Epidemiology of tetanus in Canada, 1995–2019
Bibliographic record
Abstract
OBJECTIVES: This report aims to use tetanus hospitalization data to describe the epidemiology in Canada from 1995 to 2019 and to assess progress on national reduction targets, including validating that Canada has eliminated maternal and neonatal tetanus (MNT). METHODS: Tetanus hospitalizations and fatalities occurring between 1995 and 2019 were retrieved from the Canadian Institute for Health Information (CIHI) and Statistics Canada. Cases coded with ICD-10 codes A33, A34, or A35 as the primary diagnosis (or ICD-9 equivalents) were included. The Canadian national case definition was used for generic tetanus and definitions from the World Health Organization were referenced for MNT. R version 4.0.2 was used for analyses. RESULTS: From 1995 to 2019, 155 non-MNT, 6 neonatal, and 0 maternal tetanus cases were retrieved from CIHI. However, all 6 neonatal cases were excluded after validating with provincial/territorial public health officials. In the same time period, there were 91 national notifications of tetanus. Cases were distributed relatively equally across the country, with the exception of the territories, where zero cases were reported. Adults 75 and over had significantly higher incidence rates compared to younger age groups (p<0.001). Ten deaths were reported during the timeframe. CONCLUSION: Tetanus incidence remains low and hospitalization data reveal that Canada has met its reduction target of maintaining 5 cases or fewer annually in recent years. For MNT, Canada has successfully met the elimination target of zero cases. Continued vaccination efforts must be practiced for all age groups, including those aged 75 years and older, to sustain targets moving forward.
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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 | 0.002 | 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) | 0.000 | 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".