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Unique and Interactive Effects of Depression, Age, SocioeconomicAdvantage, and Gender on Cognitive Performance of Normal HealthyOlder People

Autor(es):Rabbitt, Patrick1,4; Donlan, Christopher1;

Watson, Peter2; McInnes, Lynn3; Bent, Nuala1

Número: Volume 10(3), September 1995, p 307–313

Tipo de publicación: [Articles]

Editor:© 1995 by the American Psychological

Association

Instituciones:

1Age and Cognitive Performance Research

Centre, University of Manchester, Manchester,

England

2Medical Research Council Applied Psychology

Unit, Cambridge, England

3Department of Psychology, University of

Newcastle-upon-Tyne, Newcastle-upon-Tyne,

England.4Correspondence concerning this article should

be addressed to Patrick Rabbitt, University of

Manchester Age and Cognitive Performance

Research Centre, Manchester M13 9PL, England.

Received Date: September 3, 1993; Revised

Date: July 11, 1994; Accepted Date: October 14,

1994

AbstractA sample of 4,243 residents of Manchester, England and Newcastle-upon-Tyne, England, aged 50 to 93 years,

completed the Beck Depression Scale (A. T. Beck, C. H. Ward, M. Mendelson, J. Mock, & J. Erbaugh, 1961) and a

battery of 6 different cognitive tests. Beck scores were low, indicating gradations of dysphoria rather than clinicaldepression. Beck scores did not vary with age but were significantly higher for women than for men and for

disadvantaged than for advantaged socioeconomic groups. Measures of fluid, but not of crystallized, ability declined

as age increased. Socioeconomic disadvantage was associated with poorer performance on all cognitive tests. Men

scored higher on a test of spatial reasoning, and women scored higher on a test of word definition and on 2 tests of

verbal memory and learning. However, after variance associated with all these demographic and individual-difference

variables was considered, and within a range indicative of dysphoria rather than clinical depression, higher Beck

scores were associated with significantly poorer performance on both crystallized and fluid measures of cognitive

ability. This association was less marked in women than in men, but age, socioeconomic advantage, and estimated

lifetime intellectual ability did not act as protective or risk factors for vulnerability of cognitive processes to

dysphoria.

The current literature makes it difficult to assess whether moderate levels of depression contribute to cognitive

Ovid: Unique and Interactive Effects of Depression, Age, Socioeco... http://0-ovidsp.tx.ovid.com.columbus.uhu.es/spb/ovidweb.cgi

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changes in old age. Large epidemiological studies have identified incidence of depression in terms of diagnostic

thresholds for clinical referral (e.g., Blazer & Williams, 1980; Copeland et al., 1987; Griffiths et al., 1987). Applied

clinical studies have either not assessed cognitive performance or have only used tests designed to detect marked

cognitive loss (e.g., the Mini-Mental State Examination, Folstein, Folstein, & McHugh, 1975; the Portable Mental

Status Questionnaire, Pfeiffer, 1975; the Mental Test Score, Hodkinson, 1972; or Inventories of Activities of Daily

Living, Wiener, Hanley, & Clark, 1990). It is not yet clear whether moderate levels of depression contribute to slight

cognitive changes in old age.

Levels of depression have typically been assessed by questionnaires that differentiate more accurately between

levels of subclinical dysphoria (i.e., unhappiness), common in the population at large, than between the relative

intensities of rare, clinically significant depressions (Bech et al., 1975; Beck, Ward, Mendelson, Mock, & Erbaugh,

1961). Blazer and Williams (1980) emphasized that clinical depression represents only the extreme of a continuum of

unhappiness in older samples: “Much of what is called ‘depression’ in the elderly may actually represent decreased

life satisfaction and periodic episodes of grief secondary to the physical, social and economic circumstances

encountered by ageing individuals in the community” (p. 443). Because laboratory studies have shown that even light

and transient negative moods may reduce cognitive performance (Ellis, Thomas, & Rodriguez, 1984; Hertel & Hardin,

1990), we must consider whether even decreased life satisfaction may reduce cognitive efficiency. This is an

important issue for interpretation of data on cognitive changes in normal old age because the extent of variation incognitive ability among individuals increases in older populations (Rabbitt, 1993), and we need to discover how much

this is due to intrinsic biological aging of the central nervous system (CNS) and how much is due to increased risk

factors for unhappiness.

