Data considerations for the success of policy to restrict in‐store food promotions: A commentary from a food industry nutritionist consultation

New plans to restrict in‐store price and location‐based promotions of less healthy foods and drinks in the UK aimed to encourage healthier choices. With responsibility for implementation likely falling to food retailers, it is important to understand the feasibility of implementation and to ensure policy success. To ensure compliance, retailers will need to assess which products are restricted under the legislation. The large number of products in retailers’ portfolios poses a problem of scale. A recent research case study found the data available to retailers to be insufficient to accurately apply the rules‐based approach set out by the policy proposal. Misclassification would result in some less healthy products being incorrectly promoted and vice versa. Problems with implementation feasibility have the potential to undermine the public health goals of the legislation. Interviews were carried out with nutrition representatives from the UK food retail and manufacturing sector, to understand the real‐world implications of the proposed legislation. Industry nutritionists recommended a review of the use of the UK’s Nutrient Profiling Model as the legislative basis, proposed data‐related solutions to implementation problems and suggested a need for shared retailer‐manufacturer responsibility, given the context of data availability

Restricting Retail Food Promotions: implementation challenges could limit policy success

New plans to restrict point-of-sale promotions of less healthy foods and drinks in England, aim to encourage healthier choices. With responsibility for implementation likely falling to food retailers, it is important to understand feasibility challenges, to ensure policy success. Researchers found the data available to retailers to be insufficient to apply the rules set out by the policy proposal. This would see some products incorrectly promoted, and vice versa. We recommend a review of the legislative basis to establish rules which align public health benefit with data feasibility. Government support is needed, in the form of a free-to-use tool for consistent automated product assessment, and development of a data sharing platform, accessible to industry and the legislator

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

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

Supermarket Transaction Records In Dietary Evaluation – The STRIDE study: validation against self-reported dietary intake

Objective: Scalable methods are required for population dietary monitoring. The Supermarket Transaction Records In Dietary Evaluation (STRIDE) study compares dietary estimates from supermarket transactions with an online FFQ. Design: Participants were recruited in four waves, accounting for seasonal dietary variation. Purchases were collected for 1 year during and 1 year prior to the study. Bland–Altman agreement and limits of agreement (LoA) were calculated for energy, sugar, fat, saturated fat, protein and sodium (absolute and relative). Setting: This study was partnered with a large UK retailer. Participants: Totally, 1788 participants from four UK regions were recruited from the retailer’s loyalty card customer database, according to breadth and frequency of purchases. Six hundred and eighty-six participants were included for analysis. Results: The analysis sample were mostly female (72 %), with a mean age of 56 years (SD 13). The ratio of purchases to intakes varied depending on amounts purchased and consumed; purchases under-estimated intakes for smaller amounts on average, but over-estimated for larger amounts. For absolute measures, the LoA across households were wide, for example, for energy intake of 2000 kcal, purchases could under- or over-estimate intake by a factor of 5; values could be between 400 kcal and 10000 kcal. LoA for relative (energy-adjusted) estimates were smaller, for example, for 14 % of total energy from saturated fat, purchase estimates may be between 7 % and 27 %. Conclusions: Agreement between purchases and intake was highly variable, strongest for smaller loyal households and for relative values. For some customers, relative nutrient purchases are a reasonable proxy for dietary composition indicating utility in population-level dietary research

Restricting promotions of ‘less healthy’ foods and beverages by price and location: a big data application of UK Nutrient Profiling Models to a retail product dataset

The UK government plans to limit price-based and location-based promotions for products high in saturated fat, salt and sugars. The 2004/5 UK nutrient profile model (NPM) is the proposed legislative basis, but may be superseded by the draft 2018 NPM. This study develops an algorithm to apply both NPMs to a large food composition database (FCDB), and assesses implementation challenges. UK NPMs were applied algorithmically to the myfood24 FCDB, representing ~45,000 retail products. Pass-rates - indicating free or restricted promotions - and micronutrient compositions were compared. Challenges were assessed and recommendations addressed the legislation’s public consultation questions. For products in scope (75% of total), 6% fewer passed the 2018 NPM (36%, p<0.001) compared with the 2004/5 NPM (42%). Beverages showed the greatest reduction in pass-rate (75%). Under both models, micronutrient contents (per 100 g of product) were generally lower for products which passed; except folate, vitamin C and vitamin D that were no different for passed and failed products. Compared with products passing the 2004/5 NPM, products passing the 2018 NPM on average had marginally higher amounts of iron (0.05 mg, 95% CI: 0.02, 0.08, p<0.001) and magnesium (1.00 mg, 95% CI: 0.00, 1.17, p=0.029), but marginally lower levels of calcium (-0.42 mg, 95% CI: -2.00, -0.40, p=0.025). Missing ingredient information and heterogeneous product categories were challenges for both NPMs. Free sugar calculation further complicated 2018 NPM application. To balance feasibility and public health benefit, the proposed legislative basis may not be appropriate

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

Progress Towards Using Linked Population-Based Data For Geohealth Research: Comparisons Of Aotearoa New Zealand And The United Kingdom

Globally, geospatial concepts are becoming increasingly important in epidemiological and public health research. Individual level linked population-based data afford researchers with opportunities to undertake complex analyses unrivalled by other sources. However, there are significant challenges associated with using such data for impactful geohealth research. Issues range from extracting, linking and anonymising data, to the translation of findings into policy whilst working to often conflicting agendas of government and academia. Innovative organisational partnerships are therefore central to effective data use. To extend and develop existing collaborations between the institutions, in June 2019, authors from the Leeds Institute for Data Analytics and the Alan Turing Institute, London, visited the Geohealth Laboratory based at the University of Canterbury, New Zealand. This paper provides an overview of insight shared during a two-day workshop considering aspects of linked population-based data for impactful geohealth research. Specifically, we discuss both the collaborative partnership between New Zealand’s Ministry of Health (MoH) and the University of Canterbury’s GeoHealth Lab and novel infrastructure, and commercial partnerships enabled through the Leeds Institute for Data Analytics and the Alan Turing Institute in the UK. We consider the New Zealand Integrated Data Infrastructure as a case study approach to population-based linked health data and compare similar approaches taken by the UK towards integrated data infrastructures, including the ESRC Big Data Network centres, the UK Biobank, and longitudinal cohorts. We reflect on and compare the geohealth landscapes in New Zealand and the UK to set out recommendations and considerations for this rapidly evolving discipline