Effect of retirement on cognitive function: the Whitehall II cohort study
European Journal of Epidemiology
Effect of retirement on cognitive function: the Whitehall II cohort study
Baowen Xue 0 1 2 3
Dorina Cadar 0 1 2 3
Maria Fleischmann 0 1 2 3
Stephen Stansfeld 0 1 2 3
Ewan Carr 0 1 2 3
Mika Kivim a¨ki 0 1 2 3
Anne McMunn 0 1 2 3
Jenny Head 0 1 2 3
Retirement 0 1 2 3
0 Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, School of Life and Medical Sciences, University College London , London , UK
1 Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London , London , UK
2 Research Department of Epidemiology and Public Health, Institute of Epidemiology and Health Care, School of Life and Medical Sciences, University College London , London , UK
3 Centre for Psychiatry, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry, Queen Mary University of London , London , UK
reasoning, phonemic verbal fluency, and semantic verbal fluency before and after retirement. We found that all domains of cognition declined over time. Declines in verbal memory were 38% faster after retirement compared to before, after taking account of age-related decline. In analyses stratified by employment grade, higher employment grade was protective against verbal memory decline while people were still working, but this 'protective effect' was lost when individuals retired, resulting in a similar rate of decline post-retirement across employment grades. We did not find a significant impact of retirement on the other cognitive domains. In conclusion, these findings are consistent with the hypothesis that retirement accelerates the decline in verbal memory function. This study points to the benefits of cognitively stimulating activities associated with employment that could benefit older people's memory.
Cognition; Longitudinal study; Piecewise regression; Employment grade
Good cognitive functioning represents an essential element
of healthy ageing and independent living [
]. There is some
evidence that ageing affects cognitive functions that are
primarily associated with executive processing and other
functions of the frontal lobe [
]. Thus, fluid abilities,
such as memory, processing speed, and spatial ability tend
to decline faster with age than crystallised functions,
including vocabulary, information and comprehension
]. However, the decline in these abilities is not
necessarily homogenous across the population, as some people
maintain cognitive vitality even into extreme old age [
On the one hand, there is evidence that the adult brain
shows neuroplasticity and neurogenesis, representing the
brain’s ability to generate new neurons and rewire itself
]. On the other hand, accelerated deterioration or
impairment in one or more cognitive functions beyond the
‘normal’ age-related decline could be predictive of the
onset of dementia, a major cause of disability and
dependency among older people worldwide [
Therefore, it is important to identify and understand the
predictors of interindividual differences in cognitive
The theory of cognitive reserve proposes that some
individuals have a larger cognitive reserve than others. It
has been postulated that innate cognitive resources (such as
childhood IQ), cognitive stimulation during brain
maturation in childhood (such as education), and cognitively
engaged lifestyle during adulthood (such as cognitively
demanding occupation) can increase cognitive reserve, thus
building up a buffer against cognitive decline in old age
]. The ‘use it or lose it’ hypothesis similarly
suggests that a person can maintain cognitive function by
engaging in cognitively demanding activities, whereas
failing to keep mentally active will detrimentally affect
cognitive function and could accelerate cognitive decline
or even the onset of dementia . Accordingly, retirement
may be a potential trigger for cognitive decline, assuming
that retirees leave paid work that is cognitively demanding.
Many studies have supported this assumption showing that
retirement is associated with lower cognitive functioning
], and later retirement is associated with better
cognition and lower risk of dementia [
some studies have found no association [
] or even a
positive effect  of retirement on levels of cognition.
When studying the effects of retirement on cognition, it
is important to consider reverse causality. Declines in
cognitive function may negatively affect the management
of work tasks and thus could be a determinant of the
decision to retire [
]. For example, chronic diseases,
such as stroke, might affect both cognitive function and
retirement decisions [
]. The vast majority of studies
have compared retirees with working people to assess the
potential effect of retirement on cognition, and it is
possible that their results are biased due to a ‘healthy worker’
effect (i.e. people who remain in work are likely to be
healthier than those who have stopped working). Some
studies have relied on the use of instrumental variables,
such as state pension age or early retirement windows, to
eliminate bias due to unobserved heterogeneity and
18–21, 28, 29
]. The validity of the instrumental
variables method relies on choosing an instrument that is
not correlated with other factors that influence health and
retirement. For example, in cross-national studies that have
used state pension age as the instrument, it is possible that
differences in state pension age may have been correlated
with other national differences that affect health. A further
limitation of these studies is their short follow-ups and use
of only one measure of cognitive function. A few studies
have examined change in cognitive function before and
after retirement [
], but most have relatively short
follow-up period [
], and thus cannot estimate the
longterm effects of retirement on cognitive function.
In this report from the Whitehall II cohort study, we
compared trajectories of cognitive function before and after
retirement within same persons up to 14 years before
(mean 7.1 years) and 14 years after (mean 7.0 years)
retirement, which included up to four repeated measures of
cognitive function per person. This long period of
followup helps reduce the possibility of reverse causation from
health-related selection out of employment. To examine
whether the ‘use it or lose it’ hypothesis applies to specific
cognitive domains, we included several measures of verbal
memory, abstract reasoning, phonemic verbal fluency, and
semantic verbal fluency as cognitive outcomes. We further
hypothesised that the influence of retirement on cognition
may vary by socioeconomic status such that cognitive
decline is more marked in individuals retiring from
cognitively demanding higher employment grade jobs than in
those who retire from less cognitively demanding low
employment grade jobs [
]. Sex differences were also
tested, because men and women may show advantages in
different cognitive domains [
], and previous research has
highlighted the gendered nature of employment trajectories
and retirement [
], although men’s and women’s
employment trajectories are becoming increasingly similar
Study population and study design
This study used data from the Whitehall II prospective
occupational cohort study. All civil servants aged 35–55
working in the London offices of 20 Whitehall departments
in 1985–1988 were invited to participate. The response rate
was 73% and a sample of 6895 men and 3413 women was
recruited (phase 1). These civil servants were employed in
a wide variety of roles from clerical grades, through to
senior administrative grades, reflecting different
employment grades and salaries. Follow-up surveys were
conducted every 2–3 years. All participants provided written
consent and the University College London ethics
committee approved this study.
