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Role of Place in Explaining Racial Heterogeneity in Cognitive Outcomes among Older Adults

Published online by Cambridge University Press:  28 September 2015

Sze Yan Liu ,
M. Maria Glymour ,
Laura B. Zahodne ,
Christopher Weiss  and
Jennifer J. Manly
Sze Yan Liu*
Affiliation:
Center for Population and Development Studies, Harvard University School of Public Health, Cambridge, Massachusetts
M. Maria Glymour
Affiliation:
Human Development, and Health, Harvard School of Public Health, Boston, Massachusetts and Department of Epidemiology and Biostatistics, University of California, San Francisco, California
Laura B. Zahodne
Affiliation:
Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Medical Center, New York, New York
Christopher Weiss
Affiliation:
Vera Institute of Justice, New York, New York
Jennifer J. Manly
Affiliation:
Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Medical Center, New York, New York
*
Correspondence and reprint requests to: Sze Yan Liu, Center for Population and Development Studies, Harvard University School of Public Health, 9 Bow Street, Cambridge, MA, 02138. E-mail: [email protected]
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Abstract

Racially patterned disadvantage in Southern states, especially during the formative years of primary school, may contribute to enduring disparities in adult cognitive outcomes. Drawing on a lifecourse perspective, we examine whether state of school attendance affects cognitive outcomes in older adults and partially contributes to persistent racial disparities. Using data from older African American and white participants in the national Health and Retirement Study (HRS) and the New York based Washington Heights Inwood Cognitive Aging Project (WHICAP), we estimated age-and gender-adjusted multilevel models with random effects for states predicting years of education and cognitive outcomes (e.g., memory and vocabulary). We summarized the proportion of variation in outcomes attributable to state of school attendance and compared the magnitude of racial disparities across states. Among WHICAP African Americans, state of school attendance accounted for 9% of the variance in years of schooling, 6% of memory, and 12% of language. Among HRS African Americans, state of school attendance accounted for 13% of the variance in years of schooling and also contributed to variance in cognitive function (7%), memory (2%), and vocabulary (12%). Random slope models indicated state-level African American and white disparities in every Census region, with the largest racial differences in the South. State of school attendance may contribute to racial disparities in cognitive outcomes among older Americans. Despite tremendous within-state heterogeneity, state of school attendance also accounted for some variability in cognitive outcomes. Racial disparities in older Americans may reflect historical patterns of segregation and differential access to resources such as education. (JINS, 2015, 21, 677–687)

Keywords

AgingGeographical variationRaceCognitive functioningMemoryVisual-spatial
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 21 , Issue 9 , October 2015 , pp. 677 - 687
Copyright
Copyright © The International Neuropsychological Society 2015 

