Food Swamps Predict Obesity Rates Better Than Food Deserts in the United States
Food Swamps Predict Obesity Rates Better Than Food Deserts in the United States
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DOI：
10.3390/ijerph14111366
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发表时间：
2017-11-01
影响因子：
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通讯作者：
Brownell, Kelly D.
中科院分区：
文献类型：
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作者：
Cooksey-Stowers, Kristen;Schwartz, Marlene B.;Brownell, Kelly D.
本文探讨了食物环境，特点是食物沼泽，对成人肥胖率的影响。食物沼泽被描述为相对于更健康的食物选择，高密度的机构销售高热量快餐和垃圾食品的地区。这项研究探讨了将食品环境分类为食品沼泽和食品沙漠的多种方法，包括零售食品环境指数的替代版本。我们合并了来自美国农业部（USDA）食品环境地图集，美国社区调查和商业街参考数据集的食品出口，社会人口和肥胖数据。我们采用了工具变量（IV）策略来校正食品环境的内环境（即，个人自我选择进入社区，并在他们的决定中考虑食物的可用性）。我们的研究结果表明，食物沼泽的存在比没有提供全方位服务的杂货店更能预测肥胖率。我们发现，即使在控制了食物沙漠效应之后，食物沼泽对成人肥胖率也有积极的，统计学上显着的影响。所有三个食物沼泽措施表明相同的正相关，但反映了不同程度的食物沼泽对成人肥胖率的影响（p值范围为0.00至0.16）。我们的调整反向因果关系，使用IV方法，揭示了更强的影响，食物沼泽比将获得朴素的普通最小二乘（OLS）估计。食物沼泽效应在收入差距较大的县（p < 0.05）和居民移动的较少的县（p < 0.01）更强。根据这些发现，地方政府的政策，如分区法，同时限制进入不健康的食品店和激励健康食品零售商设在服务不足的社区值得考虑的战略，以增加健康公平。
This paper investigates the effect of food environments, characterized as food swamps, on adult obesity rates. Food swamps have been described as areas with a high-density of establishments selling high-calorie fast food and junk food, relative to healthier food options. This study examines multiple ways of categorizing food environments as food swamps and food deserts, including alternate versions of the Retail Food Environment Index. We merged food outlet, sociodemographic and obesity data from the United States Department of Agriculture (USDA) Food Environment Atlas, the American Community Survey, and a commercial street reference dataset. We employed an instrumental variables (IV) strategy to correct for the endogeneity of food environments (i.e., that individuals self-select into neighborhoods and may consider food availability in their decision). Our results suggest that the presence of a food swamp is a stronger predictor of obesity rates than the absence of full-service grocery stores. We found, even after controlling for food desert effects, food swamps have a positive, statistically significant effect on adult obesity rates. All three food swamp measures indicated the same positive association, but reflected different magnitudes of the food swamp effect on rates of adult obesity (p values ranged from 0.00 to 0.16). Our adjustment for reverse causality, using an IV approach, revealed a stronger effect of food swamps than would have been obtained by naive ordinary least squares (OLS) estimates. The food swamp effect was stronger in counties with greater income inequality (p < 0.05) and where residents are less mobile (p < 0.01). Based on these findings, local government policies such as zoning laws simultaneously restricting access to unhealthy food outlets and incentivizing healthy food retailers to locate in underserved neighborhoods warrant consideration as strategies to increase health equity.