Lamb Karen E, Thornton Lukar E, Cerin Ester, Ball Kylie
Centre for Physical Activity and Nutrition Research, School of Exercise and Nutrition Sciences, Faculty of Health, Deakin University, 221 Burwood Highway, Burwood, VIC, 3125, Australia.
AIMS Public Health. 2015 Jul 28;2(3):358-401. doi: 10.3934/publichealth.2015.3.358. eCollection 2015.
Inequalities in eating behaviours are often linked to the types of food retailers accessible in neighbourhood environments. Numerous studies have aimed to identify if access to healthy and unhealthy food retailers is socioeconomically patterned across neighbourhoods, and thus a potential risk factor for dietary inequalities. Existing reviews have examined differences between methodologies, particularly focussing on neighbourhood and food outlet access measure definitions. However, no review has informatively discussed the suitability of the statistical methodologies employed; a key issue determining the validity of study findings. Our aim was to examine the suitability of statistical approaches adopted in these analyses.
Searches were conducted for articles published from 2000-2014. Eligible studies included objective measures of the neighbourhood food environment and neighbourhood-level socio-economic status, with a statistical analysis of the association between food outlet access and socio-economic status.
Fifty-four papers were included. Outlet accessibility was typically defined as the distance to the nearest outlet from the neighbourhood centroid, or as the number of food outlets within a neighbourhood (or buffer). To assess if these measures were linked to neighbourhood disadvantage, common statistical methods included ANOVA, correlation, and Poisson or negative binomial regression. Although all studies involved spatial data, few considered spatial analysis techniques or spatial autocorrelation.
With advances in GIS software, sophisticated measures of neighbourhood outlet accessibility can be considered. However, approaches to statistical analysis often appear less sophisticated. Care should be taken to consider assumptions underlying the analysis and the possibility of spatially correlated residuals which could affect the results.
饮食行为的不平等往往与邻里环境中可及的食品零售商类型有关。许多研究旨在确定获取健康和不健康食品零售商的机会在不同邻里之间是否存在社会经济模式，从而成为饮食不平等的一个潜在风险因素。现有综述研究了方法之间的差异，尤其关注邻里和食品店可及性测量的定义。然而，尚无综述对所采用的统计方法的适用性进行有益的讨论；这是决定研究结果有效性的一个关键问题。我们的目的是检验这些分析中所采用统计方法的适用性。
检索了2000年至2014年发表的文章。符合条件的研究包括邻里食品环境和邻里层面社会经济地位的客观测量，并对食品店可及性与社会经济地位之间的关联进行统计分析。
纳入了54篇论文。店铺可及性通常定义为从邻里中心到最近店铺的距离，或邻里（或缓冲区）内食品店的数量。为评估这些测量是否与邻里劣势相关，常用的统计方法包括方差分析、相关性分析以及泊松或负二项回归。尽管所有研究都涉及空间数据，但很少有研究考虑空间分析技术或空间自相关性。
随着地理信息系统（GIS）软件的发展，可以考虑采用更复杂的邻里店铺可及性测量方法。然而，统计分析方法往往显得不够成熟。应谨慎考虑分析所依据的假设以及空间相关残差影响结果的可能性。
