Center for Public Health Nutrition, 305 Raitt Hall, #353410, University of Washington, Seattle, WA, USA.
Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA.
Int J Obes (Lond). 2021 Sep;45(9):1914-1924. doi: 10.1038/s41366-021-00836-z. Epub 2021 May 11.
To determine whether selected features of the built environment can predict weight gain in a large longitudinal cohort of adults.
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.
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.
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.
确定建成环境的某些特征是否可以预测大型成年人纵向队列的体重增加。
从 Kaiser Permanente Washington 医疗保健系统中 115260 名 18-64 岁的参保患者的电子健康记录中获得 5 年内的体重轨迹。使用 ArcGIS 对家庭地址进行地理编码。使用基于欧几里得的 SmartMaps 在 800 米缓冲区中捕获人口、住宅单元和道路交叉口密度等建成环境变量。使用基于网络的 SmartMaps 在 1600 和 5000 米缓冲区中获取区域超市和快餐店的数量。物业价值是社会经济地位的衡量标准。线性混合效应模型检验了基线建成环境变量是否与长期体重增加相关，同时调整了性别、年龄、种族/民族、医疗补助保险、体重和居住物业价值。
基线建成环境变量与基线肥胖患病率和身体质量指数的差异相关，但对体重轨迹的影响有限。全队列的平均体重增加量为 1 年时为 0.06kg（95%CI：0.03，0.10）；3 年时为 0.64kg（95%CI：0.59，0.68），5 年时为 0.95kg（95%CI：0.90，1.00）。在调整后的回归模型中，与密度指标和频数的最高三分位数相比，较低三分位数在 5 年随访时的体重增加量较低，尽管每个随访年（1、3 和 5）的体重变化平均差异不超过 0.5kg。
与基线肥胖患病率相关的建成环境变量与随时间推移的体重增加之间的独立肥胖形成能力有限。住宅单元密度与体重增加呈最强的负相关。关于建成环境变量对健康影响的进一步研究还应考虑社会背景，包括居住隔离和居住流动性。
