Primary Series and Booster Coronavirus Disease 2019 Vaccine Effectiveness in a Cohort of Healthcare Workers in Albania During a BA.1 and BA.2 Variant Period, January–May 2022
Bibliographic record
Abstract
Background: Healthcare workers (HCWs) have experienced high rates of coronavirus disease 2019 (COVID-19) morbidity and mortality. We estimated COVID-19 2-dose primary series and monovalent booster vaccine effectiveness (VE) against symptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron (BA.1 and BA.2) infection among HCWs in 3 Albanian hospitals during January-May 2022. Methods: Study participants completed weekly symptom questionnaires, underwent polymerase chain reaction (PCR) testing when symptomatic, and provided quarterly blood samples for serology. We estimated VE using Cox regression models (1 - hazard ratio), with vaccination status as the time-varying exposure and unvaccinated HCWs as the reference group, adjusting for potential confounders: age, sex, prior SARS-CoV-2 infection (detected by PCR, rapid antigen test, or serology), and household size. Results: At the start of the analysis period, 76% of 1462 HCWs had received a primary series, 10% had received a booster dose, and 9% were unvaccinated; 1307 (89%) HCWs had evidence of prior infection. Overall, 86% of primary series and 98% of booster doses received were BNT162b2. The median time interval from the second dose and the booster dose to the start of the analysis period was 289 (interquartile range [IQR], 210-292) days and 30 (IQR, 22-46) days, respectively. VE against symptomatic PCR-confirmed infection was 34% (95% confidence interval [CI], -36% to 68%) for the primary series and 88% (95% CI, 39%-98%) for the booster. Conclusions: NCT04811391.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".