Full text of DOI:10.1093/geronb/57.1.S43

Journal of Gerontology: SOCIAL SCIENCES
Copyright 2002 by The Gerontological Society of America
2002, Vol. 57B, No. 1, S43–S51
Socioeconomic Position Across the Lifecourse
and Cognitive Function in Late Middle Age
Gavin Turrell,1 John W. Lynch,2 George A. Kaplan,2 Susan A. Everson,2 Eeva-Liisa Helkala,3
Jussi Kauhanen,3,4 and Jukka T. Salonen3,4,5
1School of Public Health, Queensland University of Technology, Brisbane, Australia.
2Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor.
3Department of Public Health and General Practice, University of Kuopio, Finland.
4Research Institute of Public Health, University of Kuopio, Finland.
5Inner Savo Health Centre, Suonenjoki, Finland.
Objectives: To examine the influence of childhood and adult socioeconomic position, socioeconomic mobility, and
cumulative disadvantage across the lifecourse on cognitive function in late middle age.
Methods: Cross-sectional population-based study of 486 men age 58 and 64 from eastern Finland. Respondent’s so-
cioeconomic position in childhood was measured using parent’s education and occupation, and respondent’s position in
adulthood was indicated by attained education and personal income. Cognitive function was assessed using five neuro-
psychological tests: Trail Making, Selective Reminding, Verbal Fluency, Visual Reproduction, and the Mini-Mental
State Exam.
Results: Each indicator of socioeconomic position made statistically independent contributions to levels of cognitive
function: Respondents from poor childhood backgrounds, and those who attained a limited education and earned a low
income, performed worst on each test. Men who occupied a disadvantaged socioeconomic position in childhood and
then experienced upward mobility over the lifecourse exhibited better cognitive performance than those with similar so-
cioeconomic origins but limited or no upward mobility. Conversely, men from advantaged childhood backgrounds who
later in life experienced downward mobility scored poorer on each cognitive test than their counterparts who remained
in the most advantaged groups throughout the lifecourse. There was a strong, graded association between cumulative so-
cioeconomic disadvantage and cognitive function: Men who occupied a low socioeconomic position during both child-
hood and adulthood scored worse on every test than those who occupied a high position at all points in their lives.
Discussion: Socioeconomic conditions across all stages of the lifecourse appear to make unique contributions to
cognitive function in late middle age. These results also suggest that in terms of cognitive function, origin is not neces-
sarily destiny, as disadvantaged socioeconomic circumstances in childhood may be overcome to some extent by upward
mobility later in life.
large and growing literature has documented the asso-
ciation between socioeconomic position and adult dis-
in adulthood being greatest among those from disadvan-
taged backgrounds (Blane et al., 1996; Gilksman et al.,
1995; Goya-Wannamethee, Whincup, Shaper, & Walker,
1996; Lynch et al., 1994; Nystrom-Peck, 1994; Vagero &
Leon, 1994). In more recent times, research into the influ-
ence of childhood socioeconomic position on adult health
has been extended (and complemented) by studies that have
adopted a lifecourse perspective (Bartley, Blane, & Mont-
gomery, 1997; Kuh & Ben-Schlomo, 1997; Lynch, Kaplan,
& Salonen, 1997; Wadsworth, 1997). These studies (of
which there are few) have shown that socioeconomic vari-
ability in adult disease is due to adverse exposures experi-
enced in both early and later life, with the relative impor-
tance of each lifecourse stage depending on the health
outcome (Davey Smith, Hart, Blane, Gillis, & Hawthorne,
1997). Lifecourse research has also demonstrated that adult
disease is influenced by socioeconomic mobility (Hart et
al., 1995; Mare, 1990; Power, Manor, & Matthews, 1999).
Upward mobility appears to decrease risk and partially
compensate for earlier disadvantage, whereas downward
mobility increases risk. Moreover, the impact of socioeco-
A
ease (Kaplan & Lynch, 1997). With few exceptions, the
evidence shows that those who are socioeconomically dis-
advantaged have higher mortality rates for most major
causes of death, and their morbidity profile indicates that
they experience more ill health (Aiach & Curtis, 1990;
Davey Smith & Egger 1992; Macintyre, 1997). Moreover,
socioeconomic differences in adult health are evident for
both men and women (Arber, 1997; Koskinen & Martelin,
1994), the relationship exists irrespective of how socioeco-
nomic position and health are measured (Berkman & Mac-
intyre, 1997), and the association is found in all countries
for which there are data (Feinstein, 1993; Kunst & Macken-
bach, 1994).
During the past few decades, researchers have attempted
to understand the genesis of health inequalities in adulthood
by examining socioeconomic conditions experienced in
early life. Some, but not all, studies have shown that child-
hood socioeconomic position is an important determinant of
disease risk in later life, with propensity for adverse health
S43

S44
TURRELL ET AL.
nomic disadvantage may accumulate longitudinally, such
that the poorest health is experienced by those who have the
greatest cumulative exposure to social and economic adver-
sity.
Measurement of Socioeconomic Position
Socioeconomic position in childhood.—During the base-
line examination, respondents were asked to report on as-
pects of their family’s socioeconomic position at age 10, in-
cluding parents’ education and principal occupation.
Mother’s and father’s education were categorized as (a) did
not complete primary school and (b) primary school or
more. This categorization reflects the fact that the partici-
pants’ parents were educated in the early 1900s, and few at-
tained a high level. Parents’ occupation was coded as (a) un-
skilled manual, (b) lower white collar and skilled manual,
and (c) upper white collar and professional. If the respon-
dent’s mother was a homemaker, her socioeconomic posi-
tion was coded in accordance with her husband’s occupa-
tion. The parental socioeconomic measures for both parents
were summed to form an index that was subsequently cate-
gorized into approximate tertiles: low socioeconomic posi-
tion (n ꢀ 173, 36%), middle socioeconomic position (n ꢀ
156, 32%), and high socioeconomic position (n ꢀ 157,
32%).
