Evidence map›Paper›PMID 39040190›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Modeling the relative influence of socio-demographic variables on post-acute COVID-19 quality of life.

Tigist F Menkir, Barbara Wanjiru Citarella, Louise Sigfrid, Yash Doshi, Luis Felipe Reyes, Jose A Calvache, Anders Benjamin Kildal, Anders B Nygaard, Jan Cato Holter, Prasan Kumar Panda and 8 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

18 authors.

Tigist F MenkirCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard University, USA.
Barbara Wanjiru CitarellaISARIC, Pandemic Sciences Institute, University of Oxford, UK.
Louise SigfridISARIC, Pandemic Sciences Institute, University of Oxford, UK.ORCID 0000-0003-2764-1177
Yash DoshiTerna Speciality Hospital & Research Centre, Mumbai, India.
Luis Felipe ReyesISARIC, Pandemic Sciences Institute, University of Oxford, UK.
Jose A CalvacheDepartamento de Anestesiología, Universidad del Cauca, Colombia.
Anders Benjamin KildalDepartment of Anesthesiology and Intensive Care, University Hospital of North Norway, Tromsø, Norway.
Anders B NygaardDepartment of Microbiology, Oslo University Hospital, Oslo, Norway.
Jan Cato HolterDepartment of Microbiology, Oslo University Hospital, Oslo, Norway.
Prasan Kumar PandaAll India Institute of Medical Sciences (AIIMS), Rishikesh, India.
Waasila JassatNational Institute for Communicable Diseases, South Africa.
Laura MersonISARIC, Pandemic Sciences Institute, University of Oxford, UK.ORCID 0000-0002-4168-1960
Christl A DonnellyDepartment of Statistics, University of Oxford, Oxford, UK.ORCID 0000-0002-0195-2463
Mauricio SantillanaCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard University, USA.
Caroline BuckeeCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard University, USA.ORCID 0000-0002-8386-5899
Stéphane VerguetDepartment of Global Health and Population, Harvard TH Chan School of Public Health, Harvard University, USA.
Nima S HejaziDepartment of Biostatistics, Harvard TH Chan School of Public Health, Harvard University, USA.
ISARIC Clinical Characterisation Group

Funding

Epidemiology of Infectious Diseases and BiodefenseT32AI007535 · NIAID · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI GRAD, YONATAN H, MURRAY, MEGAN B · 1998 to 2025
$4.8M
Development of an Open-Source and Data-Driven Modeling Platform to Monitor and Forecast Disease ActivityR01GM130668 · NIGMS · NORTHEASTERN UNIVERSITY · PI SANTILLANA, MAURICIO · 2018 to 2022
$1.9M
NIAID NIH HHS T32 AI007535NIGMS NIH HHS R01 GM130668Wellcome Trust
6 · The paper itself

Abstract

Importance: Post-acute sequelae of SARS-CoV-2, referred to as "long COVID", are a globally pervasive threat. While their many clinical determinants are commonly considered, their plausible social correlates are often overlooked. Objective: To compare social and clinical predictors of differences in quality of life (QoL) with long COVID. Additionally, to measure how much adjusted associations between social factors and long COVID-associated quality of life are unexplained by important clinical intermediates. Design Setting and Participants: Data from the ISARIC long COVID multi-country prospective cohort study. Subjects from Norway, the United Kingdom (UK), and Russia, aged 16 and above, with confirmed acute SARS-CoV-2 infection reporting >= 1 long COVID-associated symptoms 1+ month following infection. Exposure: The social exposures considered were educational attainment (Norway), employment status (UK and Russia), and female vs male sex (all countries). Main outcome and measures: Quality of life-adjusted days, or QALDs, with long COVID. Results: This cohort study included a total of 3891 participants. In all three countries, educational attainment, employment status, and female sex were important predictors of long COVID QALDs. Furthermore, a majority of the estimated relationships between each of these social correlates and long COVID QALDs could not be attributed to key long COVID-predicting comorbidities. In Norway, 90% (95% CI: 77%, 100%) of the adjusted association between the top two quintiles of educational attainment and long COVID QALDs was not explained by clinical intermediates. The same was true for 86% (73%, 100%) and 93% (80%,100%) of the adjusted associations between full-time employment and long COVID QALDs in the United Kingdom (UK) and Russia. Additionally, 77% (46%,100%) and 73% (52%, 94%) of the adjusted associations between female sex and long COVID QALDs in Norway and the UK were unexplained by the clinical mediators. Conclusions and Relevance: This study highlights the role of socio-economic status indicators and female sex, in line with or beyond commonly cited clinical conditions, as predictors of long COVID-associated QoL, and further reveal that other (non-clinical) mechanisms likely drive their observed relationships. Our findings point to the importance of COVID interventions which go further than an exclusive focus on comorbidity management in order to help redress inequalities in experiences with this chronic disease.

Identifiers

PMID39040190
PMCPMC11261939

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.