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Black–White Disparities in Functional Decline in Older Persons: The Role of Cognitive Function

¹ San Francisco Veterans Affairs Medical Center, California.
² Department of Medicine, Division of Geriatrics, University of California, San Francisco.
³ School of Nursing and Center for Aging and Life Course, Purdue University, West Lafayette, Indiana.

Address correspondence to Sandra Moody-Ayers, MD, or Kenneth Covinsky, San Francisco VA Medical Center (181G), 4150 Clement Street, San Francisco, CA 94121. E-mail: sandra.moody{at}med.va.gov or covinsky{at}medicine.ucsf.edu

	Abstract

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Background. Black elders have a greater frequency of functional decline than do white elders. The impact of cognitive function on explaining black–white disparities in functional decline has not been extensively explored.

Methods. To compare the extent to which different risk domains (comorbidity, smoking, socioeconomic status (SES), self-rated health, and cognitive function) explain more frequent functional decline in black elders, we studied 779 black and 4892 white community-dwelling adults aged 70 and older from the Assets and Health Dynamics Among the Oldest Old (AHEAD), a population-based cohort study begun in 1993. Our primary outcome was worse functional status at 2 years than at baseline. We used logistic regression to compare the unadjusted with the adjusted black–white odds ratios (ORs) after adjusting for each risk domain.

Results. At baseline black participants aged 70–79 had higher rates of smoking, diabetes, and hypertension; lower SES; and worse cognitive function than did white participants (p <.05 for all). The mean cognitive score was 15.7 in black and 21.8 in white participants (p <.01). Black participants had a higher frequency of 2-year functional decline than did white participants (10.9% vs 4.7%; OR = 2.61, 95% confidence interval [CI], 1.69–4.03 adjusted for age and sex). Adjustment for comorbidity and smoking did not significantly change the black–white OR, whereas self-rated health and SES accounted for about half the risk. Adjustment for cognitive function accounted for nearly all the associated decline (OR = 1.10, 95% CI, 0.67–1.79). Among participants aged 80 and over, those who were black had significantly lower risk for functional decline after adjustment for cognitive function (OR = 0.61, 95% CI, 0.38–0.96 vs OR = 1.08, 95% CI, 0.70–1.66 adjusted for age and sex only).

Conclusions. Cognitive function mediated the higher frequency of functional decline among black elders. Efforts to understand cognitive function may enhance our understanding of black–white disparities in health outcomes.

Understanding health and social causes of ethnic differences in health status is a critical first step in reducing disparities in health outcomes. Among potential causes of ethnic differences in functional decline or disability, for example, SES and chronic diseases have been shown to partially explain the differences, although SES has been shown to play a greater role (1,15). Both physical and cognitive dysfunction are noted to influence disability in general, and each has been shown to influence decline in the other across different ethnic and/or racial groups (15).

Additional risk factors, including self-rated health and health behaviors, have been studied in the examination of disparities in health outcomes, and have been shown to partially account for these disparities (2,9,16). However, cognitive function has not been examined as a potential explanatory factor in studies attempting to explain health disparities in elders. Cognitive function is intriguing because loss of cognitive function and dementia is strongly associated with mortality and declines in physical function, and may be a marker of lifelong adversity (15). Black elders are at higher risk than are white elders for loss of cognitive function, making it possible that cognitive function may help explain black–white health disparities (15,17). If differences in cognitive function are shown to explain higher rates of poor outcomes in black elders, further study of cognitive function may reveal important mechanisms that would narrow or eliminate some of the ethnic disparities in health outcomes. Additionally, how well cognitive test performance discerns cognitive ability must be considered.

We used a nationally representative sample to examine the extent to which different risk domains, such as comorbidity, self-rated health, SES, and cognitive function account for differences in frequencies of functional decline in black and white elders. We hypothesized that lower SES and worse baseline cognitive function would account for higher frequencies of functional decline in black elders. Further, we hypothesized that, because of evidence that black–white disparities in health outcomes diminish with age (crossover effect), the impact of these variables on black–white outcome differences would be greatest in the young old, and minimal in the old-old (6,8,11,12).

