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

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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)293-298
Number of pages6
JournalAlzheimer Disease and Associated Disorders
Volume34
Issue number4
DOIs
StatePublished - 2020

Keywords

  • Alzheimer disease
  • Dependence Scale
  • Medicare expenditure
  • cluster analysis

Fingerprint

Dive into the research topics of 'Dependence Clusters in Alzheimer Disease and Medicare Expenditures: A Longitudinal Analysis from the Predictors Study'. Together they form a unique fingerprint.

Cite this