Negative correlations between expressed depression and scores on cognitive tests cannot indicate functional

causality because the risk factors for unhappiness may also, quite independently, affect cognitive competence.

Ben-Arie, Swartz, and Dickman (1987) found that socioeconomic disadvantage is not only a risk factor for depression,

but also for illnesses that are known to affect maintenance of cognitive competence in old age (Holland & Rabbitt,

1991). Blazer, Burchett, Service, and George (1991) elegantly showed that when large populations are screened,

measures of ability and of depression will not be independent, because both may reflect the impact of increasing age

on both general health and cognitive status.

Perlmutter and Nyquist (1990) illustrated further complexities in the relationships among depression, cognitive

ability, and aging. They found that for volunteers aged 60 to 90, but not for younger adults, poorer mental health

ratings, especially reports of depressive symptoms, were also associated with significantly lower scores on measures of

crystallized ability. This hints that increased age, of itself, may increase vulnerability to the cognitive impacts of

depression. This admirable small study also shows that analyses of the effects of depression on cognitive performance

must take into account the possibility that age and depression may have characteristically different impacts on

different cognitive skills.

These complexities set stringent methodological criteria for further studies: It is necessary to assess the effects of

mild as well as of clinically significant levels of depression. Because the effects of depression may be subtle, anddifferent from those of old age, it is necessary to use many different sensitive tests to detect slight as well as gross

impairments and to assess a wide range of mental abilities. In particular, performance on tests of crystallized abilities,

such as vocabulary skills (which are known to be relatively unaffected by normal aging) may be vulnerable to

depression (Perlmutter & Nyquist, 1990). Age-robust vocabulary tests must be included to allow researchers to

determine whether age increases or reduces the vulnerability of cognitive processes to depression. It is essential to

screen very large and diverse populations to detect the effects of demographic and socioeconomic variables that may

modify or determine relationships between mental ability, age, and depression. We must consider the possibility that

factors such as age, gender, socioeconomic status, and general intellectual competence not only affect the level of

depression experienced but also act as protective or risk factors that alter its impact on cognitive performance. These

criteria guided a survey of relationships among age, gender, socioeconomic status, self-reports on the Beck

Depression Inventory (Beck et al., 1961) and cognitive performance in 4,243 independently living residents of GreaterManchester, England and Newcastle-upon-Tyne, England, aged 50 to 93 years.

Method


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Participants

Data were gathered during a longitudinal study of cognitive performance in normal healthy older participants

funded by the United Kingdom (UK) Medical Research Council and Economic and Social Research Council at the

Universities of Newcastle-upon-Tyne and Manchester. Volunteers ages 50 to 93 years responded to media

advertisements or personal contacts. We screened 2,050 individuals in Newcastle-upon-Tyne between 1982 and 1985

and 2,193 individuals in Manchester between 1985 and 1988.

Socioeconomic group (SEG) categorizations followed the UK Registrar General's Occupational Categories (Office

of Population Census and Surveys, 1980). General descriptions of groups are as follows: (a) professional occupations,

(b) intermediate occupations, (c) nonmanual skilled occupations, (d) manual skilled occupations, (e) partly skilled

occupations, and (f) unskilled occupations. Married women were classified in terms of salaried occupations or, if they

had never worked for salary, in terms of their husband's occupations. Individuals were classified in terms of their most

skilled occupation. Table 1 shows distributions of gender, age and SEG. The sample included more women (71%) than

men (29%). Almost half of the participants (48%) were in their 60's. Volunteers were predominantly white-collar and

skilled industrial workers, but other groups were quite substantially represented.

Table 1 Distribution of Socioeconomic Group, Gender, and Agea

A specific estimate of years of education was obtained for a randomly selected 10% subset of this sample (n = 360)

from a questionnaire that all participants completed with this study. Years of full-time secondary education were

evaluated by asking participants to state the type of institution associated with further full-time education and anyqualifications gained. Years in further education were added to years in full-time secondary education to obtain total

time in full-time education. The modal value was 9 years (i.e., the UK minimum statutory school attendance from ages

5 to 14 during the relevant historical period), with a range from 8 to 25 years. Fifty-six percent had 10 or more years

of education, and many of these had received further education. As expected, years of education correlated very

strongly (r = .890) with SEG. Thus, for practical purposes, SEG can be taken as an equivalent index to years of

education in all analyses described below. The correlation between years of full-time education and current age was

not significant (r = -.078, p > .1).

