Cost-benefit analysis of a population-based education program on the wise use of antibiotics
Bibliographic record
Abstract
OBJECTIVE: In 2005, the Do Bugs Need Drugs (DBND) program was imported to British Columbia (BC) from Alberta with the goal of reducing unnecessary antibiotic use in the community. The objective of this study was to estimate the impact of the program on antibiotic-associated costs and cost-benefit. METHODS: We used data on antibiotic prescription and costs from BC PharmaNet for the period of 1996 to 2014. We conducted interrupted time series regression to formally interpret the impact of the DBND program. RESULTS: The average monthly prescription rate fell by 14.5%, from 54.3 to 46.4 per 1000 population between 2005 and 2014. The proportionate contribution of macrolide prescription decreased from 19.2% in 2005 to 13.2% in 2014 and for quinolones decreased from 13.1% in 2005 to 12% in 2014. The proportion of prescriptions for both penicillins and tetracyclines increased by > 35.5%. Before the program, the average monthly cost of antibiotics was increasing by CAD $8.12 per 1000 population (p < 0.001). After program introduction, average monthly cost decreased by CAD $18.19 per 1000 population (p < 0.001), creating an annual savings for BC in 2014 of CAD $83.6 million. In 2014, one Canadian dollar spent on the DBND program was associated with conservative savings of CAD $76.20. CONCLUSION: Significant cost savings have been observed in association with a community antimicrobial stewardship program focused on both public and prescribers. Such programs are an effective strategy in cost-benefit terms and should therefore be considered for universal adoption in Canadian healthcare systems.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".