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References

Anderson, J.D. (1988). The education of blacks in the South, 1860-1935. Chapel Hill: University of North Carolina Press.CrossRefGoogle Scholar
Ashendorf, L., Jefferson, A.L., Green, R.C., & Stern, R.A. (2009). Test–retest stability on the WRAT-3 reading subtest in geriatric cognitive evaluations. Journal of Clinical and Experimental Neuropsychology, 31(5), 605–610.CrossRefGoogle ScholarPubMed
Benton, A.L. (1955). The Benton Visual Retention Test. New York: The Psychological Corporation.Google Scholar
Benton, A.L. (1974). Revised Visual Retention Test (4th ed.). New York: The Psychological Corporation.Google Scholar
Benton, A.L., & Hamsher, K.d. (1976). Multilingual Aphasia Examination. Iowa City, IA: University of Iowa.Google Scholar
Blessed, G., Tomlinson, B.E., & Roth, M. (1968). The association between quantitative measures of senile change in the cerebral grey matter of elderly subjects. British Journal of Psychology, 114, 797–811.CrossRefGoogle ScholarPubMed
Brand, C. (1987). Intelligence-testing - Bryter Still and Bryter. Nature, 328(6126), 110.Google Scholar
Brandt, J., Spencer, M., & Folstein, M. (1988). The telephone interview for cognitive status. Neuropsychiatry, Neuropsychology, & Behavioral Neurology, 1(2), 111–117.Google Scholar
Buschke, H., & Fuld, P.A. (1974). Evaluating storage, retention, and retrieval in disordered memory and learning. Neurology, 24, 1019–1025.Google Scholar
Card, D., & Krueger, A.B. (1992a). Does school quality matter? Returns to education and the characteristics of public schools in the United States. The Journal of Political Economy, 100(1), 1–40.Google Scholar
Card, D., & Krueger, A.B. (1992b). School quality and black-white relative earnings: A direct assessment. The Quarterly Journal of Economics, 107(1), 151–200.Google Scholar
Corti, M.C., Guralnik, J.M., Ferrucci, L., Izmirlian, G., Leveille, S.G., Pahor, M., & Havlik, R.J. (1999). Evidence for a black-white crossover in all-cause and coronary heart disease mortality in an older population: The North Carolina EPESE. American Journal of Public Health, 89(3), 308–314.Google Scholar
Crowe, M., Clay, O.J., Martin, R.C., Howard, V.J., Wadley, V.G., Sawyer, P., &Allman, R.M. (2013). Indicators of childhood quality of education in relation to cognitive function in older adulthood. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 68(2), 198–204.Google Scholar
D’Elia, L.F., Satz, P., Uchiyama, C.L., & White, T. (1994). Color Trails Test Professional Manual. Odessa, FL: Psychological Assessment Resources.Google Scholar
Fang, J., Madhavan, S., & Alderman, M.H. (1996). The association between birthplace and mortality from cardiovascular causes among black and white residents of New York City. New England Journal of Medicine, 335(21), 1545–1551.Google Scholar
Folstein, M., Folstein, S., & McHugh, P. (1975). “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.CrossRefGoogle ScholarPubMed
Frisvold, D., & Golberstein, E. (2010). School quality and the education - health relationship: Evidence from Blacks in segregated schools. Journal of Health Economics, 30(6), 1232–1245.Google Scholar
Glymour, M.M., Avendano, M., & Berkman, L. (2007). Is the ‘stroke belt’ worn from childhood?: Risk of first stroke and state of residence in childhood and adulthood. Stroke, 38, 2415–2421.Google Scholar
Glymour, M.M., & Manly, J. (2008). Lifecourse social conditions and racial and ethnic patterns of cognitive aging. Neuropsychology Review, 18(3), 223–254.Google Scholar
Goodglass, H., & Kaplan, D. (1983). The assessment of aphasia and related disorders (2nd ed.). Philadelphia: Lea and Febiger.Google Scholar
Greenberg, M., & Schneider, D. (1992). Region of birth and mortality of blacks in the United-States. International Journal of Epidemiology, 21(2), 324–328.Google Scholar
Gurland, B., Golden, R.R., Teresi, J., & Challop, J. (1984). The SHORT-CARE: An efficient instrument for the assessment of depression, dementia, and disability. Journals of Gerontology, 39, 166–169.CrossRefGoogle ScholarPubMed
Heeringa, S.G., & Connor, J. (1995). Technical description of the Health and Retirement Study sample design (HRS/AHEAD Documentation Report No. DR-002). Ann Arbor, MI: Survey Research Center, University of Michigan.CrossRefGoogle Scholar
Hobbs, F., & Stoops, N. (2002). Demographic trends in the 20th century. In C. Bureau (Ed.), Census 2000 special reports, series CENSR-4. Washington, DC: U.S. Government Printing Office.Google Scholar
Howard, V.J., Kleindorfer, D.O., Judd, S.E., McClure, L.A., Safford, M.M., Rhodes, J.D., & Howard, G. (2011). Disparities in stroke incidence contributing to disparities in stroke mortality. Annals of Neurology, 69(4), 619–627.Google Scholar
Izquierdo-Porrera, A.M., & Waldstein, S.R. (2002). Cardiovascular risk factors and cognitive function in African Americans. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 57(4), P377–P380.Google Scholar
Johnson, N. (2000). The racial crossover in comorbidity, disability, and mortality. Demography, 37(3), 267–283.CrossRefGoogle ScholarPubMed
Johnson, R.C. (2011). Long-run impacts of school desegregation & school quality on adult attainments. NBER Working Paper 16664. Cambridge, MA: National Bureau of Economic Research.CrossRefGoogle Scholar
Kaplan, E., Goodglass, H., & Weintraub, S. (1983). Boston Naming Test. Philadelphia, PA: Lea & Febiger.Google Scholar
