Do we eat what we buy? Relative validity of grocery purchase data as an indicator of food consumption in the LoCard study

Cohort profile: The Health, Food, Purchases and Lifestyle (SMIL) cohort - a Danish open cohort

PURPOSE

The Health, Food, Purchases and Lifestyle (SMIL) cohort is a prospective open Danish cohort that collects electronic consumer purchase data, which can be linked to Danish nationwide administrative health and social registries. This paper provides an overview of the cohort's baseline characteristics and marginal differences in the monetary percentage spent on food groups by sex, age and hour of the day.

PARTICIPANTS

As of 31 December 2022, the cohort included 11 214 users of a smartphone-based receipt collection application who consented to share their unique identification number for linkage to registries in Denmark. In 2022, the composition of the cohort was as follows: 62% were men while 24% were aged 45-55. The cohort had a median of 63 (IQR 26-116) unique shopping trips. The cohort included participants with a range of health statuses. Notably, 21% of participants had a history of cardiovascular disease and 8% had diabetes before donating receipts.

FINDINGS TO DATE

The feasibility of translating consumer purchase data to operationalisable food groups and merging with registers has been demonstrated. We further demonstrated differences in marginal distributions which revealed disparities in the amount of money spent on various food groups by sex and age, as well as systematic variations by the hour of the day. For example, men under 30 spent 8.2% of their total reported expenditure on sugary drinks, while women under 30 spent 6.5%, men over 30 spent 4.3% and women over 30 spent 3.9%.

FUTURE PLANS

The SMIL cohort is characterised by its dynamic, continuously updated database, offering an opportunity to explore the relationship between diet and disease without the limitations of self-reported data. Currently encompassing data from 2018 to 2022, data collection is set to continue. We expect data collection to continue for many years and we are taking several initiatives to increase the cohort.

Tensorial Principal Component Analysis in Detecting Temporal Trajectories of Purchase Patterns in Loyalty Card Data: Retrospective Cohort Study

BACKGROUND

Loyalty card data automatically collected by retailers provide an excellent source for evaluating health-related purchase behavior of customers. The data comprise information on every grocery purchase, including expenditures on product groups and the time of purchase for each customer. Such data where customers have an expenditure value for every product group for each time can be formulated as 3D tensorial data.

OBJECTIVE

This study aimed to use the modern tensorial principal component analysis (PCA) method to uncover the characteristics of health-related purchase patterns from loyalty card data. Another aim was to identify card holders with distinct purchase patterns. We also considered the interpretation, advantages, and challenges of tensorial PCA compared with standard PCA.

METHODS

Loyalty card program members from the largest retailer in Finland were invited to participate in this study. Our LoCard data consist of the purchases of 7251 card holders who consented to the use of their data from the year 2016. The purchases were reclassified into 55 product groups and aggregated across 52 weeks. The data were then analyzed using tensorial PCA, allowing us to effectively reduce the time and product group-wise dimensions simultaneously. The augmentation method was used for selecting the suitable number of principal components for the analysis.

RESULTS

Using tensorial PCA, we were able to systematically search for typical food purchasing patterns across time and product groups as well as detect different purchasing behaviors across groups of card holders. For example, we identified customers who purchased large amounts of meat products and separated them further into groups based on time profiles, that is, customers whose purchases of meat remained stable, increased, or decreased throughout the year or varied between seasons of the year.

CONCLUSIONS

Using tensorial PCA, we can effectively examine customers' purchasing behavior in more detail than with traditional methods because it can handle time and product group dimensions simultaneously. When interpreting the results, both time and product dimensions must be considered. In further analyses, these time and product groups can be directly associated with additional consumer characteristics such as socioeconomic and demographic predictors of dietary patterns. In addition, they can be linked to external factors that impact grocery purchases such as inflation and unexpected pandemics. This enables us to identify what types of people have specific purchasing patterns, which can help in the development of ways in which consumers can be steered toward making healthier food choices.