The data for the present analyses were drawn from
phases 5 (1997–1999), 7 (2002–2004), 9 (2007–2009), and
11 (2012–2013) of the Whitehall II Study when cognitive
tests were administered during the clinical examinations.
Phase 3 (1991–1994) was not used because cognitive
testing was introduced midway through phase 3 and
consequently only half of respondents completed the cognitive
test at that phase. For the current study, participants were
eligible for inclusion if they had data on cognitive function
at least once before and once after retirement. We excluded
participants who were already not working at phase 5 and
those who did not retire during follow-up or returned to
work after retirement. There were 3691 eligible
participants who moved from work to retirement, but 258 of these
were excluded due to missing cognition outcome (i.e.
without cognition measures at least once prior and at least
once after retirement.) The final sample comprised 3433
participants (11,858 observations). The process of sample
selection is shown in Fig. 1. Participants’ average age
when taking the cognitive tests was 54.0 years (range
45–68) at phase 5, 59.5 years (range 51–74) at phase 7,
64.3 years (range 56–79) at phase 9, and 68.2 years (range
60–83) at phase 11.
The cognitive test battery, including verbal memory,
abstract reasoning, phonemic verbal fluency, and semantic
verbal fluency, was introduced to the Whitehall II cohort
study in phase 5 and was repeated using the same tests at
all subsequent assessments (phases 7, 9, and 11). The tests
have good test–retest reliability (range 0.6–0.9), assessed in
556 participants who were invited back to the clinic within
3 months of having taken the test in phase 5 [
memory was assessed with a 20-word free recall test.
Participants were presented with a list of 20 one- or
twosyllable words at two-second intervals and then had 2 min
to recall in writing as many words as possible (maximum
possible score = 20) [
]. Abstract reasoning was assessed
by the Alice Heim 4 Part 1 test (AH4). This test measures
the ability to identify patterns and to infer principles and
rules, which is composed of a series of 65 questions (32
verbal and 33 mathematical) of increasing difficulty
(maximum possible score = 65). Participants had 10 min
to complete this section [
]. Phonemic verbal fluency was
assessed by asking participants to write as many words
beginning with the letter ‘S’ as they could (maximum
score = 35), and semantic verbal fluency was assessed by
recalling as many animal names as possible (maximum
score = 35). One minute was allowed for each verbal
fluency test [
Retirement and year of retirement
Respondents’ employment status was measured by
selfreports at each phase. Participants were considered to be in
employment if they were still working in the civil service
or were in paid employment elsewhere (full or part time).
Participants were classified as retired if they moved from
work to retirement directly or moved from work to
unemployed/other, and then to retirement.
All respondents who retired from the civil service
provided their exact year of exit from the civil service, but
those who retired from employment outside the civil
service were not asked the exact year of exit. For these 1632
individuals (46% of selected sample) whose exact exit year
was unknown, we used the mid-point between the last
phase still in paid work and the subsequent phase no longer
working. We used the year of retirement as the centre point
to calculate the cognitive trajectories before and after
At each phase, participants who were not working could
indicate whether this was because of long-term sickness.
Participants who retired from the civil service answered
whether this was on health grounds. We considered
participants who were ‘long-term sick’ or who indicated that
the route of leaving the civil service was ‘retirement on
health grounds’ as health-related retirement.
3,691 eligible participants who
moved from work to retirement
3,433 final analytic sample
without missing outcome
Participated in phase 5 (n=7,870)
Excluded 49 participants without data on retirement status
Excluded 2,819 participants who were not working in phase 5
Excluded 888 participants who did not retire during follow-up
Excluded 423 participants who returned to work after
Excluded 258 participants without cognitive function at least
once before and once after retirement (n=3,433)
We included retirement age as a covariate. Because all the
analyses in this paper were centred at the year of retirement
(see statistical method section), including retirement age as
a covariate can effectively adjust for age effects. We
adjusted for birth year to take account of the possibility of
period effects. Gender and self-reported highest
educational qualification were also included as covariates.
Educational qualification was grouped into: O-level or lower
(‘low’), A-level or equivalent (‘middle’), and degree level
or higher (‘high’). To account for practice effects (i.e. gains
in scores on cognitive tests when a person was retested on
the same or similar instruments), we controlled for the
number of cognitive tests a participant had completed in
previous phases. Although cognitive test scores in phase 3
were not used in the analysis, the practice effect at this
phase was counted.
Time-fixed covariates based on the last interview before
retirement were employment grade, still working in the
civil service, psychosocial job demands, job decision
latitude, and spouse’s or partner’s employment status.
Employment grade was measured, in order of increasing
salary, as clerical/support (‘low’), professional/executive
(‘middle’), or administrative (‘high’) [
]. For those who
had left the civil service, the last employment grade before
leaving was used. Job demands were measured by four
items such as ‘Do you have to work very fast?’ Decision
latitude was measured by nine items such as ‘Do you have
a choice in deciding how to do your work?’ [
Respondents rated each question item whether it was
‘often’, ‘sometimes’, ‘seldom’ or ‘never/almost never’ the
case. Each answer was scored from 0 to 3 and was added
up so that a higher score reflected greater job demands or
higher job decision latitude. Continuous scores were
divided into tertiles [
]. Spouse’s employment status was
measured by asking whether a spouse is currently doing
any paid work. Those reporting not being
married/cohabiting were coded as ‘no spouse’.
