Whole‐of‐society monitoring framework for sugar, salt, and fat consumption and noncommunicable diseases in India
Bibliographic record
Abstract
India has experienced a rising prevalence of cardiometabolic risk factors in the past 15 years: the prevalence of diabetes has increased from 5.9% to 9.1%, hypertension from 17.2% to 29.2%, and obesity from 4% to 15%. The increase is among all socioeconomic groups and in urban and rural populations, though the quantum of change varies. A concomitant increase in per capita consumption of sugar from 22 to 55.3 g/day and total fat from 21.2 to 54 g/day was observed, with significant differences between states of high and low human development index (HDI). Per capita consumption of sugar, salt, and fat is consistently and significantly associated with overweight and obesity but variably associated with the occurrence of hypertension and diabetes. Market research shows that approximately 50-60% of total salt, sugar, and fat in Indian markets is procured by bulk purchasers, generally for manufacturing processed food items. This sector of the Indian economy is among the fastest growing, with several policy incentives. It is not clear from most of the data sets whether available information on per capita sugar, salt, and fat consumption has considered the contribution of processed and ready-to-eat food items. The unprecedented changes of rapid urbanization, mechanization, and globalization demand close monitoring of social, developmental, and economic determinants. This paper provides pieces of evidence to justify a whole-of-society (WoS) framework for monitoring the inputs, processes, and behavioral components of the National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Disease and Stroke (NPCDCS) in India.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".