In this article, we contribute to an understanding of how
socioeconomic position across the lifecourse influences
adult health by focusing on cognitive function in late middle
age. There is now a well-established literature showing that
neurological conditions such as poor cognitive function
(Cerhan et al., 1998; Elias, Elias, D’Agostino, Sibershatz, &
Wolf, 1997; Elwood et al., 1999; Freidl et al., 1996), de-
mentia (De Ronchi et al., 1998; Medical Research Council
Cognitive Function & Aging Study, 1998), and Alzheimer’s
disease (Stern et al., 1994; Stern, Tang, Denaro, & Mayeux,
1995) are more common among socioeconomically disad-
vantaged adults. Studies have also shown that children from
disadvantaged backgrounds exhibit poorer cognitive perfor-
mance than their more advantaged counterparts (Andersson,
Sommerfelt, Sonnander, & Ahlsten, 1996; Auerbach, Lerner,
Barasch, & Palti, 1992; Duncan, Brooks-Gunn, & Kle-
banov, 1994; Gottfried, 1984; Guidubaldi & Perry, 1984).
To our knowledge, however, no study has investigated
whether and to what extent socioeconomic mobility over
the lifecourse, and accumulated adverse exposure, affect
cognitive function in later life. Information about these is-
sues will have important implications for public health in-
terventions and for public policy more broadly.
Socioeconomic position in adulthood.—This was mea-
sured by using respondent’s education and personal income
received in the preceding year. Education was grouped into
two categories: (a) did not complete elementary school or
completed elementary school plus some junior high school
(n ꢀ 260, 54%) and (b) completed elementary school plus
vocational training (at least 1 year) or junior high or more
(n ꢀ 226, 47%). The mean number of formal years of edu-
cation for these two groups was 6.5 and 10.4, respectively
(p ꢀ .0001). Personal income was initially modeled using
quintiles and tertiles, and both showed a similar graded as-
sociation with cognitive function. For the purposes of this
study, income was subsequently measured as the bottom
40% of earners (n ꢀ 186) and the top 60% (n ꢀ 300). We
also considered using respondent’s occupation; however,
preliminary analyses indicated that this measure added little
to our understanding of how socioeconomic position related
to cognitive function over and above that captured by edu-
cation and income; thus, it was not included in the study.
METHODS
Participants took part in the Kuopio Ischemic Heart Dis-
ease Risk Factor Study, an ongoing population-based inves-
tigation of heart disease risk factors, mortality, and other
health-related outcomes among middle-aged eastern Finn-
ish men (Salonen, 1988). Between 1986 and 1989, an age-
stratified random population sample of 1,516 men was re-
cruited for a baseline examination. The men were ages 42
(n ꢀ 334), 48 (n ꢀ 358), 54 (n ꢀ 426), or 60 years (n ꢀ
398) and constituted 83% of eligible participants. Partway
through the baseline data collection period, ultrasound tech-
nology became available, and this was subsequently added
to the study protocol in February 1987. Those men who en-
tered the baseline study on or after this date (n ꢀ 1,229) un-
derwent an ultrasound examination of the right and left ca-
rotid arteries. Between 1991 and 1993, these 1,229 men
were invited to participate in a follow-up study; however,
52 could not take part because of death, serious illness, or
migration, leaving 1,177 eligible participants. Of this eligi-
ble group, 1,038 (88%) participated, 107 refused, and 32
could not be contacted.
Socioeconomic mobility over the lifecourse.—The so-
cioeconomic lifecourse measure was constructed by cross-
classifying the three-level childhood socioeconomic position
variable with respondent’s education and income to produce
12 pathways. This measure reflected the respondent’s socio-
economic trajectory from approximately age 10, through ad-
olescence and early adulthood, to late middle age.
The focus of this present analysis is on the two oldest
groups of men who were age 58 or 64 years at the follow-up
study; participation rates were 86% (284 of 300) and 89%
(271 of 305), respectively. During follow-up, these men un-
dertook a series of neuropsychological tests, and 98% (n ꢀ
545) completed at least one test. For this analysis, partici-
pants were excluded if they reported having no father or
mother (n ꢀ 31) or if they provided insufficient information
on their socioeconomic position (n ꢀ 28), leaving 486 men
with complete data (58 years old, n ꢀ 249; 64 years old,
n ꢀ 237).
Cumulative socioeconomic exposure.—The variable mea-
suring cumulative socioeconomic exposure across the life-
course was created by dividing the parental socioeconomic
index into two categories based on a median split and then
summing this and the education and income variables to de-
rive eight combinations of socioeconomic position. These
combinations were subsequently grouped into four catego-
ries reflecting similar levels of cumulative disadvantage:
high socioeconomic position at all three points over the life-
course (n ꢀ 120, 25%); high position at two points, low at

SOCIOECONOMIC POSITION AND COGNITIVE FUNCTION
S45
one (n ꢀ 116, 24%); low position at two points, high at one
(n ꢀ 164, 34%); and low socioeconomic position at all three
points (n ꢀ 86, 18%).
rectly recalled (potential maximum score of 60, M ꢀ 34.2,
SD ꢀ 8.3).
The Verbal Fluency Test is a test of language perfor-
mance that assesses the participant’s ability to spontane-
ously produce words under the restrictions of a limited letter
category (Strub & Black, 1985) and is also a test of frontal
lobe functioning. Participants were asked to generate as
many words as possible beginning with the letters P, A, and
S; 60 s was allocated for each letter. Different forms of the
same word and proper names of persons or places were not
counted as correct. Performance was measured by counting
the number of words produced during the 3-min period,
with higher scores indicating better language facility (M ꢀ
32.1, SD ꢀ 12.8).
The Visual Reproduction Test examines constructional
ability and visual memory for nonrepresentative figures (right
temporal lobe functioning). Participants were initially shown
a single geometric figure for 10 s, after which it was removed
from view; the participant was then asked to draw the figure
from memory. This procedure was repeated with a figure of
greater complexity, and then for a third time, although on this
occasion the participant was required to draw two figures.
Scoring was based on the degree to which the participant was
able to accurately and correctly replicate the figures (potential
maximum score of 21; M ꢀ 11.2, SD ꢀ 3.7).
The Mini-Mental State Exam has been widely used in
both clinical and population-based studies (Brayne & Callo-
way, 1990; Escobar et al., 1986) to test for the presence of
cognitive impairment and as a screening tool for dementia
(O’Connor, Pollitt, & Treasure, 1991). The test examines
orientation (10 items), registration (3 items), attention and
calculation (5 items), recall (3 items), and language (9 items).