Time-varying covariates (phases 5, 7, 9, and 11)
included smoking status, alcohol consumption, depressive
symptoms, systolic blood pressure (SBP), diastolic blood
pressure (DBP), body mass index (BMI), total blood
cholesterol, coronary heart disease (CHD), stroke, all
malignant cancers, and diabetes/intermediate
hyperglycaemia. By treating these variables as time-varying, we
account for reported changes in health conditions and
health behaviours over time. Smoking status (current,
never, ex-smoker) and alcohol consumption in the past
week (0, 1–10, more than 10 units) were based on
selfreports. Symptoms of depression were measured by the
depression subscale of the General Health Questionnaire
(GHQ), and cut-off points of four out of 12 were used to
identify depression cases [
]. Blood pressure (mm Hg),
BMI (kg/m2), and total blood cholesterol (mmol/l) were
objectively measured during the clinical examinations and
were included as continuous covariates in the model. CHD
(yes/no) includes diagnosed non-fatal myocardial
infarction (MI) and ‘definite’ angina. Non-fatal MI was defined
following MONICA criteria [
] based on study
electrocardiograms, hospital acute ECGs, and cardiac enzymes
and validated using discharge diagnoses from National
Health Service (NHS) Hospital Episode Statistics (HES)
data or General Practitioner (GP) confirmation up to the
end of phase 11. Self-reports of non-fatal MI were not used
]. ‘Definite’ angina included self-reported cases of
angina only if they were subsequently validated by these
other sources. Self-reported stroke events (yes/no) were
collected throughout follow-up, and were validated by HES
data linkage, GP’s confirmation, or retrieval of hospital
medical records up to phase 9 [
]. Cancer incidence
data (yes/no) for the period 1971–2015 were obtained from
the NHS Central Register for nearly all participants.
Diabetes/intermediate hyperglycaemia (yes/no) was defined by
the WHO criteria of oral glucose tolerance test and by a
self-reported diagnosis of diabetes .
To test a change of the response function (Y) of a varying
independent variable (X), we used piecewise linear
regression with two segments separated by a ‘knot’
]. We used year of retirement as the knot (i.e. year
0), and thus, generated two independent variables reflecting
‘years before retirement’ (- 14 to - 1) and ‘years after
retirement’ (1–14). Retired less than a year was counted as
1 year. Linear mixed models were fitted for each cognition
outcome, in turn, and these two variables were entered into
the model. The coefficients for the variable ‘years before
retirement’ (i.e. slope before) represented the average
change in cognition per year before retirement. Coefficients
for the variable ‘years after retirement’ (i.e. slope after)
represented the average change in cognition for each
additional year after retirement. If retirement did not affect
cognition, we would expect the trajectories of cognitive
function to be similar before and after retirement.
Therefore, to test whether retirement influenced cognitive
decline, independent of age-related change, we examined
differences in the slope for cognition before and after
retirement. The ‘slope change’ was defined as the ‘slope
after retirement’ minus the ‘slope before retirement’ (this
was also expressed as percentage change, calculated as
‘slope change’ divided by slope before retirement
multiplied by 100). All analyses were carried out in Stata 14. We
also examined whether a nonlinear piecewise model was
better than the linear model by adding quadratic terms of
‘years before retirement’ and ‘years after retirement’ into
each model. To take account of the clustering of the data,
mixed models with repeated measures and individuals as
the two random-effects levels were conducted. The models
allowed for both random intercepts (for each individual)
and random coefficients (for the terms ‘years before’ and
‘years after’ retirement).
To assess whether the effect of retirement differed by
cognition domains, we conducted a test of heterogeneity on
the effect of retirement using multivariate multilevel
models with all cognition outcomes included in one model.
To visualise the results from these regressions, we show
predicted trajectories of each cognitive function outcome,
both before and after retirement. These predicted
trajectories from adjusted models were calculated at the sample
mean of each covariate. In addition to piecewise linear
trajectories (where ‘years before retirement’ and ‘years
after retirement’ were treated as continuous), predicted
adjusted means at each time point (where ‘years before
retirement’ and ‘years after retirement’ were treated as
categorical) are shown as dots in the figures.
We tested for potential moderators, including
employment grade (based on last response before retirement) and
sex in the association between retirement and cognition
outcomes, by adding interaction terms (‘years before
retirement 9 employment grade’ and ‘years after
retirement 9 employment grade’; ‘years before
retirement 9 sex’ and ‘years after retirement 9 sex’) in the
model for each cognition outcome.
For time-fixed covariates (employment grade, still working
in the civil service, job demands, job decision latitude, and
partner’s employment status), missing data in the last
interview before retirement was first replaced by prior
responses. The remaining missing data of time-fixed
covariates and missing data of other covariates for the
eligible participants were imputed in Stata, using
multivariate imputation by chained equations, and 30 datasets
were imputed. We included all variables from the analyses
(i.e. independent variables, outcome variables, covariates,
and moderators) in the imputation model. After running the
imputation, we deleted imputed outcome values in the
regression. Percentage of missing data was shown in
We conducted three sensitivity analyses to assess the
reliability of our results and conclusions. Sensitivity analysis 1
aimed to assess potential bias to the results due to reverse
causality. For this analysis, we excluded 500 participants
from the analytic sample who retired due to health reasons
or had a GHQ depression value of 4 or higher at the last
interview before retirement. In addition, 172 participants
for whom cognition was measured only twice (once before
and once after retirement) were also excluded. Participants
who moved from work to retirement via ‘unemployed/
other’ (n = 278) were excluded from this sensitivity
analysis, since they are likely to have higher levels of stress
which may influence cognitive function. Some participants
fulfilled several of the exclusion criteria, thus a total of 911
participants were excluded in this sensitivity analysis.
We compared the characteristics of ‘eligible participants
but with missing cognition data (n = 258)’ and ‘the
analytic sample (n = 3433)’, and found that they had several
different demographic characteristics (online resources
Table 1S). Therefore, it is possible that our analytic sample
had different cognitive function compared to participants
with missing cognitive data. Sensitivity analysis 2 aimed to
assess the impact of missing cognitive data on results. This
sensitivity analysis included these 258 participants and
multiply imputed their missing cognitive measures.
Sensitivity analysis 3 assessed whether our results could
be influenced by physical activity level, although one
recently published Whitehall II study found no association
between physical activity and cognitive decline [
used the total physical activity level (\ 8, 8–12, C 12 h/
week) at the last interview before retirement.
