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Characterizing social environment's association with neurocognition using census and crime data linked to the Philadelphia Neurodevelopmental Cohort

  • T. M. Moore (a1), I. K. Martin (a1), O. M. Gur (a2), C. T. Jackson (a1), J. C. Scott (a1), M. E. Calkins (a1), K. Ruparel (a1), A. M. Port (a1), I. Nivar (a1), H. D. Krinsky (a1), R. E. Gur (a1) and R. C. Gur (a1)...

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.

Copyright

Corresponding author

* Address for correspondence: T. M. Moore, Ph.D., M.Sc., University of Pennsylvania, Philadelphia, Pennsylvania, USA. (Email: tymoore@upenn.edu)

References

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Barros, AJ, Victora, CG (2005). A nationwide wealth score based on the 2000 Brazilian demographic census. Revista de Saúde Pública 39, 523529.
Bentler, PM, Yuan, K-H (1998). Tests for linear trend in the smallest eigenvalues of the correlation matrix. Psychometrika 63, 131144.
Berkman, LF, Kawachi, I, Glymour, M (eds) (2014). Social Epidemiology. Oxford University Press: New York, NY.
Branas, CC, Cheney, RA, MacDonald, JM, Tam, VW, Jackson, TD, Ten Have, TR (2011). A difference-in-differences analysis of health, safety, and greening vacant urban space. American Journal of Epidemiology 171, 12961306.
Calkins, ME, Merikangas, KR, Moore, TM, Burstein, M, Behr, MA, Satterthwaite, TD, Ruparel, K, Wolf, DH, Roalf, DR, Menth, FD, Qiu, H, Chiavacci, R, Connolly, JJ, Sleiman, PMA, Gur, RC, Hakonarson, H, Gur, RE (2015). The Philadelphia Neurodevelopmental Cohort: constructing a deep phenotyping collaborative. Journal of Child Psychology and Psychiatry. Published online: 10 May 2015, doi:10.1111/jcpp.12416.
Calkins, ME, Moore, TM, Merikangas, KR, Burstein, M, Satterthwaite, TD, Bilker, WB, Ruparel, K, Chiavacci, R, Wolf, DH, Mentch, F, Qiu, H, Connolly, JJ, Sleiman, PA, Hakonarson, H, Gur, RC, Gur, RE (2014). The psychosis spectrum in a young US community sample: findings from the Philadelphia Neurodevelopmental Cohort. World Psychiatry 13, 296305.
Carey, GW (1966). The regional interpretation of Manhattan population and housing patterns through factor analysis. Geographical Review 56, 551569.
Cattell, RB (1966). The scree test for the number of factors. Multivariate Behavioral Research 1, 245276.
Ernst, JS (2001). Community-level factors and child maltreatment in a suburban county. Social Work Research 25, 133142.
Ferreira, I, Van Der Horst, K, Wendel-Vos, W, Kremers, S, Van Lenthe, FJ, Brug, J (2007). Environmental correlates of physical activity in youth – a review and update. Obesity Reviews 8, 129154.
Fuentes, M, Hart-Johnson, T, Green, CR (2007). The association among neighborhood socioeconomic status, race and chronic pain in black and white older adults. Journal of the National Medical Association 99, 1160.
Greenwood, TA, Swerdlow, NR, Gur, RE, Cadenhead, KS, Calkins, ME, Dobie, DJ, Freedman, R, Green, MF, Gur, RC, Lazzeroni, LC, Nuechterlein, KH, Olincy, A, Radant, AD, Ray, A, Schork, NJ, Seidman, LJ, Siever, LJ, Silverman, JM, Stone, WS, Sugar, CA, Tsuang, DW, Tsuang, MT, Turetsky, BI, Light, GA, Braff, DL (2013). Genome-wide linkage analyses of 12 endophenotypes for schizophrenia from the Consortium on the Genetics of Schizophrenia. American Journal of Psychiatry 170, 521532.
Gross, KS, McDermott, PA (2009). Use of city-archival data to inform dimensional structure of neighborhoods. Journal of Urban Health 86, 161182.
Gur, RC, Richard, J, Hughett, P, Calkins, ME, Macy, L, Bilker, WB, Brensinger, C, Gur, RE (2010). A cognitive neuroscience-based computerized battery for efficient measurement of individual differences: standardization and initial construct validation. Journal of Neuroscience Methods 187, 254262.
Gur, RE, Nimgaonkar, VL, Almasy, L, Calkins, ME, Ragland, JD, Pogue-Geile, MF, Kanes, S, Blanjero, J, Gur, RC (2007). Neurocognitive endophenotypes in a multiplex multigenerational family study of schizophrenia. American Journal of Psychiatry 164, 813819.
Hackman, DA, Farah, MJ (2009). Socioeconomic status and the developing brain. Trends in Cognitive Sciences 13, 6573.
Hackman, DA, Farah, MJ, Meaney, MJ (2010). Socioeconomic status and the brain: mechanistic insights from human and animal research. Nature Reviews Neuroscience 11, 651659.
Havard, S, Deguen, S, Bodin, J, Louis, K, Laurent, O, Bard, D (2008). A small-area index of socioeconomic deprivation to capture health inequalities in France. Social Science & Medicine 67, 20072016.
Herbert, DT (1968). Principal components analysis and British studies of urban-social structure. The Professional Geographer 20, 280283.
Hox, JJ (1998). Multilevel modeling: when and why. In Classification, Data Analysis, and Data Highways (ed. I. Balderjahn, R. Mathar and M. Schader), pp. 147154. New York: Springer Verlag.
James, SA, Kleinbaum, DG (1976). Socioecologic stress and hypertension related mortality rates in North Carolina. American Journal of Public Health 66, 354358.
