Generating community measures of food purchasing activities using store-level electronic grocery transaction records: an ecological study in Montreal, Canada

Objective: Geographic measurement of diets is generally not available at areas smaller than a national or provincial (state) scale, as existing nutrition surveys cannot achieve sample sizes needed for an acceptable statistical precision for small geographic units such as city subdivisions. Design: Using geocoded Nielsen grocery transaction data collected from supermarket, supercentre and pharmacy chains combined with a gravity model that transforms store-level sales into area-level purchasing, we developed small-area public health indicators of food purchasing for neighbourhood districts. We generated the area-level indicators measuring per-resident purchasing quantity for soda, diet soda, flavoured (sugar-added) yogurt and plain yogurt purchasing. We then provided an illustrative public health application of these indicators as covariates for an ecological spatial regression model to estimate spatially correlated small-area risk of type 2 diabetes mellitus (T2D) obtained from the public health administrative data. Setting: Greater Montreal, Canada in 2012. Participants: Neighbourhood districts (n 193). Results: The indicator of flavoured yogurt had a positive association with neighbourhood-level risk of T2D (1 center dot 08, 95 % credible interval (CI) 1 center dot 02, 1 center dot 14), while that of plain yogurt had a negative association (0 center dot 93, 95 % CI 0 center dot 89, 0 center dot 96). The indicator of soda had an inconclusive association, and that of diet soda was excluded due to collinearity with soda. The addition of the indicators also improved model fit of the T2D spatial regression (Watanabe-Akaike information criterion = 1765 with the indicators, 1772 without). Conclusion: Store-level grocery sales data can be used to reveal micro-scale geographic disparities and trends of food selections that would be masked by traditional survey-based estimation.