Assessing the Magnitude and Risk Factors Associated With Undiagnosed Hypertension in Rural Rwanda
Bibliographic record
Abstract
Individuals living with hypertension are predisposed to higher risk of stroke, kidney diseases and heart failure. Approximately 9.4 million people worldwide die from complications related to hypertension every year. Hypertension is often known as the silent killer because many people do not develop any symptoms until they get very sick. Early screening is particularly important for better treatment outcomes yet it remains a challenge in many countries. Worldwide, approximately 50% of people are living with undiagnosed hypertension. In Rwanda, the rate of undiagnosed hypertension is unknown, and so are the associated risk factors in rural communities. A cross-sectional descriptive study was conducted to determine the rate and risk factors of undiagnosed hypertension among adults in a rural community in Rwanda. The proportion of people having undiagnosed hypertension was found to be high. Out of 155 study participants, 41.9% had undiagnosed hypertension, with slightly more men having hypertension (52.3%) than women (47.7%). More than 98% of respondents either did not know or knew wrong information about hypertension, and only 3% knew they should have regular checkups with physicians. Age (p=0.027) and alcohol consumption (p=0.014) were found to be statistically significantly associated with hypertension. Smoking and exercise were not found to be risk factors as most Rwandans living in the rural areas are physically active. Programs to promote hypertension awareness, encourage regular physical checkups, and reduce alcohol consumption are needed to improve diagnosis and control of hypertension in Rwanda. Community programs offering free regular blood pressure checks may also be helpful in identifying early hypertension. Larger scale studies of this kind should be conducted to understand whether results can be generalized to other areas of Rwanda.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".