A Call to Action: Insights into Hypertension Prevalence and Management in an Urban Sub-Saharan African Population
Bibliographic record
Abstract
Background: Hypertension is a global health challenge, with a disproportionate burden in low- and middle-income countries like Cameroon. Urban centers, such as Yaoundé, face increasing prevalence driven by rapid urbanization and healthcare disparities. The study objective is to assess hypertension prevalence, associated risk factors, and barriers to effective management in an urban Cameroonian population. Methodology: A cross-sectional study was conducted in 2024 as part of the HEAVEN-CIEL Hypertension Awareness Campaign. Data from 181 adults were collected through structured questionnaires and physical assessments, including blood pressure measurements. Associations with demographic, socioeconomic, and lifestyle factors were analyzed using chi-square tests and logistic regression. Results: Hypertension prevalence was 59.67%, with rates peaking at 75.56% among individuals aged 46-60 years (p = 0.0019). Medium-income participants showed the highest prevalence at 75.56% (p = 0.043). Obesity (p = 0.0024) and high-fat diets (p = 0.0212) were significant risk factors. Alarmingly, 87.04% of hypertensive individuals were untreated, highlighting gaps in medication access and adherence. Conclusion: Hypertension in Yaoundé represents a major public health concern, driven by age, socioeconomic disparities, and unhealthy lifestyle factors. The low treatment rates reflect systemic healthcare barriers. Comprehensive, culturally sensitive interventions are urgently needed to improve awareness, prevention, and management. Future research should focus on the longitudinal impacts of targeted public health strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".