TY - JOUR
T1 - Comparing competing geospatial measures to capture the relationship between the neighborhood food environment and diet
AU - Rummo, Pasquale E.
AU - Algur, Yasemin
AU - McAlexander, Tara
AU - Judd, Suzanne E.
AU - Lopez, Priscilla M.
AU - Adhikari, Samrachana
AU - Brown, Janene
AU - Meeker, Melissa
AU - McClure, Leslie A.
AU - Elbel, Brian
N1 - Funding Information:
This research was conducted by the Diabetes LEAD Network, funded through CDC cooperative agreements U01DP006293 (Drexel University), U01DP006296 (Geisinger-Johns Hopkins University), U01DP006299 (New York University School of Medicine), and U01DP006302 (University of Alabama at Birmingham), along with collaboration with the U.S. Centers for Disease Control and Prevention, Division of Diabetes Translation. This work was also supported by cooperative agreement U01 NS041588 co-funded by the National Institute of Neurological Disorders and Stroke (NINDS) and the National Institute on Aging (NIA), National Institutes of Health (NIH), Department of Health and Human Services. Additional funding was provided by the NINDS of NIH under award number 1R01NS092706, the NIA of the NIH under award numbers 1R01AG049970 and 3R01AG049970-04S1, the Commonwealth Universal Research Enhancement (C.U.R.E) program funded by the Pennsylvania Department of Health - 2015 Formula award - SAP 4100072543, the Urban Health Collaborative at Drexel University, and the Built Environment and Health Research Group at Columbia University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NINDS or the NIA. Representatives of the NINDS were involved in the review of the manuscript but were not directly involved in the collection, management, analysis or interpretation of the data. The authors thank the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions can be found at: https://www.uab.edu/soph/regardsstudy/.
Funding Information:
This research was conducted by the Diabetes LEAD Network, funded through CDC cooperative agreements U01DP006293 ( Drexel University ), U01DP006296 ( Geisinger-Johns Hopkins University ), U01DP006299 ( New York University School of Medicine ), and U01DP006302 ( University of Alabama at Birmingham ), along with collaboration with the U.S. Centers for Disease Control and Prevention, Division of Diabetes Translation. This work was also supported by cooperative agreement U01 NS041588 co-funded by the National Institute of Neurological Disorders and Stroke (NINDS) and the National Institute on Aging (NIA) , National Institutes of Health (NIH) , Department of Health and Human Services. Additional funding was provided by the NINDS of NIH under award number 1R01NS092706 , the NIA of the NIH under award numbers 1R01AG049970 and 3R01AG049970-04S1 , the Commonwealth Universal Research Enhancement (C.U.R.E) program funded by the Pennsylvania Department of Health - 2015 Formula award - SAP 4100072543 , the Urban Health Collaborative at Drexel University, and the Built Environment and Health Research Group at Columbia University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NINDS or the NIA. Representatives of the NINDS were involved in the review of the manuscript but were not directly involved in the collection, management, analysis or interpretation of the data. The authors thank the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions can be found at: https://www.uab.edu/soph/regardsstudy/ .
Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/9
Y1 - 2021/9
N2 - 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< .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.
AB - 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< .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.
KW - Diet
KW - Inflammation
KW - Restaurants
KW - Food Supply
KW - Residence Characteristics
KW - Humans
KW - Fast Foods
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U2 - 10.1016/j.annepidem.2021.05.005
DO - 10.1016/j.annepidem.2021.05.005
M3 - Article
C2 - 34051343
AN - SCOPUS:85108456943
SN - 1047-2797
VL - 61
SP - 1
EP - 7
JO - Annals of Epidemiology
JF - Annals of Epidemiology
ER -