Characterizing social environment's association with neurocognition using census and crime data linked to the Philadelphia Neurodevelopmental Cohort. Issue 3 (23rd October 2015)

• Record Type: Journal Article
• Authors: Moore, T. M.; Martin, I. K.; Gur, O. M.; Jackson, C. T.; Scott, J. C.; Calkins, M. E.; Ruparel, K.; Port, A. M.; Nivar, I.; Krinsky, H. D.; Gur, R. E.; Gur, R. C.
• Is Part Of: Psychological medicine. Volume 46:Issue 3(2016)
• Page Start: 599
• Page End: 610
• Publication Date: 2015-10-23
• Subjects: Census, -- factor analysis, -- geo-coding, -- neurocognition, -- Philadelphia Neurodevelopmental Cohort, -- socioeconomic status; Psychiatry -- Periodicals; Medicine and psychology -- Periodicals; Clinical psychology -- Periodicals; 616.89
• DOI: 10.1017/S0033291715002111
• Languages: English
• ISSNs: 0033-2917

Abstract

Abstract : Background: 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. Method: 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. Results: 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). Conclusions: 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.