Protective effects of prior third dose mRNA vaccination in rural nursing home residents during SARS‐CoV‐2 outbreaks
Bibliographic record
Abstract
BACKGROUND: In Canada, mortality due to SARS-CoV-2 disproportionately impacted residents of nursing homes (NH). In November 2021, NH residents in the Canadian province of Manitoba became eligible to receive three doses of mRNA vaccine but coverage with three doses has not been universal. The objective of this study was to compare the protection from infection conferred by one, two, and three doses of COVID-19 mRNA vaccine compared to no vaccination among residents of nursing homes experiencing SARS-CoV-2 outbreaks. METHODS: Infection Prevention and Control reports from 8 rural nursing homes experiencing outbreaks of SARS-CoV-2 between January 6, 2022, and March 5, 2022, were analyzed. Attack rates and the number needed to vaccinate (NNV) were calculated. RESULTS: SARS-CoV-2 attack rate was 65% among NH residents not vaccinated, 58% among residents who received 1-2 doses of mRNA COVID-19 vaccine, and 28% among residents who had received 3 vaccine doses. The NNV to prevent one nursing home resident from SARS-CoV-2 infection during an outbreak was 3 for a vaccination with 3 doses and 14 for 1-2 doses of COVID-19 mRNA vaccine. The superiority of receiving the third dose was statistically significant compared to 1-2 doses (Chi-Squared, p < 0.00001). CONCLUSIONS: Nursing home residents who received three doses of COVID-19 mRNA vaccine were at lower risk of SARS-CoV-2 infection compared to those who received 1-2 doses. Our analyses lend support to the protective effects of the third dose of mRNA vaccine for NH residents in the event of a SARS-CoV-2 outbreak.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".