Volunteers also rated their health status at time of testing on a scale from 1 (very bad ) to 5 (very good ) and also

separately rated how their health had changed over the over the previous 5 years. Distributions of responses given by

a 43% subset of the entire sample are given in Table 2.


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Table 2 Frequencies of (%) Self-Ratings of Current and Pretest Health Status

Seventy percent of the subset reported that they were currently in good or in very good health, and 75% reported

stable or improved health during the preceding 5-year period. Volunteers' current and retrospective health ratings

showed significant but small negative correlations with their ages (r = -.049, p < .05 and r = -.051, p < .05,

respectively).

Measures of Cognitive Performance and of Depression

The cognitive test battery included the AH4 IQ test (Heim, 1970) and the Mill Hill Vocabulary Test, Parts A and B

(Raven, 1965). The AH4 is a test of general reasoning ability given in two parts, with each part consisting of 65

questions. The first, AH4 (1), tests verbal reasoning and logical and arithmetical ability. The second, AH4 (2), tests

spatial reasoning ability. The number of questions correctly answered on each part within 10 min provided indexes of fluid ability that correlated in this sample highly (r s = .760 to .920) with scores on other, well-standardized and widely

used tests of fluid intelligence such as the Raven's Progressive matrices and the Wechsler Adult Intelligence Scale

(Wechsler, 1955). Volunteers also completed the Mill Hill Vocabulary Test, Part A (MHA), which requires multiple

choice synonym selection, and Part B (MHB), which requires word definition. Both were untimed. The analyzed data

are unadjusted raw scores obtained as recommended in the test manuals, that is, total numbers of items correctly

answered without count of errors. Volunteers also completed two memory tasks. The first was cumulative learning

(CL) in which the same list of 15 nouns was balanced for frequency (Thorndike Lorge A and AA), imageability (words

over 6.0 for imageability in the sample described by Paivio, Yuille, & Madigan, 1986), and length (two, three, and four

syllables), and was presented visually at a rate of one item every 2 s in a different random order on four successive

occasions. After each presentation volunteers immediately wrote down as many items as they could recall in any

order. The analyzed scores are total words correctly reported over all four presentations. A second memory task wasimmediate free recall (FR) of a list of 30 nouns, selected as described above and presented once at a rate of one item

every 2 s. The analyzed scores are the total numbers of words correctly recalled in any order. As is usual in FR

experiments, false positives and intrusions were too rare to be informative. It would, of course, have been possible to

derive and analyze a variety of other indexes from these tasks, such as first trial recall (memory span) and rate of

subsequent learning in the CL task or primacy and recency effects from the FR task. However, the total number

correct was used because this proved sensitive to the individual-difference variables studied here and provided

indexes exactly comparable to those used in previous studies.

Depression was assessed with the Beck Depression Inventory. Many comparative studies, such as that by Bech et

al. (1975), have shown that the Beck has high validity, both in relation to clinical assessments and to other

well-validated instruments and has the further advantage of efficiency in assessing subclinical levels of depression anddysphoria (cf. Beck et al., 1961). An important practical point is that the test is easily administered to large groups.

Test batteries were administered to groups of 10 to 25 volunteers during two separate 1.5-hr sessions held in


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university lecture rooms in Newcastle-upon-Tyne, England and Manchester, England. Volunteers were paid a flat rate

of £3 per test session to cover their travel expenses. The average interval between sessions was never longer than 3

months.

Test characteristics.

Means, standard deviations, and test–retest reliabilities were evaluated for all measures. Reliability was high for

the AH4 and Mill Hill tests and at acceptable levels for other measures. These results are shown along the diagonal inTable 3, which gives correlations between all cognitive and demographic measures.

Table 3 Correlations, Reliabilities, Means, and Standard Deviations for Measures

Intercorrelations between measures.

