Epidemiology of serogroup B invasive meningococcal disease in Ontario, Canada, 2000 to 2010
Bibliographic record
Abstract
BACKGROUND: Invasive meningococcal disease (IMD) caused by serogroup B is the last major serogroup in Canada to become vaccine-preventable. The anticipated availability of vaccines targeting this serogroup prompted an assessment of the epidemiology of serogroup B disease in Ontario, Canada. METHODS: We retrieved information on confirmed IMD cases reported to Ontario's reportable disease database between January 1, 2000 and December 31, 2010 and probabilistically-linked these cases to Public Health Ontario Laboratory records. Rates were calculated with denominator data obtained from Statistics Canada. We calculated a crude number needed to vaccinate using the inverse of the infant (<1 year) age-specific incidence multiplied by expected vaccine efficacies between 70% and 80%, and assuming only direct protection (no herd effects). RESULTS: A total of 259 serogroup B IMD cases were identified in Ontario over the 11-year period. Serogroup B was the most common cause of IMD. Incidence ranged from 0.11 to 0.27/100,000/year, and fluctuated over time. Cases ranged in age from 13 days to 101 years; 21.4% occurred in infants, of which 72.7% were <6 months. Infants had the highest incidence (3.70/100,000). Case-fatality ratio was 10.7% overall. If we assume that all infant cases would be preventable by vaccination, we would need to vaccinate between 33,784 and 38,610 infants to prevent one case of disease. CONCLUSIONS: Although rare, the proportion of IMD caused by serogroup B has increased and currently causes most IMD in Ontario, with infants having the highest risk of disease. Although serogroup B meningococcal vaccines are highly anticipated, our findings suggest that decisions regarding publicly funding serogroup B meningococcal vaccines will be difficult and may not be based on disease burden alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.002 | 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 teacher head, 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".