Antihypertensive Prescribing for Uncomplicated, Incident Hypertension: Opportunities for Cost Savings
Bibliographic record
Abstract
BACKGROUND: A range of first-line similarly effective medications ranging in price are recommended for treating uncomplicated hypertension. Considering drug costs alone, thiazides and thiazide-like diuretics are the most cost-efficient option. We determined incident prescribing of thiazides for newly diagnosed hypertension as first-line treatment in Alberta, factors that predicted receiving thiazides vs more costly medications, and how much could be saved if more patients were prescribed thiazides. METHODS: Using a retrospective cohort design, factors predicting receiving thiazides vs other agents were determined using mixed effects logistic regression. Cost savings were simulated by shifting patients from other antihypertensive medications to thiazides and calculating the difference. RESULTS: Within our cohort of 89,548 adults, only 12% received thiazides as first-line treatment whereas 44% received angiotensin converting enzyme inhibitors, 17% received angiotensin receptor blockers, 16% received calcium channel blockers, and 10% received β-blockers. Antihypertensive medications were typically prescribed by office-based, general practitioners (88%). Being male and receiving a prescription from a physician with ≥ 20 years of practice and a high clinical workload were associated with increased odds of receiving nonthiazides. In the extreme case that all patients received thiazides as their first prescription, spending would have been reduced by a maximum of 95% (CAD$1.8 million). CONCLUSIONS: Only 12% of Albertan adults with incident, uncomplicated hypertension were prescribed thiazides as first-line treatment. With the opportunity for drug cost savings, future research should evaluate the risk of adverse events and side effects across the drug classes and whether the costs associated with managing those risks could offset the savings achieved through increased thiazide use.
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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) | 0.001 | 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 | 0.001 |
| 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".