Booster vaccination with inactivated whole-virus or mRNA vaccines and COVID-19–related deaths among people with multimorbidity: a cohort study
Bibliographic record
Abstract
BACKGROUND: Multimorbidity is a prevalent risk factor for COVID-19-related complications and death. We sought to evaluate the association of homologous booster vaccination using BNT162b2 (Pfizer-BioNTech) or CoronaVac (Sinovac) with COVID-19-related deaths among people with multimorbidity during the initial Omicron wave of the COVID-19 pandemic. METHODS: Using routine clinical records from public health care facilities in Hong Kong, we conducted a territory-wide retrospective cohort study comparing people aged 18 years or older with 2 or more chronic conditions who received a homologous booster (third) dose with those who received only 2 doses, between Nov. 11, 2021, and Mar. 31, 2022. The primary outcome was death related to COVID-19. RESULTS: We included 120 724 BNT162b2 recipients (including 87 289 who received a booster), followed for a median of 34 (interquartile range [IQR] 20-63) days and 127 318 CoronaVac recipients (including 94 977 who received a booster), followed for a median of 38 (IQR 22-77) days. Among BNT162b2 recipients, booster-vaccinated people had fewer COVID-19-related deaths than those who received 2 doses (5 v. 34, incidence rate 1.3 v. 23.4 per million person-days, weighted incidence rate ratio [IRR] 0.05, 95% confidence interval [CI] 0.02-0.16). We observed similar results among recipients of CoronaVac booster vaccination compared with those who received only 2 doses (26 v. 88, incidence rate 5.3 v. 53.1 per million person-days, weighted IRR 0.08, 95% CI 0.05-0.12). INTERPRETATION: Among people with multimorbidity, booster vaccination with BNT162b2 or CoronaVac was associated with reductions of more than 90% in COVID-19-related mortality rates compared with only 2 doses. These results highlight the crucial role of booster vaccination for protecting vulnerable populations as the COVID-19 pandemic continues to evolve.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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".