High Incidence of Invasive Group A Streptococcal Infections in Remote Indigenous Communities in Northwestern Ontario, Canada
Bibliographic record
Abstract
Abstract Background Worldwide, indigenous populations appear to be at increased risk for invasive group A streptococcal (iGAS) infections. Although there is empirical evidence that the burden of iGAS disease is significant among remote First Nations communities in Northwestern Ontario, Canada, the epidemiology of iGAS infections in the area remains poorly characterized. Methods Individuals that met case definition for iGAS disease and whose laboratory specimens were processed by Meno Ya Win Health Centre in Sioux Lookout, Canada or who were reported to Thunder Bay District Health Unit, Canada were identified for the period 2009 to 2014. Case demographics, clinical severity, comorbidities, and risk factors were collected through chart review. Strain typing and antibiotic susceptibility were determined when possible. Basic descriptive statistics were calculated. Results Sixty-five cases of iGAS disease were identified, for an annualized incidence of 56.2 per 100 000. Primary bacteremia was present in 26.2% of cases, and cellulitis was identified in 55.4% of cases. The most common comorbidities identified were diabetes (38.5%) and skin conditions (38.5%). Prevalent risk factors included alcohol dependence (25%). Fourteen different emm types were identified among 42 isolates, with the most common being emm114 (17.4%), emm11 (15.2%), and emm118 (13.0%). Resistance to erythromycin and clindamycin was found in 24.6% of isolates. Conclusions Rural and remote First Nations communities in Northwestern Ontario experience iGAS infections at a rate 10 times the provincial and national average. Compared with other North American series, a lower proportion of isolates causing infection were of emm types included in candidate GAS vaccines.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".