Prevalence, awareness, treatment and control of hypertension and their determinants: results from a national survey in Kenya
Bibliographic record
Abstract
BACKGROUND: Hypertension is the most important risk factor for cardiovascular diseases and the leading cause of death worldwide. Despite growing evidence that the prevalence of hypertension is rising in sub-Saharan Africa, national data on hypertension that can guide programming are missing for many countries. In this study, we estimated the prevalence of hypertension, awareness, treatment, and control. We further examined the factors associated with hypertension and awareness. METHOD: We used data from the 2015 Kenya STEPs survey, a national cross-sectional household survey targeting randomly selected people aged 18-69 years. Demographic and behavioral characteristics as well as physical measurements were collected using the World Health Organization's STEPs Survey methodology. Descriptive statistics were used to estimate the prevalence, awareness, treatment and control of hypertension. Multiple logistic regression models were used to identify the determinants of hypertension and awareness. RESULTS: The study surveyed 4485 participants. The overall age-standardized prevalence for hypertension was 24.5% (95% confidence interval (CI) 22.6% to 26.6%). Among individuals with hypertension, only 15.6% (95% CI 12.4% to 18.9%) were aware of their elevated blood pressure. Among those aware only 26.9%; (95% CI 17.1% to 36.4%) were on treatment and 51.7%; (95% CI 33.5% to 69.9%) among those on treatment had achieved blood pressure control. Factors associated with hypertension were older age (p < 0.001), higher body mass index (BMI) (p < 0.001) and harmful use of alcohol (p < 0.001). Similarly, factors associated with awareness were older age (p = 0.013) and being male (p < 0.001). CONCLUSION: This study provides the first nationally-representative estimates for hypertension in Kenya. Prevalence among adults is high, with unacceptably low levels of awareness, treatment and control. The results also reveal that men are less aware of their hypertension status hence special attention should focus on this group.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".