Education and change in cognitive function

Education

and Change in Cognitive

The Epidemiologic MARY E. FARMER, JOHN J. BARTKO,

Catchment MD, PHD,

Function

Area Study

STEVEN J. KI-I-I-NER, AND DARREL A. REGIER, MPH,

MD, MPH, MD,

DON S. RAE,

MA,

MPH

The association between educational attainment and decline in cognitive function over an inrervul of 1 year was examined for 14,883 subjects 18 years and older in the National Institute of Mentrrl Health Epidemiologic Catchment Area Study. Cognitive function was assessedat both time points by the Mini-Mental State Examination (MMSE); cognitive decline was coded as a dichotomous variable and was defined as 1 if the subject’s score had declined 3 or more points j&n the baseline MMSE scoreat the 1-year follow-up interview and as 0 otherwise. The association between educational attainment and decline in cognitive function over 1 year was examined in logistic regression models that were straifiea’ by age gmup (< 65 years, & 65 years) and by baseline MMSE level (MMSE > 23, MMSE < 23). Couariates included age, baseline MMSE score, ethniciry, residence, lifetime diagnosis of abuse of alcohol or other drugs, and gender. In those with baseline MMSE > 23, edwion was a significanr predictor of cognitive decline, not only in the elderly but also in younger subjects. Among those with baseline MMSE G 23, education was not a signijicant predictor of cognitive decline. The fact that education prwides protection against cognitive decline even in those yacnger than 6.5 years, in whom the prevalence and incidence of dementia are very low, would seem to indicate that education or its correlates provides protection against processes other than dementia that might produce a decline in rest performance in young persons. Ann Epidemiol 1995; 5:1-7. KEY

WORDS:

longitudinal

Aging, cognitive function, studies.

community

INTRODUCTION Although several studies have examined the association between age and change in cognitive function over time (l-6), there have been few studies of the association of change in cognitive function over time and low educational attainment. Low educational attainment has been linked to the prevalence of dementia and of Alzheimer’s disease (7-12) and has been associated with vascular dementia (13). However, interpretation of these findings has been difficult because of the possibility of assessment bias. The strong correlation between educational achievement and ability to perform on most screening tests for dementia may result in an assessment bias, with more of the screening test false positives being poorly educated and more of the screening test false negatives being well educated (14). A few studies on the association between change in cogFrom the National Institute of Mental Health, National Institutes of Health, Rockville (M.E.F., D.S.R., J.J.B., D.A.R.), and the Departments of Neurology and Preventive Medicine, University of Maryland Medical Center, Baltimore (S.J.K.), MD. Address reprint requests to: Mary Farmer, MD, MPH, Epidemiology and Psychopathology Research Branch/National Institute of Mental Health. Rm. lOC-09. Parklawn Building.-. 5600 Fishers Lane. Rockville. MD 20857. Received April 11, 1994; accepted August 19, 1994

Published 1995 by Elsevwr .Scicnc~ In-. 655 Avenue of rhe Americas, New York,

NY

ICOlO

surveys, educational

achievement,

nitive function and educational attainment have been published. Bank and Jarvik (15) examined the association between rate of decline in performance on neurapsychological tests and educational attainment among a small subsample of twins over 60 years old in the New York State Psychiatric Institute Study of Aging Twins. On every test, the rate of decline was faster in the less-educated group (22 twins) than in the more-educated group (32 twins). Recently, the association between educational attainment and change in cognitive function over time intervals varying from 3 to 6 years was examined in three population+based studies of community-dwelling elderly (16-19). Each of these three studies used a different method of analysis. All three studies found that low educational attainment was associated with decline in cognitive function over time, with one of the studies finding this association only in women (16). However, none of these studies examined this same association in persons younger than 60 years, to test whether the association between education and change in cognitive function is a phenomenon only of the elderly. In addition, none of these studies addressed the potential confounding effects of ethnicity and lifetime diagnosis of substance abuse. This article reports results from the Epidemiological Catchment Area Study on the association between educational attainment and decline in cognitive function over

SW1

IMi-2797/95/$9 50 1047 2?97(94)00047-w

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an interval of 1 year. The purpose was to test whether education is related to decline over time in cognitive performance on a brief screening test for dementia after adjustment for baseline cognitive performance, age, gender, ethnicity, residence, and lifetime diagnosis of substance abuse, among those younger than 65 years as well as those 65 years and older.