	METHODS

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Study Population
Participants were drawn from the Assets and Health Dynamics Among the Oldest Old (AHEAD) study, a nationally representative prospective population-based study of community-dwelling adults aged 70 years or older at the baseline interview in 1993 (N = 7447). A full description of the sampling and weighting procedures used in the AHEAD study has been described elsewhere (18). Most respondents aged 70–79 were interviewed by telephone (75%), and most respondents aged 80 or older were interviewed in person (76%). The interview type did not differ by ethnicity. The overall survey response rate was 80%, and the response rate did not differ significantly between those interviewed by telephone and those interviewed in person (19). Black participants were oversampled by 1.8 times to provide sufficient power for this group. Of the 7447 participants, we excluded the 98 participants of "other" but non-Hispanic ethnicity and the 418 participants of Hispanic ethnicity because there was insufficient power to analyze outcomes for this group. We excluded 689 participants (13.4% black vs 9.4% white, p <.01) because they had proxy respondents and 270 participants (4.9% black vs 4.2% white, p =.37) because they were missing data on essential covariates; after these exclusions, there were 5972 participants. Of these, 301 (6.8% black and 4.8% white, p <.01) were excluded because we were missing data about functional outcomes at follow-up; this exclusion left a final sample size of 5671 elders (4892 white, 779 black). The Institute for Social Research at the University of Michigan manages the data collection and obtained oral or written informed consent and Institutional Review Board approval for the AHEAD study.

Measures
Outcome measure.-- Our primary outcome measure was functional decline, defined as dependence in more basic activities of daily living (ADLs) at 2-year follow-up than at baseline. ADL dependence was determined by asking participants at each interview if they needed assistance performing any of five basic ADL (dressing, bathing, eating, transferring, and toileting). Because ADL decline often occurs before death (20), we also examined in secondary analyses a second outcome measure that was a composite of either ADL decline or death by 2 years.

Independent variable.-- To determine ethnicity participants were asked, "Do you consider yourself white or Caucasian, black or African American, American Indian, Asian, or something else?"

Explanatory variables.-- We grouped variables into domains of risk that we hypothesized might explain the relationship between black ethnicity and ADL decline. The first domain included comorbid conditions, which were obtained by asking the participant whether they had any of six comorbid conditions (hypertension, diabetes, cancer, lung disease, heart disease, or stroke). The second domain was smoking, which was determined by asking the participants whether they were current smokers at the time of the baseline interview. The third domain was self-rated health, for which participants rated their health as excellent, very good, good, fair, or poor. The fourth domain was SES and consisted of the highest grade of school or year of college completed (education), total household income, and total net worth or current value of their assets including individual retirement accounts, stocks, or mutual funds, checking and savings, and real estate (19). The last domain was cognitive function, which was measured at baseline with a validated 35-point scale developed for the AHEAD study (21). This scale is a multidimensional measure of cognitive function that is based on the Telephone Interview for Cognitive Status (TICS) (22–24) and modeled after the Mini-Mental State Examination (MMSE) (25,26). Cognitive score was derived from the following measures used on the AHEAD cognitive scale: immediate and delayed word recall (10 items), serial 7s, mental status items (current date, day of week, backward count from 20, object naming or word recognition, and president and vice president naming). Higher scores represent better cognitive functioning. Evidence supporting the validity of this scale has been published previously (21,27).

Statistical Analyses
All statistical analyses used the AHEAD sampling and design weights to account for the study's complex design (18). We stratified all analyses by age group (70–79 vs 80 years) because previous work suggests that black–white health outcome disparities decrease with advancing age, manifested as a crossover effect (6,8,11,12).