Table 1 shows that the analytical sample includes 3433
participants of whom 72% were men. Their average
retirement age was 61.2 years (SD = 4.6), which is
slightly higher than the civil service occupational pension
age (60 years). Descriptive information for the cognitive
outcomes and time-varying covariates refers to the last
interview before retirement. Most respondents’ (66.5%)
highest educational qualification was lower than degree
level. Most respondents were employed in the highest
(‘administrative’, 45.7%) or second highest (‘professional/
executive’, 42.2%) employment grade, and 62% of
respondents were still working in the civil service (rather
than working outside). 50.3% had a working spouse while
21.4% did not have a spouse. Nearly half of the sample
were never-smokers, and 15% did not consume any alcohol
in the past week. Eleven percent of respondents had a
raised GHQ depression score. Approximately, one in five
had hypertension, 77% had elevated levels of blood total
cholesterol, and nearly three out of five were overweight
(44.4%) or obese (18.1%). Imputed data showed very
similar percentages and means as observed data.
aInformation for the cognitive outcomes and time-varying covariates (smoking status, alcohol consumption, GHQ depression, BMI, blood
pressure, blood cholesterol, CHD, stroke, cancer, diabetes) were based on the last interview before retirement
bBMI, blood pressure, and blood cholesterol were used as continuous variables in regression models
cMean (SD) for systolic blood pressure
dMean (SD) for diastolic blood pressure
Table 2 shows the fully adjusted models on retirement
and cognitive function from piecewise linear regressions.
The negative pre-retirement slope for verbal memory
(- 0.103; 95% CI - 0.122, - 0.085) suggests that verbal
memory was inversely associated with ‘years before
retirement’, or in other words, verbal memory score
declined by 0.103 every year before retirement. After
retirement, verbal memory scores declined by 0.143 every
year (95% CI - 0.162, - 0.124). The slope change (the
difference between after vs. before retirement cognition
slopes) was - 0.039 (95% CI - 0.058, - 0.021;
p \ 0.001), indicating that verbal memory test scores
declined faster after retirement compared to before
retirement. The % change was 38% [calculated as:
ð 0:039 0:103Þ 100%], suggesting that retirement
was associated with, on average, 38% faster decline in
verbal memory, independent of age-related decline. The
results for other cognition domains (abstract reasoning,
phonemic and semantic verbal fluency) showed that while
there was age-related decline in cognitive function both
before and after retirement, the differences in the slope of
decline were not statistically significant at conventional
levels (p = 0.180 for abstract reasoning; p = 0.867 for
phonemic verbal fluency; p = 0.774 for semantic verbal
fluency). Adding quadratic terms in the piecewise
regressions did not improve model fit (Wald test p [ 0.1, results
are not shown). In the multivariate multilevel analysis
including all cognitive domains, the test of heterogeneity
confirmed that the effect of retirement on verbal memory
was more pronounced compared to other domains of
cognition (verbal memory vs. abstract reasoning p = 0.001;
verbal memory vs. phonemic verbal fluency p = 0.028;
verbal memory vs. semantic verbal fluency p = 0.070).
To visualise the results of verbal memory, trajectories of
adjusted means, both before and after retirement, are
shown in Fig. 2. As explained above, the decline in verbal
memory accelerated after, compared to before, retirement.
The trajectories for the other three domains are shown in
the online resources (Figs. 1S to 3S), because there was no
significant difference in the decline before and after
We then examined whether the association between
retirement and cognitive function varied by employment
grade. Including terms for the interaction between
employment grade and slopes for verbal memory revealed
that the interaction was borderline significant (Wald test
p = 0.062, results are not shown). For better interpretation,
we stratified the models for verbal memory by employment
grade. Stratified results in Table 3 show that pre-retirement
slopes were less negative among those with higher
employment grades (- 0.084 for professional/executive,
- 0.107 for administrative) compared to those in the
clerical/support grade (- 0.156), suggesting that higher
employment grade may be protective against verbal
memory decline while people were still working. This
‘protective effect’ disappeared when individuals retired, as
people had similar slopes of verbal memory after
retirement, which was - 0.152 for clerical/support grade,
- 0.142 for professional/executive grade, and - 0.144 for
administrative grade. Those retiring from
professional/executive (slope change = - 0.057; 95% CI - 0.086,
0.029; % change = 67.9%) and administrative grades
(slope change = - 0.037; 95% CI - 0.063, - 0.011; %
change = 34.6%) experienced significant changes in their
slopes of verbal memory, but those retired from the
clerical/support grade did not. Figure 3 plots trajectories of
verbal memory by employment grade. This highlights that
people in higher grades start out with better verbal memory
(i.e. higher intercept) and a slower rate of decline (i.e. less
negative slope) while in work. After retirement, rates of
decline were similar across employment grades, although
verbal memory level remains higher among participants
from higher employment grades. The interaction between
sex and slopes for verbal memory was not statistically
significant (Wald test p = 0.31), thus models were not
stratified by sex.
Three sensitivity analyses did not change the
associations between retirement and cognitive outcomes. Results
are shown in online resources Tables 2S to 4S.
.830 .55 .09
In this longitudinal study of 3433 individuals, we
compared cognitive decline before and after retirement and
examined whether trajectories varied depending on
employment grade. We found that declines in verbal
memory were faster during the 14 years after than during
the 14 years before retirement. In the stratified analysis, we
found that higher employment grade may be protective
against verbal memory decline while people were still
working, but this ‘protective effect’ was lost when
individuals retired. The other domains of cognitive function,
including abstract reasoning, phonemic verbal fluency, and
semantic verbal fluency, were not affected by retirement,
but declined steadily with age.
Our finding showing an adverse effect of retirement on
verbal memory is consistent with most previous studies
which used instrumental variables [
] and also those
which have applied other statistical methods. For instance,
Wickrama and O’Neal  used growth curve analyses in
the USA Health and Retirement Study (HRS), and found
that individuals who retired between 1998 and 2002 had a
faster memory decline between 2002 and 2006 compared
to those who were working at both instances. In another
HRS investigation, Clouston and Denier [
similar findings for retirement and episodic memory
(comprised of both verbal learning and verbal memory) by
using longitudinal regression discontinuity methods to
analyse trajectories between 1998 and 2012. A
cross-national study by Adam et al. [
] used the stochastic frontier
approach to estimate the episodic memory that individuals
would reach if they were fully efficient for a given level of
resources. They found an adverse effect of retirement on
episodic memory and highlighted the positive impact
nonprofessional activities at retirement and increased social
contacts could have for episodic memory.
