Household exposure, demographic and health characteristics associated with SARS-CoV-2 infection in a cohort study in Northern France
Bibliographic record
Abstract
In this cohort study, we aimed to study the risk of SARS-CoV-2 infection and its association with household exposure, as well as demographic and health factors. Between March 2020 and April 2022, we conducted a cohort study among adults and children aged 5 years or more in a town in northern France. Participants were screened repeatedly for post-infection anti-SARS-CoV-2 immunity. Infection dates were inferred in hierarchical order from symptomatic episodes or virological tests, or if unavailable from the timing of other cases in the household or the window of seroconversion. Household exposure to a case of SARS-CoV-2 during the infectious phase was defined as a time-varying exposure. We included 830 participants with a mean follow-up of 453 days (12,353 total person-months), during which we identified 491 infections (incidence rate 39.7 per 1,000 person-months). In adjusted analyses, exposure in the household to an infected individual was associated with an incidence rate ratio (IRR) of 16.48 (95% CI 12.29-22.09), and baseline statin use was associated with a decreased risk of infection (IRR 0.35, 95% CI 0.13-0.89). These results could contribute to the development of additional prophylactic strategies, e.g., post-exposure, for the population at high risk of severe forms of COVID-19.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".