Increasing Rates of Invasive Group A Streptococcal Disease in Alberta, Canada; 2003–2017
Bibliographic record
Abstract
Abstract Background We present an analysis of increasing rates of invasive group A streptococci (iGAS) over a 15-year period in Alberta, Canada. Methods From 2003 to 2017, the emm type of iGAS isolates was identified from patients with iGAS disease in Alberta. Demographic, clinical, and risk factor data were collected. Results A total of 3551 cases of iGAS were identified in Alberta by isolation of a GAS isolate from a sterile site. The age-standardized incidence rates of iGAS increased from 4.24/100 000 in 2003 to 10.24 in 2017. Rates (SD) were highest in those age <1 (9.69) years and 60+ (11.15) years; 57.79% of the cases were male. Commonly identified risk factors included diabetes, hepatitis C, nonsurgical wounds, addiction, alcohol abuse, drug use, and homelessness. The overall age-standardized case fatality rate was 5.11%. The most common clinical presentation was septicemia/bacteremia (41.84%), followed by cellulitis (17.25%). The top 4 emm types from 2003–2017 were emm1, 28, 59, and 12. In 2017, the top 4 emm types (emm1, 74, 101, and 59) accounted for 46.60% of cases. Conclusions The incidence of iGAS disease in Alberta, Canada, has increased from 2003 to 2017. This increase has been driven not by a single emm type, but rather what has been observed is a collection of common and emerging emm types associated with disease. In addition, it is also likely that societal factors are playing important roles in this increase as risk factors associated with marginalized populations (addiction, alcohol abuse, and drug use) were found to have increased during the survey period.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".