Effect of formulary policy decisions on antimicrobial drug utilization in British Columbia
Bibliographic record
Abstract
BACKGROUND: Formularies are used routinely for management of drug expenditures yet evaluations of their impact remain rare. The objective of this study was to analyse the impact of addition or deletion of antimicrobials from the provincial formulary on drug utilization. METHODS: We obtained data from the British Columbia PharmaNet database on all outpatient oral antimicrobial prescriptions from 1996 to 2000 and converted them to their defined daily dose (DDD) equivalents according to the ATC system. Trends in utilization associated with a changing formulary status of new antimicrobial agents were analysed. Maximum likelihood estimation was used to determine the rate of increase in utilization resulting from addition to the formulary. Models were adjusted for seasonal and temporal trends as well as serial correlation. RESULTS: During this time period, clarithromycin was on formulary, later delisted, and then relisted again. Valaciclovir and famciclovir were also added to the formulary. During the time clarithromycin was off the formulary, the rate of change in its monthly consumption was 0.0061 DDD/1000 population/day; following its relisting, the rate of change increased by 818% to 0.0560 DDD/1000 population/day (P=0.002). After the listing of valaciclovir on the formulary, the rate of change in its monthly consumption increased 57% from a baseline of 0.0014 to 0.0022 DDD/1000 population/day (P=0.07). A similar effect was seen with the addition of famciclovir to the formulary whereby the rate of change in monthly consumption increased from 0.0008 (before addition to the formulary) to 0.0018 (after addition to the formulary) (P </= 0.001). CONCLUSIONS: Listing of antimicrobials on provincial or countrywide formularies is followed temporally with increased utilization. However, before governmental agencies can institute reference-based pricing or co-payment programmes, the effect of such a programme on the emergence of antimicrobial resistance and on patient outcomes needs further study.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".