Evidence map›Paper›PMID 37953975›Full record

ArticleJournal of multimorbidity and comorbidity

Correlations between comorbidities in trials and the community: An individual-level participant data meta-analysis.

Jamie Crowther, Elaine W Butterly, Laurie J Hannigan, Bruce Guthrie, Sarah H Wild, Frances S Mair, Peter Hanlon, Fergus J Chadwick, David A McAllister

Open access · diamondAbstract read
In one paragraph

Article in Journal of multimorbidity and comorbidity. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact, top 71% 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it, 0 citations in OpenAlex.

  1. Pooled it
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

9 authors at 4 institutions in 2 countries.

Jamie CrowtherSchool of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.ORCID https://orcid.org/0000-0002-6191-7870
Elaine W ButterlySchool of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.ORCID https://orcid.org/0000-0001-9410-0237
Laurie J HanniganNic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway.
Bruce GuthrieUsher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.
Sarah H WildUsher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.ORCID https://orcid.org/0000-0001-7824-2569
Frances S MairSchool of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.ORCID https://orcid.org/0000-0001-9780-1135
Peter HanlonSchool of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.ORCID https://orcid.org/0000-0002-5828-3934
Fergus J ChadwickBiomathematics and Statistics Scotland, Edinburgh, UK.
David A McAllisterSchool of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.
University of Glasgow · GBUniversity of Edinburgh · GBBiomathematics and Statistics Scotland · GBNorwegian Institute of Public Health · NO

Funding

Medical Research Council MR/S021949/1Wellcome Trust
6 · The paper itself

Abstract

Background: People with comorbidities are under-represented in randomised controlled trials, and it is unknown whether patterns of comorbidity are similar in trials and the community. Methods: Individual-level participant data were obtained for 83 clinical trials (54,688 participants) for 16 index conditions from two trial repositories: Yale University Open Data Access (YODA) and the Centre for Global Clinical Research Data (Vivli). Community data (860,177 individuals) were extracted from the Secure Anonymised Information Linkage (SAIL) databank for the same index conditions. Comorbidities were defined using concomitant medications. For each index condition, we estimated correlations between comorbidities separately in trials and community data. For the six commonest comorbidities we estimated all pairwise correlations using Bayesian multivariate probit models, conditioning on age and sex. Correlation estimates from trials with the same index condition were combined into a single estimate. We then compared the trial and community estimates for each index condition. Results: Despite a higher prevalence of comorbidities in the community than in trials, the correlations between comorbidities were mostly similar in both settings. On comparing correlations between the community and trials, 21% of correlations were stronger in the community, 10% were stronger in the trials and 68% were similar in both. In the community, 5% of correlations were negative, 21% were null, 56% were weakly positive and 18% were strongly positive. Equivalent results for the trials were 11%, 33%, 45% and 10% respectively. Conclusions: Comorbidity correlations are generally similar in both the trials and community, providing some evidence for the reporting of comorbidity-specific findings from clinical trials.

Indexed as

communityComorbiditymultimorbiditytrials

Identifiers

PMID37953975
PMCPMC10637135
OpenAlexW4388571377

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.