Assessing the validity of commercial and municipal food environment data sets in Vancouver, Canada

Abstract

Objective: The present study assessed systematic bias and the effects of data set error on the validity of food environment measures in two municipal and two commercial secondary data sets. Design: Sensitivity, positive predictive value (PPV) and concordance were calculated by comparing two municipal and two commercial secondary data sets with ground-truthed data collected within 800 m buffers surrounding twenty-six schools. Logistic regression examined associations of sensitivity and PPV with commercial density and neighbourhood socio-economic deprivation. Kendall's τ estimated correlations between density and proximity of food outlets near schools constructed with secondary data sets v. ground-truthed data. Setting: Vancouver, Canada. Subjects: Food retailers located within 800 m of twenty-six schools Results: All data sets scored relatively poorly across validity measures, although, overall, municipal data sets had higher levels of validity than did commercial data sets. Food outlets were more likely to be missing from municipal health inspections lists and commercial data sets in neighbourhoods with higher commercial density. Still, both proximity and density measures constructed from all secondary data sets were highly correlated (Kendall's τ > 0·70) with measures constructed from ground-truthed data. Conclusions: Despite relatively low levels of validity in all secondary data sets examined, food environment measures constructed from secondary data sets remained highly correlated with ground-truthed data. Findings suggest that secondary data sets can be used to measure the food environment, although estimates should be treated with caution in areas with high commercial density.

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

“…In this sense, we found fewer differences between area-level socioeconomic status, population density and the proportion of outlets that were correctly matched in the secondary data by using location (liberal) matching. These findings are in line with previous research, which also reported no differences by area-level deprivation [13,45,48,54,55]. Yet, we found some evidence for a systematic bias when applying a stricter matching strategy.…”

“…While this idea has been suggested by previous studies [47,48], our study extends previous research because we examined the validity of secondary data sources in a European context and included multiple types of food outlets (e.g., specialized food retailers) [49,50]. This is important, because previous studies have found that independent (non-chain) food outlets are more likely to be missed in commercial datasets [31,32,33,51].…”

“…To evaluate potential differential measurement error, previous studies also examined validity statistics by area characteristics, which could lead to confounding in the association between area-level characteristics (e.g., socioeconomic status) and food environment measures [18,48]. In this sense, we found fewer differences between area-level socioeconomic status, population density and the proportion of outlets that were correctly matched in the secondary data by using location (liberal) matching.…”

“…To match outlets in the administrative dataset with outlets found during ground-truthing, we employed two strategies: (1) Location matching (henceforth referred to as liberal matching), and (2) location and name matching (henceforth referred to as strict matching) [45,46,47,48].…”

Web Data Mining: Validity of Data from Google Earth for Food Retail Evaluation

“…We also excluded food retail outlets inside institutions (e.g. schools and universities) because they are not freely available to the general public and so do not constitute part of the community food environment, but rather the organizational food environment [3,11].…”