A correct response to each item scores 1 (incorrect ꢀ 0);
scores are then summed to give a potential maximum score
of 30. Higher scores indicate better cognitive function (M ꢀ
27.0, SD ꢀ 2.2).
Measurement of Cognitive Function
Cognitive function was measured using five neuropsy-
chological tests: the Trail Making Test (Reitan, 1958), the
Selective Reminding Test (Buschke & Altman Fuld, 1974),
the Verbal Fluency Test (Borkowski, Benton, & Spreen,
1967), Russell’s adaptation of the Visual Reproduction Test
(Russell, 1975), and the Mini-Mental State Exam (Folstein,
Folstein, & McHugh, 1975). Interviewers trained in neuro-
psychological assessment administered the tests. These five
tests were chosen because they assess a range of cognitive
abilities and function and have established reliability and
validity (Borkowski et al., 1967; Buschke & Altman Fuld,
1974; Butters, Granholm, Salmon & Grant, 1987; Folstein
et al., 1975; Greencliff, Margolis, & Erker, 1985; Reitan,
1958; Russell, 1975). Moreover, the Finnish language ver-
sions of these tests have been used successfully in a popula-
tion-wide dementia screening program conducted in eastern
Finland (Koivisto et al., 1992).
The Trail Making Test is a test of frontal lobe functioning
(Hänninen et al., 1997) as indicated by visual searching and
sequencing, perceptual motor speed, and the ability to make
alternating conceptual shifts (Strub & Black, 1985). The
original Trail Making Test includes two parts in which par-
ticipants are asked to sequence on paper and in ascending
order the numbers 1–25 (Part A) or the numbers 1–13 and
the letters of the alphabet A–L in alternating fashion (Part
B). A respondent’s task was to draw a connecting line from
item to item without lifting the pencil from the paper and as
rapidly as possible. In the present study, we also adminis-
tered an alternate form of Part B in which letters of the al-
phabet were replaced with names of the months and respon-
dents were asked to sequence the items, alternating between
numbers and months. This version of the test was developed
for use in elderly Finnish populations as part of a popula-
tion-wide dementia screening program (Koivisto et al.,
1992) and was shown to differentiate normotensive and hy-
pertensive individuals in a sample of older Finnish adults
(Kuusisto et al., 1993). The correlation between scores on
the original Trail Making Test, Part B, and the alternate
form of Part B was .67 (p ꢁ .0001) in the present study.
Owing to a higher proportion of missing data on the original
Part B (23%), we chose to report results from the alternate
version of Part B, which had less than 3% missing data. Per-
formance on the Trail Making Test was judged in terms of
the number of seconds required to complete the sequencing
(M ꢀ 125.9, SD ꢀ 66.3).
Data Analyses
The influence of socioeconomic position, changes in po-
sition across the lifecourse, and cumulative disadvantage on
cognitive ability were investigated using the general linear
models procedure in SAS (SAS Institute, 1990). This proce-
dure produces cognitive function means and 95% confi-
dence intervals for the levels of each socioeconomic indica-
tor. Three sets of analyses were conducted. The first set
examined the association between respondent’s childhood
socioeconomic position, education, and income and perfor-
mance on each of the cognitive tests. The results are initially
presented only with age adjustment and then with simulta-
neous adjustment for age and each measure of socioeco-
nomic position. The second set examined how each of the
12 lifecourse pathways related to cognitive function score
and also how changes in socioeconomic position from
childhood to adulthood affected these scores. The third set
investigated the association between cumulative socioeco-
nomic disadvantage and cognitive performance. Impor-
tantly, when undertaking the analyses models were adjusted
for morbidity indicators that were likely to represent path-
ways through which socioeconomic factors affected cogni-
tive function. These indicators included disease measures
The Selective Reminding Test examines learning ability
and storage, retention, and retrieval of information from
short- and long-term memory. Participants were initially
read 10 unrelated words in approximately 20 s and asked
to recall the entire list in any order. Participants were then
read only those words that they failed to recall after the
first reading and were again asked to recall the entire list
of 10 words. This procedure was repeated six times, and
the participant’s score was the total number of words cor-

S46
TURRELL ET AL.
(history and incidence of stroke, ischemic heart disease, ath-
erosclerosis, and diabetes), risk markers for these diseases
(hypertension, blood lipids, fibrinogen, glucose, and insu-
lin), and pharmacological agents (medications for control
of hypertension and cholesterol). These measures were
modeled in numerous ways—both separately and simulta-
neously, using single indicators and composite indexes—
however, their inclusion in the general linear models analy-
sis did not change the relationship between socioeconomic
position and cognitive function. In short, the patterning and
magnitude of association between socioeconomic position
and score on each of the neuropsychological tests was the
same prior to and after adjustment for the morbidity indica-
tors. In light of this, we report only the age-adjusted results.
dent contributions to levels of cognitive functioning in
adulthood.
Socioeconomic Mobility and Cognitive Function
Table 2 presents age-adjusted mean scores for each cog-
nitive function test according to different socioeconomic
pathways from childhood to adulthood. The strong and con-
sistent patterning of results in this table attests to the impact
of socioeconomic mobility across the lifecourse on cogni-
tive function in late middle age. Those who occupied a dis-
advantaged childhood position and then experienced up-
ward mobility over the lifecourse had better adult cognitive
performance than those with similar socioeconomic origins
but with limited or no upward mobility. Respondents in the
low childhood category who subsequently attained a high
education and income, for example, produced an average of
33.8 words during the 3-min duration of the Verbal Fluency
Test compared with 24.1 words (p ꢁ .0001) for those who
occupied a disadvantaged position at all three lifecourse
stages. A near identical trend is observed for each of the
other four tests. Further, respondents who occupied an ad-
vantaged socioeconomic position in childhood but then later
in life experienced downward mobility scored poorer on
each cognitive function test than their counterparts who re-
mained in the most advantaged groups throughout the life-
course. The mean score on the Trail Making Test, for exam-
ple, was 91.5 s for those who experienced an advantaged
childhood, were better educated, and were among the top
60% of income earners; their counterparts, who also experi-
enced an advantaged childhood but then later attained a low
education and earned a lower income, took an average of
151.1 s (p ꢁ .0001).
