Assessing the Retail Food Environment in Madrid: An Evaluation of Administrative Data against Ground Truthing

Previous studies have suggested that European settings face unique food environment issues; however, retail food environments (RFE) outside Anglo-Saxon contexts remain understudied. We assessed the completeness and accuracy of an administrative dataset against ground truthing, using the example of Madrid (Spain). Further, we tested whether its completeness differed by its area-level socioeconomic status (SES) and population density. First, we collected data on the RFE through the ground truthing of 42 census tracts. Second, we retrieved data on the RFE from an administrative dataset covering the entire city (n = 2412 census tracts), and matched outlets using location matching and location/name matching. Third, we validated the administrative dataset against the gold standard of ground truthing. Using location matching, the administrative dataset had a high sensitivity (0.95; [95% CI = 0.89, 0.98]) and positive predictive values (PPV) (0.79; [95% CI = 0.70, 0.85]), while these values were substantially lower using location/name matching (0.55 and 0.45, respectively). Accuracy was slightly higher using location/name matching (k = 0.71 vs 0.62). We found some evidence for systematic differences in PPV by area-level SES using location matching, and in both sensitivity and PPV by population density using location/name matching. Administrative datasets may offer a reliable and cost-effective source to measure retail food access; however, their accuracy needs to be evaluated before using them for research purposes.