Evidence map›Paper›PMID 34174838›Full record

ArticleBMC medical research methodology2021

Multiple imputation to quantify misclassification in observational studies of the cognitively impaired: an application for pain assessment in nursing home residents.

Anthony P Nunes, Danni Zhao, William M Jesdale, Kate L Lapane

Open access · goldAbstract read
In one paragraph

Article in BMC medical research methodology, 2021. 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
0.5field-weighted citation impact, top 35% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Anthony P NunesDivision of Epidemiology, Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA, 01605, USA.
Danni ZhaoDivision of Epidemiology, Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA, 01605, USA.
William M JesdaleDivision of Epidemiology, Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA, 01605, USA.
Kate L LapaneDivision of Epidemiology, Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA, 01605, USA. Kate.lapane@umassmed.edu.ORCID 0000-0001-9957-7747
University of Massachusetts Chan Medical School · US

Funding

Non-malignant Pain in Nursing Home ResidentsR01NR016977 · NINR · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LAPANE, KATE L · 2017 to 2021
$2.8M
NINR NIH HHS 5R01NR016977NINR NIH HHS R01 NR016977
6 · The paper itself

Abstract

backgroundDespite experimental evidence suggesting that pain sensitivity is not impaired by cognitive impairment, observational studies in nursing home residents have observed an inverse association between cognitive impairment and resident-reported or staff-assessed pain. Under the hypothesis that the inverse association may be partially attributable to differential misclassification due to recall and communication limitations, this study implemented a missing data approach to quantify the absolute magnitude of misclassification of pain, pain frequency, and pain intensity by level of cognitive impairment.

methodsUsing the 2016 Minimum Data Set 3.0, we conducted a cross-sectional study among newly admitted US nursing home residents. Pain presence, severity, and frequency is assessed via resident-reported measures. For residents unable to communicate their pain, nursing home staff document pain based on direct resident observation and record review. We estimate a counterfactual expected level of pain in the absence of cognitive impairment by multiply imputing modified pain indicators for which the values were retained for residents with no/mild cognitive impairment and set to missing for residents with moderate/severe cognitive impairment. Absolute differences (∆) in the presence and magnitude of pain were calculated as the difference between documented pain and the expected level of pain.

resultsThe difference between observed and expected resident reported pain was greater in residents with severe cognitive impairment (∆ = -10.2%, 95% Confidence Interval (CI): -10.9% to -9.4%) than those with moderate cognitive impairment (∆ = -4.5%, 95% CI: -5.4% to -3.6%). For staff-assessed pain, the magnitude of apparent underreporting was similar between residents with moderate impairment (∆ = -7.2%, 95% CI: -8.3% to -6.0%) and residents with severe impairment (∆ = -7.2%, 95% CI: -8.0% to -6.3%). Pain characterized as "mild" had the highest magnitude of apparent underreporting.

conclusionsIn residents with moderate to severe cognitive impairment, documentation of any pain was lower than expected in the absence of cognitive impairment. This finding supports the hypothesis that an inverse association between pain and cognitive impairment may be explained by differential misclassification. This study highlights the need to develop analytic and/or procedural solutions to correct for recall/reporter bias resulting from cognitive impairment.

Indexed as

Cognitive DysfunctionNursing HomesCross-Sectional StudiesHumansPainPain MeasurementMisclassificationMultiple imputationNursing homesPain

Identifiers

PMID34174838
PMCPMC8235835
OpenAlexW3176092093

What OpenQuestion holds

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LicenceCC BY
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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.