Post-Acute Sequelae After Severe Acute Respiratory Syndrome Coronavirus 2 Infection by Viral Variant and Vaccination Status: A Multicenter Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Disentangling the effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and vaccination on the occurrence of post-acute sequelae of SARS-CoV-2 (PASC) is crucial to estimate and reduce the burden of PASC. METHODS: We performed a cross-sectional analysis (May/June 2022) within a prospective multicenter healthcare worker (HCW) cohort in north-eastern Switzerland. HCWs were stratified by viral variant and vaccination status at time of their first positive SARS-CoV-2 nasopharyngeal swab. HCWs without positive swab and with negative serology served as controls. The sum of 18 self-reported PASC symptoms was modeled with univariable and multivariable negative-binomial regression to analyze the association of mean symptom number with viral variant and vaccination status. RESULTS: Among 2912 participants (median age: 44 years; 81.3% female), PASC symptoms were significantly more frequent after wild-type infection (estimated mean symptom number: 1.12; P < .001; median time since infection: 18.3 months), after Alpha/Delta infection (0.67 symptoms; P < .001; 6.5 months), and after Omicron BA.1 infections (0.52 symptoms; P = .005; 3.1 months) versus uninfected controls (0.39 symptoms). After Omicron BA.1 infection, the estimated mean symptom number was 0.36 for unvaccinated individuals versus 0.71 with 1-2 vaccinations (P = .028) and 0.49 with ≥3 prior vaccinations (P = .30). Adjusting for confounders, only wild-type (adjusted rate ratio [aRR]: 2.81; 95% confidence interval [CI]: 2.08-3.83) and Alpha/Delta infections (aRR: 1.93; 95% CI: 1.10-3.46) were significantly associated with the outcome. CONCLUSIONS: Previous infection with pre-Omicron variants was the strongest risk factor for PASC symptoms among our HCWs. Vaccination before Omicron BA.1 infection was not associated with a clear protective effect against PASC symptoms in this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".