Our finding on the retirement-associated decline in
verbal memory supports the ‘use it or lose it’ hypothesis
suggesting that failing to keep mentally active may
accelerate the rate of cognitive decline in post-retirement
]. On a similar note, our findings are also
consistent with the theory of ‘mental retirement’ proposed
by Rohwedder and Willis [
], suggesting that the work
environment could be more cognitively stimulating than
the leisure environment as a retiree. Besides the direct
effects of an absence of cognitive activities related to
work, retirement may also affect cognitive function
indirectly via loss of work-related forms of self-organisation,
communications and collaborations [
], which are
important factors potentially contributing to the
maintenance of cognitive reserve [
]. For example, social
networks could be more extensive during employment, and
-14 -12 10 -8 -6 -4 -2 0 2 4 6
Years before and after retirement
Adjusted means at each time point
Piecewise trajectories of verbal memory
8 10 12 14
accordingly, Bo¨ rsch-Supan and Schuth [
] estimated that
at least one-third of the decline in cognition after
retirement could be attributed to a reduction in the size and
composition of social networks.
Our results showing a significant effect of retirement for
verbal memory but not for other cognitive domains suggest
that retirement may affect some cognitive domains more
than others. Age-related neuronal modifications that are at
the root of Alzheimer’s disease have been observed to have
heterogeneous effects on cognitive functioning. For
example, episodic memory deficits are largely considered
as a hallmark symptom of Alzheimer’s disease [
this is less the case for other domains of cognition. It may
also be that verbal memory is a more sensitive indicator of
cognitive decline than the other indicators. Few studies
have assessed the effect of retirement on different domains
of cognition. Using the USA Wisconsin Longitudinal
-14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14
Years before and after retirement
Administrative Prof/Exec Clerical/support
Fig. 3 Trajectories of verbal memory by employment grade
Study, Denier et al. [
] found that those who had retired
voluntarily or for family reasons had improved reasoning
abilities, which is contrary to our findings. Denier et al.
used the similarities construct of the Weschler Adult
Intelligence Scale to measure reasoning abilities.
Respondents were asked to relate two words; for instance, ‘How
are an apple and orange alike?’ to which they should
respond that both are fruits. In contrast, the AH4
questionnaire used in our study consists of both verbal and
mathematical questions. Inconsistent results may originate
from different assessments of cognitive functioning across
studies. One SHARE study tested only memory and
numeracy and found that both domains were negatively
affected by retirement [
]. Also using SHARE data,
Mazzonnaa and Peracchi found that retirement was
negatively associated with verbal memory, orientation, and
numeracy for both men and women. Retirement was not
aAdjusted for retirement age, birth cohort, highest educational qualification, gender, practice effects, spouse employment status, still working in
the civil service, job demands, job decision latitude, smoking status, alcohol consumption, depressive symptoms, systolic blood pressure,
diastolic blood pressure, body mass index, total blood cholesterol, coronary heart disease, stroke, malignant cancers, and diabetes/intermediate
bCalculated as ‘slope after retirement’ minus ‘slope before retirement’
cCalculated as ‘slope change’ divided by ‘slope before retirement’ and multiplied by 100%
associated with verbal fluency, with the exception that
retired women without a high-school degree showed a
faster decline in verbal fluency [
]. Our Whitehall II study
did not measure orientation or numeracy, but our results of
the negative effect on memory and no influence on verbal
fluency are generally consistent with these two SHARE
studies. Roberts et al. [
] previously used Whitehall II to
show that mean cognitive test scores increased between
two assessments over 5 years, and discussed that this is
possibly due to practice effects. They found that those
retired increased less than those still working. Their
findings could not be confirmed by our study using longer
follow-up of Whitehall II, where we have taken account of
practice effects by adjusting for the number of cognitive
tests a participant has completed in previous phases.
Stratified analyses showed that higher employment
grade may be protective against cognitive decline while
people were still working, but this ‘protective effect’ went
away when individuals retired. According to the cognitive
reserve hypothesis, engagement in mentally challenging
activities can yield additional neuronal resources that may
prevent cognitive decline [
]. Higher grade jobs have
higher levels of skill discretion implying more
opportunities for the use of skills and variety of work [
suggests higher levels of mental processing than
clerical/support jobs. Thus, our observation that employees in
higher grades had slower decline during employment is
plausible. This protection of higher grades no longer exists
after retirement. As expected by the ‘use it or lose it’
hypothesis, the decline in verbal memory was similar in all
participants irrespective of their pre-retirement
Our findings on employment grade are in agreement
with the observations from SHARE, showing that the
average effect of retirement on cognition was negative, and
the negative effect of retirement disappeared when the
sample was restricted to people who worked in more
physically demanding occupations [
]. Our stratified
results are also in line with Finkel et al.’s work, which
found that retirement from more complex jobs was related
to a faster rate of cognitive decline in the longitudinal
Swedish Adoption/Twin Study of Aging [
Fisher et al. [
] found that participants from occupations
characterised by higher levels of mental demands showed
slightly higher cognitive performance and less steep
decline both before and after retirement, compared with
individuals who were engaged in fewer mental demands. It
is likely that employment grades may not only represent
job mental demands but also serve as an indicator of
broader working environments as well as post-retirement
financial resources and social support. People in higher
employment grades may have a stronger attachment to
their work role, and thus retirement may be more
detrimental to them because of this role loss. Future studies
might investigate different preretirement occupational
characteristics in order to understand the nature and
mechanisms underlying the cognitive effects of retirement.
Our findings on employment grade should be interpreted
cautiously because only 12.1% of our analytic sample was
retired from clerical/support grade, and the interaction by
employment grade was only marginally significant.
Therefore, we cannot rule out the possibility that the
different results found for clerical/support grade were due to
selection bias. It is also worth pointing out that, even
though individuals in higher grades had a faster rate of
cognitive decline after retirement compared to before
retirement, they still had higher average levels of cognition
than people in the lowest grade, both before and after
retirement. This suggests that although retirement seems to
be more detrimental for those in higher grades, people in
the lowest grade remain at greatest risk of developing
We found no significant sex differences in terms of the
effect of retirement on verbal memory. However, less than
30% of our sample are women, with even fewer women in
the higher grades, so it is possible that the study lacked the
power to detect potential sex differences.