METHODS

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AND MATERIALS

Study Population TheNational Instituteof Mental Health (NIMH) Epidemiologic Catchment Area (RCA) program is a collaborative research project involving the NIMH and five university research teams: Yale, Johns Hopkins, Washington University at St. Louis, Duke, and University of California at Los Angeles. Data collection for the wave 1 survey took place between 1980 and 1984. Earlier publications cover the historical context (19), major objectives (19), methods (20,21), and study population characteristics (19, 22, 23). In brief, stratified sampling procedures were used to select for inperson interviews of adults aged 18 years and older from the community and from each of three types of institutions: menta1 hospitals (including mental retardation facilities), long-term care facilities (including board and care homes and long-term hospitals), and prisons. For the community sample, one adult within selected households from a sample of households was interviewed. Residents of institutions serving geographically defined catchment area populations were sampled at higher probability rates than the community population (2 1). The combined community and institutional population sample size was 20,291 persons, who were interviewed directly, without use of proxies, at wave 1. At the wave 2 follow-up 12 months later, 4442 (21.9%) were not able to be reinterviewed (24). Subjects in this analysis were 19,758 adults aged 18 and older in the combined community and institutional samples who completed the test of cognitive functioning at baseline (wave 1) and had information available on educational attainment. At the wave 2 follow-up interview 12 months later, 14,883 (75%) of the 19,758 study subjects completed the test of cognitive function. Mental Status Questionnaire Cognitive function was assessedat both timepoints by the Mini-Mental State Examination (MMSE) (25). The MMSE measures orientation to time, orientation to place, registration of three words, attention and calculation, recall of three words, language, and visual construction. The range of test scores is 0 to 30 points, with a lower score indicating worse cognitive functioning. Although previous reports from the ECA have used a cutoff score of 17 or less as an indication

of severe cognitive impairment (24), a baseline MMSE score of 23 or less was used in this study as a means of stratifying the analyses according to baseline cognitive performance because a score of 23 or less has been generally accepted as indicating the possible presence of cognitive impairment (26). Cognitive decline was coded as a dichotomous variable and was defined as 1 if the subject’s score at the l-year follow-up interview had declined 3 or more points from the baseline MMSE score and as 0 otherwise. A decline of 3 or more points on the MMSE was chosen because studies of patients with Alzheimer’s disease found a decrement of around 2 to 4 points to be an average annual rate of decline on the MMSE (27-33). Covariates Candidate independent variables for these models were years of education, residential status (whether participant resided in a household, a long-term care facility, jail, or mental institution), income, and lifetime history of substance abuse. Terms for age, gender, and baseline MMSE score were included in all models. Education was measured by asking at the initial interview, What’s the highest grade in school or year in college you completed?” Income was as reported by participants. Lifetime diagnosis of substance abuse was obtained by using the Diagnostic Interview Schedule. It was not possible to consider alcohol abuse separately from drug abuse since the frequency of drug abuse in the elderly was so low. Statistical Methods Logistic regression was used to estimate odds ratios for the effect of education on a decline of 3 or more points in the MMSE, adjusted for potential confounding variables.

RESULTS Table 1 shows characteristics of the baseline study cohort by participation status at follow-up. Those who did not complete the MMSE at follow-up were significantly more likely to be older and to have completed fewer years of education; more likely to be a resident of a long-term care facility, jail, or mental hospital; to be male; to be Hispanic; and to have a baseline MMSE score less than or equal to 23. Most subjects who participated at follow-up were white, between the ages of 18 and 64 years, and living in households. Characteristics of the study population at baseline are shown by educational level in Table 2. About 80% of those with some college experience were less than 65 years old. Whites were more likely than blacks or Hispanics to have some college experience; those with higher incomes were also more likely to have some college experience. Not unex-

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TABLE 1. Characteristics of cohort according to

