Estimating diet costs: Bridging the gap between food supply price databases and dietary intake data. (30th September 2020)

• Record Type: Journal Article
• Authors: Luongo, G; Tarasuk, V; Yi, Y; Mah, C L
• Is Part Of: European journal of public health. Volume 30(2020)Supplement 5
• Publication Date: 2020-09-30
• Subjects: Epidemiology -- Europe -- Periodicals; Public health -- Europe -- Periodicals; 362.109405
• DOI: 10.1093/eurpub/ckaa165.900
• Languages: English
• ISSNs: 1101-1262

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.