First, we compared the baseline characteristics of black and white participants in both age strata using chi-square tests for categorical variables and t tests for continuous variables. Next, we used logistic regression to compare the odds of the outcome ADL decline in black and white elders, adjusting for only age and sex (base model). We then examined the effect of each risk domain on black–white ADL decline differences, with the overall goal to examine the extent to which each risk domain explained black–white differences in outcomes. We created a series of logistic regression models, one for each risk domain (comorbid conditions, smoking, self-assessed health, SES, and cognitive function). In each model, we adjusted for the variables in the risk domain, in addition to age and sex. We then examined the differences between odds ratio (OR) for black ethnicity in the base model, which only controlled for age and sex, to the OR in each risk domain model. We repeated these analyses for the combined outcome of ADL decline or mortality. We also repeated these analyses using ordinal logistic regression in which the outcome was change in ADL score instead of our dichotomous outcome. We used the Software for the Statistical Analysis of Correlated Data (SUDAAN) for PCs (release 8.0, 2001; Research Triangle Institute; Research Triangle Park, NC) to perform all statistical analyses.

	RESULTS

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Characteristics of Participants
Selected baseline characteristics of the study participants stratified by age and ethnicity are presented in Table 1. In the 70- to 79-year-old group, black participants were more likely to be women, widowed, or unmarried. The prevalence of comorbid conditions varied across race groups. For example, black participants had a higher prevalence of hypertension, diabetes, and stroke whereas white participants had a higher prevalence of cancer, lung disease, and heart disease. Black participants were more likely than white participants to report current smoking (15% vs 11%) and to rate their health as fair or poor (48% vs 28%). Black participants also had worse functional status than white participants as measured by both ADL function and Instrumental Activities of Daily Living (IADL) score. For example, 24% of black participants were dependent in at least one IADL function compared with 16% of white participants (p =.01). Black participants had lower SES across all measures (education, household income, and net worth). For example, 41% of black participants had low total net worth (<$20,000) compared with 13% of white participants (p <.01). Cognitive test scores were significantly lower in black participants than in white participants (15.7 vs 21.7, p <.01).

2-Year Outcomes
At 2-year follow-up (Table 2), among participants aged 70–79, ADL decline was more frequent in black participants than in white participants (10.9% vs 4.7%, p <.01, unadjusted OR = 1.98, 95% confidence interval [CI], 1.37–2.86). Among participants aged 80 and older, ADL decline was essentially the same in both groups. Among participants aged 70–79, much of the difference in the combined ADL decline or mortality outcome was explained by more frequent ADL decline.

Impact of Individual Risk Domains on the Black–White ADL Decline or Mortality Differential
When we changed the outcome to a composite of either ADL decline or mortality, the results were consistent with the results for ADL decline alone (Table 4). Among participants aged 70–79, black participants had a greater risk for ADL decline or mortality than did white participants (OR = 2.02, 95% CI, 1.40–2.92) after adjusting for age and sex (the base model). Adjustment for comorbid conditions and smoking in addition to age and sex had little explanatory effect on the black–white ADL decline or mortality difference in participants aged 70–79 (OR declined from 2.02 to 2.00 and 1.98, respectively). In contrast, adjustment for self-rated health explained nearly half of the black–white ADL decline or mortality difference (OR dropped from 2.02 to 1.56). Adjustment for SES explained over half of the black–white ADL decline or mortality difference (OR dropped from 2.02 to 1.41), and adjustment for cognitive function accounted for the entire ADL decline or mortality difference in this age group (OR dropped from 2.02 to 1.04).

	DISCUSSION

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In this study, we examined the potential causes or mechanisms of black–white disparities in ADL decline in a cohort of community-dwelling elders. Potential explanatory variables grouped into risk domains were examined to determine the impact of each risk domain on the relationship between black ethnicity and ADL decline. First, this study confirms past work that black ethnicity is associated with a markedly increased risk of ADL decline in the young old and that the risk associated with black ethnicity lessens with increasing age. Second, baseline differences in self-rated health and SES (education, household income, and net worth) partially explained the impact of ethnicity on declining function. Third, cognitive function (or test performance) fully accounted for the large black–white differences in ADL decline in the young old (participants aged 70–79). Our results do not explain why cognitive function (or test performance) differs between black and white participants, or why these differences explain differentials in outcomes. However, our findings strongly suggest that cognitive function may be a target condition through which we can better learn the etiology of black–white disparities in health outcomes.