Educarion

Did not complete MMSE (%) (n = 4875)

p Value

Age < 65 y a 65 y

71.3 28.7

64.2 35.8

< O.Klol

Education o-9 y 10-12 y Some college +

28.0 40. I 31.9

37.6 37.2 25.2

< O.coOl

24.4 7.1 67.6 0.9

22.8 11.2 64.1 1.9

< O.KOl

2.5 2.8 0.7 94.0

5.7 5.8 2.6 85.9

< o.ooo1

Ethnicity Black Hispanic White and other Missing information Residence Nursing home Jail Mental institution Household

o-9 (n = 4165)

Substance abuse (lifetime diagnosis) Yes NO

19.6 80.4

19.1 80.9

= 0.473

Baseline MMSE > 23 < 23

88.6 11.4

78.6 21.4

< O.oool

41.6 58.4

46.0 54.0

< O.oool

Sex Male Female

3

Farmer et al. FIINCTION

IN COGNITIVE

status

Completed MMSE (%) (n = 14,883)

Variable

CHANGE

TABLE 2. Characteristics by education of parricipants with two MMSE scores over a l-year period: The NIMH Epidemiokraic Catchment Area Study

participation status at follow-up: The NIMH Epidemiologic Catchment Area Study Follow-up

AND

score

pectedly, those with some college experience were more likely to score higher on the baseline MMSE than those with fewer years of education. The percentages within different educational groups whose MMSE score declined 3 or more points over a l-year time period are shown in Table 3 by age and by quintiles of baseline MMSE score. With the exception of those 65 years and older who scored 0 to 23 on the baseline MMSE, the percentage for those whose score declined 3 or more points over a l-year time period decreased with increasing educational experience within each strata. To assess the independent association of education with decline in MMSE score in the presence of other important variables, logistic regression models were developed. These models, shown in Tables 4 and 5, include all the covariates listed in the tables. When stratified by age and by baseline MMSE level, fewer years of education was a significant predictor of cognitive decline in those with baseline MMSE scores higher than 23 (scores indicative of better cognitive performance at baseline) (see Table 4). Of interest is the finding that among those with baseline MMSE scores higher

io- I2 (n = 5968)

Iv) Some cdiege + (n = 4750)

Age (Y) < 65 65-74 2 75 sex Female Male Ethnicity

45.5 31.1 23.4

Y9.7 !4.2 h,@

83.3 10.9 5.8

60.3 39.7

61.1 18.9

53.3 46.7

Black Hispanic White Other Residential status Household Nursing home Jail Mental institution Income Low Medium-low High-medium High Baseline MMSE score 30 29 28 27-24 23-O Substance abuse (lifetime diagnosis)

28.8 11.3 58.0 1.9

27. 8 6.4 63.5 2.3

16.7 4.4 75.5 3.4

90.6 5.7 2.9 0.8

93.4 I.7 4.1 0.X

97.8 0.8 1.1 0.3

32.4 29.8 16.6 21.2

I :.s 18.7

5.2 9.2

18.5 49.3

13.2 72.4

9.2 13.3 14.0 32.8 30.6

30.8 26.9 16. L 20.6 5.6

49.1 27.3 11.8 10.0 1.8

13.8

17.1

15.7

than 23, fewer years of education predicted cognitive decline not only in those 65 and older but also in those Iess than 65 years old (see Table 4). Other significant predictors of cognitive decline for those with MMSE scores higher than 23 at baseline included age and a lifetime diagnosis of substance abuse in those 65 and older, ethnicity, residence, and in those under 65, gender. In those with MMSE scores of 23 or lower at baseline (indicative of worse cognitive performance) (see Table 5), significant predictors of cognitive decline included age (in those 65 and over), ethnicity, residence and in those under 65, gender. Education was not a significant predictor of cognitive decline in those with baseline MMSE scores less than or equal to 23.

