High Rates of Undiagnosed and Uncontrolled Hypertension Upon a Screening Campaign in Rural Rwanda: a Cross-sectional Study
Bibliographic record
Abstract
Abstract Background: Hypertension remains the major risk factor for cardiovascular diseases (CVDs) worldwide with a prevalence and mortality in low- and middle-income countries (LMICs) among the highest. The early detection of hypertension risk factors is a crucial pillar for CVD prevention. Design and method: This cross-sectional study included 4284 subjects, mean age 46±16SD, 56.4% females and mean BMI 26.6±3.7 SD. Data were collected through a screening campaign in rural area of Kirehe District, Eastern of Rwanda, with the objective to characterize and examine the prevalence of elevated blood pressure (BP) and other CVD risk factors. An adapted tool from the World Health Organization STEPwise Approach was used for data collection. Elevated BP was defined as ≥140/90 mm/Hg and elevated blood glucose as blood glucose ≥100 mg/dl after a six-hours fast. Results: Of the sampled population, 21.2% (n=910) had an elevated BP at screening; BP was elevated among individuals not previously known to have HTN in 18.7% (n=752). Among individuals with a prior diagnosis of HTN, 62.2% (n=158 of 254) BP was uncontrolled. Age, weight, smoking, alcohol history and waist circumference were associated with BP in both univariate analyses and multivariate analysis. Conclusion: High rates of elevated BP identified through a health screening campaign in this Rwandan district were surprising given the rural characteristics of the district and relatively low population age. These data highlight the need to implement an adequate strategy for the prevention, diagnosis, and control of HTN that includes rural areas of Rwanda as part of a multicomponent strategy for CVD prevention.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".