Cognitive function is sensitive to both environmental (e.g., educational and cultural) (28–33) and genetic (e.g., apolipoprotein E4 and intracellular adhesion molecules) (34–41) factors, both of which manifest differentially in different racial and ethnic groups. Several studies (31–33) have shown that lower educational levels are associated with lower test scores on cognitive measures used to diagnose cognitive impairment or dementia. In a review by Froehlich and colleagues (33), they highlight evidence showing that cognitive test scores and a diagnosis of dementia may be influenced by both educational and cultural biases. Other studies highlight potential genetic differences. For example, Fillenbaum and colleagues (39) showed that the E4 allele was more prevalent in African American than in white participants, and that the prevalence of E4 decreased with increasing age. Blazer and colleagues (40) found that the presence of the E4 allele was not independently associated with functional decline.

Many other factors that contribute to the differential manifestation of cognitive function among ethnic groups have been identified, including variation in the prevalence of specific chronic diseases (13,42), attainment and quality of education (31,33), and lifelong economic standing (1). These are some of the very factors that have been shown to contribute to the disparity in health outcomes between racial and ethnic groups, and are therefore important variables to explore in the context of cognitive function.

One reason cognitive function differences may explain black–white health disparities may be that cognitive function may tell us more about the impact of chronic conditions than simply their presence or absence. For many chronic diseases, cognitive function could be an end-organ consequence of disease and therefore be a marker of disease severity and inadequate or lack of access to treatment. For example, hypertension and stroke are not only more prevalent in black Americans, but often more severe, and are more likely to go untreated (43). Consequently, black Americans with hypertension are more likely to suffer hypertensive or vascular strokes than are white Americans, and therefore may be at higher risk for cognitive decline (44,45).

A second reason cognitive function may explain black–white health disparities is that cognitive function may be a marker of some life-course social factors that are more informative than "static" measures of SES. Thus, cognitive function may reflect unmeasured differences in life-course SES. For example, the quality of formal education may more powerfully influence lifetime cognitive function than may quantity alone, affecting reading and other cognitive skills (33,46,47). As argued by Manly and colleagues (31), matching on or controlling for quantity of education does not guarantee that the quality of education received by each ethnic group is comparable. Furthermore, how cognitive function is measured may be biased and confounded by measures of SES that are known to explain black–white differences in health outcomes.

We expected to find that our measures of SES would more fully explain the differences in black–white ADL decline because we included a measure (net worth) that potentially captured lifetime economic standing. However, SES only partially explained the differential in outcome. Assets accumulated during adulthood may not sufficiently capture the full impact of SES. Some have speculated that childhood SES has a greater impact than expected in the development of inequalities in adult diseases (48). Education and income have also been shown to be inadequate markers of past and current economic standing among elderly persons (3). Additionally, our measures did not capture the full spectrum of social status, as we did not use measures related to employment status, for example. In this study, SES was a self-reported measure and therefore is subject to recall bias. Nevertheless, we believe that our finding provides stronger support that SES alone does not fully explain racial or ethnic health disparities, but that there are other important factors that may be operating.

Although we demonstrated that black participants had worse scores on cognitive function tests than did white participants, it is not clear to what extent these differences reflect accumulated declines in cognitive function as opposed to biases in tests of cognitive function that result in overestimating cognitive impairment in black elders (30,46,47). For example, part of the AHEAD cognitive function score consisted of word recall, and it is possible that the words used are more familiar to white elders than to black elders because of black–white cultural differences. However, even if cultural bias in test measurement explained differences in cognitive performance, our findings would remain important because they would suggest that the same sociocultural biases that result in differential performance on cognitive tests may also contribute to ADL decline.

Contrary to our hypothesis that, among participants aged 80 and older, adjustment for SES and cognitive function would have little effect on black–white differences in outcome, adjustment for cognitive function decreased the OR for functional decline from 1.08 to 0.61, suggesting that among the oldest old, black elders have better functional outcomes after adjusting for cognitive function. Additionally, it is possible that black participants had more ADL decline at 2 years because they had lower ADL function than did white participants at baseline. However, when we controlled for baseline ADL function in additional analyses, cognitive function remained a powerful explanatory factor for the ethnicity difference in ADL decline.