Intertest score correlations were positive and highly significant ( p < .001). The MHA, MHB, and AH4, Parts 1 and 2,

correlated highly with each other (r > .490) and moderately well with the memory tests (.340 < r < .500). AH4 (1) and

AH4 (2) scores correlated negatively and significantly with age (r = -.330 and r = -.420, respectively), as did memory

test scores (CL: r = -.300; FR: r = -.280), but MH scores did not (MHA: r = .004; MHB: r = .030). Beck scores showed

small but significant negative correlations with all cognitive measures (r > -.080). Gender correlated modestly in

various directions with all cognitive measures (-.070 < r < .130), and SEG showed significant strong negativecorrelations (r > -.250) with all cognitive measures.

Beck scores and demographics.

The Kolmogorov–Smirnov test, (Kolmogorov, 1933) showed that the distribution of Beck scores was negatively

skewed (z = 3.95, p < .001). Most volunteers had very low Beck scores, with very few cases beyond the thresholds for

clinical depression. The mean of 7.6 (SD = 6.1) contrasts to values of 18, 25, and 30 that Beck et al. (1961) estimated

as thresholds for mild, moderate, and severe clinical depression, respectively.

Associations between Beck scores and demographic variables were broadly consistent with findings from earlier

studies. Chronic illness, functional disability, and severe cognitive impairment were minimized in this sample of

healthy, independently living, and highly motivated volunteers. Thus, the absence of any increase in Beck scores withage is consistent with Blazer et al.'s (1991) finding that the association of age with increased incidence of depression is

mediated by factors such as chronic illness, functional disability, and severe cognitive impairment. However, a new

finding is that Beck scores were significantly associated with gender and with SEG. Note that these relationships


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cannot be explained by mediating associations of gender and SEG with medical problems because no such relationships

appeared in the subset of the sample whose self-ratings of health were examined (see McInnes & Rabbitt, 1994).

Beck scores and cognitive performance.

Because MHA scores were independent of age in this sample, they could be used both as a dependent variable (so

that the unique effect of Beck scores on MHA scores could be evaluated) and, where appropriate, as a factor in the

statistical model used to evaluate the unique effect of Beck scores on other cognitive measures. Only complete caseswere analyzed. In the following regression analyses, Beck scores were divided into three groups with ranges of 0–6,

7–13, and 14+ representing low, middle, and high scoring groups, respectively. Because of low numbers in SEGs 1 and

5, SEG 1 was pooled with 2, and 5 was pooled with 4.

ResultsThe analysis was performed using GLIM (Baker, 1985). The analysis consisted of a series of hierarchical regressions

that were conducted on the two AH4 scores, the two MH scores, and the CL and FR tasks. First, variation in test score

that was due to age, gender, SEG, and, where appropriate, MHA, was removed. Beck scores were then entered to

identify their unique relationship with test score. Any significant interaction terms were then added using the forward

stepwise method and beginning with second order terms. Results are presented in Table 4.


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Table 4 Hierarchical Regressions of Cognitive Scores

When interactions involved a group variable, regression coefficients given in the tables represent the cognitive

effect of an explanatory variable involved in the interaction within each group.

The R2 given in Table 4 is given by

Equation (Uncited)

where TSS represents total variation in a test score, RSS represents the variation in test score not explained by

the regressors, and DF represents the appropriate degrees of freedom.


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Model Checking

Residuals, which are obtained from fitting models that result from using hierarchical regressions, should be

normally distributed. This assumption was checked by computing correlations proposed by Filliben (1975). In all cases,

the Filliben correlation was at least .99 (to two decimal places), indicating a close fit to the normal distribution.

Direction of variable influence was obtained by examining the signs of associated regression coefficients, and strength

of association was evaluated by F ratios. The familywise error rate (Howell, 1987) was used in assessing significance of

group multiple comparisons when there was a significant overall group effect. To minimize the number of chancefindings the number of multiple comparisons was limited to those involving lowest valued groups (i.e., the Beck group

containing scores <= 6, or SEGs 1 and 2), and model terms were regarded as statistically significant only if their p value

was less than .01.

AH4 (1) and AH4 (2) Scores

Both AH4 (1) and (2) scores decreased with age. This decline was more marked on AH4 (1) in high MHA scorers.

