1 Channing Laboratory, Department of Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA.
2 Department of Health and Social Behavior, Harvard School of Public Health, Boston, MA.
3 Department of Epidemiology, Harvard School of Public Health, Boston, MA.
Received for publication April 8, 2002; accepted for publication November 4, 2002.
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ABSTRACT |
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cognition; education; income; social class; socioeconomic factors
Abbreviations: Abbreviations: CI, confidence interval; EBMT, East Boston Memory Test; MMSE, Mini-Mental State Examination; OR, odds ratio; SD, standard deviation; TICS, Telephone Interview for Cognitive Status.
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INTRODUCTION |
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To address these issues, we utilized data from the Nurses Health Study, a cohort study of female nurses with at least 15 years of education, in which we collected information regarding subjects educational attainment and other socioeconomic variables. Since the start of the Nurses Health Study, women have additionally provided extensive data on diet and health characteristics, allowing us to adjust for a wide array of potentially confounding factors. In the present analysis, we investigated the relation of educational attainment and other socioeconomic status indicators to baseline performance on a battery of cognitive tests among 19,319 women, as well as change in score over 2 years among 15,594 subjects. To our knowledge, this is the largest such study conducted to date.
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MATERIALS AND METHODS |
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Population for analysis
Nurses Health Study participants aged 70 years or more who were free of diagnosed stroke and had answered the most recent mailed questionnaire were eligible to participate in the cognitive study. From 1995 to 2000, we contacted 22,213 women to administer a brief telephone interview assessing cognitive function; 19,510 women (88 percent) completed the interview, 7 percent refused, and we had inaccurate telephone numbers for 5 percent (i.e., of those contacted, 92 percent agreed to participate). The baseline results presented here included 19,319 subjects who answered a question about educational attainment.
Second interviews, conducted approximately 2 years after the baseline assessment, are ongoing among all participants; as of April 2002, 81 percent (n = 15,594) were complete. Participation remains high, with 5 percent loss to follow-up among women we have attempted to contact so far.
Assessment of cognitive function
The initial assessment consisted of the Telephone Interview for Cognitive Status (TICS) (25), which is modeled on the Mini-Mental State Examination (MMSE). After we had established participants acceptance of the telephone testing, we gradually included five additional tests to broaden the scope and accuracy of our assessment. Specifically, from 1995 to 1996, we administered the TICS alone to 862 women; from 1997 to 1999, we administered the TICS, the immediate and delayed recall portions of the East Boston Memory Test (EBMT), and a verbal fluency test to 1,732 women; and from 1999 to 2001, we added delayed recall of the TICS 10-word list and the digit span backwards test. The majority of participants (n = 16,859) were administered our final battery of six tests. Participation rates in the cognitive study remained unchanged over time and were unrelated to socioeconomic status.
Telephone Interview for Cognitive Status
Brandt et al. (25) reported a strong linear relation between performance on the TICS and performance on the MMSE (Pearson correlation = 0.94). In our population at baseline (n = 19,319), scores ranged from 8 to 41 (41 is a perfect score); the mean was 33.7 (standard deviation (SD), 2.8), and 11.4 percent of the women scored below 31, a cutpoint for poor cognition (25). In our second interview (n = 15,594), 42 percent of subjects declined in score (because of an expected learning effect, the mean score was somewhat higher on the second assessment (mean = 33.9; SD, 2.9)). We separately examined a subsection of the TICS, the 10-word list; scores ranged from 0 to 10 (mean = 4.6; SD, 1.7) at baseline. In our second interview, 35 percent of subjects scores declined, and the mean score was 4.9 (SD, 1.8).
Delayed recall of 10-word list
We administered a delayed recall of the TICS 10-word list at the end of our interview (approximately 15 minutes later); baseline scores (n = 16,859) ranged from 0 to 10 (mean = 2.3; SD, 2.0). In the second interview, 26 percent of scores declined, and the mean score was 2.7 (SD, 2.2).
East Boston Memory Test
In the EBMT (5), a short story is read to the respondent. Twelve key elements must be repeated immediately; a test of delayed recall is given 15 minutes later. Scores for the immediate recall at baseline (n = 18,591) ranged from 0 to 12; the mean was 9.4 (SD, 1.7). On the second interview (n = 15,593), 36 percent of scores declined, and the mean was 9.5 (SD, 1.8). For the delayed recall, baseline scores (n = 18,569) ranged from 0 to 12 (mean = 9.0; SD, 2.0); on the second interview (n = 15,586), 36 percent of scores declined, with a mean score of 9.1 (SD, 2.1).
Verbal fluency
In the test of verbal fluency (26), women name as many animals as they can during 1 minute. Scores ranged from 1 to 38 at baseline (n = 18,584); the mean was 16.8 (SD, 4.6). On the second interview (n = 15,593), 44 percent of scores declined, and scores ranged from 0 to 44 (mean = 16.9; SD, 4.8).