Jones, FL (1965). A social profile of Canberra, 1961. Journal of Sociology 1, 107120.
Krabbendam, L, Hooker, CI, Aleman, A (2014). Neural effects of the social environment. Schizophrenia Bulletin 40, 248251.
Langlois, A, Kitchen, P (2001). Identifying and measuring dimensions of urban deprivation in Montreal: an analysis of the 1996 census data. Urban Studies 38, 119139.
Lauer, K (1994). The risk of multiple sclerosis in the USA in relation to sociogeographic features: a factor-analytic study. Journal of Clinical Epidemiology 47, 4348.
Li, G, Weng, Q (2007). Measuring the quality of life in city of Indianapolis by integration of remote sensing and census data. International Journal of Remote Sensing 28, 249267.
Lo, CP, Faber, BJ (1997). Integration of Landsat Thematic Mapper and census data for quality of life assessment. Remote Sensing of Environment 62, 143157.
Lovasi, GS, Hutson, MA, Guerra, M, Neckerman, KM (2009). Built environments and obesity in disadvantaged populations. Epidemiologic Reviews 31, 720.
Manolio, TA, Bailey-Wilson, JE, Collins, FS (2006). Genes, environment and the value of prospective cohort studies. Nature Reviews Genetics 7, 812820.
McEwen, BS (2012). Brain on stress: how the social environment gets under the skin. Proceedings of the National Academy of Sciences of the United States of America 109(Suppl. 2), 1718017185.
McEwen, BS, Gianaros, PJ (2010). Central role of the brain in stress and adaptation: links to socioeconomic status, health, and disease. Annals of the New York Academy of Sciences 1186, 190222.
McGinn, AP, Evenson, KR, Herring, AH, Huston, SL, Rodriguez, DA (2008). The association of perceived and objectively measured crime with physical activity: a cross-sectional analysis. Journal of Physical Activity & Health 5, 117.
Mezuk, B, Li, X, Cederin, K, Concha, J, Kendler, KS, Sundquist, J, Sundquist, K (2015). Ethnic enclaves and risk of psychiatric disorders among first- and second-generation immigrants in Sweden. Social Psychiatry and Psychiatric Epidemiology. Published online: 27 August 2015. doi:10.1007/s00127-015-1107-1.
Moore, TM, Reise, SP, Gur, RE, Hakonarson, H, Gur, RC (2015). Psychometric properties of the Penn Computerized Neurocognitive Battery. Neuropsychology 29, 235246.
Muthén, LK, Muthén, BO (1998–2013). Mplus User's Guide, 7th edn. Muthén & Muthén: Los Angeles, CA.
Noble, KG, McCandliss, BD, Farah, MJ (2007). Socioeconomic gradients predict individual differences in neurocognitive abilities. Developmental Science 10, 464480.
R Core Team (2014). R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria (http://www.R-project.org/).
Ray, DM (1971). From factorial to canonical ecology: the spatial interrelationships of economic and cultural differences in Canada. Economic Geography, 47, 344367.
Revelle, W (2013). psych: Procedures for personality and psychological research . Northwestern University: Evanston, Illinois, USA (http://CRAN.R-project.org/package=psych).
Roberts, RE, McBee, GW (1968). Modernization and economic development in Mexico: a factor analytic approach. Economic Development and Cultural Change, 16, 603612.
Smoller, JW (2015). The genetics of stress-related disorders: PTSD, depression and anxiety disorders. Neuropsychopharmacology. Published online: 31 August 2015. doi:10.1038/npp.2015.266.
Sörbom, D (1989). Model modification. Psychometrika 54, 371384.
Tello, JE, Jones, J, Bonizzato, P, Mazzi, M, Amaddeo, F, Tansella, M (2005). A census-based socio-economic status (SES) index as a tool to examine the relationship between mental health services use and deprivation. Social Science & Medicine 61, 20962105.
Temkin, K, Rohe, WM (1998). Social capital and neighborhood stability: an empirical investigation. Housing Policy Debate 9, 6188.
Thurstone, LL (1935). The Vectors of Mind. University of Chicago Press: Chicago.
Vespa, J, Lewis, JM, Kreider, RM (2013). America's families and living arrangements: 2012. In Current Population Reports, pp. 20570. U.S. Census Bureau: Washington, DC.
Wang, MC, Kim, S, Gonzalez, AA, MacLeod, KE, Winkleby, MA (2007). Socioeconomic and food-related physical characteristics of the neighbourhood environment are associated with body mass index. Journal of Epidemiology and Community Health 61, 491498.
Warnecke, RB, Oh, A, Breen, N, Gehlert, S, Paskett, E, Tucker, KL, Lurie, N, Rebbeck, T, Goodwin, J, Flack, J, Srinivasan, S, Kerner, J, Heurtin-Roberts, S, Abeles, R, Tyson, FL, Patmios, G, Hiatt, RA (2008). Approaching health disparities from a population perspective: the National Institutes of Health Centers for Population Health and Health Disparities. American Journal of Public Health 98, 16081615.
Yen, IH, Syme, SL (1999). The social environment and health: a discussion of the epidemiologic literature. Annual Review of Public Health 20, 287308.

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