Lemann, N. (1991). The promised land: The great black migration and how it changed America. New York: AA Knopf.Google Scholar
Manly, J.J., Bell-McGinty, S., Tang, M.X., Schupf, N., Stern, Y., & Mayeux, R. (2005). Implementing diagnostic criteria and estimating frequency of mild cognitive impairment in an urban community. Archives of Neurology, 62, 1739–1746.Google Scholar
Margo, R.A. (1985). Disenfranchisement, School finance, and the economics of segregated schools in the United States South, 1980-1910. New York: Garland Publishing.Google Scholar
Margo, R.A. (1990). Race and schooling in the South, 1880-1950: An economic history. Chicago: University of Chicago Press.Google Scholar
Mattis, S. (1988). Dementia Rating Scale: Professional manual. Odessa, FL: Psychological Assessment Resources.Google Scholar
Mehta, K.M., Simonsick, E.M., Rooks, R., Newman, A.B., Pope, S.K., Rubin, S.M., &Yaffe, K. (2004). Black and white differences in cognitive function test scores: What explains the difference? Journal of the American Geriatrics Society, 52(12), 2120–2127.Google Scholar
Morgan, A.A., Marsiske, M., & Whitfield, K.E. (2007). Characterizing and explaining differences in cognitive test performance between African American and European American older adults. Experimental Aging Research, 34(1), 80–100.Google Scholar
Obisesan, T.O., Vargas, C.M., & Gillum, R.F. (2000). Geographic variation in stroke risk in the United States - Region, urbanization, and hypertension in the Third National Health and Nutrition Examination Survey. Stroke, 31(1), 19–25.Google Scholar
Ofstedal, M.B., McAuley, G.F., & Herzog, A.R. (2002). Documentation of cognitive functioning measures in the health and retirement study (HRS/AHEAD Documentation Report). Ann Arbor, MI: Survey Research Center, University of Michigan.Google Scholar
Rosen, W. (1981). The Rosen Drawing Test. Bronx, NY: Veterans Administration Medical Center.Google Scholar
Sayegh, P., Arentoft, A., Thaler, N.S., Dean, A.C., & Thames, A.D. (2014). Quality of education predicts performance on the wide range achievement test-4th edition word reading subtest. Archives of Clinical Neuropsychology, 29(8), 731–736.Google Scholar
Schneider, D., Greenberg, M.R., & Lu, L.L. (1997). Region of birth and mortality from circulatory diseases among black Americans. American Journal of Public Health, 87(5), 800–804.Google Scholar
Schwartz, B.S., Glass, T.A., Bolla, K.I., Stewart, W.F., Glass, G., Rasmussen, M., & Bandeen-Roche, K. (2004). Disparities in cognitive functioning by race/ethnicity in the Baltimore Memory Study. Environmental Health Perspectives, 112(3), 314–320.CrossRefGoogle Scholar
Sellers, A.H., Burns, W.J., & Guyrke, J. (2002). Differences in young children’s IQs on the Wechsler Preschool and Primary Scale of Intelligence-Revised as a function of stratification variables. Applied Neuropsychology, 9(2), 65–73.Google Scholar
Siedlecki, K.L., Honig, L.S., & Stern, Y. (2008). Exploring the structure of a neuropsychological battery across healthy elders and those with questionable dementia and Alzheimer’s disease. Neuropsychology, 22, 400–411.CrossRefGoogle ScholarPubMed
Siedlecki, K.L., Manly, J.J., Brickman, A.M., Schupf, N., Tang, M.X., & Stern, Y. (2010). Do neuropsychological tests have the same meaning in Spanish speakers as they do in English speakers? Neuropsychology, 22, 402–411.CrossRefGoogle Scholar
Sloan, F.A., & Wang, J. (2005). Disparities among older adults in measures of cognitive function by race or ethnicity. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 60(5), P242–P250.Google Scholar
Snijders, T.A.B., & Bosker, R.J. (1994). Modeled variance in two-level models. Sociological Methods & Research, 22(3), 342–363.Google Scholar
Stern, Y., Andrews, H., Pittman, J., Sano, M., Tatemichi, T., Lantigua, R., & Mayeux, R. (1992). Diagnosis of dementia in a heterogeneous population. Development of a neuropsychological paradigm-based diagnosis of dementia and quantified correction for the effects of education. Archives of Neurology, 49, 453–460.CrossRefGoogle Scholar
Tang, M.X., Cross, P., Andrews, H., Jacobs, D.M., Small, S., Bell, K., Merchant, C., & Mayeux, R. (2001). Incidence of Alzheimer’s disease in African-Americans, Caribbean Hispanics and Caucasians in northern Manhattan. Neurology, 56, 49–56.Google Scholar
Tolnay, S.E. (2003). The African American “Great Migration” and beyond. Annual Review of Sociology, 29, 209–232.CrossRefGoogle Scholar
Wallace, R.B., & Herzog, A. (1995). Overview of the health measures in the health and retirement study. Journal of Human Resources, 30(Suppl.), S84–S107.Google Scholar
Wechsler, D. (1981). Wechsler Adult Intelligence Scale-Revised. New York, NY: The Psychological Corporation.Google Scholar
Wilkinson, G.S. (1993). Wide Range Achievement Test 3 - administration manual. Wilmington, DE: Jastak Associates, Inc.Google Scholar
Williams, W. (1998). Are we raising smarter children today? School and home influences on IQ. In U. Neisser (Ed.), The rising curve: Long-term gains in IQ and related measures (pp. 125–154). Washington, DC: American Psychological Association.Google Scholar
Yao, L., & Robert, S.A. (2011). Examining the racial crossover in mortality between African American and white older adults: A multilevel survival analysis of race, individual socioeconomic status, and neighborhood socioeconomic context. Journal of Aging Research, 2011, 132073.Google Scholar