Estimating diet costs: Bridging the gap between food supply price databases and dietary intake data
Bibliographic record
Abstract
Abstract Introduction The cost of food is a key influence on diet. The majority of diet cost studies match intake data from population-based surveys to a single source of food supply prices such as the Consumer Price Index (CPI). Our aim was to examine the nutritional significance of using food supply data to price dietary intakes in Canada. Methods We examined food groups and nutrients in dietary intakes captured by the CPI. For prices, we used 2015 Canadian CPI average monthly item prices. For dietary intakes, we used reported intakes from the 2015 Canadian Community Health Survey (CCHS)-Nutrition, 1st 24-hour recall (n = 20,487). i) 2015 CPI item prices ($/g) were matched to the 156 food items from the 2015 CCHS-Nutrition as full, partial, or non-match; ii) CPI capture (full or partial match) per total intake (g), without water, was calculated for each respondent; iii) descriptive statistics and quantile regression (α = 0.05) were used to compare intakes of Canadian Nutrient File food groups and nutrients by quantile of CPI capture. Results The CPI captured on average 74% of total dietary intake (g) without water. A greater proportion of protein and fat intake was captured by the CPI as compared to carbohydrate, sodium, fibre, and sugar intake. Intakes of beef, poultry, sausages, pork, and breakfast foods had among the best match; snack foods, nuts, veal, and alcoholic beverages had among the worst. Individuals in the poorest CPI capture quantile consumed the greatest fibre (g), carbohydrates (g), total sugar (g), fat (g), protein (g), and energy (kcal) as compared to those with best CPI capture. Conclusions The poorest quantile of CPI capture reflects individuals with high intakes of nutrients of concern including fat, carbohydrates, and sugar; potential bias in estimating fibre and protein intake was also detected. Researchers and decision makers should attend to differential misclassification bias and opportunities for tailored datasets to price dietary intakes. Key messages Given the proliferation of diet cost studies using food supply prices, this novel study highlights the importance of understanding the biases in using food supply data to price dietary intakes. Nutrition researchers and decision makers can use these findings to strengthen food supply price data to support the monitoring of diet costs in relation to diet quality and health outcomes.
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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 | 0.018 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".