Prevalence and predictors of overweight and obesity among school-aged children in urban Ghana
Bibliographic record
Abstract
BACKGROUND: Childhood overnutrition is a serious public health problem, with consequences that extend into adulthood. The aim of this study was to determine the prevalence and determinants of overweight and obesity among school-age children in two urban settings in Ghana. METHODS: This cross-sectional study involved 3089 children (9-15 years) recruited between December 2009 and February 2012 in Accra and Kumasi, Ghana. Socio-demographic, dietary, and physical activity data were collected using pretested questionnaires. BMI-for-age z-scores were used to categorize anthropometric data of the children as thin, normal, or overweight/obese. Determinants of overweight were examined using multiple logistic regressions. RESULTS: Seventeen percent of children were overweight or obese. Children who reported lower participation (< 3 times/week) in sports activity were 44% more likely to be overweight or obese (AOR = 1.44; 95% CI: 1.07, 1.94). Maternal tertiary education (AOR = 1.91, 95% CI: 1.07, 3.42), higher household socioeconomic status (AOR = 1.56, 95% CI: 1.18, 2.06), and attending private school (AOR = 1.74, 95% CI: 1.31, 2.32) were also associated with elevated risk of overweight and obesity. CONCLUSIONS: Physical inactivity is a modifiable independent determinant of overweight or obesity among Ghanaian school-aged children. Promoting and supporting a physically active lifestyle in this population is likely to reduce risk of childhood overnutrition.
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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 | 0.001 | 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".