Our study has several limitations. First, the measure of cognitive function used in this study, though based on common tests such as the MMSE and TICS, is not commonly used clinically. Thus, we cannot fully define how scores on our cognitive measure translate to clinical diagnoses. Second, we did not have sufficient power to examine the impact of cognitive function on outcomes in other ethnic groups. Third, it is possible that our results are explained by unmeasured differences in baseline function between black and white participants. We think that this is unlikely, however, as our results were similar when adjusted for baseline ADL function. Fourth, the AHEAD did not measure physical activity, which is likely to impact on disparities in outcomes. Lastly, the frequency of proxy interviews and self-respondents who had missing follow-up ADL information was moderately higher in black participants than in white participants. Although the difference was statistically significant, the magnitude of the difference was small.

Summary
Although SES and self-assessed health partially accounted for the difference in ADL decline in black and white participants, cognitive function fully accounted for the impact of ethnicity on the difference in the outcome. Because cognitive function is likely influenced by multiple factors that may be the result of ethnic disparities, including attainment and quality of education, economic standing, and complications and inadequate treatment of comorbidities, examining the influence that these factors have on cognitive function may be a window through which we can begin to see potential mechanisms that underlie ethnic health disparities.

	Acknowledgments

 
Dr. Moody-Ayers was supported in part by a Minority Research Supplement grant (1R01AG019827-01) from the National Institute on Aging and a pilot investigator grant through the Resource Centers for Minority Aging Research/Center for Aging in Diverse Communities at the University of California, San Francisco, funded by the National Institute on Aging and the National Institute on Nursing Research. Dr. Covinsky was supported in part by a grant from the National Institute on Aging (1R01AG019827-01) and an independent scientist award (K02HS000006-01).

An abstract of this study was presented at the 26th Annual Meeting of the Society of General Internal Medicine in Vancouver, Canada, May 2003, and at the American Geriatrics Society 2003 Annual Scientific Meeting in Baltimore, Maryland, May 2003.

We are indebted to Drs. C. Seth Landefeld and Louise Walter for their review of the manuscript.

	Footnotes

 
Decision Editor: John E. Morley, MB, BCh

Received December 24, 2003

Accepted April 21, 2004

	References

Top Abstract Methods Results Discussion References

 

