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 - Funding Information:
C.W.Z. also is supported by the Department of Veterans Affairs, Veterans Health Administration. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.
Funding Information:
Supported by Federal grant AG07370, with additional support from federal grants, RR00645, and U01AG010483.
Funding Information:
C.W.Z. also is supported by the Department of Veterans Affairs, Vet-erans Health Administration. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.
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 -