: (A) high initial dependence, faster decline, and (B) low initial dependence, slower decline. Adjusting for patient characteristics, 6-month Medicare expenditures increased over time with widening between-cluster differences.

Discussion: Dependence captures dementia care costs over time. Better characterization of dependence clusters has significant implications for understanding disease progression, trial design and care planning.

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Title
Alzheimer Disease & Associated Disorders
DOI
https://doi.org/10.1097/WAD.0000000000000402

More About This Work

Academic Units
Neurology
Published Here
May 4, 2021