Digit span backwards
In the digit span backwards test, women repeat backwards increasingly long series of digits (12 series). The mean score was 6.7 (SD, 2.4), and scores ranged from 1 to 12 at baseline (n = 16,848). At the second interview (n = 14,964), 34 percent of scores declined; the range in scores was 0 to 12, with a mean of 6.7 (SD, 2.4).
Global score
To estimate overall performance, we calculated a global cognitive score among women who had completed all tests: 1) the TICS, 2) delayed recall of the TICS 10-word list, 3) immediate and 4) delayed recalls of the EBMT, 5) verbal fluency, and 6) the digit span backwards test (16,812 women at baseline and 13,429 for decline). We created z scores by taking the difference between the participants score on each test and the mean and dividing the result by the standard deviation. We added z scores to calculate a global score.
The telephone cognitive assessments were administered by registered nurses who had been trained to conduct these interviews. A study of inter-interviewer reliability demonstrated greater than 95 percent correlation on each of our tests. Substantial data support the validity of telephone tests of cognitive function, with reported correlations of 0.85 to 0.96 for comparison of telephone and in-person administrations (27, 28). In a validation study we conducted among 61 nuns from the Rush Religious Order Study (29)women of similar age and educational status as our Nurses Health Study participantswe found a correlation of 0.81 when comparing the global score from our telephone-administered interview to the global score from an in-person interview consisting of 21 tests.
Assessment of socioeconomic variables
Participants provided information on educational attainment in 1992. In our analyses, we created categories according to the highest degree achieved: Registered Nurse diploma (a 3-year diploma), bachelors degree, or doctoral/masters degree.
We inquired about husbands education in 1992 among women who were married or widowed. For the analysis, we created four categories: high school or less, college degree, graduate school, and missing information (divorced or separated or no response).
In 1976, women were asked, "When you were 16 years of age, what was your fathers occupation?". Written responses to this open-ended question were categorized into nine groups (professional, managerial, clerical, sales, craftsmen, service, laborers, farmers, and unknown/retired/deceased). We combined these groups as upper white-collar (professional, managerial), lower white-collar and skilled manual (clerical, sales, service, craftsmen), unskilled manual (laborers), farming, and other/missing (deceased, retired, unknown).
We classified subjects according to median annual household income on the basis of census tract of residence, geocoded to the 1990 US Census. We divided womens median household income into quartiles (<$27,600, $27,600$36,699, $36,700$48,599, and $48,600).
Statistical analysis
In baseline analyses, we used logistic regression to estimate age- and multivariate-adjusted odds ratios for low scores and corresponding 95 percent confidence intervals. For the TICS, we used a score of <31 points to define low scorers (an established cutoff (25)). For the remaining tests, we defined low score as the bottom 10th percentile and compared low scorers with the remaining women. The following cutpoints were used: immediate recall of the TICS 10-word list, 2 (for delayed recall, 0); immediate recall of the EBMT, <7 (for delayed recall, <6); verbal fluency task, <11 points; and digit span backwards test, <3.
We performed linear regression to obtain multivariate-adjusted mean differences in baseline test scores among women with different levels of education and other socioeconomic indicators and 95 percent confidence intervals.
In multivariate regression models, we included the primary variables of interest and potentially confounding variables: age at interview (2-year age groups), history of diabetes (yes/no), physician-diagnosed high blood pressure (yes/no), heart disease (yes/no), use of vitamin E supplements (yes/no), use of aspirin (<1 day/month, 12 days/week, 3 days/week), use of postmenopausal hormones (never, current, past), body mass index (weight (kg)/height (m)2) in four groups (<22, 2224.9, 2529.9,
30), smoking (current, past, never), daily alcohol consumption (0, 04.9, 5.014.9,
15 g), use of antidepressants (yes/no), age at menopause (<50, 5052,
53 years), and mental health index (051, 52100) and vitality index (049, 50100) from the Medical Outcomes Short Form-36 (better known as the SF-36 (30)). In controlling for history of hypertension, diabetes, and heart disease, we constructed a cumulative variable such that a history was considered present if that disease had been diagnosed at any time prior to baseline cognitive assessment. For other covariates, the most recently updated information prior to the baseline cognitive assessment was used.
In prospective analysis in the subset of women (n = 15,594) for whom we had completed a second cognitive assessment to date, we determined change by subtracting the second test score from the baseline score. Using logistic regression analyses, we obtained odds ratios for substantial decline and 95 percent confidence intervals. We defined as "cases" those women in the worst 10 percent of the distribution of change and as "noncases" the remaining women. The following cutpoints were used: for the TICS, a decline of 3; for immediate recall of the TICS 10-word list, a decline of
3 (for delayed recall,
3); for immediate recall of the EBMT,
3 (for delayed recall,
3); for the verbal fluency task,
6 points; and for the digit span backwards test,
3. We also performed linear regression to examine mean declines and corresponding 95 percent confidence intervals.
Covariates included in multivariate models of change included baseline test score and time between first and second interview, as well as the variables described above.
