Overweight, Obesity, and Perception of Body Image Among Slum Residents in Nairobi, Kenya, 2008–2009
Bibliographic record
Abstract
INTRODUCTION: The increase in cardiovascular diseases in sub-Saharan Africa has been attributed in part to the changes in lifestyle, and the prevalence of risk factors for cardiovascular disease is higher among urban populations than among nonurban populations. The objective of this study was to determine the prevalence of overweight and obesity and examine perceptions of body size differentiated by sex and other determinants among slum dwellers in Nairobi, Kenya. METHODS: Analysis included 4,934 adults randomly selected from the Korogocho and Viwandani slums of Nairobi. Height and weight were measured during interviews; body mass index (BMI) was calculated. Perceptions of current and ideal body image were determined by using 18 silhouette drawings of body sizes ranging from very thin to very obese. We used multivariate logistic regression analysis to determine predictors of underestimation of body weight among overweight and obese respondents. RESULTS: Overall, 43.4% of women and 17.3% of men in the study population were overweight or obese. More than half (53%) of those who were overweight or obese underestimated their weight; 34.6% of women and 16.9% of men did so. In all BMI categories, more than one-third of women and men preferred body sizes classified as overweight or obese. CONCLUSION: This study highlights the prevalence of overweight and obesity and the strong preference for larger body size among adults in the slums of Nairobi. Interventions to educate residents on the health risks associated with excess body weight are necessary as a part of strategies to reduce the prevalence of risk factors for cardiovascular disease in these settlements.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".