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Comparing competing geospatial measures to capture the relationship between the neighborhood food environment and diet
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9 2021
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Source: Ann Epidemiol. 61:1-7
[PDF-348.72 KB]
Details:
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Alternative Title:Ann Epidemiol
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Personal Author:
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Description:Purpose:
To examine how the choice of neighborhood food environment definition impacts the association with diet.
Methods:
Using food frequency questionnaire data from the Reasons for Geographic and Racial Differences in Stroke study at baseline (2003–2007), we calculated participants’ dietary inflammation score (DIS) (n=20,331); higher scores indicate greater pro-inflammatory exposure. We characterized availability of supermarkets and fast food restaurants using several geospatial measures, including density (i.e., counts/km2) and relative measures (i.e., percentage of all food stores or restaurants); and various buffer distances, including administrative units (census tract) and empirically-derived buffers (“classic” network, “sausage” network) tailored to community type (higher-density urban, lower-density urban, suburban/small town, rural). Using generalized estimating equations, we estimated the association between each geospatial measure and DIS, controlling for individual- and neighborhood-level sociodemographics.
Results:
The choice of buffer-based measure did not change the direction or magnitude of associations with DIS. Effect estimates derived from administrative units were smaller than those derived from tailored empirically-derived buffer measures. Substantively, a 10% increase in the percentage of fast food restaurants using a “classic” network buffer was associated with a 6.3 (SE=1.17) point higher DIS (p<0.001). The relationship between the percentage of supermarkets and DIS, however, was null. We observed high correlation coefficients between buffer-based density measures of supermarkets and fast food restaurants (r=0.73–0.83), which made it difficult to estimate independent associations by food outlet type.
Conclusions:
Researchers should tailor buffer-based measures to community type in future studies, and carefully consider the theoretical and statistical implications for choosing relative (vs. absolute) measures.
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Pubmed ID:34051343
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Pubmed Central ID:PMC8592302
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