Characterizing social environment's association with neurocognition using census and crime data linked to the Philadelphia Neurodevelopmental Cohort.
Characterizing social environment's association with neurocognition using census and crime data linked to the Philadelphia Neurodevelopmental Cohort.
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DOI：
10.1017/s0033291715002111
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发表时间：
2016-02
影响因子：
6.9
通讯作者：
Gur RC
中科院分区：
文献类型：
--
作者：
Moore TM;Martin IK;Gur OM;Jackson CT;Scott JC;Calkins ME;Ruparel K;Port AM;Nivar I;Krinsky HD;Gur RE;Gur RC
“环境”的贡献已在与健康有关的各种和多个领域进行了调查。然而，在大规模基因组研究的背景下，重点一直是获得个体水平的内在表型，并为未来的分解留下环境。地理社会研究表明，环境层面的变量可以减少，这些复合材料可以与其他变量一起使用，作为研究中环境的直观，精确的表示。使用来自费城地区的大型社区样本（N = 9498），参与者地址与2010年人口普查和犯罪数据相关联。然后对这些数据进行因素分析（探索性因素分析; EFA），以得出参与者环境的社会和犯罪维度。这些被用来计算环境水平的分数，这是与个人层面的变量合并。我们估计了一个探索性的多层次结构方程模型（MSEM），探索不同社区的环境和个人层面的变量之间的关联。EFA显示，人口普查数据最好由两个因素代表，一个是社会经济地位，另一个是家庭/语言。犯罪数据最好用单一犯罪因素来表示。MSEM变量具有良好的拟合（例如，比较拟合指数= 0.98），并揭示环境与神经认知表现的关联最大（β = 0.41，p < 0.0005），其次是父母教育（β = 0.23，p < 0.0005）。环境层面的变量可以组合起来，以创建用于更大的统计模型的因子得分或复合数据。我们的结果与文献一致，表明个人层面的社会人口特征（例如种族和性别）和家庭社会资本的各个方面（例如父母教育）与神经认知表现具有统计关系。
The contribution of ‘environment’ has been investigated across diverse and multiple domains related to health. However, in the context of large-scale genomic studies the focus has been on obtaining individual-level endophenotypes with environment left for future decomposition. Geo-social research has indicated that environment-level variables can be reduced, and these composites can then be used with other variables as intuitive, precise representations of environment in research. Using a large community sample (N = 9498) from the Philadelphia area, participant addresses were linked to 2010 census and crime data. These were then factor analyzed (exploratory factor analysis; EFA) to arrive at social and criminal dimensions of participants’ environments. These were used to calculate environment-level scores, which were merged with individual-level variables. We estimated an exploratory multilevel structural equation model (MSEM) exploring associations among environment- and individual-level variables in diverse communities. The EFAs revealed that census data was best represented by two factors, one socioeconomic status and one household/language. Crime data was best represented by a single crime factor. The MSEM variables had good fit (e.g. comparative fit index = 0.98), and revealed that environment had the largest association with neurocognitive performance (β = 0.41, p < 0.0005), followed by parent education (β = 0.23, p < 0.0005). Environment-level variables can be combined to create factor scores or composites for use in larger statistical models. Our results are consistent with literature indicating that individual-level socio-demographic characteristics (e.g. race and gender) and aspects of familial social capital (e.g. parental education) have statistical relationships with neurocognitive performance.