Risk factors for developing COVID-19: a population-based longitudinal study (COVIDENCE UK)

Holt, Hayley; Talaei, Mohammad; Greenig, Matthew; Zenner, Dominik; Symons, Jane; Relton, Clare; Young, Katherine S.; Davies, Molly R.; Thompson, Katherine N.; Ashman, Jed; Rajpoot, Sultan Saeed; Kayyale, Ahmed Ali; Rifai, Sarah El; Lloyd, Philippa; Jolliffe, David A.; Timmis, Olivia; Finer, Sarah; Iliodromiti, Stamatina; Miners, Alec; Hopkinson, Nicholas S; Alam, Bodrul; Lloyd-Jones, Graham; Thränhardt, Dietrich; Chapple, Iain; Pfeffer, Paul; McCoy, David; Davies, Gwyneth; Lyons, Ronan A; Griffiths, Chris; Kee, Frank; Sheikh, Aziz; Breen, Gerome; Shaheen, Seif O.; Martineau, Adrian R.

doi:10.1136/thoraxjnl-2021-217487

Cited by 59 publications

(56 citation statements)

References 23 publications

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“…In pre-vaccination studies, high body-mass index (BMI), 22 Asian ethnicity, 23 and working in a healthcare setting 24 have all been found to be risk factors for SARS-CoV-2 infection and COVID-19, including in the same COVIDENCE UK cohort studied here. 18 By contrast, we observed no association with BMI, either after primary or booster vaccinations, and a reduced risk of breakthrough infection among healthcare workers after their primary vaccination. While underpowered to investigate ethnicity, previous studies on our cohort have consistently shown increased risk of pre-vaccination infection among south Asian participants, 18,19 which has not remained after vaccination.…”

Section: Discussioncontrasting

confidence: 82%

“…Further details on COVIDENCE UK have been published elsewhere. 18,19 This analysis is based on monthly follow-up data to Feb 21, 2022.…”

Section: Methodsmentioning

confidence: 99%

“…All included factors have previously been found to be independently associated with risk of COVID-19 or pre-vaccination or post-vaccination SARS-CoV-2 antibody response. 18,19,21 Previous infection was defined as reporting a positive result on a lateral flow or RT-PCR test for SARS-CoV-2 before start of the post-vaccination follow-up period.…”

Section: Methodsmentioning

confidence: 99%

“…To produce participant-level covariates for each class of medications investigated, questionnaire responses were mapped to drug classes listed in the British National Formulary or the DrugBank and Electronic Medicines Compendium databases if not explicitly listed in the British National Formulary, as previously described. 18 Participants were assigned Index of Multiple Deprivation (IMD) 2019 scores, or equivalent scores for devolved administrations, according to their postcode.…”

Section: Methodsmentioning

confidence: 99%

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Risk factors for SARS-CoV-2 infection after primary vaccination with ChAdOx1 nCoV-19 or BNT1262b2 and after booster vaccination with BNT1262b2 or mRNA-1273: a population-based cohort study (COVIDENCE UK)

Vivaldi

Jolliffe

Holt

et al. 2022

Preprint

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Background: Little is known about the relative influence of demographic, behavioural, and vaccine-related factors on risk of post-vaccination SARS-CoV-2 infection. We aimed to identify risk factors for SARS-CoV-2 infection after primary and booster vaccinations. Methods: We undertook a prospective population-based study in UK adults (≥16 years) vaccinated against SARS-CoV-2, including data from Jan 12, 2021, to Feb 21, 2022. We modelled risk of post-vaccination SARS-CoV-2 infection separately for participants who had completed a primary course of vaccination (two-dose or, in the immunosuppressed, three-dose course of either ChAdOx1 nCoV-19 [ChAdOx1] or BNT1262b2) and for those who had additionally received a booster dose (BNT1262b2 or mRNA-1273). Cox regression models were used to explore associations between sociodemographic, behavioural, clinical, pharmacological, and nutritional factors and breakthrough infection, defined as a self-reported positive result on a lateral flow or reverse transcription PCR (RT-PCR) test for SARS-CoV-2. Models were further adjusted for weekly SARS-CoV-2 incidence at the local (lower tier local authority) level. Findings: 14,713 participants were included in the post-primary analysis and 10,665 in the post-booster analysis, with a median follow-up of 203 days (IQR 195-216) in the post-primary cohort and 85 days (66-103) in the post-booster cohort. 1051 (7.1%) participants in the post-primary cohort and 1009 (9.4%) participants in the post-booster cohort reported a breakthrough SARS-CoV-2 infection. A primary course of ChAdOx1 (vs BNT182b2) was associated with higher risk of infection, both in the post-primary cohort (adjusted hazard ratio 1.63, 95% CI 1.41-1.88) and in the post-booster cohort after boosting with mRNA-1273 (1.29 [1.03-1.61] vs BNT162b2 primary plus BNT162b2 booster). A lower risk of breakthrough infection was associated with older age (post-primary: 0.96 [0.96-0.97] per year; post-booster: 0.97 [0.96-0.98]), whereas a higher risk of breakthrough infection was associated with lower levels of education (post-primary: 1.66 [1.35-2.06] for primary or secondary vs postgraduate; post-booster: 1.36 [1.08-1.71]) and at least three weekly visits to indoor public places (post-primary: 1.38 [1.15-1.66] vs none; post-booster: 1.33 [1.10-1.60]). Conclusions: Vaccine type, socioeconomic status, age, and behaviours affect risk of breakthrough SARS-CoV-2 infection following a primary schedule and a booster dose.