Strengths and limitations
The main strength of our study is the assessment of
multiple cognitive domains and a long observation period both
before and after retirement. To our knowledge, this is the
first study on cognition with such extended periods of
preand post-retirement measures.
It is also a strength that we could examine the rate of
change in cognition before and after retirement rather than
comparing levels of cognition. Cognitive decline could
lead to retirement but the analysis reduced this problem by
comparing slopes from long follow-up before and after for
the same group of individuals. This way, our method could
lower the risk of reverse causality. The consistent findings
in the sensitivity analysis reassured us of our results. The
use of a multilevel framework in this study was able to
account for clustering of observations within participants.
Missing values in the covariates were multiply imputed
using chained equations, allowing us to use information
from all cases. Predicted piecewise cognition trajectories
showed the trajectories before and after retirement.
There are also several limitations to this study. First, a
great challenge with this type of research is the possibility
of reverse causality. Although this piecewise study has
several strengths in attempting to reduce the influence of
reverse causality, the possibility of reverse causality is still
not fully eliminated. Loss-to-follow-up bias is also possible
because we only included retired participants with repeated
observations both before and after retirement. Some
participants dropped out of the study earlier than others, and
thus had fewer repeated measures of cognition, which may
lead to nonignorable missingness. Also, the level of
cognitive function was measured at every other wave rather
than at each wave, which increased the likelihood of
lossto-follow-up for older participants. We adjusted for health
conditions that are related to cognitive decline, but
confounding from unknown characteristics is still possible.
Random intercepts and random coefficients accounted for
individual trajectories of cognitive function before and
after retirement and captured such unobserved variability
to some extent. However, even though the proportion of
participants who died or were diagnosed with dementia is
very small in this cohort, results may be biased by
accelerating declines of cognition occurring before dementia
] or prior to death [
]. Our study focuses on the
average slope change of cognitive function as a response to
retirement. We did not examine whether post-retirement
activities, such as voluntary work, social activities, and
physical activities may modify the risk of cognitive
decline. Further research may focus on factors explaining
heterogeneity in declines in cognitive functioning after
The Whitehall II Study uses a sample of civil servants in
the UK. Compared to the general population, their type of
work may be more mentally challenging. For example,
verbal memory may be especially important for their
paperwork. Thus, their cognition trajectories cannot be
regarded as being representative of the general population,
although the sample covers the entire range of occupations
from administrative to clerical/support, ensuring some
level of variability. Moreover, the sample size at the two
ends of the analytic period is smaller than those at other
time points, leading to larger confidence intervals at the
two ends. However, the estimated coefficients of our
piecewise model are based on the overall linear trend of
cognition, and showed a good fit between the estimated
line and observed values at each time point.
In support of the ‘use it or lose it’ hypothesis, we found that
retirement is associated with faster declines in verbal
memory function over time, but has little impact on other
domains of cognitive functions, such as abstract reasoning
and verbal fluency. The smaller cognitive decline before
retirement in employees from high employment grade jobs
points to the potential benefits of cognitively stimulating
activities associated with employment that could benefit
older people’s memory.
Acknowledgements We thank all participating civil service
departments and their welfare personnel and establishment officers; the
Occupational Health and Safety Agency; the Council of Civil Service
Unions; all participating civil servants in the Whitehall II Study; and
all members of the Whitehall II Study Team. The Whitehall II Study
Team comprises research scientists, statisticians, study coordinators,
nurses, data managers, administrative assistants, and data entry staff,
who make the study possible.
Funding This work was funded by the Economic and Social Research
Council and the Medical Research Council as part of the Lifelong
Health and Well-Being (LLHW) initiative (Grant Number ES/
L002892/1). The Whitehall II study is also supported by British
Medical Research Council Grant (G0902037 & K013351). The
funding organisations had no role in the design and conduct of the
study; collection, management, analysis, and interpretation of data;
and preparation, review, or approval of the manuscript. SAS was (in
part) supported by the National Institute for Health Research (NIHR)
Collaboration for Leadership in Applied Health Research and Care
(CLAHRC) North Thames at Bart’s Health NHS Trust. The views
expressed are those of the authors and not necessarily those of the
NHS, the NIHR or the Department of Health.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://creative
commons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
1. Salthouse T. Consequences of age-related cognitive declines . Annu Rev Psychol . 2012 ; 63 : 201 - 26 .
2. Salthouse TA . Robust cognitive change . J Int Neuropsychol Soc . 2012 ; 18 : 749 - 56 .
3. Aizpurua A , Koutstaal W. Aging and flexible remembering: contributions of conceptual span, fluid intelligence, and frontal functioning . Psychol Aging . 2010 ; 25 : 193 - 207 .
4. Schaie K. The course of adult intellectual development . Am Psychol . 1994 ; 49 : 304 - 13 .
5. Singer T , Verhaeghen P , Ghisletta P , Lindenberger U , Baltes PB . The fate of cognition in very old age: six-year longitudinal findings in the Berlin aging study (BASE) . Psychol Aging . 2010 ; 18 : 318 - 31 .
6. Murman DL . The impact of age on cognition . Semin Hear . 2015 ; 36 : 111 - 21 . https://doi.org/10.1055/s-0035-1555115.
7. Silver M , Newell K , Hyman B , Growdon J , Hedley-Whyte ET , Perls T. Unraveling the mystery of cognitive changes in old age: correlation of neuropsychological evaluation with neuropathological findings in the extreme old . Int Psychogeriatr . 1998 ; 10 : 25 - 41 .
8. Silver MH , Jilinskaia E , Perls TT . Cognitive functional status of age-confirmed centenarians in a population-based study . J Gerontol Ser B . 2001 ; 56 : 134 - 40 .
9. Fjell AM , McEvoy L , Holland D , Dale AM , Walhovd KB . What is normal in normal aging? Effects of aging, amyloid and Alzheimer's disease on the cerebral cortex and the hippocampus . Prog Neurobiol Pergamon . 2014 ; 117 : 20 - 40 .