AH4 scores were lower in SEGs that were Grade 3 nonmanual workers (3N) than in SEGs 1 and 2 and increased with

MHA, (although AH4 [1] scores increased at different rates with MHA across the SEGs). Men had lower AH4 (1) scores

than women in the high Beck group and had higher AH4 (2) scores overall. For both AH4 (1) and (2), the middle and

high Beck groups had significantly lower scores than the low Beck group.

MHA and MHB Scores

MHA and MHB scores did not differ with age; they were lower in SEGs 3N and below than in the pooled SEGs 1 and

2. Women had lower MHA scores but higher MHB scores; MHB had a strong positive association with MHA, though this

association varied with SEG. The difference in MHB score between the two pooled SEGs was greatest in the high Beck

group. Beck scores had a weak but significant negative association with MHA score and a strong negative association

with MHB score, with the low Beck group differing from both of the other groups.

CL and FR Scores

Both FR and CL scores decreased with age. The age decline in FR was more pronounced in higher MHA scorers.

Both FR and CL scores were higher in women and lower in SEGs that were Grade 3 skilled manual workers (3M) and

below (whereas, for FR, additionally being lower in SEG 3N compared with the pooled SEGs 1 and 2). Both correlatedpositively with MHA and negatively with Beck scores. The high Beck group had significantly lower scores than the low

Beck group.

General DiscussionIn a demographically representative UK sample there were strong relationships between illness, age, and gender

and, probably, significant interactions among Beck scores, age, and gender. Furthermore, because incidence of poor

health, disability, and negative life events was greater in disadvantaged SEGs, we might also expect complex

interaction among Beck scores, age, gender, and SEG. Such information might be very useful to epidemiologists and

health economists but would prevent us from asking the questions answered here. Because this self-selected sample

shows no statistical relation between age and SEG or between age and health status, we are able to replicate and

extend findings from earlier studies: There appears to be no residual relationship between Beck scores and age whenrisk factors are eliminated by sample self-selection rather than by post hoc partialing of variance. Data from this elite

sample also allow us to show that when health factors are controlled by sample selection rather than by post hoc

statistical analysis, variation within a very low range of Beck scores (indicative of dysphoria rather than depression)

significantly affects cognitive performance. There is every reason to suppose that these relationships are general,

with the caution necessary for all extrapolations—that comparable control of variance must be exercised, whether by

statistical analysis or by cohort selection.

Demographic Variables and Beck Scores

Beck scores did not increase with age. This confirms that depression is not necessarily related to age but rather to

socioeconomic status and to medical risk factors related to age (Blazer et al., 1991).

The finding that Beck scores increase with socioeconomic disadvantage is familiar (Blazer & Williams, 1980; Blazer

et al., 1991). A new finding is that this relationship persists within an elite population with a very low range of Beck

scores in which self-selection has eliminated some major risk factors associated with socioeconomic disadvantage such


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as poor health. We might expect an interaction among Beck score, age, and SEG to appear in a demographically

normal sample in which increased age would probably also imply increased risk of socioeconomic deprivation.

Self-selection probably explains its absence in this sample.

The finding that women had higher Beck scores than men is familiar (Griffiths et al., 1987). A new finding is that

although both gender and SEG affected Beck scores their impacts did not interact. Apparently the risk factors for

depression associated with being a woman may be independent of, and so possibly qualitatively different from, thoseassociated with socioeconomic disadvantage.

Beck Scores and Cognitive Performance

People with higher Beck scores performed less well on all cognitive tests. Most affected were scores on the AH4

(1) and (2) then scores on CL and MHB, and then FR and MHA scores. Previous studies have shown effects of

depression on cognitive function but not within a range of Beck scores well below conventional threshold values for

diagnoses of clinical depression. As expected, both greater age and higher Beck scores were associated with lower

levels of cognitive performance on all cognitive tasks. A new finding is that their effects did not interact. There is no

evidence that greater age, of itself, increases vulnerability to the effects of depression on cognitive performance.

Another new finding is that age and Beck scores had qualitatively different patterns of effects across cognitivetasks. Both increased age and Beck scores and reduced AH4, CL, and FR scores. However, though age between 50 and

93 years did not affect MHA or MHB scores, Beck scores did. With earlier findings that depression lowers performance

on tests of crystallized ability (Perlmutter & Nyquist, 1990), although age, on its own does not (Horn, 1982, 1986; Horn

& Cattell, 1966), this result suggests that, in contrast to age, depression not only reduces the speed of problem solving

and efficiency of verbal learning but also fluent access to a vocabulary that has been built up over a lifetime.

