The Impact of the Meningococcal Serogroup C Conjugate Vaccine in Canada Between 2002 and 2012
Bibliographic record
Abstract
Background. Before 2001, the incidence of invasive meningococcal disease (IMD) in Canada was 1.0 per 100 000 per year, with 40% of cases caused by serogroup C organisms. During 2001–2005 all provinces introduced the meningococcal serogroup C conjugate vaccine (MCCV) into their routine infant immunization schedule. Methods. Active, prospective, population-based surveillance of IMD in children and adults was conducted by the Canadian Immunization Monitoring Program, ACTive (IMPACT) during 2002–2012. Inclusion criteria were admission to hospital and identification of Neisseria meningitidis from a sterile site. Incidence was estimated using population census data from Statistics Canada. Results. Prior to MCCV introduction, serogroup C disease incidence was 0.07–0.25 per 100 000 per year depending on the province. Following vaccine introduction, serogroup C disease decreased to <0.05 per 100 000 per year, with a reduction of 14% per year (P = .0014). A decrease occurred in all provinces, despite differing schedules being implemented. The largest decrease of 83% (from 0.27 to 0.05 per 100 000 per year) occurred in the 15–24 year age group (P = .0100) who were not vaccinated in all provinces. There was no impact on the incidence of nonserogroup C disease over the same period (P = .9811). Conclusions. MCCV dramatically reduced the incidence of serogroup C IMD in Canada through both direct and indirect effects. The observation that disease incidence decreased with different schedules suggests that the doses at 12 months (common to all provinces) and adolescence (7 of 8 provinces studied) were critical in achieving disease control.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".