DISCUSSION We found that in those with better initial cognitive performance, education was a significant predictor of cognitive

Farmer et al. EDUCATION

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TABLE 3. Percent whose MMSE score declined 3 or more

TABLE 5. Estimated odds ratioa by age group for declining

points over a l-year period within different education groups, by age and initial MMSE score: The NIMH Epidemiologic Catchment Area Studv

3 or more points on the MMSE over a l-year period among participants with baseline MMSE score of 23 or below: The NIMH Epidemiologic Catchment Area Study

Baseline MMSE score

Age group (9

30 29

< 65

28 27-24 23-O

65-74

75 +

Education (y) 10-12

o-9 19.0 20.6 17.1 17.1 11.7

Some college

9.6 7.8

7.2

30 29 28 27-24 23-O

24.3 20.7 15.2 15.5 14.2

12.3 11.1 8.6 9.4 9.5

8.0 7.1 2.2 9.0 17.6

30 29 28

44.4 31.0 31.8 23.2 23.7

21.9 25.9 17.5 13.9 20.8

16.7 11.1 10.9

27-24 23-O

Variable

7.4 7.5 6.1 9.9 6.7

7.5 9.6

Baseline MMSE

+

<

65 y

< 23 2 65 y

Age (~1”

1.1 (0.9, 1.3)

Education (y) Ethnicity

1.0 (0.6, 1.5)

1.6 (1.4, 2.0) 0.9 (0.7, 1.3)

1.6 (1.03, 2.5) 2.2 (1.2, 4.0) 1.O (reference)

1.3 (0.97, 1.7) 1.1 (0.5, 2.2) 1.O (reference)

Nursing home

2.2 (1.1, 4.2)

2.2 (1.6, 3.1)

Jail Mental institution Household

3.4 (1.6, 7.4) 4.7 (2.5, 8.8) 1.O (reference)

5.0 (2.0, 12.0) 1.O (reference)

1.1 (0.7, 1.7) 1.O (reference) 1.0 (1.001, 1.08)

1.4 (0.9, 2.2) 1.O (reference) 1.0 (0.98, 1.03)

Black

Hispanic White and other Residence

Substanceabuse (lifetime diagnosis) Yes No Baseline MMSE scored

4.3 23.8

Sex

decline, not only in the elderly, who are at increased risk for dementing disorders such as Alzheimer’s disease and multi-infarct dementia, but also in younger subjects. This

Male 1.6 (1.05, 2.4) Female 1.O (reference) 695% confidenceintervalsin parentheses. bAssociatedwith an incrementof 10 y.

1.0 (0.8, 1.4) 1.0 (reference)

’ Associated with an increment of 8 y of education. d Associated with an increment of 1 MMSE error.

TABLE 4. Estimatedodds ratio” by age group for declining 3 or more points on the MMSE over a l-year period among participants with baseline MMSE score above 23: The NIMH Epidemiologic Catchment Area Study Baseline MMSE Variable

< 65 y

Age (Y)*

1.1 (1.05, 1.2) 0.5 (0.4, 0.6)

Education (y) Ethnicity 1.4 (1.2, 1.7) Black 2.3 (1.9, 2.8) Hispanic 1.O (reference) White and other Residence 3.6 (1.9, 6.7) Nursing home 2.6 (2.0, 3.4) Jail 9.5 (6.0, 14.9) Mental institution 1.0 (reference) Household Substanceabuse (lifetime diagnosis) Yes 1.1 (0.9, 1.2) No 1.O (reference) Baseline MMSE scored 0.9 (0.9, 0.98) Sex Male 1.2 (1.05. 1.4) Female 1.O ireferLn&) n95% confidenceintervalsin parentheses. b Associated with an increment of 10 y. ’ Associated with an increment of 8 y of education. d Associated

with

an increment

of 1 MMSE

error.

> 23 2 65 y

2.0 (1.7, 2.4) 0.3 (0.2, 0.4) 2.4 (1.9, 3.1) 2.0 (1.2, 3.4) 1.0 (reference) 3.0 (1.9, 4.7)

1.O (reference)

1.6 (1.1, 2.3) 1.0 (reference) 0.8 (0.8, 0.89) 1.0 (0.8. 1.2) I

I

1.O(reference)

association was not explained by differences in age, baseline MMSE score, ethnicity, residence, lifetime diagnosis of abuse of alcohol or other drugs, or gender. Among those with worse baseline cognitive performance, education was not a significant predictor of cognitive decline. An understanding of the relationship between educational attainment and decline in cognitive function is particularly important since education has been found to be associated with the prevalence of dementia and with the prevalence of Alzheimer’s disease and multi-infarct dementia. Thus, different rates of decline in cognitive performance with education may be a reflection of different real or apparent rates of dementia with education. This is unlikely to be the explanation of our findings regarding the association between education and decline in cognitive performance in persons younger than 65, in whom the incidence of dementia is very low.

