Rural-Urban Differences in Prevalence and Associated Factors of Underweight and Overweight/Obesity among Bangladeshi Adults: Evidence from Bangladesh Demographic and Health Survey 2017–2018
Bibliographic record
Abstract
The aim of this study was to identify the differences in prevalence and associated factors of underweight and overweight/obesity among Bangladeshi adults (≥18 years) by analyzing the cross-sectional Bangladesh Demographic and Health Survey 2017-2018 data. Multilevel multivariable logistic regression was applied to identify the factors associated with underweight and overweight/obesity in urban and rural areas. The prevalence of underweight was 12.24% and 19.34% in urban and rural areas, respectively. The prevalence of overweight/obesity was 50.23% and 35.96%, respectively, in urban and rural areas. In the final multivariable analysis in both urban and rural areas, 30-49 years of age, female sex, being educated up to college or higher level, living in the wealthiest household, and being currently married or being separated/divorced/widowed had higher odds of being overweight/obese compared to other categories. Residence in the Mymensingh and Sylhet region was associated with decreased odds of overweight/obesity in urban and rural areas. On the other hand, being educated up to college or higher level, living in the wealthiest household, and being married were associated with reduced odds of being underweight in both areas. These high-risk groups should be brought under targeted health promotion programs to curb malnutrition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".