411. Significance of a Known Epidemiological Link to a COVID-19 Index Case and Severity of COVID-19 Infection
Bibliographic record
Abstract
Abstract Background Several factors have been associated with severity of COVID-19 disease, but there remains a paucity of data surrounding whether the nature of exposure is impactful. Evidence demonstrating the correlation between initial viral exposure dose and disease severity exists for many viral infections. Observational studies have suggested that the exposure context, which can be considered a proxy for magnitude of viral inoculum, may influence severity of COVID-19 infection. We aimed to assess whether having a known exposure, as a proxy for higher inoculum dose to COVID-19, was associated with more severe outcomes for individuals hospitalized with COVID-19. Methods We created a retrospective cohort of community-dwelling adults hospitalized for COVID-19 in south-central Ontario from April 1, 2020 - January 14, 2021. Individuals or next of kin were contacted to ascertain exposure history. The primary outcome was death, intensive care unit (ICU) admission, or mechanical ventilation (MV) within 30 days of admission. A multivariable logistic regression model was used to determine whether a known exposure was associated with worse outcomes. Results 1097 individuals with community acquired COVID-19 required hospitalization; of these, 942 (86%) had available exposure data. In this group, the median age was 65, 44% were women, 84% lived in a private residence, 59% had a frailty score (FS) of 1 – 3 while 40% had a FS of 4 – 9, and 28% had a known exposure. Overall, the primary outcome occurred in 368/942 (39%) patients. Having a known exposure was not associated with worse outcome (OR 1.14, 95% CI 0.84–1.54, p = 0.41). Male gender (OR 1.41, 95% CI 1.06–1.89; p = 0.018), age (OR 1.01/year, 95% CI 1.00–1.03, p = 0.03), frailty (OR 1.22/point, 95% CI 1.09–1.36, p = 0.001) and living with at least one other person (OR 1.57, 95% CI 1.09–2.28, p = 0.017) were all associated with death, ICU admission, or MV within 30 days of admission. Conclusion While having a known exposure to a person with COVID-19 was not associated with worse outcome, the identified increased severity of illness associated with cohabitation suggests context of exposure may have a role in disease severity. This data and future studies can be used to guide public health recommendations to not only minimize transmission, but severity of COVID-19 infection. Disclosures All Authors: No reported disclosures
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".