1. Kington RS, Smith JP. Socioeconomic status and racial and ethnic differences in functional status associated with chronic diseases. Am J Public Health. 1997;87:805-810.[Abstract/Free Full Text]
2. Wong MD, Shapiro MF, Boscardin WJ, Ettner SL. Contribution of major diseases to disparities in mortality. N Engl J Med. 2002;347:1585-1592.[Abstract/Free Full Text]
3. Mendes de Leon CF, Beckett LA, Fillenbaum GG, et al. Black-white differences in risk of becoming disabled and recovering from disability in old age: a longitudinal analysis of two EPESE populations. Am J Epidemiol. 1997;145:488-497.[Abstract/Free Full Text]
4. Otten Jr MW, Teutsch SM, Williamson DF, Marks JS. The effect of known risk factors on the excess mortality of black adults in the United States. JAMA. 1990;263:845-850.[Abstract]
5. Pappas F, Queen S, Hadden W, Fisher G. The increasing disparity in mortality between socioeconomic groups in the United States, 1960 and 1986. N Engl J Med. 1993;329:103-109.[Abstract/Free Full Text]
6. Guralnik JM, Land KC, Blazer D, Fillenbaum GG, Branch LG. Educational status and active life expectancy among older blacks and whites. N Engl J Med. 1993;329:110-116.[Abstract/Free Full Text]
7. Liao Y, McGee DL, Cao G, Cooper RS. Black-white differences in disability and morbidity in the last years of life. Am J Epidemiol. 1999;149:1097-1103.[Abstract/Free Full Text]
8. Mendes de Leon CF, Fillenbaum GG, Williams CS, Brock DB, Beckett LA, Berkman LF. Functional disability among elderly blacks and whites in two diverse areas: the New Haven and North Carolina EPESE. Established Populations for the Epidemiologic Studies of the Elderly. Am J Public Health. 1995;85:994-998.[Abstract/Free Full Text]
9. Manton KG, Gu X. Changes in the prevalence of chronic disability in the United States black and nonblack population above age 65 from 1982 to 1999. Proc Natl Acad Sci U S A. 2001;98:6354-6359.[Abstract/Free Full Text]
10. Gibson RC. Age-by-race differences in the health and functioning of elderly persons. J Aging Health. 1991;3:335-351.[Abstract/Free Full Text]
11. Johnson NE. The racial crossover in comorbidity, disability, and mortality. Demography. 2000;37:267-283.[Medline]
12. Corti MC, Guralnik JM, Ferrucci L, et al. Evidence for a black-white crossover in all-cause and coronary heart disease mortality in an older population: the North Carolina EPESE. Am J Public Health. 1999;89:308-314.[Abstract/Free Full Text]
13. Fried LP, Guralnik JM. Disability in older adults: evidence regarding significance, etiology, and risk. J Am Geriatr Soc. 1997;45:92-100.[Medline]
14. Gill TM, Baker DI, Gottschalk M, Peduzzi PN, Allore H, Byers A. A program to prevent functional decline in physically frail, elderly persons who live at home. N Engl J Med. 2002;347:1068-1074.[Abstract/Free Full Text]
15. Black SA, Rush RD. Cognitive and functional decline in adults aged 75 and older. J Am Geriatr Soc. 2002;50:1978-1986.[Medline]
16. Gibson RC. Race and the self-reported health of elderly persons. J Gerontol. 1991;46:S235-S242.[Medline]
17. Green RC, Cupples LA, Go R, et al. Risk of dementia among white and African American relatives of patients with Alzheimer disease. JAMA. 2002;287:329-336.[Abstract/Free Full Text]
18. Heeringa SG. Technical Description of the Asset and Health Dynamics (AHEAD) Sample Design. Ann Arbor: University of Michigan; 1995:1–64.
19. Soldo BJ, Hurd MD, Rodgers WL, Wallace RB. Asset and health dynamics among the oldest old: an overview of the AHEAD study. J Gerontol B Psychol Sci Soc Sci. 1997;52:Spec No: 1-20.
20. Covinsky KE, Eng C, Lui LY, Sands LP, Yaffe K. The last 2 years of life: functional trajectories of frail older people. J Am Geriatr Soc. 2003;51:492-498.[Medline]
21. Langa KM, Chernew ME, Kabeto MU, et al. National estimates of the quantity and cost of informal caregiving for the elderly with dementia. J Gen Intern Med. 2001;16:770-778.[Medline]
22. Brandt J, Spencer M, Folstein M. The telephone interview for cognitive status. Neuropsychiatry Neuropsychol Behav Neurol. 1988;1:111-117.
23. Plassman BL, Newman T, Welsh K, Breitner J. Properties of the telephone interview for cognitive status. Application in epidemiological and longitudinal studies. Neuropsychiatry Neuropsychol Behav Neurol. 1994;7:235-241.
24. Herzog AR, Wallace RB. Measures of cognitive functioning in the AHEAD Study. J Gerontol B Psychol Sci Soc Sci. 1997;52:Spec No: 37-48.
25. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189-198.[Medline]
