Measuring Hypertension Progression With Transition Probabilities: Estimates From the WHO SAGE Longitudinal Study
Bibliographic record
Abstract
This paper assessed the transition probabilities between the stages of hypertension severity and the length of time an individual might spend at a particular disease state using the new American College of Cardiology/American Heart Association hypertension blood pressure guidelines. Data for this study were drawn from the Ghana WHO SAGE longitudinal study, with an analytical sample of 1884 across two waves. Using a multistate Markov model, we estimated a seven-year transition probability between normal/elevated blood pressure (systolic ≤ 129 mm Hg & diastolic <80 mm Hg), stage 1 (systolic 130-139 mm Hg & diastolic 80-89 mm Hg), and stage 2 (systolic ≥140mm Hg & diastolic≥90 mm Hg) hypertension and adjusted for the individual effects of anthropometric, lifestyle, and socio-demographic factors. At baseline, 22.5% had stage 1 hypertension and 52.2% had stage 2 hypertension. The estimated seven-year transition probability for the general population was 19.0% (95% CI: 16.4, 21.8) from normal/elevated blood pressure to stage 1 hypertension, 31.6% (95% CI: 27.6, 35.4%) from stage 1 hypertension to stage 2 hypertension, and 48.5% (45.6, 52.1%) for remaining at stage 2. Other factors such as being overweight, obese, female, aged 60+ years, urban residence, low education and high income were associated with an increased probability of remaining at stage 2 hypertension. However, consumption of recommended servings of fruits and vegetables per day was associated with a delay in the onset of stage 1 hypertension and a recovery to normal/elevated blood pressure. This is the first study to show estimated transition probabilities between the stages of hypertension severity across the lifespan in sub-Saharan Africa. The results are important for understanding progression through hypertension severity and can be used in simulating cost-effective models to evaluate policies and the burden of future healthcare.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".