RESULTS
Child and Adult Socioeconomic Position and
Cognitive Function
Table 1 presents the respondents’ mean scores for each
neuropsychological test according to their childhood socio-
economic position, education, and personal income. The
age-adjusted associations (Model 1) show significant and
consistently graded relationships between socioeconomic
position and all indicators of cognitive function in adult-
hood. After simultaneously adjusting for age and the other
measures of socioeconomic position (Model 2), these asso-
ciations were attenuated somewhat, but remained graded. In
every case, those in the bottom socioeconomic category per-
formed worst and those in the top group did best. Overall,
these results show that each indicator of socioeconomic po-
sition—childhood, education, and income—made indepen-
Table 1. Respondent’s Childhood and Adult Socioeconomic Position and Cognitive Function Score
Trail Making
Test
(No. Seconds)
Selective Reminding
Test
Verbal Fluency
Test
(No. Words)
Visual
Reproduction Test
(No. Correct)
Mini-Mental
State Exam
(Score)
(No. Words)
Measure
Model 1 Model 2 Model 1
Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
Childhood socioeconomic position
High
Middle
108.8
122.2
146.1*
.0001
120.4
123.9
139.4*
.01
35.8
34.4
32.7*
.003
35.0
34.2
33.2
.15
36.0
34.2
32.4
30.0*
.007
12.0
11.4
10.3*
.0002
11.5
11.3
10.7*
.08
27.5
27.1
26.5*
.0001
27.2
27.0
26.6*
.03
32.4*
28.4*
.0001
Low
pa
Educationb
Elementary school plus vocational, or junior
high or more
100.1
109.2
36.2
35.5
37.6
36.4
12.4
12.1
27.6
27.4
Part of elementary school, or elementary school
plus part of junior high
149.6*
.0001
146.9*
.0001
32.4*
.0001
32.7*
.0005
27.3*
.0001
27.9*
.0001
10.2*
.0001
10.3*
.0001
26.4*
.0001
26.5*
.0001
pa
Personal incomeb
Top 60%
110.9
151.4*
.0001
115.1
140.7*
.0001
35.4
32.3*
.0001
35.1
33.1*
.01
34.4
28.6*
.0001
33.5
30.9*
.03
11.8
10.2*
.0001
11.6
10.7*
.006
27.3
26.4*
.0001
27.2
26.7*
.007
Bottom 40%
pa
No. respondentsc
474
468
483
486
485
Note: Least square means adjusted for age (Model 1), and age and each measure of socioeconomic position (Model 2).
ap value (F test) for the association between socioeconomic position and cognitive function simultaneously adjusted for age and each measure of socioeconomic
position.
bRespondent’s highest attained education and personal income.
cPopulation-based sample of men aged 58 and 64 years.
*p ꢂ .05 for comparison with the high socioeconomic group (high childhood, elementary school plus vocational or junior high or more, top 60% of income).

SOCIOECONOMIC POSITION AND COGNITIVE FUNCTION
S47
Table 2. Socioeconomic Mobility and Cognitive Function Score
Socioeconomic Mobility
Trail Making
Test
(No. Seconds)
Selective
Reminding Test
(No. Words)
Verbal Fluency
Test
(No. Words)
Visual Reproduction
Test
Mini-Mental
State Exam
(Score)
Childhood
Education
Income
n
(No. Correct)
High
High
High
High
Middle
Middle
Middle
Middle
Low
Low
Low
Low
p valuea
high
high
low
low
high
high
low
low
high
high
low
low
high
low
high
low
high
low
high
low
high
low
high
low
88
14
23
32
51
21
48
36
36
16
54
67
91.5 (79.3–103.7)
100.4 (69.8–131.0)
126.3 (102.4–150.1)
151.1 (129.8–172.3)
89.7 (73.5–105.9)
126.0 (100.4–151.6)
131.2 (114.3–148.0)
152.9 (133.8–172.0)
109.5 (90.3–128.6)
126.0 (96.3–155.8)
141.3 (125.3–157.4)
175.0 (160.7–189.3)
.0001
37.5 (35.7–39.2)
39.3 (35.1–43.5)
33.3 (29.9–36.6)
31.6 (28.7–34.4)
37.4 (35.2–39.6)
33.0 (29.5–36.4)
33.1 (30.8–35.4)
32.4 (29.7–35.2)
34.9 (32.2–37.5)
30.9 (26.9–34.8)
33.4 (31.3–35.6)
31.3 (29.3–33.2)
.0001
41.5 (39.0–43.9)
36.1 (30.1–42.2)
27.1 (22.3–31.8)
27.3 (23.3–31.4)
37.1 (33.9–40.2)
35.7 (30.8–40.7)
27.7 (24.4–31.0)
30.0 (26.2–33.8)
33.8 (30.0–37.6)
30.5 (24.8–36.2)
29.3 (26.2–32.5)
24.1 (21.3–26.9)
.0001
12.9 (12.1–13.6)
11.9 (10.1–13.7)
11.1 (9.7–12.5)
10.2 (9.0–11.4)
13.1 (12.2–14.0)
11.0 (9.5–12.4)
10.7 (9.7–11.7)
10.2 (9.0–11.3)
12.3 (11.2–13.5)
10.3 (8.6–12.0)
9.9 (9.0–10.8)
9.7 (8.8–10.5)
.0001
28.0 (27.6–28.4)
27.4 (26.2–28.5)
26.9 (26.0–27.8)
26.5 (25.8–27.3)
27.6 (27.0–28.2)
27.5 (26.6–28.4)
26.8 (26.2–27.4)
26.5 (25.9–27.2)
27.6 (26.9–28.3)
26.2 (25.1–27.2)
26.6 (26.0–27.1)
25.8 (25.3–26.3)
.0001
No. of respondentsb
486
474
468
483
486
485
Note: Least square means and 95% confidence intervals adjusted for age.
ap value (F test) for the association between the socioeconomic trajectory measure and each cognitive function test adjusted for age.
bPopulation-based sample of men aged 58 and 64 years.
