Differences in Antihypertensive Medication Prescription Profiles Between 2009 and 2021: A Retrospective Cohort Study of CARTaGENE
Bibliographic record
Abstract
Background: Although blood pressure (BP) control is critical to prevent cardiovascular diseases, hypertension control rates in Canada are in decline. Objective: To assess this issue, we sought to evaluate the differences in antihypertensive medication prescription profiles in the province of Quebec between 2009 and 2021. Design: This is a retrospective cohort study. Setting: We used data from the CARTaGENE population–based cohort linked to administrative health databases. Patients: Participants with any drug claim in the 6 months prior to the end of follow-up were included. Measurements: Guideline-recommended antihypertensive drug prescription profiles were assessed at the time of enrollment (2009-2010) and end of follow-up (March 2021). Methods: Prescriptions practices from the 2 time periods were compared using Pearson’s chi-square tests. A sensitivity analysis was performed by excluding participants in which antihypertensive drugs may not have been prescribed solely to treat hypertension (presence of atrial fibrillation/flutter, ischemic heart disease, heart failure, chronic kidney disease, or migraines documented prior to or during follow-up). Results: Of 8447 participants included in the study, 31.4% and 51.3% filled prescriptions for antihypertensive drugs at the beginning and end of follow-up. In both study periods, guideline-recommended monotherapy was applied in most participants with hypertension (77.9% vs 79.5%, P = .3), whereas optimal 2 and 3-drug combinations were used less frequently (62.0% vs 61.4%, P = .77, 51.9% vs 46.7%, P = .066, respectively). Only the use of long-acting thiazide-like diuretics (9.5% vs 27.7%, P < .001) and spironolactone as a fourth-line agent (8.3% vs 15.9%, P = .054) increased with time but nonetheless remained infrequent. Results were similar in the sensitivity analysis. Limitations: Specific indication of the prescribed antihypertensive medications and follow-up BP data was not available. Conclusions: Application of hypertension guidelines for the choice of antihypertensive drugs remains suboptimal, highlighting the need for education initiatives. This may be an important step to raise BP control rates in Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".