Identifying Patterns of Multimorbidity in Older Americans: Application of Latent Class Analysis.
To define multimorbidity "classes" empirically based on patterns of disease co-occurrence in older Americans and to examine how class membership predicts healthcare use.



Retrospective cohort study.



Nationally representative sample of Medicare beneficiaries in file years 1999-2007.



Individuals aged 65 and older in the Medicare Beneficiary Survey who had data available for at least 1 year after index interview (N = 14,052).



Surveys (self-report) were used to assess chronic conditions, and latent class analysis (LCA) was used to define multimorbidity classes based on the presence or absence of 13 conditions. All participants were assigned to a best-fit class. Primary outcomes were hospitalizations and emergency department visits over 1 year.



The primary LCA identified six classes. The largest portion of participants (32.7%) was assigned to the minimal disease class, in which most persons had fewer than two of the conditions. The other five classes represented various degrees and patterns of multimorbidity. Usage rates were higher in classes with greater morbidity, but many individuals could not be assigned to a particular class with confidence (sample misclassification error estimate = 0.36). Number of conditions predicted outcomes at least as well as class membership.



Although recognition of general patterns of disease co-occurrence is useful for policy planning, the heterogeneity of persons with significant multimorbidity (≥3 conditions) defies neat classification. A simple count of conditions may be preferable for predicting usage.
Department of Medicine, Duke University Medical Center, Durham, North Carolina.|Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina.|Department of Duke Aging Center, Duke University Medical Center, Durham, North Carolina.|Geriatrics Research Education and Clinical Center, Department of Veterans Affairs Durham, Durham, North Carolina.
Heather E
HE
Whitson
Department of Medicine, Duke University Medical Center, Durham, North Carolina.|Department of Duke Aging Center, Duke University Medical Center, Durham, North Carolina.|Geriatrics Research Education and Clinical Center, Department of Veterans Affairs Durham, Durham, North Carolina.
Kimberly S
KS
Johnson
Department of Duke Aging Center, Duke University Medical Center, Durham, North Carolina.|Geriatrics Research Education and Clinical Center, Department of Veterans Affairs Durham, Durham, North Carolina.
Richard
R
Sloane
Department of Family Medicine, University of Michigan, Ann Arbor, Michigan.|Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan.|Geriatrics Research, Education and Clinical Center, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan.
Christine T
CT
Cigolle
Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina.|Department of Duke Aging Center, Duke University Medical Center, Durham, North Carolina.
Carl F
CF
Pieper
Department of Duke Aging Center, Duke University Medical Center, Durham, North Carolina.
Lawrence
L
Landerman
Department of Medicine, Duke University Medical Center, Durham, North Carolina.|Department of Duke Aging Center, Duke University Medical Center, Durham, North Carolina.|Geriatrics Research Education and Clinical Center, Department of Veterans Affairs Durham, Durham, North Carolina.|Center for Health Services Research in Primary Care, Department of Veterans Affairs Durham, Durham, North Carolina.
Susan N
SN
Hastings
D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D015897:Comorbidity; D006306:Health Surveys; D006801:Humans; D016013:Likelihood Functions; D006278:Medicare / Q000706:statistics & numerical data; D015233:Models, Statistical; D011336:Probability; D012189:Retrospective Studies; D014481:United States
comorbidity; complexity; health service use