A comparison of the healthiness of packaged foods and beverages from 12 countries using the Health Star Rating nutrient profiling system, 2013–2018
Bibliographic record
Abstract
We compared the healthiness of packaged foods and beverages between selected countries using the Health Star Rating (HSR) nutrient profiling system. Packaged food and beverage data collected 2013-2018 were obtained for Australia, Canada, Chile, China, India, Hong Kong, Mexico, New Zealand, Slovenia, South Africa, the UK, and USA. Each product was assigned to a food or beverage category and mean HSR was calculated overall by category and by country. Median energy density (kJ/100 g), saturated fat (g/100 g), total sugars (g/100 g) and sodium (mg/100 g) contents were calculated. Countries were ranked by mean HSR and median nutrient levels. Mean HSR for all products (n = 394,815) was 2.73 (SD 1.38) out of 5.0 (healthiest profile). The UK, USA, Australia and Canada ranked highest for overall nutrient profile (HSR 2.74-2.83) and India, Hong Kong, China and Chile ranked lowest (HSR 2.27-2.44). Countries with higher overall HSR generally ranked better with respect to nutrient levels. India ranked consistently in the least healthy third for all measures. There is considerable variability in the healthiness of packaged foods and beverages in different countries. The finding that packaged foods and beverages are less healthy in middle-income countries such as China and India suggests that nutrient profiling is an important tool to enable policymakers and industry actors to reformulate products available in the marketplace to reduce the risk of obesity and NCDs among populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".