Impact of the COVID-19 pandemic on primary care for hypertension in the UK: a population-based cohort study
Bibliographic record
Abstract
OBJECTIVES: To describe the impact of the COVID-19 pandemic on hypertension diagnosis and management in UK primary care. DESIGN: Population-based cohort study. SETTING: Over 2000 general practices across the UK contributing to the Clinical Practice Research Datalink. PARTICIPANTS: A cohort of 23 076 390 patients over 18 years of age and registered with their general practice for at least 1 year between 2011 and 2022, who did not have a previous diagnosis of hypertension. From these patients, a subcohort of 712 461 patients diagnosed with hypertension between 2011 and 2022 was selected. PRIMARY AND SECONDARY OUTCOME MEASURES: Coprimary outcomes included rates of hypertension diagnosis and rates of antihypertensive treatment initiation, treatment change and blood pressure measurement in patients newly diagnosed with hypertension. RESULTS: In April 2020, the first month of lockdown, incident hypertension diagnosis rates fell by 65% (95% CI 64% to 67%) compared with historical trends and remained depressed until November 2021, leading to 51 000 fewer diagnoses than expected by March 2022. However, by March 2022, there were 2.6% fewer diagnoses than expected in Scotland, compared with 20%-30% fewer in other UK Nations. Rates of treatment initiation and change fell by 47% (95% CI 43% to 51%) and 36% (95% CI 33% to 38%), respectively, in April 2020. However, initiation rates rebounded above expectations and remained elevated until March 2022. Blood pressure measurements fell by 69% (95% CI 65% to 72%) in April 2020, recovering in February 2021. CONCLUSIONS: Hypertension diagnosis and management in UK primary care were significantly disrupted during the COVID-19 pandemic. Future studies should investigate the potential clinical implications for the cardiovascular health of the UK population.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".