The need for multisectoral food chain approaches to reduce trans fat consumption in India
Bibliographic record
Abstract
BACKGROUND: The World Health Organization (WHO) recommends virtually eliminating trans fat from the global food supply. Although several high-income countries have successfully reduced trans fat levels in foods, low- and middle-income countries such as India face additional challenges to its removal from the food supply. This study provides a systems analysis of the Indian food chain to assess intervention options for reducing trans fat intake in low-income consumers. METHODS: Data were collected at the manufacturer, retailer and consumer levels. Qualitative interviews were conducted with vanaspati manufacturers (n = 13) and local food vendors (n = 44). Laboratory analyses (n = 39) of street foods/snacks sold by the vendors were also conducted. Trans fat and snack intakes were also examined in low-income consumers in two rural villages (n = 260) and an urban slum (n = 261). RESULTS: Manufacturers of vanaspati described reducing trans fat levels as feasible but identified challenges in using healthier oils. The fat content of sampled oils from street vendors contained high levels of saturated fat (24.7-69.3 % of total fat) and trans fat (0.1-29.9 % of total fat). Households were consuming snacks high in trans fat as part of daily diets (31 % village and 84.3 % of slum households) and 4 % of rural and 13 % of urban households exceeded WHO recommendations for trans fat intakes. CONCLUSIONS: A multisectoral food chain approach to reducing trans fat is needed in India and likely in other low- and middle-income countries worldwide. This will require investment in development of competitively priced bakery shortenings and economic incentives for manufacturing foods using healthier oils. Increased production of healthier oils will also be required alongside these investments, which will become increasingly important as more and more countries begin investing in palm oil production.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".