Evidence map›Paper›PMID 27309908›Full record

ArticleJournal of the American Geriatrics Society2016

Identifying Patterns of Multimorbidity in Older Americans: Application of Latent Class Analysis.

Heather E Whitson, Kimberly S Johnson, Richard Sloane, Christine T Cigolle, Carl F Pieper, Lawrence Landerman, Susan N Hastings

Open access · bronzeAbstract read
In one paragraph

Article in Journal of the American Geriatrics Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
79citing papers in PubMed, 5 pooled it
8.8field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

79 citing papers in PubMed, 5 syntheses or guidelines pooled it, 153 citations in OpenAlex.

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  6. High Symptom Burden and Low Functional Status in the Setting of Multimorbidity.Journal of the American Geriatrics Society · 2017 · on this map
    Trial
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19 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 1 country.

Heather E WhitsonDepartment of Medicine, Duke University Medical Center, Durham, North Carolina.
Kimberly S JohnsonDepartment of Medicine, Duke University Medical Center, Durham, North Carolina.
Richard SloaneDepartment of Duke Aging Center, Duke University Medical Center, Durham, North Carolina.
Christine T CigolleDepartment of Family Medicine, University of Michigan, Ann Arbor, Michigan.
Carl F PieperDepartment of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina.
Lawrence LandermanDepartment of Duke Aging Center, Duke University Medical Center, Durham, North Carolina.
Susan N HastingsDepartment of Medicine, Duke University Medical Center, Durham, North Carolina.
Duke University · USDuke Medical Center · USDurham VA Medical Center · USVA Ann Arbor Healthcare System · US

Funding

Resource Core 3 - Metabolomics CoreP30AG028716 · NIA · DUKE UNIVERSITY · PI Virginia B Kraus · 2006 to 2026
$24.6M
Advancing Geriatrics Infrastructure & Network Growth - Admin Supplement: Designing and Testing an Informatics Tool to Aid Prescribers in Reducing Anticholinergic Burden in Older AdultsR24AG045050 · NIA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI GURWITZ, JERRY H · 2014 to 2017
$3.6M
Cognitive Changes and Brain Connectivity in Age-Related Macular DegenerationR01AG043438 · NIA · DUKE UNIVERSITY · PI WHITSON, HEATHER E. · 2013 to 2017
$2.1M
Geriatric Conditions and Disablement in the Older Adult PopulationK08AG031837 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CIGOLLE, CHRISTINE T · 2010 to 2014
$639k
Cognitive Impairment in Low Vision Rehabilitation: Prevalence and ConsequencesI21RX001721 · VA · DURHAM VA MEDICAL CENTER · PI WHITSON, HEATHER E. · 2015 to 2016
–
NIA NIH HHS K08 AG031837NIA NIH HHS P30 AG028716NIA NIH HHS R01 AG043438NIA NIH HHS R24 AG045050RRD VA I21 RX001721
6 · The paper itself

Abstract

objectivesTo define multimorbidity "classes" empirically based on patterns of disease co-occurrence in older Americans and to examine how class membership predicts healthcare use.

designRetrospective cohort study.

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

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

resultsThe 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.

conclusionAlthough 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.

Indexed as

ComorbidityModels, StatisticalAgedAged, 80 and overCohort StudiesHealth SurveysHumansLikelihood FunctionsMedicareProbabilityRetrospective StudiesUnited Statescomorbiditycomplexityhealth service use

Identifiers

PMID27309908
PMCPMC4988894
OpenAlexW2437173508

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.