Longitudinal Surveillance of Outpatient Tetracycline, Sulfonamide‐Trimethoprim and ‘Other’ Antimicrobial Use in Canada, 1995 to 2010
Bibliographic record
Abstract
INTRODUCTION: Monitoring the volume and patterns of use of antimicrobial agents is important in light of antimicrobial resistance. OBJECTIVE: To assess the use of three antimicrobial groups - tetracycline, sulfonamide-trimethoprim and 'other' antimicrobials - within Canadian provinces over time. METHODS: Prescription counts from 1995 to 2010 were acquired for the tetracycline and sulfonamide-trimethoprim groups of antimicrobials, and from 2001 to 2010 for the 'other' antimicrobial group. Linear mixed models were produced to assess differences among provinces and over time while accounting for repeated measurements. Prescription rate, defined daily dose per 1000 inhabitant-days and defined daily doses per prescription measures for the year 2009 were also compared with those reported by participating European Union countries to determine where Canadian provinces rank in terms of antimicrobial use among these countries. RESULTS: Prescribing of all three groups varied according to province and over time. Tetracycline and sulfonamide-trimethoprim group prescribing were significantly reduced over the study period, by 36% and 61%, respectively. Prescribing of the 'other' antimicrobial group increased in all provinces from 2001 to 2010 with the exception of Prince Edward Island, although by varying amounts (10% to 61% increases). DISCUSSION: The overall use of antimicrobials in Canada has dropped from 1995 to 2010, and the tetracycline and sulfonamide-trimethoprim groups have contributed to this decline. The use of the 'other' antimicrobials has increased, however. These results may suggest that switches are being made among these groups, particularly among the antimicrobials used to treat urinary tract infections.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".