Prevalence and Trend of Overweight and Obesity among Schoolchildren in Ahvaz, Southwest of Iran
Bibliographic record
Abstract
INTRODUCTION: Obesity is an important risk factor for some chronic diseases. Since the effect of obesity is long-standing, monitoring childhood obesity should be the first step in the health policy for interventions regarding early prevention of chronic diseases. In this study we aim to determine the prevalence of overweight and obesity among school children in the city of Ahvaz. METHODS: A cross-sectional survey was designed. A sample of 5811 children, 2904 (49.97%) boys and 2907 (50.03%) girls, was selected and their heights and weights were measured in 2012-2013 academic year. Measurements of height and weight were made by using calibrated equipment and according to standardized protocol with the children having light clothes and without wearing shoes. The adjusted odds ratio of obesity and overweight for age and sex were calculated from multiple logistic regression model. RESULTS: A total 685 (23.6%) of boys and 561 (19.3%) of girls were overweight. and 190(6.05%) of boys and 130 (4.5%) of girls were obese. The proportion of overweight and obese boys was significantly higher than that of girls (p<0.001). Logistic regression showed significant increase in the likelihood of being overweight with the increasing age OR=1.50, C.I.95%: (1.43, 1.57). CONCLUSION: The prevalence of overweight and obesity increased markedly with age. This shows the importance of early prevention by doing interventions and training since the first year of primary school.
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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) | 0.001 | 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".