Neighborhood-level vaccine impact on COVID-19 infection and hospital admission in Quebec, Canada, during the Delta and early Omicron periods
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of COVID-19 vaccination on COVID-19 infection and hospitalisation at the population-level, and to assess the indirect effects of vaccination in the province of Quebec, Canada. METHODS: We performed a time-stratified, neighborhood-level ecologic study. The exposure was neighborhood-level vaccination (primary series) coverage; outcomes were COVID-19 infection and hospitalisation rates. We used robust Poisson regression to estimate weekly relative rates of infection and hospitalisation versus vaccination. RESULTS: Higher vaccination coverage was associated with lower COVID-19 infection rates from July 18-December 4 for the year 2021 (Delta period) (RR≈0.46 [0.39; 0.54] - 0.94 [0.83; 1.05], 85-100% vs. 60-74% coverage). From December 5-December 25, this association reversed (RR≈1.28 [1.16; 1.41] - 1.41 [1.31; 1.52]), possibly due to the Omicron variant, social behaviors and accumulation of susceptibles in more vaccinated neighborhoods. Vaccine impact against hospitalisation was maintained throughout (RR≈0.43 [0.29; 0.65] - 0.88 [0.64; 1.22]). Vaccination provided substantial indirect protection (RR≈0.43 [0.34; 0.54] - 0.81 [0.65; 1.03]). CONCLUSIONS: This study confirmed the protective impact of vaccination against severe disease regardless of variant, at the population level. Ecological analyses are a valuable strategy to evaluate vaccination programs. Population-level effects can have substantial effects and should be accounted for in public health and vaccination program planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".