Cumulative Socioeconomic Position and
Cognitive Function
Figure 1 shows the age-adjusted association between cu-
mulative socioeconomic disadvantage and cognitive func-
tion. Strong and finely graded associations between cumula-
tive disadvantage and each indicator of cognitive performance
were observed. The poorest scores were always found
among men who occupied a disadvantaged socioeconomic
Figure 1. Cumulative socioeconomic position across the lifecourse and cognitive function in late middle age (mean test score and 95% confi-
dence intervals).

S48
TURRELL ET AL.
position at all three life stages, whereas the best perfor-
mance was observed among those who occupied a high po-
sition at all points.
that this process makes only a relatively minor contribution
to socioeconomic health inequalities (Blane, Davey Smith,
& Bartley, 1993; Independent Inquiry into Inequalities in
Health, 1998).
Taken together, a causal interpretation of these results
suggests a number of things. First, in terms of adult cogni-
tive function, origin is not necessarily destiny. Disadvan-
taged socioeconomic circumstances in childhood may be
overcome to some extent by upward mobility later in life.
Similar mobility effects have been shown for mortality
(Hart et al., 1995; Mare, 1990), self-rated health (Power,
Manor, & Matthews, 1999), and some health behaviors
(Lynch, 2000). The converse and more sobering aspect of
this process, however, is that positive foundations estab-
lished in childhood may be undermined by events that result
in downward mobility over the lifecourse. Importantly, in
this study we were not able to ascertain why some men were
socioeconomically mobile (either up or down), nor did we
have any information about the cognitive status of these
men in early life. Thus, the strongly patterned association
between socioeconomic mobility and cognitive function
may in part reflect the upward and downward movement of
cognitively healthy and impaired men, respectively. This
notwithstanding, however, data from longitudinal studies
have shown that socioeconomic health inequalities are pri-
marily the result of socioeconomic factors impacting on
health. Clearly, for some people, poor health contributes to
downward mobility or makes upward mobility difficult;
however, its overall contribution to population disease gra-
dients is estimated to be small (Blane et al., 1993; Lynch,
Kaplan, & Shema, 1997; Power, Matthews, & Manor,
1996).
Second, the results of the mobility analysis are consistent
with the idea that not all types of adult chronic disease are
inevitably set or programmed in childhood (Barker, 1992;
Davey Smith, Gunnell, & Ben-Shlomo, 2000; Joseph &
Kramer, 1996). Clearly, childhood is important, but child-
hood status is not the sole determinant of adult cognitive
functioning. Indeed, although our study was not able to as-
certain the men’s cognitive status in childhood, a causal in-
terpretation of our findings suggests that cognitive state
may be malleable and responsive to changing socioeco-
nomic conditions, including those that occur well beyond
the childhood stage of physiological development. This sit-
uation may also pertain to other outcomes such as dementia
and Alzheimer’s disease, which are seen by some research-
ers as different points along the same disease continuum as
cognitive function, and not discrete diseases with unique
etiologies (Brayne & Calloway, 1988). At present, we know
very little about the mechanisms and processes that link so-
cioeconomic position at each lifecourse stage with adult
cognitive function. It seems plausible, however, that being
in a high socioeconomic position at any point is associated
with greater exposure to more stimulating environments, re-
sulting in more extensive brain development as indicated by
increased cortical thickness and dendritic branching and im-
proved communication among neuron networks, which in
turn may reduce one’s propensity to experience dementia or
Alzheimer’s disease in later life. Recent reviews of the rela-
tionship between education, cognitive function, and demen-
DISCUSSION
In this sample of eastern Finnish men, cognitive function
in late middle age was independently associated with child-
hood socioeconomic position and adult education and in-
come. Those who experienced a socioeconomically dis-
advantaged childhood, attained a limited education, or
received a low income exhibited the poorest cognitive per-
formance. These results add to existing evidence showing
independent effects of child and adult socioeconomic posi-
tion on a variety of health outcomes (Blane et al., 1996;
Brunner et al., 1996; Nystrom-Peck, 1994; Vagero & Leon,
1994) and are indicative of the influence of cumulative dis-
advantage in the chronic disease process. It should be noted
that due to selective survival, these and all other associa-
tions between socioeconomic position and cognitive func-
tion reported in this article are probably smaller than they
otherwise would have been if all socioeconomic groups had
the same probability of living to late middle age. Men in this
study needed to survive to ages 58 and 64 years to be in-
cluded in our analysis, and it has been well established in
earlier papers based on the Kuopio Ischemic Heart Disease
cohort that men from lower socioeconomic groups experi-
ence the highest premature mortality (Lynch et al., 1994,
1996). This disproportionate attrition of men from the most
disadvantaged backgrounds resulting from selective sur-
vival will therefore very likely bias against our findings.
This study found that socioeconomic mobility across the
lifecourse was an important determinant of adult cognitive
functioning. Respondents in the high childhood group who
experienced downward mobility into low education and/or
income exhibited a poorer cognitive profile than those who
had the same socioeconomic origins and retained a high (or
a similar) position at each lifecourse stage. An identical (but
reverse) pattern was evident for those who were upwardly
mobile. Those who started life in a socioeconomically dis-
advantaged position and then attained a high education and/
or income performed better on every test than their less up-
wardly mobile counterparts. We should also note that those
who experienced a high socioeconomic position in child-
hood and then attained a low education and income per-
formed significantly worse on four tests than those who ex-
perienced a disadvantaged childhood and subsequently
attained a high education and income. Implicit in this inter-
pretation of the evidence is a presumed causal link between
socioeconomic position across the lifecourse and cognitive
function in adulthood. It must be acknowledged, however,
that a reverse causal interpretation of these findings is possi-
ble. Specifically, if cognitive function were in some way in-
nately determined or set in early life, then this could con-
ceivably influence later socioeconomic position and adult
cognitive functioning. Given the cross-sectional design of
this study, we were unable to disentangle the causal and re-
verse-causation interpretations as these relate to cognitive
function, although it should be noted that studies examining
reverse causation for other types of health outcomes find

SOCIOECONOMIC POSITION AND COGNITIVE FUNCTION
S49
tia provide some support for this process (Albert, 1995;
Katzman, 1993; Orrell & Sahakian, 1995), as do a number
of animal studies (Mohammed, Winblad, Ebendal, & Lark-
fors, 1990; Swaab, 1991).
priately targeted interventions can make a measurable dif-
ference to cognitive and socioemotional functioning (Power
& Hertzman, 1997).
