P-1906. Early COVID-19 and Severity of Subsequent Omicron Infection in Ontario, Canada
Bibliographic record
Abstract
Abstract Background As SARS-CoV-2 evolves, assessing changes in COVID-19 severity over time, and the impact of prior infection on repeat infection, is important. We determined whether developing COVID-19 early in the pandemic was associated with reduced severity of subsequent infection with Omicron sub-lineages. Methods We evaluated COVID-19 severity among patients infected during the Omicron wave. Severity was measured in 3 ways:(1) an ordinal measure combining activities of daily living (ADL) and presence of fever,(2) healthcare required (yes/no),(3) an ordinal measure of illness duration. We compared these outcomes in a study of age and time-period matched cohorts in Toronto, Canada: one with symptomatic COVID-19 between Mar 1 & Sep 30/2020 ('early COVID-19') and another who did not test positive for SARS-CoV-2 during the same period. Participants completed baseline, then biweekly surveys to identify SARS-CoV-2 infection episodes from Jan 2020 to Jan 2023. Multivariable binary and ordinal logistic regression models were used to construct ORs and 95% CI for impact of early COVID-19 on severity, adjusted for social/demographic characteristics, comorbidities, COVID-19 vaccination status, and time from early COVID-19 to first Omicron infection. Results Of 261 participants with COVID-19 due to Omicron (Table 1), 177 had early COVID-19 at a median of 26 months prior. In adjusted analyses, those with early COVID-19 occurring < 24 months prior to their Omicron infection had lower odds of having severe Omicron-related illness; OR 0.35 (95%CI 0.15-0.80); with non-significant lower odds of requirement for healthcare (OR 0.49,95%CI 0.14-1.8) and illness duration (OR 0.80, 95%CI 0.32-2.0) (Table 2). Immunocompromise was associated with more severe illness based on all 3 outcomes; other non-immunocompromising comorbidities were associated with requiring healthcare and longer illness duration, and females reported longer illness duration (Table 2). Conclusion Developing COVID-19 early in the pandemic was associated with reduced severity of first Omicron infection if it occurred < 24 months later. Immunocompromise and the presence of other underlying comorbidities were associated with increased severity, and women reported longer duration of illness. Disclosures Moe H. Kyaw, PhD, Pfizer: Employee Catherine Martin, PhD, Pfizer: employee|Pfizer: Stocks/Bonds (Private Company) Maria Major, B.Sc., M.P.H., Pfizer: Employee Samira Mubareka, MD, Pfizer: Grant/Research Support Srinivas Valluri, PhD, Pfizer: Employee John M. McLaughlin, PhD, Pfizer: Employee|Pfizer: Stocks/Bonds (Public Company) Allison McGeer, MD, AstraZeneca: Honoraria|GSK: Honoraria|Merck: Honoraria|Moderna: Honoraria|Novavax: Honoraria|Pfizer: Grant/Research Support|Pfizer: Honoraria|Roche: Honoraria|Seqirus: Grant/Research Support|Seqirus: Honoraria
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".