Since we were concerned about possible floor and ceiling effects in examining change, we performed an alternate analysis grouping participants according to baseline score and, within each value, ranked the change in scores; we then normalized the ranks by dividing the difference between an individuals rank and the mean rank by the standard deviation (3). Results using this method were virtually identical to those obtained from the analyses that simply included baseline score in the model; thus, we present only results using the baseline adjustment, since those are more easily interpretable.
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RESULTS |
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The average ages of the women were similar regardless of educational attainment (table 1). More educated women tended to have a more educated husband and a higher median household income, and they used hormone therapy and vitamin supplements slightly more often than women with less education. They were somewhat less likely to smoke cigarettes, and they reported a lower body mass index.
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In contrast, on the global score, there was no difference in odds of a low score for women whose husbands had higher levels of education as compared with a high school education or less (i.e., for a husband with a graduate degree, OR = 0.99, 95 percent CI: 0.85, 1.14). We also found little difference in the odds of low cognitive function across levels of household income ($27,600$36,699: OR = 0.90, 95 percent CI: 0.86, 1.12; $36,700$48,599: OR = 0.99, 95 percent CI: 0.86, 1.14; $48,600: OR = 0.89, 95 percent CI: 0.76, 1.03; comparison group, <$27,599). Finally, fathers occupation at age 16 was not related to odds of low cognitive function; we observed only a marginal increase in the odds of a low global score for women whose fathers were farmers as compared with upper white-collar professionals (OR = 1.19, 95 percent CI: 0.99, 1.43).
Findings from analyses examining the baseline cognitive test results as continuous data were almost identical to logistic regression results.
Analyses of cognitive decline
After multivariate adjustment, we found statistically significant trends of decreasing odds of cognitive decline with increasing level of education on all six tests (TICS: p < 0.01; TICS 10-word delayed recall: p = 0.03; verbal fluency: p < 0.01; EBTM immediate and delayed recall: p < 0.01 for both; digit span backwards: p = 0.02). On the global score, women with a graduate degree had a 35 percent decrease in odds of substantial decline (OR = 0.65, 95 percent CI: 0.50, 0.86) compared with women with a Registered Nurse diploma, and women with a bachelors degree had 20 percent lower odds (OR = 0.80, 95 percent CI: 0.680.94) in multivariate-adjusted analyses (table 2).
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There were few relations between other socioeconomic status markers and womens cognitive decline. For example, on the global score, mean declines were similar for women whose husbands had a higher level of education and women whose husbands had a high school education or less (linear regression estimate of mean difference = 0.09, 95 percent CI: 0.25, 0.07). We found little difference in mean cognitive decline for household income (i.e., comparing the top income level with the bottom income level, the mean difference was 0.11 (95 percent CI: 0.26, 0.04)). Finally, fathers occupation at age 16 had no association with mean decline (i.e., comparing an upper white-collar father with a laborer, the linear regression estimate of mean difference was 0.003 (95 percent CI: 0.30, 0.31)).
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DISCUSSION |
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Several aspects of this study are unique in comparison with previous investigations. First, to our knowledge, this is the only study that has examined the effects of education and socioeconomic status among highly educated, professional women. Second, we were able to carefully control for confounding by a wide array of health and behavioral covariates. Finally, our large sample size allowed us to estimate effects with great precision.
Since exposure information was self-reported or census-derived, there may have been misclassification. In particular, census-derived information on household income and information on fathers occupation at age 16 may have been randomly misclassified, which may partly explain those generally null results. However, participants and husbands education were probably well-reported; thus, we expect that there was little bias toward the null for these estimates. In these subjects, who all had the same occupation, husbands education is an extremely strong determinant of socioeconomic status. Therefore, despite the possibility of some misclassification, we believe our finding that subjects education was highly related to cognitive function and decline while husbands education was not indicates that education is more important in cognition than is socioeconomic status.
Another limitation may have been the somewhat short follow-up period for measuring cognitive decline, possibly leading us to underestimate true relations with decline. However, the average 2-year interval between interviews was sufficient to identify relations with education, as well as with established risk factors for cognitive decline; for example, on the global score, we found a linear 19 percent elevation in the odds of decline with each 2-year increase in age (OR = 1.19, 95 percent CI: 1.08, 1.31).
