Anthocyanins are a class of polyphenols that have received widespread recent attention due to their potential health benefits. However, estimating the dietary intake of anthocyanins at a population level is a challenging task, due to the difficulty of scaling dietary surveys. Further, there is limited evidence as to who regularly consumes anthocyanins, whether temporally, spatially, or culturally according to levels of socioeconomic deprivation. Leveraging a massive retail loyalty card dataset in the UK, we pair two years of real-world purchasing data for 619,524 regular shoppers and 207 million shopping baskets with anthocyanin estimates drawn from polyphenol databases. We subsequently analyse relative deprivation levels of the neighbourhoods in which shoppers reside, illustrating how anthocyanin intake varies according to affluence. Results indicate that deprivation is linked dramatically with both lower total intake of anthocyanins and lower breadth of dietary sources for them, potentially aggravating the incidence of diet-related diseases in the poorest sections of society.

Life satisfaction significantly contributes to wellbeing and is linked to positive outcomes for individual people and society more broadly. However, previous research demonstrates that many factors contribute to the life satisfaction of an individual person, including: demography, socioeconomic status, health, deprivation, family life, friendships, social networks, living environment, and the broad range of behaviours enacted by the person, such as helping or volunteering. Consequently, it is challenging to disentangle the factors that contribute most significantly to life satisfaction, and thus more importantly, inform public policies designed to help foster positive wellbeing. We analyse primary survey data $(\mathrm{n}=2849)$ on self-reported life satisfaction in relation to a range of self-reported and observed variables associated with wellbeing. Specifically, we draw on a massive paired dataset related to use of a food sharing application in London, to augment the analysis using additional socioeconomic, environmental, and behavioural variables. Through a random forest machine learning approach and variable importance measures, we evaluate how a range of factors, that are often only evaluated individually, provide relative contributions towards life satisfaction. Result reveal that factors such as employment and social reliance contribute most significantly towards the experience of life satisfaction.