Effectiveness of one dose of MVA-BN vaccine against mpox infection in males in Ontario, Canada: A target trial emulation
Bibliographic record
Abstract
ABSTRACT Background Limited evidence is available on the real-world effectiveness of modified vaccinia Ankara-Bavarian Nordic vaccine (MVA-BN) against mpox infection. Methods We emulated a target trial using linked databases in Ontario, Canada to estimate the effectiveness of one dose of MVA-BN. Our study included males aged ≥18 years who: (1) had a history of syphilis testing and a laboratory-confirmed bacterial sexually transmitted infection (STI) in the prior year; or (2) filled a prescription for HIV pre-exposure prophylaxis in the prior year. On each day between June 12, 2022 and October 27, 2022, males who had been vaccinated 15 days prior were matched 1:1 with unvaccinated males by age, geographic region, prior HIV diagnosis, number of bacterial STI diagnoses in the previous three years, and receipt of any non- MVA-BN vaccine in the previous year. We used a Cox proportional hazards model to estimate the hazard ratio comparing the incidence of mpox between the two groups, and calculated vaccine effectiveness as (1–HR)x100. Results Each group included 3,204 males. A total of 71 mpox infections were diagnosed over the study period, with 0.09 (95% confidence interval [CI], 0.05–0.13) per 1000 person-days for the vaccinated group and 0.20 (95%CI, 0.15–0.27) per 1000 person-days for the unvaccinated group. Estimated vaccine effectiveness of one dose of MVA-BN against mpox infection was 59% (95%CI, 31–76%). Conclusions This study, conducted in the context of a targeted vaccination program and evolving outbreak, suggests that one dose of MVA-BN is moderately effective in preventing mpox infection.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".