26. Ofstedal MB, McAuley GF, Herzog RA, HRS Working Group. HRS/AHEAD Documentation Report: Documentation of Cognitive Functioning Measures in the Health and Retirement Study, 2002. Available at: http://hrsonline.isr.umich.edu/docs/sho_refs.php?hfyle=index&xtyp=3. Accessed 12/19/2003.
27. de Jager CA, Budge MM, Clarke R. Utility of TICS-M for the assessment of cognitive function in older adults. Int J Geriatr Psychiatry. 2003;18:318-324.[Medline]
28. Silverman JM, Smith CJ, Marin DB, Mohs RC, Propper CB. Familial patterns of risk in very late-onset Alzheimer disease. Arch Gen Psychiatry. 2003;60:190-197.[Abstract/Free Full Text]
29. Wilson RS, Bennett DA, Bienias JL, et al. Cognitive activity and incident AD in a population-based sample of older persons. Neurology. 2002;59:1910-1914.[Abstract/Free Full Text]
30. Shadlen MF, McCormick WC, Larson EB. Research agenda for understanding Alzheimer disease in diverse populations: work group on cultural diversity, Alzheimer's association. Alzheimer Dis Assoc Disord. 2002;16:Suppl 2: S96-S100.
31. Manly, JJ, Jacobs DM, Sano M, et al. Cognitive test performance among nondemented elderly African Americans and whites. Neurology. 1998;50:1238-1245.[Abstract/Free Full Text]
32. Murden RA, McRae TD, Kaner S, Bucknam ME. Mini-Mental State Exam scores vary with education in blacks and whites. J Am Geriatr Soc. 1991;39:149-155.[Medline]
33. Froehlich TE, Bogardus ST, Inouye SK. Dementia and race: are there differences between African Americans and Caucasians? J Am Geriatr Soc. 2001;49:477-484.[Medline]
34. Bretsky P, Guralnik JM, Launer L, Albert M, Seeman TE. The role of APOE-epsilon4 in longitudinal cognitive decline: MacArthur Studies of Successful Aging. Neurology. 2003;60:1077-1081.[Abstract/Free Full Text]
35. Barak Y, Aizenberg D, Achiron A. Concordance for cognitive impairment: a study of 50 community-dwelling elderly female-female twin pairs. Compr Psychiatry. 2003;44:117-120.[Medline]
36. Almkvist O, Axelman K, Basun H, et al. Clinical findings in nondemented mutation carriers predisposed to Alzheimer's disease: a model of mild cognitive impairment. Acta Neurol Scand Suppl. 2003;179:77-82.[Medline]
37. Deary IJ, Whiteman MC, Pattie A, et al. Cognitive change and the APOE epsilon 4 allele. Nature. 2002;418:932.[Medline]
38. Graff-Radford NR, Green RC, Go RCP, et al. Association between apolipoprotein E genotype and Alzheimer disease in African American subjects. Arch Neurol. 2002;59:594-600.[Abstract/Free Full Text]
39. Fillenbaum GG, Landerman LR, Blazer DG, Saunders AM, Harris TB, Launer LJ. The relationship of APOE genotype to cognitive functioning in older African-American and Caucasian community residents. J Am Geriatr Soc. 2001;49:1148-1155.[Medline]
40. Blazer DG, Fillenbaum G, Burchett B. The APOE-E4 Allele and the risk of functional decline in a community sample of African American and White older adults. J Gerontol A Biol Sci Med Sci. 2001;56:M785-M789.[Abstract/Free Full Text]
41. Goldstein FC, Ashley AV, Gearing M, et al. Apolipoprotein E and age at onset of Alzheimer's disease in African American patients. Neurology. 2001;57:1923-1925.[Abstract/Free Full Text]
42. Whitfield KE, Seeman TE, Miles TP, et al. Health indices as predictors of cognition among older African Americans: MacArthur studies of successful aging. Ethn Dis. 1997;7:127-136.[Medline]
43. Ashaye MO, Giles WH. Hypertension in Blacks: a literature review. Ethn Dis. 2003;13:456-462.[Medline]
44. Vermeer SE, Prins ND, den Heijer T, Hofman A, Koudstaal PJ, Breteler MM. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med. 2003;348:1215-1222.[Abstract/Free Full Text]
45. Bohannon AD, Fillenbaum GG, Pieper CF, Hanlon JT, Blazer DG. Relationship of race/ethnicity and blood pressure to change in cognitive function. J Am Geriatr Soc. 2002;50:424-429.[Medline]
46. Manly JJ, Jacobs DM, Touradji P, Small SA, Stern Y. Reading level attenuates differences in neuropsychological test performance between African American and White elders. J Int Neuropsychol Soc. 2002;8:341-348.[Medline]
47. Albert SM, Teresi JA. Reading ability, education, and cognitive status assessment among older adults in Harlem, New York City. Am J Public Health. 1999;89:95-97.[Abstract/Free Full Text]
48. Power C, Manor O, Li L. Are inequalities in height underestimated by adult social position? Effects of changing social structure and height selection in a cohort study. BMJ. 2002;325:131-134.[Abstract/Free Full Text]

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