Moodie Erica

Obesity and related chronic diseases cost Canadians several billion dollars annually. Dietary intake, and in particular consumption of carbonated sweetened drinks (soda), has a strong effect on the incidence of obesity and other illness. Marketing research suggests that in-store promotion, and more specifically price discounting, has a strong effect on the purchase of energy-dense products such as soda. Attempts by public health authorities to monitor price discounts are currently limited by a lack of data and methods. Although rarely used in public health surveillance, electronic retail sales data collected around the world by marketing companies such as the Nielsen Corporation have an immense potential to measure dietary choices at high geographical resolution. These scanned sales data are recorded in real-time and they include a detailed product description, price, purchased quantity, store location, and product-specific advertising activities.

Objective

To assess the influence of in-store price discounts on soda purchasing by neighborhood socio-economic status in Montreal, Canada using digital grocery store-level sales data.

Unhealthy diet is becoming the most important preventable cause of chronic disease burden. Dietary patterns vary across neighborhoods as a function of policy, marketing, social support, economy, and the commercial food environment. Assessment of community-specific response to these socio-ecological factors is critical for the development and evaluation policy interventions and identification of nutrition inequality. Mass administration of dietary surveys is impractical and prohibitory expensive, and surveys typically fail to address variation of food selection at high geographic resolution. Marketing companies such as the Nielsen cooperation continuously collect and centralize scanned grocery transaction records from a geographically representative sample of retail food outlets to guide product promotions. These data can be harnessed to develop a model for the demand of specific foods using store and neighborhood attributes, providing a rich and detailed picture of the “foodscape” in an urban environment. In this study, we generated a spatial profile of food selection from estimated sales in food outlets in the Census Metropolitan Area (CMA) of Montreal, Canada, using regular carbonated soft drinks (i.e. non-diet soda) as an initial example.

Objective

To demonstrate a method for estimating neighborhood food selection with secondary use of digital marketing data; grocery transaction records and retail business registry.