Prescription Patterns for the Use of Antihypertensive Drugs for Primary Prevention Among Patients With Hypertension in the United Kingdom
Bibliographic record
Abstract
BACKGROUND: Several antihypertensive drugs are available for the primary prevention of cardiovascular disease (CVD). However, existing evidence on prescription patterns was primarily generated among patients at high CVD risk with short-term follow-up, and failed to capture impacts of time and patient characteristics. Our objective was therefore to describe longitudinal prescription patterns for antihypertensive drugs for the primary prevention of CVD among patients with arterial hypertension in the United Kingdom. METHODS: This population-based cohort study used data from the Clinical Practice Research Datalink, included 660,545 patients with hypertension who initiated an antihypertensive drug between 1998 and 2018. Antihypertensive treatments were measured by drug class and described overall and in subgroups, focusing on first-line therapy (first antihypertensive drug(s) recorded after a diagnosis of hypertension) and second-line therapy (antihypertensive drug(s) prescribed as part of a treatment change following first-line therapy). RESULTS: Angiotensin-converting enzyme (ACE) inhibitors (29.0%), thiazide diuretics (22.1%), and calcium-channel blockers (CCBs) (21.0%) were the most prescribed first-line therapies. ACE inhibitors have been increasingly prescribed as first-line therapy since 2001. Men were more likely to be prescribed ACE inhibitors than women (43.5% vs. 32.1%; difference: 11.4%; 95% confidence interval [CI], 11.0%-11.8%), and Black patients were more likely to be prescribed CCBs than White patients (63.6% vs. 37.0%; difference: 26.6%; 95% CI, 24.8%-28.4%). CONCLUSIONS: Antihypertensive prescription patterns for the primary prevention of CVD among patients with hypertension are consistent with treatment guidelines that were in place during the study period, providing reassurance regarding the use of evidence-based prescribing.
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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 | 0.001 |
| 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".