Risk factors for developing COVID-19: a population-based longitudinal study (COVIDENCE UK)
Bibliographic record
Abstract
Summary Background Risk factors for severe COVID-19 include older age, male sex, obesity, Black or Asian ethnicity and underlying medical conditions. Whether these factors also influence susceptibility to developing COVID-19 is uncertain. Methods We undertook a prospective, population-based cohort study (COVIDENCE UK) from 1 st May 2020 to 5 th February 2021. Baseline information on potential risk factors was captured by an online questionnaire. Monthly follow-up questionnaires captured incident COVID-19. We used logistic regression models to estimate multivariable-adjusted odds ratios (aORs) for associations between potential risk factors and risk of COVID-19. Findings We recorded 446 incident cases of COVID-19 in 15,227 participants (2.9%). Increased risk of developing COVID-19 was independently associated with Asian/Asian British vs . White ethnicity (aOR 2.31, 95% CI 1.35-3.95), household overcrowding (aOR per additional 0.5 people/bedroom 1.26, 1.11-1.43), any vs . no visits to/from other households in previous week (aOR 1.33, 1.07-1.64), number of visits to indoor public places (aOR per extra visit per week 1.05, 1.01-1.09), frontline occupation excluding health/social care vs . no frontline occupation (aOR 1.49, 1.12-1.98), and raised body mass index (BMI) (aOR 1.51 [1.20-1.90] for BMI 25.0-30.0 kg/m 2 and 1.38 [1.05-1.82] for BMI >30.0 kg/m 2 vs . BMI <25.0 kg/m 2 ). Atopic disease was independently associated with decreased risk (aOR 0.76, 0.59-0.98). No independent associations were seen for age, sex, other medical conditions, diet, or micronutrient supplement use. Interpretation After rigorous adjustment for factors influencing exposure to SARS-CoV-2, Asian/Asian British ethnicity and raised BMI were associated with increased risk of developing COVID-19, while atopic disease was associated with decreased risk. Funding Barts Charity, Health Data Research UK
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".