Evidence map›Paper›PMID 26956616›Full record

ArticleBMC geriatrics2016

Understanding non-performance reports for instrumental activity of daily living items in population analyses: a cross sectional study.

Margaret G Stineman, Dawei Xie, Qiang Pan, Jibby E Kurichi, Debra Saliba, Sophia Miryam Schüssler-Fiorenza Rose, Joel E Streim

Abstract readMulticenter Study
In one paragraph

Article in BMC geriatrics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Margaret G StinemanDepartment of Physical Medicine and Rehabilitation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. mstinema@exchange.upenn.edu.
Dawei XieDepartment of Biostatistics and Epidemiology, The Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvani, 423 Guardian Drive, 617 Blockley Hall, Philadelphia, PA, 19104, USA. dxie@mail.med.upenn.edu.
Qiang PanDepartment of Biostatistics and Epidemiology, The Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvani, 423 Guardian Drive, 617 Blockley Hall, Philadelphia, PA, 19104, USA. panqiang@mail.med.upenn.edu.
Jibby E KurichiDepartment of Biostatistics and Epidemiology, The Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvani, 423 Guardian Drive, 617 Blockley Hall, Philadelphia, PA, 19104, USA. jkurichi@mail.med.upenn.edu.
Debra SalibaAnna and Harry Borun Chair in Geriatrics and Gerontology at UCLA, RAND, Santa Monica, CA, USA. saliba@rand.org.
Sophia Miryam Schüssler-Fiorenza RoseSpinal Cord Injury Service, Veterans Affairs Palo Health Care System, 3801 Miranda Ave, Palo Alto, CA, 94304, USA. cms@alumni.med.upenn.edu.
Joel E StreimGeriatric Psychiatry Section of the Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Joel.Streim@uphs.upenn.edu.

Funding

RESEARCH TRAINING IN NEUROLOGIC REHABILITATIONT32HD007425 · NICHD · UNIVERSITY OF PENNSYLVANIA · PI SALCIDO, RICHARD · 1992 to 2011
$2.4M
Staging Activity LimitationR01AG040105 · NIA · UNIVERSITY OF PENNSYLVANIA · PI HENNESSY, SEAN · 2012 to 2016
$1.2M
Impairment Activity StagingR01AG032420 · NIA · UNIVERSITY OF PENNSYLVANIA · PI STINEMAN, MARGARET GRACE · 2009 to 2010
$686k
NIA NIH HHS AG032420-01A1NIA NIH HHS AG040105NIA NIH HHS R01 AG032420NIA NIH HHS R01 AG040105NICHD NIH HHS 5-T32-HD-007425NICHD NIH HHS T32 HD007425
6 · The paper itself

Abstract

backgroundConcerns about using Instrumental Activities of Daily Living (IADLs) in national surveys come up frequently in geriatric and rehabilitation medicine due to high rates of non-performance for reasons other than health. We aim to evaluate the effect of different strategies of classifying "does not do" responses to IADL questions when estimating prevalence of IADL limitations in a national survey.

methodsCross-sectional analysis of a nationally representative sample of 13,879 non-institutionalized adult Medicare beneficiaries included in the 2010 Medicare Current Beneficiary Survey (MCBS). Sample persons or proxies were asked about difficulties performing six IADLs. Tested strategies to classify non-performance of IADL(s) for reasons other than health were to 1) derive through multiple imputation, 2) exclude (for incomplete data), 3) classify as "no difficulty," or 4) classify as "difficulty." IADL stage prevalence estimates were compared across these four strategies.

resultsIn the sample, 1853 sample persons (12.4 % weighted) did not do one or more IADLs for reasons other than physical problems or health. Yet, IADL stage prevalence estimates differed little across the four alternative strategies. Classification as "no difficulty" led to slightly lower, while classification as "difficulty" raised the estimated population prevalence of disability.

conclusionsThese analyses encourage clinicians, researchers, and policy end-users of IADL survey data to be cognizant of possible small differences that can result from alternative ways of handling unrated IADL information. At the population-level, the resulting differences appear trivial when applying MCBS data, providing reassurance that IADL items can be used to estimate the prevalence of activity limitation despite high rates of non-performance.

Indexed as

Activities of Daily LivingDisability EvaluationAdultAgedAged, 80 and overCross-Sectional StudiesFemaleHumansMaleMiddle AgedPersons with DisabilitiesUnited StatesYoung Adult

Identifiers

PMID26956616
PMCPMC4784362

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Registered trials

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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.