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Section: Discussioncontrasting

confidence: 82%

“…Further details on COVIDENCE UK have been published elsewhere. 18,19 This analysis is based on monthly follow-up data to Feb 21, 2022.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Risk factors for SARS-CoV-2 infection after primary vaccination with ChAdOx1 nCoV-19 or BNT1262b2 and after booster vaccination with BNT1262b2 or mRNA-1273: a population-based cohort study (COVIDENCE UK)

Vivaldi

Jolliffe

Holt

et al. 2022

Preprint

Self Cite

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“…Throughout the pandemic, there has been consistent epidemiological and clinical evidence that age and sex/gender (male) are major risk factors for more severe COVID-19 symptoms. [131]–[133] Earlier studies have shown that GISAID data also supported a correlation between the demographic metadata which are available—age and gender—to clinical severity. [76], [77] Figs.…”

Section: Resultsmentioning

confidence: 94%

Interpretable and Predictive Deep Modeling of the SARS-CoV-2 Spike Protein Sequence

Sokhansanj

Zhao

Rosen

2021

Preprint

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As the COVID-19 pandemic continues, the SARS-CoV-2 virus continues to rapidly mutate and change in ways that impact virulence, transmissibility, and immune evasion. Genome sequencing is a critical tool, as other biological techniques can be more costly, time-consuming, and difficult. However, the rapid and complex evolution of SARS-CoV-2 challenges conventional sequence analysis methods like phylogenetic analysis. The virus picks up and loses mutations independently in multiple subclades, often in novel or unexpected combinations, and, as for the newly emerged Omicron variant, sometimes with long explained branches. We propose interpretable deep sequence models trained by machine learning to complement conventional methods. We apply Transformer-based neural network models developed for natural language processing to analyze protein sequences. We add network layers to generate sample embeddings and sequence-wide attention to interpret models and visualize multiscale patterns. We demonstrate and validate our framework by modeling SARS-CoV-2 and coronavirus taxonomy. We then develop an interpretable predictive model of disease severity that integrates SARS-CoV-2 spike protein sequence and patient demographic variables, using publicly available data from the GISAID database. We also apply our model to Omicron. Based on knowledge prior to the availability of empirical data for Omicron, we predict: 1) reduced neutralization antibody activity (15-50 fold) greater than any previously characterized variant, varying between Omicron sublineages, and 2) reduced risk of severe disease (by 35-40%) relative to Delta. Both predictions are in accord with recent epidemiological and experimental data.

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Clinical and Demographic Factors Associated With COVID-19, Severe COVID-19, and SARS-CoV-2 Infection in Adults

