Obesity and cardio-metabolic risk factors in urban adults of Benin: Relationship with socio-economic status, urbanisation, and lifestyle patterns
Bibliographic record
Abstract
BACKGROUND: There is a dearth of information on diet-related chronic diseases in West Africa. This cross-sectional study assessed the rate of obesity and other cardiovascular disease (CVD) risk factors in a random sample of 200 urban adults in Benin and explored the associations between these factors and socio-economic status (SES), urbanisation as well as lifestyle patterns. METHODS: Anthropometric parameters (height, weight and waist circumference), blood pressure, fasting plasma glucose, and serum lipids (HDL-cholesterol and triglycerides) were measured. WHO cut-offs were used to define CVD risk factors. Food intake and physical activity were assessed with three non-consecutive 24-hour recalls. Information on tobacco use and alcohol consumption was collected using a questionnaire. An overall lifestyle score (OLS) was created based on diet quality, alcohol consumption, smoking, and physical activity. A SES score was computed based on education, main occupation and household amenities (as proxy for income). RESULTS: The most prevalent CVD risk factors were overall obesity (18%), abdominal obesity (32%), hypertension (23%), and low HDL-cholesterol (13%). Diabetes and hypertriglyceridemia were uncommon. The prevalence of overall obesity was roughly four times higher in women than in men (28 vs. 8%). After controlling for age and sex, the odds of obesity increased significantly with SES, while a longer exposure to the urban environment was associated with higher odds of hypertension. Of the single lifestyle factors examined, physical activity was the most strongly associated with several CVD risk factors. Logistic regression analyses revealed that the likelihood of obesity and hypertension decreased significantly as the OLS improved, while controlling for potential confounding factors. CONCLUSION: Our data show that obesity and cardio-metabolic risk factors are highly prevalent among urban adults in Benin, which calls for urgent measures to avert the rise of diet-related chronic diseases. People with higher SES and those with a longer exposure to the urban environment are priority target groups for interventions focusing on environmental risk factors that are amenable to change in this population. Lifestyle interventions would appear appropriate, with particular emphasis on physical activity.
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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 | 0.001 |
| 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) | 0.002 | 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".