10. Gage FH . Neurogenesis in the adult brain . J Neurosci . 2002 ; 22 : 612 - 3 .
11. Couillard-Despre´s S. Hippocampal neurogenesis and ageing . Neurogenes Neural Plast . 2012 ; 15 : 343 - 55 .
12. Cameron HA , Glover LR . Adult neurogenesis: beyond learning and memory . Annu Rev Psychol Ann Rev . 2015 ; 66 : 53 - 81 .
13. Amieva H , Jacqmin-Gadda H , Orgogozo J-M , Le Carret N , Helmer C , Letenneur L , et al. The 9 year cognitive decline before dementia of the Alzheimer type: a prospective population-based study . Brain . 2005 ; 128 : 1093 - 101 .
14. Rabin LA , Smart CM , Amariglio RE . Subjective cognitive decline in preclinical Alzheimer's disease . Annu Rev Clin Psychol Annu Rev . 2017 ; 13 : 369 - 96 .
15. Stern Y. Cognitive reserve in ageing and Alzheimer's disease . Lancet Neurol . 2012 ; 11 : 1006 - 12 .
16. Stern Y. Cognitive reserve. Neuropsychologia Pergamon . 2009 ; 47 : 2015 - 28 .
17. Hultsch DF , Hertzog C , Small BJ , Dixon RA . Use it or lose it: engaged lifestyle as a buffer of cognitive decline in aging? Psychol Aging Am Psychol Assoc . 1999 ; 14 : 245 - 63 .
18. Behncke S. Does retirement trigger ill health? Health Econ . 2012 ; 21 : 282 - 300 .
19. Bonsang E , Adam S , Perelman S. Does retirement affect cognitive functioning? J Health Econ . 2012 ; 31 : 490 - 501 .
20. Mazzonna F , Peracchi F. Ageing, cognitive abilities and retirement . Eur Econ Rev . 2012 ; 56 : 691 - 710 .
21. Rohwedder S , Willis RJ . Mental retirement . J Econ Perspect . 2010 ; 24 : 119 - 38 .
22. Clouston SAP , Denier N. Mental retirement and health selection: analyses from the U.S. Health and Retirement Study . Soc Sci Med . 2017 ; 178 : 78 - 86 .
23. Kajitani S , Sakata K , McKenzie C. Occupation, retirement and cognitive functioning . Ageing Soc . 2017 ; 37 : 1568 - 96 .
24. Grotz C , Meillon C , Amieva H , Stern Y , Dartigues J-F , Adam S , et al. Why is later age at retirement beneficial for cognition? Results from a French population-based study . J Nutr Health Aging . 2016 ; 20 : 514 - 9 .
25. Dufouil C , Pereira E , Cheˆne G , Glymour MM , Alpe´rovitch A , Saubusse E , et al. Older age at retirement is associated with decreased risk of dementia . Eur J Epidemiol . 2014 ; 29 : 353 - 61 .
26. Wickrama K , O'Neal CW , Kwag KH , Lee TK . Is working later in life good or bad for health? An investigation of multiple health outcomes . J Gerontol Ser B Psychol Sci Soc Sci . 2013 ; 68 : 807 - 15 .
27. Lupton MK , Stahl D , Archer N , Foy C , Poppe M , Lovestone S , et al. Education, occupation and retirement age effects on the age of onset of Alzheimer's disease . Int J Geriatr Psychiatry . 2010 ; 25 : 30 - 6 .
28. Coe NB , von Gaudecker H-M , Lindeboom M , Maurer J. The effect of retirement on cognitive functioning . Health Econ . 2012 ; 21 : 913 - 27 .
29. Coe NB , Zamarro G . Retirement effects on health in Europe . J Health Econ . 2011 ; 30 : 77 - 86 .
30. Fisher G , Stachowski A , Infurna F . Mental work demands, retirement, and longitudinal trajectories of cognitive functioning . J Occup Health Psychol . 2014 ; 19 : 231 - 42 .
31. Belbase A , Khan MR , Munnell AH , Webb A . Slowed or sidelined? The effect of ''normal'' cognitive decline on job performance among the elderly . Chestnut Hill: Center for Retirement Research at Boston College; 2015 .
32. Stafford M , Cooper R , Cadar D , Carr E , Murray E , Richards M , et al. Physical and cognitive capability in mid-adulthood as determinants of retirement and extended working life in a British cohort study . Scand J Work Environ Health . 2017 ; 43 : 15 - 23 .
33. Karpansalo M , Manninen P , Kauhanen J , Lakka TA , Salonen JT . Perceived health as a predictor of early retirement . Scand J Work Environ Health . 2004 ; 30 : 287 - 92 .
34. Viswanathan A , Macklin EA , Betensky R , Hyman B , Smith EE , Blacker D. The influence of vascular risk factors and stroke on cognition in late life: analysis of the NACC cohort . Alzheimer Dis Assoc Disord . 2015 ; 29 : 287 - 93 .
35. Wickrama KK , O'Neal CW. The influence of working later in life on memory functioning . Adv Life Course Res . 2013 ; 18 : 288 - 95 .
36. Roberts B , Fuhrer R , Marmot M. Does retirement influence cognitive performance? The Whitehall II study . J Epidemiol Commun Health . 2010 ; 65 : 958 - 63 .
37. Marmot MG , Stansfeld S , Patel C , North F , Head J , White I , et al. Health inequalities among British civil servants: the Whitehall II study . Lancet . 1991 ; 337 : 1387 - 93 .
38. van Hooren SAH , Valentijn AM , Bosma H , Ponds RWHM , van Boxtel MPJ , Jolles J. cognitive functioning in healthy older adults aged 64-81: a cohort study into the effects of age, sex, and education . Aging Neuropsychol Cogn . 2007 ; 14 : 40 - 54 .
39. Loretto W , Vickerstaff S. The domestic and gendered context for retirement . Hum Relat . 2013 ; 66 : 65 - 86 .
40. McMunn A , Lacey R , Worts D , McDonough P , Stafford M , Booker C , et al. De-standardization and gender convergence in work-family life courses in Great Britain: a multi-channel sequence analysis . Adv Life Course Res . 2015 ; 26 : 60 - 75 .