et al. 2023

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ImportanceCurrent data identifying COVID-19 risk factors lack standardized outcomes and insufficiently control for confounders.ObjectiveTo identify risk factors associated with COVID-19, severe COVID-19, and SARS-CoV-2 infection.Design, Setting, and ParticipantsThis secondary cross-protocol analysis included 4 multicenter, international, randomized, blinded, placebo-controlled, COVID-19 vaccine efficacy trials with harmonized protocols established by the COVID-19 Prevention Network. Individual-level data from participants randomized to receive placebo within each trial were combined and analyzed. Enrollment began July 2020 and the last data cutoff was in July 2021. Participants included adults in stable health, at risk for SARS-CoV-2, and assigned to the placebo group within each vaccine trial. Data were analyzed from April 2022 to February 2023.ExposuresComorbid conditions, demographic factors, and SARS-CoV-2 exposure risk at the time of enrollment.Main Outcomes and MeasuresCoprimary outcomes were COVID-19 and severe COVID-19. Multivariate Cox proportional regression models estimated adjusted hazard ratios (aHRs) and 95% CIs for baseline covariates, accounting for trial, region, and calendar time. Secondary outcomes included severe COVID-19 among people with COVID-19, subclinical SARS-CoV-2 infection, and SARS-CoV-2 infection.ResultsA total of 57 692 participants (median [range] age, 51 [18-95] years; 11 720 participants [20.3%] aged ≥65 years; 31 058 participants [53.8%] assigned male at birth) were included. The analysis population included 3270 American Indian or Alaska Native participants (5.7%), 7849 Black or African American participants (13.6%), 17 678 Hispanic or Latino participants (30.6%), and 40 745 White participants (70.6%). Annualized incidence was 13.9% (95% CI, 13.3%-14.4%) for COVID-19 and 2.0% (95% CI, 1.8%-2.2%) for severe COVID-19. Factors associated with increased rates of COVID-19 included workplace exposure (high vs low: aHR, 1.35 [95% CI, 1.16-1.58]; medium vs low: aHR, 1.41 [95% CI, 1.21-1.65]; P &lt; .001) and living condition risk (very high vs low risk: aHR, 1.41 [95% CI, 1.21-1.66]; medium vs low risk: aHR, 1.19 [95% CI, 1.08-1.32]; P &lt; .001). Factors associated with decreased rates of COVID-19 included previous SARS-CoV-2 infection (aHR, 0.13 [95% CI, 0.09-0.19]; P &lt; .001), age 65 years or older (aHR vs age &lt;65 years, 0.57 [95% CI, 0.50-0.64]; P &lt; .001) and Black or African American race (aHR vs White race, 0.78 [95% CI, 0.67-0.91]; P = .002). Factors associated with increased rates of severe COVID-19 included race (American Indian or Alaska Native vs White: aHR, 2.61 [95% CI, 1.85-3.69]; multiracial vs White: aHR, 2.19 [95% CI, 1.50-3.20]; P &lt; .001), diabetes (aHR, 1.54 [95% CI, 1.14-2.08]; P = .005) and at least 2 comorbidities (aHR vs none, 1.39 [95% CI, 1.09-1.76]; P = .008). In analyses restricted to participants who contracted COVID-19, increased severe COVID-19 rates were associated with age 65 years or older (aHR vs &lt;65 years, 1.75 [95% CI, 1.32-2.31]; P &lt; .001), race (American Indian or Alaska Native vs White: aHR, 1.98 [95% CI, 1.38-2.83]; Black or African American vs White: aHR, 1.49 [95% CI, 1.03-2.14]; multiracial: aHR, 1.81 [95% CI, 1.21-2.69]; overall P = .001), body mass index (aHR per 1-unit increase, 1.03 [95% CI, 1.01-1.04]; P = .001), and diabetes (aHR, 1.85 [95% CI, 1.37-2.49]; P &lt; .001). Previous SARS-CoV-2 infection was associated with decreased severe COVID-19 rates (aHR, 0.04 [95% CI, 0.01-0.14]; P &lt; .001).Conclusions and RelevanceIn this secondary cross-protocol analysis of 4 randomized clinical trials, exposure and demographic factors had the strongest associations with outcomes; results could inform mitigation strategies for SARS-CoV-2 and viruses with comparable epidemiological characteristics.

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Risk factors for developing COVID-19: a population-based longitudinal study (COVIDENCE UK)

Cited by 59 publications

References 23 publications

Risk factors for SARS-CoV-2 infection after primary vaccination with ChAdOx1 nCoV-19 or BNT1262b2 and after booster vaccination with BNT1262b2 or mRNA-1273: a population-based cohort study (COVIDENCE UK)

Risk factors for SARS-CoV-2 infection after primary vaccination with ChAdOx1 nCoV-19 or BNT1262b2 and after booster vaccination with BNT1262b2 or mRNA-1273: a population-based cohort study (COVIDENCE UK)

Interpretable and Predictive Deep Modeling of the SARS-CoV-2 Spike Protein Sequence

Clinical and Demographic Factors Associated With COVID-19, Severe COVID-19, and SARS-CoV-2 Infection in Adults

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