Feuillet T, Charreire H, Roda C, Ben Rebah M, Mackenbach J D, Compernolle S, Glonti K, Bárdos H, Rutter H, De Bourdeaudhuij I, McKee M, Brug J, Lakerveld J, Oppert J-M
Equipe de Recherche en Epidémiologie Nutritionnelle (EREN), Centre de Recherche en Epidémiologie et Statistiques, Cnam, COMUE Sorbonne Paris Cité, Université Paris 13, Bobigny, France.
Paris Est University, Lab-Urba, UPEC, Urban School of Paris, Créteil, France.
Obes Rev. 2016 Jan;17 Suppl 1:19-30. doi: 10.1111/obr.12378.
Virtual audit (using tools such as Google Street View) can help assess multiple characteristics of the physical environment. This exposure assessment can then be associated with health outcomes such as obesity. Strengths of virtual audit include collection of large amount of data, from various geographical contexts, following standard protocols. Using data from a virtual audit of obesity-related features carried out in five urban European regions, the current study aimed to (i) describe this international virtual audit dataset and (ii) identify neighbourhood patterns that can synthesize the complexity of such data and compare patterns across regions. Data were obtained from 4,486 street segments across urban regions in Belgium, France, Hungary, the Netherlands and the UK. We used multiple factor analysis and hierarchical clustering on principal components to build a typology of neighbourhoods and to identify similar/dissimilar neighbourhoods, regardless of region. Four neighbourhood clusters emerged, which differed in terms of food environment, recreational facilities and active mobility features, i.e. the three indicators derived from factor analysis. Clusters were unequally distributed across urban regions. Neighbourhoods mostly characterized by a high level of outdoor recreational facilities were predominantly located in Greater London, whereas neighbourhoods characterized by high urban density and large amounts of food outlets were mostly located in Paris. Neighbourhoods in the Randstad conurbation, Ghent and Budapest appeared to be very similar, characterized by relatively lower residential densities, greener areas and a very low percentage of streets offering food and recreational facility items. These results provide multidimensional constructs of obesogenic characteristics that may help target at-risk neighbourhoods more effectively than isolated features.
虚拟审计（使用谷歌街景等工具）有助于评估物理环境的多个特征。然后，这种暴露评估可以与肥胖等健康结果相关联。虚拟审计的优势包括按照标准协议从各种地理环境中收集大量数据。本研究利用在欧洲五个城市地区进行的与肥胖相关特征的虚拟审计数据，旨在（i）描述这一国际虚拟审计数据集，以及（ii）识别能够综合此类数据复杂性并比较不同地区模式的邻里模式。数据来自比利时、法国、匈牙利、荷兰和英国城市地区的4486个街道段。我们对主成分进行多因素分析和层次聚类，以构建邻里类型学，并识别相似/不同的邻里，而不考虑地区。出现了四个邻里集群，它们在食物环境、娱乐设施和积极出行特征方面存在差异，即从因素分析得出的三个指标。集群在城市地区分布不均。以高水平户外娱乐设施为主的邻里主要位于大伦敦，而以高城市密度和大量食品店为特征的邻里大多位于巴黎。兰斯塔德城市群、根特和布达佩斯的邻里似乎非常相似，其特征是居住密度相对较低、绿化面积较大，提供食品和娱乐设施的街道比例非常低。这些结果提供了致肥胖特征的多维结构，可能有助于比孤立特征更有效地针对高危邻里。