41. Singh-Manoux A , Kivimaki M , Glymour MM , Elbaz A , Berr C , Ebmeier KP , et al. Timing of onset of cognitive decline: results from Whitehall II prospective cohort study . BMJ . 2012 ; 344 : d7622 .
42. Sabia S , Kivimaki M , Shipley MJ , Marmot MG , Singh-Manoux A . Body mass index over the adult life course and cognition in late midlife: the Whitehall II Cohort Study . Am J Clin Nutr . 2009 ; 89 : 601 - 7 .
43. Heim A. AH 4 group test of general intelligence . Windsor: NFER-Nelson Publishing Company Ltd; 1970 .
44. Borkowski JG , Benton AL , Spreen O . Word fluency and brain damage . Neuropsychologia . 1967 ; 5 : 135 - 40 .
45. Fleischmann M , Carr E , Stansfeld SA , Xue B , Head J . Can favourable psychosocial working conditions in midlife moderate the risk of work exit for chronically ill workers? A 20-year follow-up of the Whitehall II study . Occup Environ Med . 2017 . https://doi.org/10.1136/oemed-2017-104452.
46. Joensuu M , Kivimaki M , Pentti J , Virtanen M , Vaananen A , Vahtera J . Components of job control and mortality: the Finnish Public Sector Study . Occup Environ Med . 2014 ; 71 : 536 - 42 .
47. Singh-Manoux A , Akbaraly TN , Marmot M , Melchior M , Ankri J , Sabia S , et al. Persistent depressive symptoms and cognitive function in late midlife: the Whitehall II study . J Clin Psychiatry Inserm . 2010 ; 71 : 1379 - 85 .
48. Tunstall-Pedoe H , Kuulasmaa K , Amouyel P. Myocardial infarction and coronary deaths in the World Health Organization MONICA Project . Registration procedures, event rates, and casefatality rates in 38 . Circulation. 1994 ; 90 : 583 - 612 .
49. Brunner EJ , Shipley MJ , Britton AR , Stansfeld SA , Heuschmann PU , Rudd AG , et al. Depressive disorder, coronary heart disease, and stroke: dose-response and reverse causation effects in the Whitehall II cohort study . Eur J Prev Cardiol . 2014 ; 21 : 340 - 6 .
50. Britton A , Milne B , Butler T , Sanchez-Galvez A , Shipley M , Rudd A , et al. Validating self-reported strokes in a longitudinal UK cohort study (Whitehall II): extracting information from hospital medical records versus the Hospital Episode Statistics database . BMC Med Res Methodol . 2012 ; 12 : 83 .
51. World Health Organization, International Diabetes Federation. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia: report of a WHO/IDF consultation [Internet] . Geneva: WHO Press; 2006 [cited 13 Jun 2017 ]. http://apps.who. int/iris/bitstream/10665/43588/1/9241594934_eng.pdf.
52. Weisberg S. Applied linear regression . Hoboken: Wiley; 2005 .
53. Xue B , Head J , McMunn A . The associations between retirement and cardiovascular disease risk factors in China: a 20-year prospective study . Am J Epidemiol . 2017 ; 185 : 688 - 96 .
54. Sabia S , Dugravot A , Dartigues J-F , Abell J , Elbaz A , Kivima¨ki M, et al. Physical activity, cognitive decline, and risk of dementia: 28 year follow-up of Whitehall II cohort study . BMJ . 2017 ; 357 : j2709 .
55. Adam S , Bonsang E , Germain S , Perelman S. Retirement and cognitive reserve: a stochastic frontier approach applied to survey data 1 . 2007 . http://orbi.ulg.ac.be/bitstream/2268/35657/1/101% 20WP_HECULG_20070402_Adam_Bonsang_Germain_ Perel man . pdf. Accessed 19 Dec 2017 .
56. Wang M , Shi J . Psychological research on retirement. Annu Rev Psychol . 2014 ; 65 : 209 - 33 .
57. Scarmeas N , Stern Y. Cognitive reserve and lifestyle . J Clin Exp Neuropsychol . 2010 ; 25 : 625 - 33 .
58. Bo¨ rsch-Supan A , Schuth M . Early retirement, mental health and social networks . In: Bo¨ rsch-Supan A , Brandt M , Litwin H , Weber G , editors. Active ageing solidarity between generations Europe: first results from SHARE after economic crisis . Berlin: Walter de Gruyter; 2013 . p. 337 .
59. Dubois B , Feldman HH , Jacova C , DeKosky ST , BarbergerGateau P , Cummings J , et al. Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria . Lancet Neurol . 2007 ; 6 : 734 - 46 .
60. Denier N , Clouston SAP , Richards M , Hofer SM . Retirement and cognition: a life course view . Adv Life Course Res . 2017 ; 31 : 11 - 21 .
61. Mazzonna F , Peracchi F. Unhealthy retirement? J Hum Resour . 2017 ; 52 : 128 - 51 . http://jhr.uwpress.org/content/52/1/128.full. pdf?html. Accessed 19 Dec 2017 .
62. Finkel D , Andel R , Gatz M , Pedersen NL . The role of occupational complexity in trajectories of cognitive aging before and after retirement . Psychol Aging . 2009 ; 24 : 563 - 73 .
63. Cadar D , Piccinin AM , Hofer SM , Johansson B , Muniz-Terrera G . Education, occupational class, and cognitive decline in preclinical dementia . GeroPsyc Hogrefe AG . 2016 ; 29 : 5 - 15 .
64. Cadar D , Stephan BCM , Jagger C , Johansson B , Hofer SM , Piccinin AM , et al. The role of cognitive reserve on terminal decline: a cross-cohort analysis from two European studies: OCTO- Twin , Sweden, and Newcastle 85?, UK. Int J Geriatr Psychiatry . 2016 ; 31 : 601 - 10 .
65. Muniz-Terrera G , van den Hout A , Piccinin AM , Matthews FE , Hofer SM . Investigating terminal decline: results from a UK population-based study of aging . Psychol Aging Am Psychol Assoc . 2013 ; 28 : 377 - 85 .