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Epidemiology and Population Health

Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain

Abstract

Objective

To determine whether selected features of the built environment can predict weight gain in a large longitudinal cohort of adults.

Methods

Weight trajectories over a 5-year period were obtained from electronic health records for 115,260 insured patients aged 18–64 years in the Kaiser Permanente Washington health care system. Home addresses were geocoded using ArcGIS. Built environment variables were population, residential unit, and road intersection densities captured using Euclidean-based SmartMaps at 800-m buffers. Counts of area supermarkets and fast food restaurants were obtained using network-based SmartMaps at 1600, and 5000-m buffers. Property values were a measure of socioeconomic status. Linear mixed effects models tested whether built environment variables at baseline were associated with long-term weight gain, adjusting for sex, age, race/ethnicity, Medicaid insurance, body weight, and residential property values.

Results

Built environment variables at baseline were associated with differences in baseline obesity prevalence and body mass index but had limited impact on weight trajectories. Mean weight gain for the full cohort was 0.06 kg at 1 year (95% CI: 0.03, 0.10); 0.64 kg at 3 years (95% CI: 0.59, 0.68), and 0.95 kg at 5 years (95% CI: 0.90, 1.00). In adjusted regression models, the top tertile of density metrics and frequency counts were associated with lower weight gain at 5-years follow-up compared to the bottom tertiles, though the mean differences in weight change for each follow-up year (1, 3, and 5) did not exceed 0.5 kg.

Conclusions

Built environment variables that were associated with higher obesity prevalence at baseline had limited independent obesogenic power with respect to weight gain over time. Residential unit density had the strongest negative association with weight gain. Future work on the influence of built environment variables on health should also examine social context, including residential segregation and residential mobility.

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Fig. 1: Patient analytic sample exclusion/inclusion decision flow diagram.
Fig. 2: Mean difference in weight at baseline comparing the first and third tertiles of built environment characteristics at different buffer sizes, after adjusting for baseline demographics, height, and year-specific patient property values.

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Funding

This manuscript was supported by three grants from the National Institutes of Health: 1 R01 DK 114196, 5 R01 DK076608, and 4 R00LM012868.

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Correspondence to James H. Buszkiewicz.

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AD has received grants, honoraria, and consulting fees from numerous food, beverage, and ingredient companies and from other commercial and nonprofit entities with an interest in diet quality and nutrient density of foods. The University of Washington receives research funding from public and private sectors. The remaining authors declare no competing interests.

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Buszkiewicz, J.H., Bobb, J.F., Hurvitz, P.M. et al. Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain. Int J Obes 45, 1914–1924 (2021). https://doi.org/10.1038/s41366-021-00836-z

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