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.
Keywords: Built environment; Data validation; Food environment; Public health.