Prevalence of Obesity and its Associated Factors Among the 35-70-Year-Old Population of Bandare-Kong: A Cross-sectional Survey (Findings of the Persian Cohort Study)
Bibliographic record
Abstract
Background: Obesity is a major health problem in many countries such as Iran. This study aimed to evaluate the prevalence of overweight and obesity and their associated risk factors in Bandare-Kong, Hormozgan, Iran. Materials and Methods: This cross-sectional survey included 3921 participants of the Bandare-Kong Cohort Study (BKNCD). Their baseline data were used for analysis. General obesity was defined as the body mass index (BMI)≥30 kg/m2 and overweight as 25≤BMI<30. Central obesity was defined as waist circumference (WC)≥95 cm. Results: The prevalence of overweight, general, and central obesity was 39%, 24%, and 30.5%, respectively. Female gender (adjusted odds ratio [aOR]=5.11, 95% confidence interval [CI]: 3.74-6.96 and aOR=1.70, 95% CI: 1.34-2.16), hypertension (aOR=2.43, 95% CI: 1.81-3.26 and aOR=1.26, 95% CI: 1.04-1.52), and hypertriglyceridemia (aOR=1.76, 95% CI: 1.31-2.38 and aOR=1.26, 95% CI: 1.05-1.51) were significantly associated with both general and central obesity. Higher WC (aOR=503.89, 95% CI: 331.76-765.32), higher calorie intake (aOR=1.03, 95% CI: 1.02-1.04), and urban residency (aOR=2.99, 95% CI: 2.06-4.32) were correlated with general obesity. BMI≥25 kg/m² (aOR=46.81, 95% CI: 35.53-61.67), higher fasting plasma glucose (aOR=1.03, 95% CI: 1.01-1.04), older age (aOR=1.03, 95% CI: 1.02-1.04) and being unemployed (aOR=1.49, 95% CI: 1.18-1.89) were significantly associated with central obesity. Conclusion: Overall, a significant correlation was found among female gender, hypertension, and hypertriglyceridemia with general and central obesity in this study. Given the high prevalence of obesity in this population, regional public health authorities should take appropriate measures to reduce these rates in order to prevent obesity-associated complications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".