■了解2型糖尿病(T2D)的地理差异需要考虑社区多维性质的方法。
■在一项电子健康记录嵌套病例对照研究中,我们确定了2008年至2016年的15884例新发T2D病例,使用相遇诊断定义,用药命令,和实验室测试结果,和没有T2D的频率匹配对照(79,400;65,069个独特人员)。我们使用有限混合模型从社会,自然,身体活动,和食品环境措施。我们使用逻辑广义估计方程模型以95%置信区间(CI)估计了T2D赔率比(OR),根据社会人口统计学变量进行调整。我们仅检查了与个人资料的关联,并将其与基于行政边界的社区类型或基于人口普查的城市/农村状况相结合。
■我们在宾夕法尼亚州中部和东北部沿城乡梯度的1069个社区中确定了四个剖面:“稀疏的农村,“”农村发达,\"\"内郊区,“和”被剥夺的城市核心。“城市地区人口稠密,拥有大量的体力活动资源和食物出口;然而,他们也有较高的社会经济匮乏和较低的绿色度。与“发达农村”相比,“T2D发病几率在“贫困城市核心”(1.24,CI=1.16-1.33)和“内郊区”(1.10,CI=1.04-1.17)中较高。这些与基于模型的社区概况的关联比与行政边界或城市/农村地位相结合时要弱。
■我们的研究结果表明,在城市地区,糖尿病性特征压倒了T2D保护特征。社区概况支持行政社区类型和城乡地位的建构有效性,此前报道,评估该地区T2D发病的地理差异。
UNASSIGNED: Understanding geographic disparities in type 2 diabetes (T2D) requires approaches that account for communities\' multidimensional nature.
UNASSIGNED: In an electronic health record nested case-control study, we identified 15,884 cases of new-onset T2D from 2008 to 2016, defined using encounter diagnoses, medication orders, and laboratory test results, and frequency-matched controls without T2D (79,400; 65,069 unique persons). We used finite mixture models to construct community profiles from social, natural, physical activity, and food environment measures. We estimated T2D odds ratios (OR) with 95% confidence intervals (CI) using logistic generalized estimating equation models, adjusted for sociodemographic variables. We examined associations with the profiles alone and combined them with either community type based on administrative boundaries or Census-based urban/rural status.
UNASSIGNED: We identified four profiles in 1069 communities in central and northeastern Pennsylvania along a rural-urban gradient: \"sparse rural,\" \"developed rural,\" \"inner suburb,\" and \"deprived urban core.\" Urban areas were densely populated with high physical activity resources and food outlets; however, they also had high socioeconomic deprivation and low greenness. Compared with \"developed rural,\" T2D onset odds were higher in \"deprived urban core\" (1.24, CI = 1.16-1.33) and \"inner suburb\" (1.10, CI = 1.04-1.17). These associations with model-based community profiles were weaker than when combined with administrative boundaries or urban/rural status.
UNASSIGNED: Our findings suggest that in urban areas, diabetogenic features overwhelm T2D-protective features. The community profiles support the construct validity of administrative-community type and urban/rural status, previously reported, to evaluate geographic disparities in T2D onset in this geography.