Nevertheless, it is instructive to review our findings in the context of several potential explanations of the dementiaeducation association. One interpretation is that lower educational attainment may be a risk factor for the underlying pathologic processesof dementia, possibly through its association with other potential risk factors such asoccupational hazards, lifetime patterns of health care use, health behav-

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iors such as alcohol and tobacco use, stress, or prior exposures such as nutritional deprivation in early life (34, 35). As noted above, this interpretation does not entirely explain the association we found between education and decline in cognitive function in patients under the age of 65, in whom the incidence of dementia is low. Others have argued that low educational attainment is not a risk factor for the underlying pathologic process of dementia but that education may provide a reserve of brain capacity that must be depleted to a certain threshold before dementia is clinically manifested and in that sense, it protects against the emergence of the clinical features of dementia (36,37). Studies in animals suggest that complex environments stimulate dendritic growth and increase brain weight, and that this association is maintained throughout adulthood (34, 38). In humans, dendrites continue to grow well into old age (39). One study that supports this brain reserve hypothesis examined the association between educational attainment and parietotemporal perfusion deficit in 58 patients with Alzheimer’s disease of equal severity (36). Parietotemporal perfusion deficit is a specific pattern of flow reduction in the parietotemporal cortex that corresponds to the pathology of Alzheimer’s disease. It was found that the parietotemporal perfusion deficit was significantly greater in the group with the highest level of education, indicating that Alzheimer’s disease was more advanced in this group. The authors concluded that education or its covariates or both may provide a reserve that compensates for the neuropathologic changes of Alzheimer’s disease and delays the onset of clinical manifestations. Another study examined cognitive abnormalities among asymptomatic subjects from the Multicenter AIDS Cohort Study who were infected with human immunodeficiency virus type 1; the mean age of these subjects was 36 years (40). The study found that although there was little effect of education on cognitive performance among seronegative subjects and no effect of serostatus among subjects with more than 12 years of education, there was a substantial effect on cognitive performance of being both seropositive and less educated (odds ratio = 3.01). The authors hypothesized that low educational attainment represents an indirect measure of brain reserve capacity that may alter the threshold for symptom onset after acquired brain injury. This study demonstrates that the effect of education in cognitive change is not specific to old age dementias. However, it seems unlikely that our findings in a general population sample under age 65 are due to acquired brain injury. Another interpretation of the association between the prevalence of dementia and education is detection bias; that is, individuals with lower educational levels perform more poorly on screening tests for dementia, which results in a higher rate of detection. This in turn introduces high false-positive rates in individuals with low education and high false-negative rates in those with more education. De-