The fact that childhood socioeconomic position had an
enduring effect on test performance separate from the im-
pact of more proximate (and strongly determinative) influ-
ences such as formal education, attests to the importance of
early life circumstances and environments in shaping and
circumscribing human cognitive development. Moreover,
this result underscores and extends evidence from the child
development literature showing poorer cognitive function
and school performance among children from socioeco-
nomically disadvantaged backgrounds. The unique contri-
bution made by education and income to adult cognitive
function also serves to further illustrate that although these
constructs are used widely and often interchangeably in the
health inequalities literature, they constitute distinct socio-
economic domains that may demand different policy re-
sponses and intervention options. The independent con-
tribution of education and income to socioeconomic
variability in health outcomes has also been shown for
physical functioning and mobility limitation. (Kaplan,
1992; Kaplan, Strawbridge, Camacho, & Cohen, 1993).
In the last section of this study, we examined the impact
of cumulative socioeconomic disadvantage on cognitive
function and found a direct correspondence between the du-
ration of disadvantage over the lifecourse and test perfor-
mance in adulthood. The group who experienced the longest
exposure to socioeconomic disadvantage recorded the worst
scores, and those who remained in a high socioeconomic
position at all three lifecourse stages did best. Using U.S.
data, Lynch, Kaplan, and Shema (1997) found a similar as-
sociation between sustained economic hardship and self-
reported cognitive problems, and other studies have shown
that duration of adverse socioeconomic exposure increases
risk for premature death and morbidity (Wadsworth, 1997)
and poor/fair self-rated health at age 33 (Power et al., 1999).
These earlier studies and our present results indicate that an
understanding of the genesis of adult disease and function-
ing will not be greatly furthered by research that focuses on
socioeconomic position at any single timepoint. Moreover,
this evidence highlights the need for future cross-sectional
and longitudinal research to collect data on socioeconomic
conditions that span the lifecourse. In addition, an important
question for future research is whether women’s socioeco-
nomic position over the lifecourse influences their cognitive
function in late middle age in ways similar to or different
from those observed here for their male counterparts. We
were not able to address this issue because our sample con-
sisted solely of men.
Acknowledgments
Dr. Turrell was a visiting postdoctoral research fellow at the Department
of Epidemiology, School of Public Health, University of Michigan, funded
by the National Health and Medical Research Council (Australia) at the
time this research was conducted.
This research was supported by Grant HL44199 from the National
Heart, Lung, and Blood Institute; Grant AG13199 from the National Insti-
tute on Aging; and grants from the Academy of Finland and the Finnish
Ministry of Education.
Address correspondence to Dr. Gavin Turrell, School of Public Health,
Queensland University of Technology, Victoria Park Road, Kelvin Grove,
Brisbane, Queensland 4059, Australia. E-mail: g.turrell@qut.edu.au
References
Aiach, P., & Curtis, S. (1990). Social inequalities in self-reported morbid-
ity: Interpretation and comparison of data from Britain and France.
Social Science and Medicine, 31, 267–274.
Albert, M. S. (1995). How does education affect cognitive function? An-
nals of Epidemiology, 5, 76–78.
Arber, S. (1997). Comparing inequalities in women’s and men’s health:
Britain in the 1990’s. Social Science and Medicine, 44, 773–787.
Andersson, H. W., Sommerfelt, K., Sonnander, K., & Ahlsten, G. (1996).
Maternal child-rearing attitudes, IQ, and socioeconomic status as re-
lated to cognitive abilities of five-year-old children. Psychological
Reports, 79, 3–14.
Auerbach, J., Lerner, Y., Barasch, M., & Palti, H. (1992). Maternal and en-
vironmental characteristics as predictors of child behavior problems
and cognitive competence. American Journal of Orthopsychiatry, 62,
409–420.
Barker, D. J. P. (1992). Fetal and infant origins of adult disease. London:
BMJ Publishing Group.
Bartley, M., Blane, D., & Montgomery, S. (1997). Health and the life
course: Why safety nets matter. British Medical Journal, 314, 1194–
1196.
Berkman, L. F., & Macintyre, S. (1997). The measurement of social class
in health studies: Old measures and new formulations. International
Agency for Research on Cancer, 138, 51–64.
Blane, D., Davey Smith, G., & Bartley, M. (1993). Social selection: What
does it contribute to social class differentials in health? Sociology of
Health and Illness, 15, 1–15.
Blane, D., Hart, C. L., Davey Smith, G., Gillis, C. R., Hole, D. J., & Haw-
thorne, V. M. (1996). Association of cardiovascular disease risk factors
with socioeconomic position during childhood and during adulthood.
British Medical Journal, 313, 1434–1438.
Borkowski, J. G., Benton, A. L., & Spreen, O. (1967). Word fluency and
brain damage. Neuropsychologica, 5, 135–140.
Brayne, C., & Calloway, P. (1988). Normal aging, impaired cognitive
function, and senile dementia of the Alzheimer’s type: A continuum?
Lancet, 1, 1265–1267.
Brayne, C., & Calloway, P. (1990). The association of education and socio-
economic status with the Mini Mental State Examination and the
clinical diagnosis of dementia in elderly people. Age and Ageing, 19,
91–96.
Brunner, E., Davey Smith, G., Marmot, M., Canner, R., Beksinska, M., &
O’Brien, J. (1996). Childhood social circumstances and psychosocial
and behavioural factors as determinants of plasma fibrinogen. Lancet,
347, 1008–1013.
Buschke, H., & Altman Fuld, P. (1974). Evaluating storage, retention, and
retrieval in disordered memory and learning. Neurology, 24, 1019–1025.
Butters, N., Granholm, E., Salmon, D. P., & Grant, I. (1987). Episodic and
semantic memory: A comparison of amnesic and demented patients.
Journal of Clinical and Experimental Neuropsychology, 9, 479–497.
