A method for neighborhood‐level surveillance of food purchasing

Abstract

Added sugar, particularly in carbonated soft drinks (CSDs), represents a considerable proportion of caloric intake in North America. Interventions to decrease the intake of added sugar have been proposed, but monitoring their effectiveness can be difficult due to the costs and limitations of dietary surveys. We developed, assessed the accuracy of, and took an initial step toward validating an indicator of neighborhood-level purchases of CSDs using automatically captured store scanner data in Montreal, Canada, between 2008 and 2010 and census data describing neighborhood socioeconomic characteristics. Our indicator predicted total monthly neighborhood sales based on historical sales and promotions and characteristics of the stores and neighborhoods. The prediction error for monthly sales in sampled stores was low (2.2%), and we demonstrated a negative association between predicted total sales and median personal income. For each $10,000 decrease in median personal income, we observed a fivefold increase in predicted monthly sales of CSDs. This indicator can be used by public health agencies to implement automated systems for neighborhood-level monitoring of an important upstream determinant of health. Future refinement of this indicator is possible to account for factors such as store catchment areas and to incorporate nutritional information about products.

A nutrition/health mindset on commercial Big Data and drivers of food demand in modern and traditional systems

“…Such systems are critical in providing information to drive public health interventions in an effective manner. Buckeridge et al . looked at one component of such a system and used real‐time aggregate sales data to complement the limited amounts of survey and other types of health data currently available in order to monitor neighborhood‐level purchases in real time.…”

Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation

“…Informatics research has helped to advance surveillance systems in many ways, including by facilitating access to new data streams from clinical and other sources (16,29,43,52); automating surveillance processes, such as case detection from free text (22) and aberration detection (38); and enabling the rapid dissemination of information produced by surveillance to a wide range of stakeholders (20).…”

Agriculture, health, and wealth convergence: bridging traditional food systems and modern agribusiness solutions

“…The fourth paper, by Buckridge et al ., pushes the boundaries of metrics and analytics research design further, to support integration among sectors and geographic scales, and target population segments of varying socioeconomic backgrounds, taking this time as a laboratory the rich‐country context (more specifically, Canada). The authors introduce a method for neighborhood‐level surveillance of food purchasing, motivated by the increasing share worldwide of caloric intake represented by sugar‐rich, nutrient‐poor food, such as carbonated soft drinks (CSDs) .…”

“…The roadmap paper by Dubé et al . proposes to capitalize on 21st‐century Big Data and advanced analytics, such as those used by Buckridge et al . above to set the foundations for an integrative knowledge platform that bridges sectoral practices and disciplinary theories, as well as metrics and models to better inform and monitor behavioral change and ecosystem transformation.…”