Risks of COVID-19-related hospitalisation and mortality among individuals with mental disorders following BNT162b2 and CoronaVac vaccinations: A case-control study
Bibliographic record
Abstract
Concerns have been raised regarding potential weaker vaccine immunogenicity with higher immune suppression for individuals with pre-existing mental disorders. Yet, data on the effectiveness of COVID-19 vaccinations among this vulnerable population are limited. A case-control study was conducted to investigate the risks of COVID-19-related hospitalisation and mortality among individuals with mental disorders following one to three doses of BNT162b2 and CoronaVac vaccinations in Hong Kong. Data were extracted from electronic health records, vaccination and COVID-19 confirmed case records. Conditional logistic regression was applied with adjustment for comorbidities and medication history. Subgroup analyses were performed with stratification: by age (< 65 and ≥ 65) and mental disorders diagnosis (depression, schizophrenia, anxiety disorder, and bipolar disorder). Two doses of BNT162b2 and CoronaVac significantly reduced COVID-19-related hospitalisation and mortality. Further protection for both outcomes was provided after three doses of BNT162b2 and CoronaVac. The vaccine effectiveness magnitude of BNT162b2 was generally higher than CoronaVac, but the difference diminished after the third dose. Individuals with mental disorders should be prioritised in future mass vaccination programmes of booster doses or bivalent COVID-19 vaccines. Targeted strategies should be developed to resolve the reasons behind vaccine hesitancy among this population and increase their awareness on the benefits of vaccination.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".