Patterns of change in the association between socioeconomic status and body mass index distribution in India, 1999–2021
Bibliographic record
Abstract
Background: Body mass index (BMI) is an important indicator of human health. However, trends in socioeconomic inequalities in BMI over time throughout India are understudied. Filling this gap will elucidate which socioeconomic groups are still at risk for adverse BMI values. Methods: ). We examined the prevalence, standardised absolute change, and odds ratios estimated by multivariable regression models by household wealth and levels of education, two important measures of socioeconomic status (SES). Results: The study population consisted of 1 244 149 women and 227 585 men. We found that those in the lowest SES categories were more likely to be severely/moderately thin or mildly thin. Conversely, those in the highest SES groups were more likely to be overweight or obese. The gradients were steepest for wealth, and this was substantiated by the results of regression models for every wave. There has been a decline in the difference in the prevalence of severely/moderately thin or mildly thin between SES groups when comparing the years 1999 and 2021. Conclusions: SES-based inequalities in BMI were smaller in 2021 compared to 1999. However, those in low SES groups were most likely to be severely/moderately thin or mildly thin while those in high SES groups were more likely to be overweight or obese. Future research should explore the pathways that link SES with BMI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".