Effectiveness of Serogroup C Meningococcal Conjugate Vaccine
Bibliographic record
Abstract
BACKGROUND: A mass immunization campaign was implemented in 2001 to control a serogroup C meningococcal disease outbreak, and a newly licensed serogroup C meningococcal conjugate vaccine (C-MCV) was used. In 2002, 1 C-MCV dose was routinely offered to children 12 months of age. OBJECTIVE: To assess the epidemiologic effect of the campaign and C-MCV effectiveness during a 7-year period according to age at vaccination and delay since vaccine administration. METHODS: Cases of invasive meningococcal infection reported to public health authorities and the reference laboratory during the period 1990 to 2008 were obtained to calculate year- and age-specific incidence rates. Multiple sources were used to ascertain the immunization status of cases. Immunization registry data were used to estimate age-specific C-MCV uptake rates in different birth cohorts. Vaccine effectiveness was estimated by Mantel-Haenszel method and logistic regression models. RESULTS: After mass immunization campaign, meningococcal C disease incidence decreased markedly not only in highly vaccinated but also in poorly vaccinated and nonvaccinated birth cohorts. Overall vaccine effectiveness was 87.4% (95% CI: 75.4%-94.2%) with lower protection in children vaccinated <2 years of age and waning of protection of higher magnitude in this age group. CONCLUSION: Results support the current Canadian recommendation to provide booster vaccination for adolescents.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".