Impact of administrative restrictions on antibiotic use and expenditure in Ontario: time series analysis
Bibliographic record
Abstract
OBJECTIVE: In a potential attempt to guide antibiotic prescribing based on current clinical evidence and mitigate the spread of antibiotic resistance, in March 2001 the Ontario Drug Benefit programme restricted reimbursement of two fluoroquinolone antibiotics--ciprofloxacin and ofloxacin--to its beneficiaries. Our objective was to determine the impact of this policy on the volume and cost of antibiotic prescribing. METHOD: Weekly administrative data on antibiotic prescribing volumes and expenditures were analysed between January 1999 and September 2002 to estimate the effect of the policy changes using time series analysis. RESULTS: The policy changes were associated with a statistically significant shift downwards for the fluoroquinolones as a category (1905 fewer prescriptions each week, representing a saving of Can$105,707 a week), driven by a decrease in prescriptions for ciprofloxacin (2084 fewer prescriptions a week, saving Can$129,421 a week). Nitrofurantoin (200 more prescriptions a week, costing an extra Can$2082 a week) and trimethoprim-sulphamethoxazole (532 more prescriptions a week, costing an extra Can$1473 a week) demonstrated a statistically significant shift upwards. The latter also showed a decrease in trend and nitrofurantoin an increase in trend during the time period. There was no statistically significant change in either the total number of antibiotic prescriptions or expenditures associated with the policy of limiting their use. CONCLUSIONS: Although no direct cause and effect can be shown with these observational data, the results suggest that the change in reimbursement policy to restrict prescribing of fluoroquinolones decreased their use and associated expenditures. These decreases were offset by increases in the use of other antibiotics. The balance of consequent benefit and harm of these shifts in prescribing patterns needs to be examined carefully. Alternative solutions to encourage appropriate use of antibiotics deserve exploration.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".