Comparison of the adherence and persistence to inhaled corticosteroids among adult patients with public and private drug insurance plans.
Bibliographic record
Abstract
BACKGROUND: Despite important differences in reimbursement procedures between private and public drug insurance plans in Quebec (Canada), no study has evaluated the impact of the type of drug insurance on the use of essential medications such as inhaled corticosteroids (ICS). The lack of data might be attributable, at least in part, to the absence of a provincial medication database for patients with private drug insurance. OBJECTIVES: To compare patient's adherence and persistence to ICS between Quebec residents (Canada) with private and public drug insurance. METHODS: A matched cohort design with patients selected from the database of the Régie de l'assurance maladie du Québec (RAMQ) and from reMed, a database that we have put in place for Quebec residents covered by a private drug insurance, was used. ICS users with private drug insurance were selected from reMed between 2008 and 2010 and matched to ICS users with public drug insurance selected from the RAMQ database. Patient's adherence, measured with the proportion of prescribed days covered (PPDC) and persistence over one year, was compared between patients privately and publicly insured using linear regression and Cox regression models. RESULTS: This study included 330 and 1,109 ICS users with private and public drug insurance, respectively. Patients privately insured were significantly less adherent than patients publicly insured (adjusted mean difference of PPDC: -9.7%; 95% CI: -13.2% to -6.5%). Moreover, patients privately insured were found to be 52% more likely to stop ICS during the first year than patients publicly insured (adjusted HR=1.5; 95% CI: 1.2 to 2.0). CONCLUSIONS: Although adherence and persistence were rather low in both groups, patients with public drug insurance appeared to have greater adherence and persistence to ICS than patients with private drug insurance. Differences in reimbursement policies might explain the observed differences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".