Are Foods of Higher Nutritional Quality More Expensive Than Their Less Healthy Counterparts? An Analysis of Canadian Packaged Foods
Bibliographic record
Abstract
Price‐related differences in the nutritional quality of food products may create unintended disparities in healthy food availability between different socioeconomic segments of the population. This study assessed whether healthier products are more expensive than less healthy products within specific food categories in the Canadian food supply. Data on nutritional composition and price were obtained from the University of Toronto Food Label Information Program (FLIP) 2013 database, which contains information on n=15,401 packaged products from the four largest national retailers by sales. Ready‐to‐eat breakfast cereals (n=250) and yogurts (n=236) were included in the present analyses. The Food Standards Australia New Zealand Nutrient Profiling Scoring Criterion (FSANZ‐NPSC) was used to calculate a summary score of the healthfulness of each product based on its content of nutrients to limit (e.g. sodium) and nutrients to encourage (e.g. fibre). Additionally, food products were classified either as “healthier” or “less healthy” based on the established cutoff score in the FSANZ‐NPSC model. The Student's t‐test was used to compare different price measures (price per 100 g, per serving, and per 100 Kcal) between “healthier” and “less healthy” breakfast cereals and yogurts. “Healthier” breakfast cereals had a higher price per serving than breakfast cereals of lower nutritional quality (geometric mean [95% confidence interval] = CAD $0.53 [0.49–0.57] vs. 0.44 [0.41–0.47], respectively, P =0.0004). Price per 100 g and price per 100 Kcal did not differ according to the degree of healthfulness of breakfast cereals (both P ≥0.60). “Healthier” yogurts had a lower price per serving and price per 100 g than yogurts of lower nutritional quality (per serving: CAD $0.82 [0.77–0.87] vs. 1.13 [0.91–1.40], respectively, P =0.006; per 100g: CAD $0.67 [0.64–0.70] vs. 0.81 [0.72–0.92], P =0.03), but a higher price per 100 Kcal (CAD $0.93 [0.88–0.98] vs. 0.61 [0.57–0.65], P <0.0001). These data suggest that healthier food products are not necessarily more expensive than less healthy products. Importantly, the relationship between the healthfulness of foods and their price varies depending on the type of food products evaluated and the selected price measures. These factors must be taken into consideration when communicating research results to a general audience. Support or Funding Information Canadian Institutes of Health Research (CIHR) Fellowship (MEL); CIHR Open Operating Grant (ML); and Burroughs Wellcome Fund – Innovation in Regulatory Science (ML)
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".