The question of causality arises: Laboratory demonstrations have shown that negative mood states can reduce

cognitive efficiency (Ellis et al., 1984; Hertel & Hardin, 1990). However, in this study, we must ask whether Beck

scores correlated negatively with scores on cognitive tests because unhappiness reduced cognitive efficiency or

because participants were made unhappy if they noticed that their cognitive efficiency had declined.

Concurrent with the Beck scores, volunteers in this sample also rated the frequency with which they experienced

everyday cognitive lapses and memory failures (on the Broadbent Cognitive Failures Questionnaire [CFQ], Broadbent,

Cooper, Fitzgerald, & Parkes, 1982; and the Sunderland and Harris Memory Failures Questionnaire [MFQ], Sunderland,

Watts, & Baddeley, 1983). Within the entire sample MFQ scores did not correlate with Beck scores, thus giving no

evidence that individuals with high Beck scores noticed more memory problems. CFQ scores correlated positively with

Beck scores (r = .38). Previous studies have also found that the CFQ loads for depression (e.g., Broadbent et al., 1982;

Rabbitt & Abson, 1990). Nevertheless, across this entire sample there was no relationship between MFQ or CFQ scores

and scores on any cognitive test (for the entire sample r ; see also a detailed analysis from a random subset of 442

members of the present sample by Rabbitt & Abson, 1990). The association between CFQ and Beck scores suggests that

people who are unhappy have relatively poor views of their own competence or, indeed, vice versa. However, the

absence of any association between volunteers' perceptions of their everyday cognitive efficiency and their scores onthese cognitive tests suggests no relationship between their levels of satisfaction with their own abilities and their

cognitive test scores. Thus, although we know that dysphoria can reduce cognitive competence, and it is reasonable

to suppose that perceptions of loss of cognitive competence may increase dysphoria, on balance the trends found in

this analysis seem to reflect the former relationship. However, these data do not settle the matter, and the way

forward seems to be through a detailed joint analysis of cognitive self-assessment, depression, and objective ability.

Possible Variables Modifying the Effects of Depression on Cognition

It was possible that age, general intellectual ability, gender, or socioeconomic advantage might have modified the

impact of unhappiness on cognitive performance. As expected, people in advantaged SEGs performed better on all

cognitive tasks and also had lower Beck scores. However, the sizes and qualitative profiles of the effects of Beck

scores on cognitive test scores were the same for all social groups. It seems that, in this sample, level of socioeconomic advantage was neither a protective nor a risk factor for the impact of unhappiness on cognitive

efficiency.


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Because MHA scores were unaffected by ages between 50 and 93 years they could be included in the GLIM model

both as an index of lifetime general ability, which might be affected by all other factors, and also as an

age-independent predictor variable. The strength of the negative relationship between Beck scores on the one hand

and AH4 (1) and (2), MHB, CL, and FR scores on the other remained the same across the range of MHA scores. Thus,

there was no evidence that lifetime level of general intellectual ability modified the impact of depression on

cognition.

Gender played a much more complicated role. As in most previous studies, women performed as well as men on an

IQ test assessing verbal and logical reasoning and arithmetical ability [the AH4 (1)] but less well at comparison and

manipulation of shapes and spatial reasoning [the AH4 (2)]. Most studies have found that women perform better than

men on vocabulary tests. In our study, this was true for the more difficult vocabulary test, MHB, but not for the easier

MHA. A sufficient explanation for this discrepancy is that the women in the present sample belonged to generations

that were educationally disadvantaged and had poorer job opportunities than their male peers. The new finding is that

women performed significantly better than men at cumulative learning of lists of 15 nouns and at free recall of lists of

30 nouns. This difference remained robust even after individual differences in age, SEG, and MHA scores were

considered.

The entry of gender into higher order interactions is complex. An interesting finding is that the effects of increasing Beck scores on AH4 (1) were significantly less marked for women than for men. This raises the possibility

that women may be more resistant than men to the cognitive impact of low levels of depression. It may also mean that

women are more willing than men to express their unhappiness, so that levels of complaints expressed on the Beck by

the two sexes reflect different levels of actual depression.

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