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Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain
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 Perm...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8592117/ https://www.ncbi.nlm.nih.gov/pubmed/33976378 http://dx.doi.org/10.1038/s41366-021-00836-z |
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author | Buszkiewicz, James H. Bobb, Jennifer F. Hurvitz, Philip M Arterburn, David Moudon, Anne Vernez Cook, Andrea Mooney, Stephen J. Cruz, Maricela Gupta, Shilpi Lozano, Paula Rosenberg, Dori E. Theis, Mary Kay Anau, Jane Drewnowski, Adam |
author_facet | Buszkiewicz, James H. Bobb, Jennifer F. Hurvitz, Philip M Arterburn, David Moudon, Anne Vernez Cook, Andrea Mooney, Stephen J. Cruz, Maricela Gupta, Shilpi Lozano, Paula Rosenberg, Dori E. Theis, Mary Kay Anau, Jane Drewnowski, Adam |
author_sort | Buszkiewicz, James H. |
collection | PubMed |
description | 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-meter buffers. Counts of area supermarkets and fast food restaurants were obtained using network-based SmartMaps at 1,600, and 5,000-meter 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 kilograms at 1 year (95% CI: 0.03, 0.10); 0.64 kilograms at 3 years (95% CI: 0.59, 0.68), and 0.95 kilograms 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 kilograms. CONCLUSION: 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. |
format | Online Article Text |
id | pubmed-8592117 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
spelling | pubmed-85921172021-11-15 Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain Buszkiewicz, James H. Bobb, Jennifer F. Hurvitz, Philip M Arterburn, David Moudon, Anne Vernez Cook, Andrea Mooney, Stephen J. Cruz, Maricela Gupta, Shilpi Lozano, Paula Rosenberg, Dori E. Theis, Mary Kay Anau, Jane Drewnowski, Adam Int J Obes (Lond) Article 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-meter buffers. Counts of area supermarkets and fast food restaurants were obtained using network-based SmartMaps at 1,600, and 5,000-meter 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 kilograms at 1 year (95% CI: 0.03, 0.10); 0.64 kilograms at 3 years (95% CI: 0.59, 0.68), and 0.95 kilograms 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 kilograms. CONCLUSION: 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. 2021-05-11 2021-09 /pmc/articles/PMC8592117/ /pubmed/33976378 http://dx.doi.org/10.1038/s41366-021-00836-z Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Buszkiewicz, James H. Bobb, Jennifer F. Hurvitz, Philip M Arterburn, David Moudon, Anne Vernez Cook, Andrea Mooney, Stephen J. Cruz, Maricela Gupta, Shilpi Lozano, Paula Rosenberg, Dori E. Theis, Mary Kay Anau, Jane Drewnowski, Adam Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain |
title | Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain |
title_full | Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain |
title_fullStr | Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain |
title_full_unstemmed | Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain |
title_short | Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain |
title_sort | does the built environment have independent obesogenic power? urban form and trajectories of weight gain |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8592117/ https://www.ncbi.nlm.nih.gov/pubmed/33976378 http://dx.doi.org/10.1038/s41366-021-00836-z |
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