Using food composition tables to estimate decreases in sodium intake due to the reformulation of packaged and ultra-processed foods in a young population in South Africa
Bibliographic record
Abstract
Abstract Objective: In response to increasing hypertension rates, South Africa implemented a regulation which set a maximum total Na content for certain packaged food categories. We assess changes in reported Na intake among 18–39 years old adults living in one township in the Western Cape as a result of the implementation of the regulation in 2016. Design: By linking one set of 24-h dietary recall data to two versions of the South Africa Food Composition Database which reflect the pre-regulation and post-regulation periods, we calculated changes in Na intake due to reformulation of food products, not behaviour change. We statistically tested differences in mean consumption in this sample with paired t tests. Setting: Langa, Western Cape, South Africa Participants: Surveyed participants were residents of Langa between 18 and 39 years old ( n 2148) Results: Before and after the implementation of the regulation, there was a statistically significant decrease in the estimated Na intake among adults of 189·4 mg (137·5, 241·4; P = 0·00). Reported Na from cured meat (such as Russians) and certain types of soup powder, cereals and salted peanuts had a 9 to 33 per cent lower calculated Na consumption. Conclusions: Our conclusions show that independent of any behavioural changes on the part of consumers, it is possible to lower Na intake by using regulations to induce food manufacturers to lower the Na levels in their products. As countries explore similar regulatory strategies, this work can add to that body of evidence to inform policies to improve the food system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".