Difference in drug cost between private and public drug plans in Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: We expect a difference in drug cost between private drug plans and the Public Drug Plan (PDP) because the dispensing fee is fixed and regulated by the PDP for publicly insured patients, whereas it is determined freely by the pharmacy owner for privately insured patients. This study compared the drug cost of Quebec residents covered by private drug plans with those covered by PDP. METHODS: We used a sample of prescriptions filled between 1 January 2015 and 23 May 2019 selected from reMed, a database of Quebecers' drug claims. We created strata of prescriptions filled by privately insured patients and matched them with strata of prescriptions filled by publicly insured patients based on the Drug Identification Number, quantity dispensed, number of days of supply, pharmacy identifier, and a date corresponding to the publication of List of Medications of Régie de l'Assurance Maladie du Québec. The differences in drug cost between private plans and the PDP were analyzed with linear regression models using prescription strata as the unit of analysis. RESULTS: Based on 38 896 prescription strata, we observed that privately insured patients payed $9·35 (95% confidence interval [CI]: 5·58; 13·01) more on average per drug prescription than publicly insured patients, representing a difference of 17·6%. CONCLUSIONS: This study showed that, on average, drug cost is substantially higher for privately insured Quebecers. Knowing that adherence to treatment is affected by drug cost, these results will help public health authorities to make informed decisions about drug policies.
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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 | 0.001 | 0.004 |
| 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) | 0.004 | 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".