Other large studies of cognitive function have generally reported inverse associations between educational attainment and cognitive function. In one cross-sectional study, Gallacher et al. (4) administered cognitive tests to 1,870 men aged 5569 years in the Caerphilly Study. There was a significant trend of increasing scores with increasing education (from none to a college degree) after adjustment for age, mood, and social class; the adjusted mean difference in score between college graduates and men with no educational qualification was 1.6 for the MMSE and 5.9 for the Cambridge Cognitive Examination (4). Another recent cross-sectional study by Cagney and Lauderdale (6) examined 6,577 men and women aged 70 years or older; they found that education had a strong impact on cognition after adjustment for wealth and income.
In a prospective study of 3,266 community-dwelling subjects over age 65 years, Evans et al. (3) administered cognitive tests twice over a 3-year interval and found that each year of education predicted significantly less decline, after adjustment for age, birthplace, language of interview, occupation, income, and common chronic diseases. However, this population was substantially less educated than ours (75 percent of subjects had less than a high school education) (3). Another study of change in MMSE over 1 year by Farmer et al. (1) included 14,833 subjects aged 18 years or more. Each 8-year increment in education was related to a 70 percent lower risk of decline on the MMSE (relative risk = 0.3, 95 percent CI: 0.2, 0.4) after adjustment for age, sex, ethnicity, residential status, income, baseline score, and substance abuse. However, there was little control for confounders, and most participants had a high school education or less (1). Finally, in a study of 1,488 men and women aged 18 years or more with 11.5-years of follow-up, Lyketsos et al. (2) found that having more than 8 years of formal education was associated with significantly less decline on the MMSE after adjustment for age and race; unlike us, they found no further protection against decline with increasing levels of education after 8 years. However, participants may have been too young for such a decline to be detected; over half of the subjects were less than 41 years of age, and just 53 were over age 70 years.
A cross-sectional study of Finnish men (n = 496) aged 5864 years investigated parents education and occupation and cognitive function as measured on five tests, including the MMSE (31). After adjustment for subjects education, only mothers education (less than primary school vs. more) was significantly predictive of sons cognition; for example, the mean MMSE score for subjects whose mothers did not complete primary school was 26.6, and for the others it was 27.2 (p = 0.01). Similarly to our study, there was no relation between fathers occupation and participants cognitive function.
In studies of adult socioeconomic status, Gallacher et al. (4) used the Caerphilly Study to examine cross-sectionally the association between occupation/social class and cognitive function in 1,870 men aged 5569 years. Social class was strongly related to each test in univariate analysis; after adjustment for age, mood, and education, social class effects universally declined (3045 percent), principally because of adjustment for education. The adjusted mean difference between the highest and lowest social classes was 1.9 units for the MMSE and 5.3 units for the Cambridge Cognitive Examination; it is possible that adjustment for additional confounders would have eliminated these observed associations. Evans et al. (3) demonstrated that increases in occupational prestige score and income predicted significantly less cognitive decline on a test of immediate memory after adjustment for sex, age, education, birthplace, and language of interview. However, neither occupation nor income was related to decline in their general test of cognitive function (3), which is similar to our results.
There are several possible mechanisms supporting our finding that less education is related to cognitive decline. First, education may exert direct effects on brain structure early in life by increasing synapse number or vascularization and creating cognitive reserves (32); this has been called the "reserve capacity" hypothesis. Second, education in early life may have effects in later life if persons with more education continue searching for mental stimulation (the "use it or lose it" hypothesis), which may lead to beneficial neurochemical or structural alterations in the brain (32). Indeed, one study found that among men and women aged 65 years or older, cognitive function was positively related to composite measures of recent mental stimulation (activities such as playing games and puzzles, etc.) (33).
In addition, education may simply represent better health or other behavioral choices that in turn lead to maintenance of cognitive function (32). Our study may contribute to the elimination of this hypothesis, since we found strong effects of education after carefully adjusting for numerous health and behavioral characteristics in a population of registered nurses, who are relatively homogeneous in terms of their health habits and access to health care.
Finally, higher levels of education may improve an individuals ability to perform on tests of cognitive function or to hide mild cognitive deficits (32). Again, in our group of educated professionals, it does not seem likely that this could completely explain the substantial differences in performance that we observed between women with a 3-year Registered Nurse diploma and those with a masters or doctoral degree. Thus, overall, our data would suggest that the education effect is most likely explained by the "cognitive reserve" hypothesis or the "use it or lose it" hypothesis.
In conclusion, this appears to have been the first study of educational attainment and cognitive decline in community-dwelling, older women, all of whom are well-educated. We found strongly decreased odds of cognitive decline among women with higher levels of education; we found little association with other markers of socioeconomic status. Further exploration of these findings, through research specifically investigating whether continued mental stimulation in old age may protect against cognitive decline, seems warranted for establishing productive public health recommendations.
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ACKNOWLEDGMENTS |
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NOTES |
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REFERENCES |
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