Antibiotic use among twelve Canadian First Nations communities: a retrospective chart review of skin and soft tissue infections
Bibliographic record
Abstract
BACKGROUND: Previous publications indicated an emerging issue with community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA), particularly skin and soft tissue infections (SSTIs), in Indigenous communities in Canada. The objectives of this analysis were to explore the prevalence of SSTIs due to CA-MRSA and patterns of antimicrobial use in the community setting. METHODS: A retrospective chart review was conducted as part of an environmental scan to assess antibiotic prescriptions in 12 First Nations communities across five provinces in Canada including Alberta, Saskatchewan, Manitoba, Ontario, and Québec. Charts were randomly selected from nursing stations and patients who had accessed care in the previous 12 months and were ≥ 18 years were included in the review. Data was collected from September to December, 2013 on antibiotic prescriptions, including SSTIs, clinical symptoms, diagnostic information including presence of CA-MRSA infection, and treatment. RESULTS: A total of 372 charts were reviewed, 60 from Alberta, 70 from Saskatchewan, 120 from Manitoba, 100 from Ontario, and 22 from Québec. Among 372 patients, 224 (60.2%) patients had at least one antibiotic prescription in the previous 12 months and 569 prescriptions were written in total. The prevalence of SSTIs was estimated at 36.8% (137 cases of SSTIs in 372 charts reviewed). In 137 cases of SSTIs, 34 (24.8%) were purulent infections, and 55 (40.2%) were due to CA-MRSA. CONCLUSIONS: This study has identified a high prevalence of antibiotic use and SSTIs due to CA-MRSA in remote and isolated Indigenous communities across Canada. This population is currently hard to reach and under-represented in standard surveillance system and randomized retrospective chart reviews can offer complimentary methodology for monitoring disease burden, treatment and prevention.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".