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Record W3137686445 · doi:10.1101/2021.03.09.21253218

An observational cohort study on the incidence of SARS-CoV-2 infection and B.1.1.7 variant infection in healthcare workers by antibody and vaccination status

2021· preprint· en· W3137686445 on OpenAlexfundno aff
Sheila Lumley, Gillian Rodger, Bede Constantinides, Nicholas Sanderson, Kevin Chau, Teresa Street, Denise O’Donnell, Alison Howarth, Stephanie B. Hatch, Brian D. Marsden, Stuart Cox, Tim James, Fiona C Warren, Liam J Peck, Thomas Ritter, Zoe de Toledo, Laura Warren, David Axten, Richard J. Cornall, E. Yvonne Jones, David I. Stuart, Gavin Screaton, Daniel Ebner, Sarah Hoosdally, Meera Chand, Derrick W. Crook, Christopher P. Conlon, Koen B. Pouwels, A. Sarah Walker, Tim Peto, Susan Hopkins, A Sarah Walker, Nicole Stoesser, Philippa C Matthews, Katie Jeffery, David W. Eyre

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
FundersHuo Family FoundationPublic Health EnglandUniversity of OxfordDepartment of Health and Social CareNational Institute for Health and Care ResearchMedical Research CouncilKennedy Trust for Rheumatology ResearchGovernment of the United KingdomEuropean Federation of Pharmaceutical Industries and AssociationsRobertson FoundationWellcome TrustNovartis PharmaOntario Genomics InstituteMerck KGaAPfizerNational Institute for Health Research Health Protection Research UnitFundação de Amparo à Pesquisa do Estado de São PauloGenome CanadaOntario Genomics
KeywordsMedicineVaccinationIncidence (geometry)AsymptomaticCohortPoisson regressionCohort studyHerd immunityImmunityImmunologyInternal medicineImmune systemPopulationEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background Natural and vaccine-induced immunity will play a key role in controlling the SARS-CoV-2 pandemic. SARS-CoV-2 variants have the potential to evade natural and vaccine-induced immunity. Methods In a longitudinal cohort study of healthcare workers (HCWs) in Oxfordshire, UK, we investigated the protection from symptomatic and asymptomatic PCR-confirmed SARS-CoV-2 infection conferred by vaccination (Pfizer-BioNTech BNT162b2, Oxford-AstraZeneca ChAdOx1 nCOV-19) and prior infection (determined using anti-spike antibody status), using Poisson regression adjusted for age, sex, temporal changes in incidence and role. We estimated protection conferred after one versus two vaccinations and from infections with the B.1.1.7 variant identified using whole genome sequencing. Results 13,109 HCWs participated; 8285 received the Pfizer-BioNTech vaccine (1407 two doses) and 2738 the Oxford-AstraZeneca vaccine (49 two doses). Compared to unvaccinated seronegative HCWs, natural immunity and two vaccination doses provided similar protection against symptomatic infection: no HCW vaccinated twice had symptomatic infection, and incidence was 98% lower in seropositive HCWs (adjusted incidence rate ratio 0.02 [95%CI <0.01-0.18]). Two vaccine doses or seropositivity reduced the incidence of any PCR-positive result with or without symptoms by 90% (0.10 [0.02-0.38]) and 85% (0.15 [0.08-0.26]) respectively. Single-dose vaccination reduced the incidence of symptomatic infection by 67% (0.33 [0.21-0.52]) and any PCR-positive result by 64% (0.36 [0.26-0.50]). There was no evidence of differences in immunity induced by natural infection and vaccination for infections with S-gene target failure and B.1.1.7. Conclusion Natural infection resulting in detectable anti-spike antibodies and two vaccine doses both provide robust protection against SARS-CoV-2 infection, including against the B.1.1.7 variant. Summary Natural infection resulting in detectable anti-spike antibodies and two vaccine doses both provided ≥ 85% protection against symptomatic and asymptomatic SARS-CoV-2 infection in healthcare workers, including against the B.1.1.7 variant. Single dose vaccination reduced symptomatic infection by 67%.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.091
GPT teacher head0.417
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations55
Published2021
Admission routes1
Has abstractyes

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