Potential Impact of the 2017 High Blood Pressure Guideline Beyond the United States: A Case Study of the People’s Republic of China
Bibliographic record
Abstract
BACKGROUND: The 2017 American College of Cardiology (ACC)/American Heart Association (AHA) (US) Guideline for the Prevention, Detection, Evaluation and Management of High Blood Pressure in Adults expanded the definition of hypertension and now considers atherosclerotic cardiovascular disease (ASCVD) risk in determining treatment for people with hypertension. US guidelines are influential around the world and it is therefore justified to study their impact in other settings. Our study determined the impact of adopting the 2017 ACC/AHA guideline in China. METHODS: We analyzed the population impact of the 2017 ACC/AHA guideline using the 2011-2012 year of the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative sample of Chinese adults 45-74 years of age (n = 11,822). Our analysis was unique because for the first time it used a population-appropriate equation to calculate ASCVD risk instead of the US Pooled Cohort Equation (the latter misrepresents risk in non-US populations). RESULTS: Adopting the 2017 ACC/AHA guideline in China would increase the prevalence of hypertension from 44.1% to 56.4% (12.3 percentage points) and increase the number of adults recommended for antihypertensive medication from 41.6% to 49.1% (7.5 percentage points) in the 45-74-year age range. According to Chinese (but not US) risk calculations, the 2017 ACC/AHA guideline more selectively assigns antihypertensive medication to patients at higher risk for ASCVD. CONCLUSIONS: The 2017 ACC/AHA guideline brings potential for risk reduction in China and selectively recommends medication for those who would benefit most. Realizing such benefits would ultimately depend on the acceptance, adherence, and feasibility of adopting this guideline.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".