COVID-19 booster vaccination uptake and infection breakthrough amongst health care workers in Wales: A national prospective cohort study
Bibliographic record
Abstract
BACKGROUND: From September 2021, Health Care Workers (HCWs) in Wales began receiving a COVID-19 booster vaccination. This is the first dose beyond the primary vaccination schedule. Given the emergence of new variants, vaccine waning vaccine, and increasing vaccination hesitancy, there is a need to understand booster vaccine uptake and subsequent breakthrough in this high-risk population. METHODS: We conducted a prospective, national-scale, observational cohort study of HCWs in Wales using anonymised, linked data from the SAIL Databank. We analysed uptake of COVID-19 booster vaccinations from September 2021 to February 2022, with comparisons against uptake of the initial primary vaccination schedule. We also analysed booster breakthrough, in the form of PCR-confirmed SARS-Cov-2 infection, comparing to the second primary dose. Cox proportional hazard models were used to estimate associations for vaccination uptake and breakthrough regarding staff roles, socio-demographics, household composition, and other factors. RESULTS: We derived a cohort of 73,030 HCWs living in Wales (78% female, 60% 18-49 years old). Uptake was quickest amongst HCWs aged 60 + years old (aHR 2.54, 95%CI 2.45-2.63), compared with those aged 18-29. Asian HCWs had quicker uptake (aHR 1.18, 95%CI 1.14-1.22), whilst Black HCWs had slower uptake (aHR 0.67, 95%CI 0.61-0.74), compared to white HCWs. HCWs residing in the least deprived areas were slightly quicker to have received a booster dose (aHR 1.12, 95%CI 1.09-1.16), compared with those in the most deprived areas. Strongest associations with breakthrough infections were found for those living with children (aHR 1.52, 95%CI 1.41-1.63), compared to two-adult only households. HCWs aged 60 + years old were less likely to get breakthrough infections, compared to those aged 18-29 (aHR 0.42, 95%CI 0.38-0.47). CONCLUSION: Vaccination uptake was consistently lower among black HCWs, as well as those from deprived areas. Whilst breakthrough infections were highest in households with children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".