Evidence map›Paper›PMID 40018233›Full record

ArticleBMJ public health2024

Association between chronic pain and attrition: a prospective analysis of a national sample of midlife adults in the USA, 2004-2014.

Yunlong Liang

Abstract read
In one paragraph

Article in BMJ public health, 2024. 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

1 author.

Yunlong LiangUniversity of Essex, Colchester, UK.ORCID 0000-0002-6744-8956

Funding

U19 MIDlife in the United States-Diversity Supplement-WellsU19AG051426 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI CHRISTOPHER L COE · 2016 to 2026
$74.8M
STATISTICS COREP01AG020166 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI RYFF, CAROL D. · 2002 to 2015
$50.6M
NIA NIH HHS P01 AG020166NIA NIH HHS U19 AG051426
6 · The paper itself

Abstract

Background: Health conditions of participants can significantly affect longitudinal drop-out in population-based epidemiological surveys, yet few studies have examined the association between chronic pain (CP) and follow-up attrition. Methods: The Midlife in the United States study (MIDUS) was used to explore the longitudinal association between CP and survey attrition. CP was assessed by three measures: the presence of CP, CP interference and the number of pain sites at MIDUS 2. The types of sample attrition at MIDUS 3 encompassed several categories: complete, refusal to participate, inability to participate due to physical or mental constraints, deceased, non-working numbers, participants consistently unavailable for interviews, global refusal or withdrew from the study and not fielded. Multinomial logistic regression was employed to examine these relationships and to explore the moderation effects of sociodemographic variables and multiple chronic conditions on these associations. Results: High-interference pain was associated with a 162% increased risk (RR 2.62, 95% CI 1.12 to 6.16, p=0.026) of being physically and mentally unable to participate in MIDUS 3. Individuals reporting the presence of CP (RR 0.65, 95% CI 0.45 to 0.95, p=0.028) and those with three or more CP sites (RR 0.48, 95% CI 0.27 to 0.87, p=0.016) were less likely to refuse participation in MIDUS 3. However, no further significant associations or moderating effects were identified. Conclusion: Population-based epidemiological surveys may be susceptible to attrition bias from participants with CP, necessitating the adoption of adaptive survey methodologies.

Indexed as

biascontact tracingdata collectionepidemiologypublic health

Identifiers

PMID40018233
PMCPMC11812813

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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.