TY - JOUR
T1 - Dependence Clusters in Alzheimer Disease and Medicare Expenditures
T2 - A Longitudinal Analysis from the Predictors Study
AU - Zhu, Carolyn W.
AU - Lee, Seonjoo
AU - Ornstein, Katherine A.
AU - Cosentino, Stephanie
AU - Gu, Yian
AU - Andrews, Howard
AU - Stern, Yaakov
N1 - Publisher Copyright:
© 2020 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Alzheimer disease
KW - Dependence Scale
KW - Medicare expenditure
KW - cluster analysis
UR - http://www.scopus.com/inward/record.url?scp=85091451379&partnerID=8YFLogxK
U2 - 10.1097/WAD.0000000000000402
DO - 10.1097/WAD.0000000000000402
M3 - Article
C2 - 32826426
AN - SCOPUS:85091451379
SN - 0893-0341
VL - 34
SP - 293
EP - 298
JO - Alzheimer Disease and Associated Disorders
JF - Alzheimer Disease and Associated Disorders
IS - 4
ER -