Testing a conceptual Hierarchy of Effects model of food marketing exposure and associations with children and adolescents' diet-related outcomes

OBJECTIVE

Children's exposure to unhealthy food marketing contributes to poor diets by influencing the foods that children like, request, buy and consume. This study aimed to use confirmatory mediational analyses to test a hypothetical model of marketing effects, to better understand the mechanisms behind food marketing's impacts on children.

DESIGN

Children responded to a cross-sectional online survey about their attitudes towards, and purchase and consumption behaviours of, ten frequently promoted food/beverage brands and their media use. Structural equation modelling tested a priori potential pathways for the effects of food marketing exposure on children's diets.

PARTICIPANTS

10-16-year-old children (n 400).

SETTING

Australia.

RESULTS

There was a significant positive correlation between children's commercial screen media use and their attitudes towards brands (related to perceived social norms) and their brand purchasing behaviours, including their own purchases and requests to parents. The use of strategies to avoid advertising in commercial screen media reduced but did not remove the association between media use and brand purchases. Other brand exposures (on clothing, outdoor advertising, sponsorships) had a positive association with children's perceived social norms about brands and their brand purchases and requests. Non-commercial screen media use was not associated with any brand-related outcomes.

CONCLUSIONS

Commercial screen media use and other brand exposures were strongly positively associated with children's perceptions and purchasing behaviours of frequently marketed food/beverages. Regulations to restrict children's exposures to food marketing on-screen and through other media are required to reduce the effect of marketing exposure on children's food purchasing behaviours.

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.

Food purchase behaviour in a Finnish population: patterns, carbon footprints and expenditures

OBJECTIVE

To identify food purchase patterns and to assess their carbon footprint and expenditure.

DESIGN

Cross-sectional.

SETTING

Purchase patterns were identified by factor analysis from the annual purchases of 3435 product groups. The associations between purchase patterns and the total purchases' carbon footprints (based on life-cycle assessment) and expenditure were analysed using linear regression and adjusted for nutritional energy content of the purchases.

PARTICIPANTS

Loyalty card holders (n 22 860) of the largest food retailer in Finland.

RESULTS

Eight patterns explained 55 % of the variation in food purchases. The Animal-based pattern made the greatest contribution to the annual carbon footprint, followed by the Easy-cooking, and Ready-to-eat patterns. High-energy, Traditional and Plant-based patterns made the smallest contribution to the carbon footprint of the purchases. Animal-based, Ready-to-eat, Plant-based and High-energy patterns made the greatest contribution, whereas the Traditional and Easy-cooking patterns made the smallest contribution to food expenditure. Carbon footprint per euros spent increased with stronger adherence to the Traditional, Animal-based and Easy-cooking patterns.

CONCLUSIONS

The Animal-based, Ready-to-eat and High-energy patterns were associated with relatively high expenditure on food, suggesting no economic barrier to a potential shift towards a plant-based diet for consumers adherent to those patterns. Strong adherence to the Traditional pattern resulted in a low energy-adjusted carbon footprint but high carbon footprint per euro. This suggests a preference for cheap nutritional energy rather than environment-conscious purchase behaviour. Whether a shift towards a plant-based pattern would be affordable for those with more traditional and cheaper purchase patterns requires more research.

Exploring the Geographic Variation in Fruit and Vegetable Purchasing Behaviour Using Supermarket Transaction Data

The existence of dietary inequalities is well-known. Dietary behaviours are impacted by the food environment and are thus likely to follow a spatial pattern. Using 12 months of transaction records for around 50,000 ‘primary’ supermarket loyalty card holders, this study explores fruit and vegetable purchasing at the neighbourhood level across the city of Leeds, England. Determinants of small-area-level fruit and vegetable purchasing were identified using multiple linear regression. Results show that fruit and vegetable purchasing is spatially clustered. Areas purchasing fewer fruit and vegetable portions typically had younger residents, were less affluent, and spent less per month with the retailer.