tection bias is less likely in studies of decline in cognitive functioning, however, since variables that affect the first measurement of cognitive function are likely to be similar to those affecting subsequent measurements; therefore, the difference between measurements should be unbiased (17). Two recently published studies, in addition, provided data indicating that the association between low educational attainment and cognitive impairment in the elderly is not due to test bias according to education. The first, a study of 269 subjects over 70 years old living in the community, assessed whether the MMSE is a biased indicator of cognitive impairment according to education by examining predictive validity (using daily living skilis as a criterion), age correlations, principal components analysis, item c&Iiculties, and reliability coefficients by education groups (4 1). None of these analyses provided evidence to support the hypothesis that the MMSE is biased against poorly educated elderly in assessing cognitive impairment. The second, a study of education, survival, and independence in 306 elderly Roman Catholic nuns, found that sisters with at least a bachelor’s degree were more likely than ather sisters to survive to old age while maintaining their ability to perform self-care activities (42). The findings from this study suggest that higher educational attainment may be protective against cognitive impairment, assuming that cognitive impairment will result in impaired daily living skills. Our findings do not provide definitive answers to these questions. However, these findings may shed more light on the issues involved. The fact that education provides protection against cognitive decline even in those under 65 years old, in whom the incidence of dementia is very low, would seem to indicate that education or its correlates might provide protection against processes other than dementia that could produce a decline in test performance in younger persons. Such processes might include deficits in test-taking skills (such as apathy, carelessness, or decreased attention), the onset of psychiatric or behavioral disorders, or the onset of medical disorders. Education or its correlates might also provide a reserve of brain capacity that protects against the clinical manifestations of any kind of acquired brain injury. It is important to emphasize that the association between education and cognitive decline and the association between education and dementia may not be a direct consequence of years of schooling. As with all observational studies (and particularly relevant here), it is possible that a third unmeasured factor is associated both with education and with cognitive decline or dementia. Innate characteristics such as intellectual ability, perseverance or drive, early childhood stimulation, and factors associated with a higher socioeconomic status would be candidates for this confounder. Several potential limitations in this study should be noted. First, those in the cohort who did not take the MMSE at the follow-up interview were significantly more

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likely to be older, performed less well on the baseline MMSE, and had less education; therefore, there may be attrition bias. We were unable to determine whether the declines in test scores represent permanent changes in capacity or were temporary changes in performance due to medical illness, medications being taken, or apathy. Also, some loss of statistical precision from dichotomization of decline in test score may have resulted in wider confidence intervals around the estimates. We were not able to control for potential mediators such as current alcohol consumption and smoking or the presence of medical illnesses. No information was available on foreign birth, which may account for some differences in test scores (if English is not the native language), differences in quality of education, or differences in culture (which may be a surrogate for environmental exposures). In addition, the ethnic differences seen in cognitive decline over time may not be adequately controlled by years of education since there may be qualitative differences in education between ethnic groups. Additional research is necessary to investigate the questions of the association of education and change in cognitive performance and the association of education and dementia. However, clinical studies are more likely to be biased according to education in making diagnoses of dementia. This is true for two reasons: Neuropsychological tests for diagnosing dementia exhibit educational bias, and clinical examiners who make diagnoses are usually not blind to the educational attainment of subjects (17). When change in cognitive performance is the outcome, such as inthe present study, educational bias is less likely because variables that affect the first measurement of cognitive function are likely to be similar to those a&cting subsequent measurements; therefore, the difference between measurements should be unbiased (17). Further studies of both types, particularly studies of cognitive change in population samples including subjects with a wide age range, may help to clarify the nature of the relationship between education and dementia.

The Epidemiologic Catchment Area program is a series Oc five ep&miologic research studies pet&mnd by independent research teams in collaboration with staff of the Division of Biometry and Epidemiology-reorganized in 1985 and in 1992 with components now in the Division of Epidemiokqgy and Services Research-of the National Institute of Mental Health (NIMH), Rockville, MD. The NIMH Prirwipal Collaborators are Darrel A. Regier, MD, MPH, Ben Z. Locke, MSPH, William W. Eaton (19781983), and Jack D. Burke, Jr., MD, MPH (1983-1991); the NIMI-I Project CXlicer was Carl A. Taube (1978 1985) and is now William I-h&r (starting in 1985). The Principal Investigators and co-investigators from the five sites are as follows: Yale University, New Haven, CT, IJGl MH 34224: Jerome K. Myers, PhD, Myma M. Weissman, PhD, and Gary L. Tischler, MD; the Johns Hopkins University, Baltimore, MD, UGl MI-I 33870: Morton Kramer, ScD, Ernest Gruenberg, MD, and Sam Shaprio, MS; Washington University St. Louis, MO, UOl MH 33883: Lee N. Robins, PhD, and John Heher, MD; Duke University, Durham, NC, UGl MH 35386: Linda George, PhD, and Dan Blazer, MD; and University of California, Los Angeles, CA, UOI MH 35865: Marvin Karno, MD, Rich-

ard L. Hough, PhD, Javier I. Escobar, MD, M. Audrey Burnam, PhD, and Diane M. Timbers, PhD.

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