Treatment and prescribing trends of antihypertensive drugs in 2.7 million UK primary care patients over 31 years: a population-based cohort study
Bibliographic record
Abstract
OBJECTIVES: To describe the prescribing trends of antihypertensive drugs in primary care patients and assess the trajectory of antihypertensive drug prescriptions, from first-line to third-line, in patients with hypertension according to changes to the United Kingdom (UK) hypertension management guidelines. DESIGN: Population-based cohort study. SETTING AND PARTICIPANTS: We used the UK Clinical Practice Research Datalink, an electronic primary care database representative of the UK population. Between 1988 and 2018, we identified all adult patients with at least one prescription for a thiazide diuretic, angiotensin-converting enzyme (ACE) inhibitor, angiotensin receptor blocker, beta-blocker or calcium channel blocker (CCB). PRIMARY AND SECONDARY OUTCOME MEASURES: We estimated the period prevalence of patients with antihypertensive drug prescriptions for each calendar year over a 31-year period. Treatment trajectory was assessed by identifying patients with hypertension newly initiating an antihypertensive drug, and treatment changes were defined by a switch or add-on of a new class. This cohort was stratified before and after 2007, the year following important changes to UK hypertension management guidelines. RESULTS: The cohort included 2 709 241 patients. The prevalence of primary care patients with antihypertensive drug prescriptions increased from 7.8% (1988) to 21.9% (2018) and was observed for all major classes except thiazide diuretics. Patients with hypertension initiated thiazide diuretics (36.8%) and beta-blockers (23.6%) as first-line drugs before 2007, and ACE inhibitors (39.9%) and CCBs (31.8%) after 2007. After 2007, 17.3% were not prescribed guideline-recommended first-line agents. Overall, patients were prescribed a median of 2 classes (IQR 1-2) after first-line treatment. CONCLUSION: Nearly one-quarter of primary care patients were prescribed antihypertensive drugs by the end of the study period. Most patients with hypertension initiated guideline-recommended first-line agents. Not all patients, particularly females, were prescribed recommended agents however, potentially leading to suboptimal cardiovascular outcomes. Future research should aim to better understand the implication of this finding.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".