Background: Globally, unhealthy diet is one of the leading global risks to health, thus it is central to consider aspects of the food environment that are modifiable and may enable healthy eating. Food retail data can be used to present and facilitate analyses of food environments that in turn may direct strategies towards improving dietary patterns among populations. Though food retail data are available in many countries, their completeness and accuracy differ.
Methods: We applied a systematically name-based procedure combined with a manual procedure on Danish administrative food retailer data (i.e. the Smiley register) to identify, locate and classify food outlets. Food outlets were classified into the most commonly used classifications (i.e. fast food, restaurants, convenience stores, supermarkets, fruit and vegetable stores and miscellaneous) each divided into three commonly used definitions; narrow, moderate and broad. Classifications were based on branch code, name, and/or information on the internal and external appearance of the food outlet. From ground-truthing we validated the information in the register for its sensitivity and positive predictive value.
Results: In 361 randomly selected areas of the Capital region of Denmark we identified a total of 1887 food outlets compared with 1861 identified in the register. We obtained a sensitivity of 0.75 and a positive predictive value of 0.76. Across classifications, the positive predictive values varied with highest values for the moderate and broad definitions of fast food, convenience stores and supermarkets (ranging from 0.89 to 0.97).
Conclusion: Information from the Smiley Register is considered to be representative to the Danish food environment and may be used for future research.
Keywords: Administrative food retail data; Foodscape; Ground-truthing; Positive predictive value; Retail food environment; Sensitivity; Validity.
© 2022. The Author(s).