Cerhan, J. R., Folson, A. R., Mortimer, J. A., Shahar, E., Knopman, D. S.,
McGovern, P. G., Hays, M. A., Crum, L. D., & Heiss, G. (1998). Cor-
relates of cognitive function in middle aged adults. Gerontology, 44,
95–105.
In sum, the results of this study suggest that cognitive
function in late middle age is both shaped by and sensitive
to socioeconomic conditions experienced during childhood
and adulthood, as well as changes in socioeconomic posi-
tion over the lifecourse and cumulative disadvantage. Thus,
all stages of the lifecourse play a role in influencing adult
cognitive function, indicating that intervention efforts be di-
rected at both early and later life. Importantly, there is now
a well-established literature showing that timely and appro-

S50
TURRELL ET AL.
Davey Smith, G., & Egger, M. (1992). Socioeconomic differences in mor-
tality in Britain and the United States. American Journal of Public
Health, 82, 1079–1081.
Davey Smith, G., Gunnell, D., & Ben Shlomo, Y. (2000). Lifecourse ap-
proaches to socioeconomic differentials in cause-specific adult
mortality. In D. Leon & G. Walt (Eds.), Poverty, inequality, and health
(pp. 88–124). Oxford, England: Oxford University Press.
Davey Smith, G., Hart, C., Blane, D., Gillis, C., & Hawthorne, V. (1997).
Lifetime socioeconomic position and mortality: Prospective observa-
tional study. British Medical Journal, 314, 547–552.
De Ronchi, D., Fratiglioni, L., Rucci, P., Paternico, A., Graziani, S., &
Dalmonte, E. (1998). The effect of education on dementia occurrence
in an Italian population with middle to high socioeconomic status. Neu-
rology, 50, 1231–1238.
Factors associated with change in physical functioning in the elderly: A
six-year prospective study. Journal of Aging and Health, 5, 40–53.
Katzman, R. (1993). Education and the prevalence of dementia and Alz-
heimer’s disease. Neurology, 43, 13–20.
Koivisto, K., Helkala, E. L., Reinikainen, K. J., Hänninen, T., Mykkänen,
L., Laakso, M., Pyörälä, K., & Riekkinen, P. J. (1992). Population-
based dementia screening program in Kuopio: The effect of education,
age, and sex on brief neuropsychological tests. Journal of Geriatric
Psychiatry Neurology, 5, 162–171.
Koskinen, S., & Martelin, T. (1994). Why are socioeconomic mortality dif-
ferences smaller among women than among men? Social Science and
Medicine, 38, 1385–1396.
Kuh, D., & Ben-Schlomo, Y. (1997). A life course approach to chronic
disease epidemiology. Oxford, England: Oxford University Press.
Kunst, A. E., & Mackenbach, J. P. (1994). The size of mortality differences
associated with educational level in nine industrialized countries.
American Journal of Public Health, 84, 932–937.
Duncan, G. J., Brooks-Gunn, J., & Klebanov, P. K. (1994). Economic dep-
rivation and early childhood development. Child Development, 65,
296–318.
Elias, M. F., Elias, P. K., D’Agostino, R. B., Sibershatz, H., & Wolf, P. A.
(1997). Role of age, education, and gender on cognitive performance in
the Framingham heart study: Community based norms. Experimental
Aging Research, 23, 201–235.
Kuusisto, J., Koivisto, K., Mykkänen, L., Helkala, E.-L., Vanhanen, M.,
Hänninen, T., Pyörälä, K., Riekkinen, P., & Laakso, M. (1993). Essen-
tial hypertension and cognitive function. The role of hyperinsulinemia.
Hypertension, 22, 771–779.
Elwood, P. C., Gallacher, J. E., Hopkinson, C. A., Pickering, J., Stollery, B.,
Brayne, C., Huppert, F. A., & Bayer, A. (1999). Smoking, drinking, and
other life style factors of cognitive function in men in the Caerphilly co-
hort. Journal of Epidemiology and Community Health, 53, 9–14.
Escobar, J., Burnam, A., Karno, M., Forsythe, A., Landsverk, J., & Gold-
ing, J. M. (1986). Use of the Mini-Mental State Examination (MMSE)
in a community population of mixed ethnicity. Journal of Nervous and
Mental Disorders, 174, 607–613.
Feinstein, J. S. (1993). The relationship between socioeconomic status and
health: A review of the literature. The Milbank Quarterly, 71, 279–322.
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). Mini-Mental
State: A practical method for grading the cognitive state of patients for
the clinician. Journal of Psychiatric Research, 12, 189–198.
Freidl, W., Schmidt, R., Stonegger, W. J., Irmler, A., Reinhart, B., & Koch,
M. (1996). Mini Mental State Exam: Influence of sociodemographic,
environmental and behavioral factors, and vascular risk factors. Jour-
nal of Clinical Epidemiology, 49, 73–78.
Lynch, J. W. (2000). Socioeconomic factors in the behavioral and psychoso-
cial epidemiology of cardiovascular disease. In N. Schneiderman, J.
Gentry, J. M. da Silva, M. Speers, & H. Tomes (Eds.), Integrating behav-
ioral and social sciences with public health (pp. 51–71). Washington,
DC: American Psychological Association.
Lynch, J. W., Kaplan, G. A., Cohen, R. D., Kauhanen, J., Wilson, T. W.,
Smith, N. L., & Salonen, J. T. (1994). Childhood and adult socioeco-
nomic status as predictors of mortality in Finland. Lancet, 343, 524–527.
Lynch, J. W., Kaplan, G. A., Cohen, R. D., Tuomilehto, J., & Salonen, J. T.
(1996). Do known risk factors explain the relationship between socio-
economic status, risk of all-cause mortality, cardiovascular mortality,
and acute myocardial infarction? American Journal of Epidemiology,
144, 934–942.
Lynch, J. W., Kaplan, G. A., & Salonen, J. T. (1997). Why do poor people
behave poorly? Variation in adult health behaviours and psychosocial
characteristics by stages of the socioeconomic lifecourse. Social Sci-
ence and Medicine, 44, 809–819.
Gilksman, M. D., Kawachi, I., Hunter, D., Colditz, G. A., Manson, J. E.,
Stampfer, M. J., Speizer, F. E., Willett, W. C., & Hennekens, C. H.
