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Dietary Patterns Derived from UK Supermarket Transaction Data with Nutrient and Socioeconomic Profiles

Clark, Stephen D.; Shute, Becky; Jenneson, Victoria; Rains, Tim; Birkin, Mark; Morris, Michelle A.

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April 27, 2021

10.3390/nu13051481

PMID: 33925712 PMCID: PMC8147024

Abstract:

Poor diet is a leading cause of death in the United Kingdom (UK) and around the world. Methods to collect quality dietary information at scale for population research are time consuming, expensive and biased. Novel data sources offer potential to overcome these challenges and better understand population dietary patterns. In this research we will use 12 months of supermarket sales transaction data, from 2016, for primary shoppers residing in the Yorkshire and Humber region of the UK (n = 299,260), to identify dietary patterns and profile these according to their nutrient composition and the sociodemographic characteristics of the consumer purchasing with these patterns. Results identified seven dietary purchase patterns that we named: Fruity; Meat alternatives; Carnivores; Hydrators; Afternoon tea; Beer and wine lovers; and Sweet tooth. On average the daily energy intake of loyalty card holders -who may buy as an individual or for a household- is less than the adult reference intake, but this varies according to dietary purchase pattern. In general loyalty card holders meet the recommended salt intake, do not purchase enough carbohydrates, and purchase too much fat and protein, but not enough fibre. The dietary purchase pattern containing the highest amount of fibre (as an indicator of healthiness) is bought by the least deprived customers and the pattern with lowest fibre by the most deprived. In conclusion, supermarket sales data offer significant potential for understanding population dietary patterns.

Automatic Tags

Female; Humans; Male; Adolescent; Adult; Young Adult; Aged; Middle Aged; Diet; Socioeconomic Factors; United Kingdom; Nutritive Value; dietary patterns; nutrients; Consumer Behavior; big data; dietary assessment; nutrition analytics; socioeconomic; Supermarkets; transaction data

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