Carnegie School of Sport, Leeds Beckett University, Leeds, LS6 3QT, United Kingdom; GeoHealth Laboratory, Geospatial Research Institute, University of Canterbury, Christchurch, New Zealand.
School of Environmental Sciences, University of Liverpool, Liverpool, United Kingdom.
Soc Sci Med. 2019 Oct;239:112528. doi: 10.1016/j.socscimed.2019.112528. Epub 2019 Aug 31.
International research linking food outlets and body mass index (BMI) is largely cross-sectional, yielding inconsistent findings. However, addressing the exposure of food outlets is increasingly considered as an important adult obesity prevention strategy. Our study investigates associations between baseline food environment types and change in BMI over time. Survey data were used from the Yorkshire Health Study (n=8,864; wave one: 2010-2012, wave two: 2013-2015) for adults aged 18-86. BMI was calculated using self-reported height (cm) and weight (kg). Restaurants, cafés, fast-food, speciality, convenience and large supermarkets were identified from the Ordnance Survey Point of Interest database within 1600m radial buffer of home postcodes. K-means cluster analysis developed food environment typologies based on food outlets and population density. Large supermarkets, restaurants, cafés, fast-food, speciality and convenience food outlets all clustered together to some extent. Three neighbourhood typologies were identified. However, multilevel models revealed that relative to cluster one all were unrelated to change in BMI (cluster 2, b= -0.146 [-0.274, 0.566]; cluster 3, b= 0.065 [-0.224, 0.356]). There was also little evidence of gender-based differences in these associations when examined in a three-way interaction. Policymakers may need to begin to consider multiple types of food outlet clusters, while further research is needed to confirm how these relate to changed BMI.
国际上关于食品店与体重指数(BMI)的研究主要是横断面研究,得出的结果并不一致。然而,越来越多的人认为,解决食品店的暴露问题是预防成年人肥胖的一个重要策略。我们的研究调查了基线食品环境类型与随时间变化的 BMI 之间的关联。该研究使用了来自约克郡健康研究(n=8864;第一波:2010-2012 年,第二波:2013-2015 年)的成年人(年龄 18-86 岁)的调查数据。BMI 通过自我报告的身高(cm)和体重(kg)计算得出。在家庭邮政编码 1600 米的辐射缓冲区范围内,从 Ordnance Survey Point of Interest 数据库中识别出餐馆、咖啡馆、快餐店、专卖店、便利店和大型超市。基于食品店和人口密度的 K-均值聚类分析开发了食品环境类型学。大型超市、餐馆、咖啡馆、快餐店、专卖店和便利店在某种程度上都聚集在一起。确定了三种社区类型。然而,多层次模型显示,与簇 1 相比,所有这些都与 BMI 的变化无关(簇 2,b=-0.146[-0.274,0.566];簇 3,b=0.065[-0.224,0.356])。在三向交互作用中进行检查时,这些关联也几乎没有证据表明存在性别差异。政策制定者可能需要开始考虑多种类型的食品店集群,同时需要进一步研究来确认这些集群与 BMI 的变化有何关系。