(1995). Childhood socioeconomic status and risk of cardiovascular dis-
ease in middle aged US women: A prospective study. Journal of
Epidemiology and Community Health, 49, 10–15.
Lynch, J. W., Kaplan, G. A., & Shema, S. J. (1997). Cumulative impact of
sustained economic hardship on physical, cognitive, and psychosocial
functioning. New England Journal of Medicine, 337, 1889–1895.
Macintyre, S. (1997). The Black Report and beyond: What are the issues?
Social Science and Medicine, 44, 723–745.
Gottfried, A. W. (1984). Home environment and early cognitive develop-
ment: Longitudinal research. New York: Academic Press.
Goya-Wannamethee, S., Whincup, P. H., Shaper, G., & Walker, M.
(1996). Influence of fathers’ social class on cardiovascular disease in
middle-aged men. Lancet, 348, 1259–1263.
Greencliff, C. L., Margolis, R. B., & Erker, G. J. (1985). Application of the
Trail Making Test in differentiating neuropsychological impairment of
elderly persons. Perceptual and Motor Skills, 61, 1283–1289.
Guidubaldi, J., & Perry, J. D. (1984). Divorce, socioeconomic status, and
children’s cognitive-social competence at school entry. American Jour-
nal of Orthopsychiatry, 54, 459–468.
Mare, R. D. (1990). Socioeconomic careers and differential mortality
among older men in the United States. In J. Vallin, S. D’Souza, & A.
Palloni (Eds.), Measurement and analysis of mortality: New ap-
proaches (pp. 362–387). New York: Clarendon Press.
Medical Research Council Cognitive Function & Aging Study. (1998).
Cognitive function and dementia in six areas of England and Wales:
The distribution of MMSE and prevalence of GMS organicity level in
the MRC CFA study. Psychological Medicine, 28, 319–335.
Mohammed, A. K., Winblad, B., Ebendal, T., & Larkfors, L. (1990). Envi-
ronmental influence on behavior and nerve growth factor in the brain.
Brain Research, 528, 62–72.
Hänninen, T., Hallikainen, M., Koivisto, K., Partanen, K., Laakso, M. P.,
Riekkinen, P. J., & Soininen, H. (1997). Decline of frontal lobe func-
tions in subjects with age-associated memory impairment. Neurology,
48, 148–153.
Hart, C., Davey Smith, G., Blane, D., Hole, D., Gillis, C., & Hawthorne, V.
(1995). Social mobility, health and cardiovascular disease. Journal of
Epidemiology and Community Health, 49, 552–553.
Independent Inquiry into Inequalities in Health. (1998). Independent In-
quiry into Inequalities in Health report (the Acheson Report). London:
The Stationery Office.
Joseph, K. S., & Kramer, M. S. (1996). Review of the evidence on fetal
and early childhood antecedents of adult chronic disease. Epidemiolog-
ical Reviews, 18, 158–174.
Nystrom-Peck, M. (1994). The importance of childhood socioeconomic
group for adult health. Social Science and Medicine, 39, 553–562.
O’Connor, D. W., Pollitt, P. A., & Treasure, F. P. (1991). The influence of
education and social class on the diagnosis of dementia in a community
population. Psychological Medicine, 21, 219–224.
Orrell, M., & Sahakian, B. (1995). Education and dementia: Research evi-
dence supports the concept of “use it or lose it.” British Medical
Journal, 310, 951–952.
Power, C., & Hertzman, C. (1997). Social and biological pathways linking
early life and adult disease. British Medical Bulletin, 53, 210–221.
Power, C., Manor, O., & Matthews, S. (1999). The duration and timing of
exposure: Effects of socioeconomic environment on adult health.
American Journal of Public Health, 89, 1059–1065.
Kaplan, G. A. (1992). Maintenance of functioning in the elderly. Annals of
Epidemiology, 2, 823–834.
Kaplan, G. A., & Lynch, J. W. (1997). Whither studies on the socioeco-
nomic foundations of population health? American Journal of Public
Health, 87, 1409–1411.
Power, C., Matthews, S., & Manor, O. (1996). Inequalities in self-rated
health in the 1958 birth cohort: Lifetime social circumstances or social
mobility? British Medical Journal, 313, 449–453.
Reitan, R. M. (1958). Validity of the Trail Making Test as an indicator of
organic brain damage. Perceptual Motor Skills, 8, 271–276.
Russell, E. W. (1975). A multiple scoring method for the assessment of
Kaplan, G. A., Strawbridge, W. J., Camacho, T., & Cohen, R. D. (1993).

SOCIOECONOMIC POSITION AND COGNITIVE FUNCTION
S51
complex memory functions. Journal of Consulting and Clinical Psy-
chology, 43, 800–809.
Strub, R. L., & Black, F. W. (1985). The Mental Status Examination in
neurology (2nd ed). Philadelphia: FA Davis.
Salonen, J. T. (1988). Is there a continuing need for longitudinal epidemio-
logical research? The Kuopio ischemic heart disease risk factor study.
Annals of Clinical Research, 20, 46–50.
SAS Institute. (1990). SAS user’s guide: Statistics (Version 6.09). Cary,
NC: Author.
Stern, Y., Gurland, B., Tatemichi, T. K., Tang, M. X., Wilder, D., & May-
eux, R. (1994). Influence of education and occupation on the incidence
of Alzheimer’s disease. Journal of the American Medical Association,
271, 1004–1010.
Swaab, D. F. (1991). Brain aging and Alzheimer’s disease: “Wear and
tear” versus “use it or lose it.” Neurobiological Aging, 12, 317–324.
Vagero, D., & Leon, D. (1994). Effect of social class in childhood and
adulthood on adult mortality. Lancet, 343, 1224–1225.
Wadsworth, M. E. J. (1997). Health inequalities in the life course perspec-
tive. Social Science Medicine, 44, 859–869.
Stern, Y., Tang, M. X., Denaro, J., & Mayeux, R. (1995). Increased risk of
mortality in Alzheimer’s disease patients with more advanced educa-
tional and occupational attainment. Annals of Neurology, 37, 590–595.
Received May 30, 2000
Accepted June 22, 2001
Decision Editor: Fredric D. Wolinsky, PhD