Identifying foods with good nutritional quality and price for the Opticourses intervention research project

The revised Healthy Purchase Index (r-HPI): a validated tool for exploring the nutritional quality of household food purchases

BACKGROUND

The Healthy Purchase Index (HPI) assesses the nutritional quality of food purchases (FP) from food group expenditure shares only. However, it was developed from the FP of a disadvantaged population.

OBJECTIVE

To adapt and validate the HPI for a general population.

METHODS

FP were obtained from a representative sample of French households (Kantar WorldPanel) subdivided into two subsamples. The first sample (n = 4375) was used to adapt and validate the score; the second sample (n = 2188) was used to test external validity. The revised-HPI (r-HPI) includes 2 subscores: the diversity subscore and the quality subscore. Diversity subscore points were awarded when expenditure shares were above the 25th percentile for 5 food groups ("Fruits", "Vegetables", "Starches", "Dairy", "Meat, Fish and Eggs"). Regression models between the expenditure shares of each food group and the Mean Adequacy Ratio (MAR) and the Mean Excess Ratio (MER) of FP were used to select quality subscore components and define cut-offs for point allocation. Construct validity was assessed on the first sample using Spearman's correlations between the r-HPI and the four nutritional quality indicators (NRF9.3, MAR, MER, energy density), and also by comparing the r-HPI of monthly FP of sub-populations defined by criteria known to influence diet quality (age, gender, income, education) and between households having a monthly food basket of higher (MAR > median and MER and energy density < median) vs. lower nutritional quality within the population, using Wilcoxon tests or pairwise comparisons of contrasts. External validity was tested by performing the same analyses on the 2nd sample of 2188 households.

RESULTS

The adaptation led to include new components (e.g. red meat) and define new cut-offs (e.g. - 1 point when budget share for red meat > 21%). The r-HPI (mean = 6.50 ± 3.58) was strongly correlated with NRF9.3, MAR, MER and energy density (0.59, 0.52, - 0.41 and - 0.65, respectively, p < 0.01) and poorly correlated with total energy content (- 0.096, p < 0.001). The r-HPI was significantly higher in women (β = 1.41 [0.20], p < 0.01), households having a food basket of higher nutritional quality (β = 4.15 [0.11], p < 0.001), and increased significantly with age, income and education levels. Similar results were obtained in the 2nd sample.

CONCLUSION

We showed the validity of the r-HPI in a large sample of French households. As it does not require food quantity or nutrient content, it can be used as a valuable tool to explore FP behaviours. Cut-offs can be used in health promotion to provide nutri-economic counselling.

Evaluating the Nutritional Properties of Food: A Scoping Review

There are many methods or indicators used for evaluating the nutritional value of foods; however, it is difficult to accurately reflect the comprehensive nutritional value of a food with a single indicator, and a systematic evaluation system is lacking. In this article, we systematically summarize the common evaluation methods and indicators of the nutritional value of foods. The purpose of this review was to establish an evaluation procedure for nutritional properties of foodstuffs and to help scientists choose more direct and economical evaluation methods according to food types or relevant indicators. The procedure involves the selection of a three-level evaluation method that covers the whole spectrum of a food’s nutritional characteristics. It is applicable to scientific research in the fields of agricultural science, food science, nutrition, and so on.

Nutrient density and affordability of foods in Brazil by food group and degree of processing

OBJECTIVE

Affordable nutrition refers to the relation between nutrient density of foods and their monetary cost. There are limited data on affordable nutrition in low- and middle-income countries. The present study aimed to develop a nutrient density score and nutrient affordability metrics for 377 most consumed foods in Brazil.

DESIGN

The foods were aggregated into seven major food groups and four NOVA food categories. Nutrient composition data were obtained from the Brazilian Institute of Geography and Statistics. Food prices were obtained from retailer websites and were converted to prices per 100 g and 418 kJ. The Nutrient Rich Food (NRF8.2) score was based on protein, fiber, vitamins A, C and E, Ca, Fe and K. Nutrients to limit were sugar and Na. Affordability was measured as kcal/R$ and nutrients/R$.

RESULTS

Grains, fats and sweets were more energy dense and had lower NRF8.2 scores than dairy, vegetables and fruits. Grains, fats and sweets were the lowest cost sources of energy. Vegetables and fruits, beans, nuts and seeds and eggs and dairy were the lowest cost sources of multiple nutrients. Ultra-processed foods (48 % of total) had higher energy density and lower NRF8.2 scores than did unprocessed foods. In Brazil, fruits, vegetables and dairy products offered the most nutrients per real.

CONCLUSIONS

Analysis of the relationship between nutrient density of foods and their cost can help identify locally available foods that are nutrient rich, affordable and culturally acceptable. Achieving high nutrient density at an affordable cost should be the goal of Brazil's food systems.

Nutrient profile and energy cost of food sold by informal food vendors to learners in primary and secondary schools in the Eastern Cape, South Africa

OBJECTIVE

To assess type, nutrient profile and cost of food items sold by informal vendors to learners; and to determine nutrient content of corn-based processed snacks frequently sold.

DESIGN

Cross-sectional survey.

SETTING

Quintile 1 to 3 schools (n 36) randomly selected from six education districts; Eastern Cape, South Africa.ParticipantsInformal food vendors (n 92) selling inside or immediately outside the school premises.

RESULTS

Food items sold at most schools were corn-based processed snacks (94 % of schools), sweets (89 %), lollipops (72 %) and biscuits (62 %). Based on the South African Nutrient Profiling model, none of these foods were profiled as healthy. Foods less commonly sold were fruits (28 % of schools) and animal-source foods; these foods were profiled as healthy. Mean (sd) energy cost (per 418 kJ (100 kcal)) was highest for animal-source foods (R2·95 (1·16)) and lowest for bread and vetkoek (R0·76 (0·21)), snacks (R0·76 (0·30)) and confectionery products (R0·70 (0·28)). The nutrient profiling score was inversely related to the energy cost of the food item (r = -0·562, P = 0·010). Compared with brand-name corn-based processed snacks, non-branded snacks had lower energy (2177 v. 2061 kJ; P = 0·031) content per 100 g. None of the brand-name samples contained sucrose; six of the nine non-branded samples contained sucrose, ranging from 4·4 to 6·2 g/100 g.

CONCLUSIONS

Foods mostly sold were unhealthy options, with the healthier food items being more expensive sources of energy.