Dyslipidemia, obesity and other cardiovascular risk factors in the adult population in Senegal
Bibliographic record
Abstract
INTRODUCTION: According to the WHO, 50% of deaths worldwide (40.1% in developing countries) are due to chronic non-communicable diseases (NCDs). Of these chronic NCDs, cardiovascular diseases remain the leading cause of death and disability in developed countries. The Framingham study has shown the importance of hypercholesterolemia as a primary risk factor. In Senegal, the epidemiology of dyslipidemia and obesity are still poorly understood due to the lack of comprehensive studies on their impact on the general population. This motivated this study to look into the key epidemiologic and socio-demographic determinants of these risk factors. METHODS: It was a cross-sectional descriptive epidemiological survey which included 1037 individuals selected by cluster sampling. Data were collected using a questionnaire following the WHO STEPwise approach. Socio-demographic, health and biomedical variables were collected. P value <0.05 was considered to be statistically significant. RESULTS: The average age was 48 years with a female predominance (M: F of 0.6). The literacy rate was 65.2% and 44.7% of participants were from rural areas. The prevalence of hypercholesterolemia, hyperLDLemia, hypoHDLemia, hypertriglyceridemia and mixed hyperlipidemia were 56%, 22.5%, 12.4%, 7.11% and 1.9% respectively. One in four was obese (BMI> 30kg/m2) and 34.8% had abdominal obesity. The main factors significantly associated with dyslipidemia were obesity, urban dwelling, physical inactivity and a family history of dyslipidemia. CONCLUSION: The prevalence of dyslipidemia, obesity and other risk factors in the population was high needing immediate care for those affected and implementation of prevention strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".