Evidence map›Paper›PMID 32814959›Full record

ArticleInternational journal of epidemiology2020

Risk factors for positive and negative COVID-19 tests: a cautious and in-depth analysis of UK biobank data.

Marc Chadeau-Hyam, Barbara Bodinier, Joshua Elliott, Matthew D Whitaker, Ioanna Tzoulaki, Roel Vermeulen, Michelle Kelly-Irving, Cyrille Delpierre, Paul Elliott

Open access · hybridAbstract read
In one paragraph

Article in International journal of epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
81citing papers in PubMed, 6 pooled it
3.1field-weighted citation impact, top 5% 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

81 citing papers in PubMed, 6 syntheses or guidelines pooled it, 151 citations in OpenAlex.

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  16. Prospective study design and data analysis in UK Biobank.Science translational medicine · 2024
    Review
  17. Sociodemographic inequalities in the incidence of COVID-19 in National Household Sample Survey cohort, Brazil, 2020.Revista brasileira de epidemiologia = Brazilian journal of epidemiology · 2024
    Article
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21 more citing papers are in PubMed but not listed here.

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 5 institutions in 4 countries.

Marc Chadeau-HyamDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Barbara BodinierDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Joshua ElliottDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Matthew D WhitakerDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Ioanna TzoulakiDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Roel VermeulenInstitute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands.
Michelle Kelly-IrvingUMR LEASP, Université de Toulouse III, UPS, Inserm, Toulouse, France.
Cyrille DelpierreUMR LEASP, Université de Toulouse III, UPS, Inserm, Toulouse, France.
Paul ElliottDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
MRC Centre for Environment and HealthUniversité Toulouse III - Paul Sabatier · FRRoyal Surrey County Hospital · GBUniversity of Ioannina · GRUtrecht University · NL

Funding

Cancer Research UK 22184Medical Research Council MR/S019669/1
6 · The paper itself

Abstract

backgroundThe recent COVID-19 outbreak has generated an unprecedented public health crisis, with millions of infections and hundreds of thousands of deaths worldwide. Using hospital-based or mortality data, several COVID-19 risk factors have been identified, but these may be confounded or biased.

methodsUsing SARS-CoV-2 infection test data (n = 4509 tests; 1325 positive) from Public Health England, linked to the UK Biobank study, we explored the contribution of demographic, social, health risk, medical and environmental factors to COVID-19 risk. We used multivariable and penalized logistic regression models for the risk of (i) being tested, (ii) testing positive/negative in the study population and, adopting a test negative design, (iii) the risk of testing positive within the tested population.

resultsIn the fully adjusted model, variables independently associated with the risk of being tested for COVID-19 with odds ratio >1.05 were: male sex; Black ethnicity; social disadvantage (as measured by education, housing and income); occupation (healthcare worker, retired, unemployed); ever smoker; severely obese; comorbidities; and greater exposure to particulate matter (PM) 2.5 absorbance. Of these, only male sex, non-White ethnicity and lower educational attainment, and none of the comorbidities or health risk factors, were associated with testing positive among tested individuals.

conclusionsWe adopted a careful and exhaustive approach within a large population-based cohort, which enabled us to triangulate evidence linking male sex, lower educational attainment and non-White ethnicity with the risk of COVID-19. The elucidation of the joint and independent effects of these factors is a high-priority area for further research to inform on the natural history of COVID-19.

Indexed as

Confounding Factors, EpidemiologicCOVID-19COVID-19 TestingBiological Specimen BanksFemaleHumansMaleMiddle AgedPredictive Value of TestsRisk AssessmentRisk FactorsSARS-CoV-2United KingdomCOVID-19infectionprospective cohortSARS-CoV-2test dataUK Biobank

Identifiers

PMID32814959
PMCPMC7454561
OpenAlexW3055355304

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.