A novel application of point-of-sales grocery transaction data to enhance community nutrition monitoring.

Unhealthy eating is the most important preventable cause of global death and disability. Effective development and evaluation of preventive initiatives and the identification of disparities in dietary patterns require surveillance of nutrition at a community level. However, nutrition monitoring currently relies on dietary surveys, which cannot efficiently assess food selection at high spatial resolution. However, marketing companies continuously collect and centralize digital grocery transaction data from a geographically representative sample of chain retail food outlets through scanner technologies. We used these data to develop a model to predict store-level sales of carbonated soft drinks, which was applied to all chain food outlets in Montreal, Canada. The resulting map of purchase patterns provides a foundation for developing novel, high-resolution nutrition indicators that reflect dietary preferences at a community level. These detailed nutrition portraits will allow health agencies to tailor healthy eating interventions and promotion programs precisely to meet specific community needs.