Associations between neighborhood built environment, residential property values, and adult BMI change: The Seattle Obesity Study III. (September 2022)

• Record Type: Journal Article
• Authors: Buszkiewicz, James H.; Rose, Chelsea M.; Ko, Linda K.; Mou, Jin; Moudon, Anne Vernez; Hurvitz, Philip M.; Cook, Andrea J.; Drewnowski, Adam
• Is Part Of: SSM - population health. Volume 19(2022)
• Publication Date: 2022-09
• Subjects: Social medicine -- Periodicals; Medical anthropology -- Periodicals; Public health -- Periodicals; Psychology -- Periodicals; Medicine -- Periodicals; 362.105
• DOI: 10.1016/j.ssmph.2022.101158
• Languages: English
• ISSNs: 2352-8273

Abstract

Abstract: Objective: To examine associations between neighborhood built environment (BE) variables, residential property values, and longitudinal 1- and 2-year changes in body mass index (BMI). Methods: The Seattle Obesity Study III was a prospective cohort study of adults with geocoded residential addresses, conducted in King, Pierce, and Yakima Counties in Washington State. Measured heights and weights were obtained at baseline (n = 879), year 1 (n = 727), and year 2 (n = 679). Tax parcel residential property values served as proxies for individual socioeconomic status. Residential unit and road intersection density were captured using Euclidean-based SmartMaps at 800 m buffers. Counts of supermarket (0 versus. 1+) and fast-food restaurant availability (0, 1–3, 4+) were measured using network based SmartMaps at 1600 m buffers. Density measures and residential property values were categorized into tertiles. Linear mixed-effects models tested whether baseline BE variables and property values were associated with differential changes in BMI at year 1 or year 2, adjusting for age, gender, race/ethnicity, education, home ownership, and county of residence. These associations were then tested for potential disparities by age group, gender, race/ethnicity, and education. Results: Road intersection density, access to food sources, and residential property values were inversely associated with BMI at baseline. At year 1, participants in the 3rd tertile of density metrics and with 4+ fast-food restaurants nearby showed less BMI gain compared to those in the 1st tertile or with 0 restaurants. At year 2, higher residential property values were predictive of lower BMI gain. There was evidence of differential associations by age group, gender, and education but not race/ethnicity. Conclusion: Inverse associations between BE metrics and residential property values at baseline demonstrated mixed associations with 1- and 2-year BMI change. More work is needed to understand how individual-level sociodemographic factors moderate associations between the BE, property values, and BMI change. Highlights: Strong, inverse cross-sectional relationships between the built environment, residential property values (a proxy for individual socioeconomic status), and measured BMI were observed. Measures of the built environment and residential property values showed modest and inconsistent associations with 1- and 2-year BMI change. There was suggestive evidence that age may moderate the association between urban density and 1- and 2-year BMI change while education may moderate the association between residential property values and 2-year BMI change.