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Socioeconomic position, psychosocial work
environment and disability in an ageing workforce:
a longitudinal analysis of SHARE data from
11 European countries
Jan D Reinhardt,1,2 Morten Wahrendorf,3 Johannes Siegrist4
▸ An additional
supplementary appendix is
published online only. To view
this file please visit the journal
online (http://dx.doi.org/10.
1136/oemed-2012-100924)
1
Epidemiology of Functioning
and Disability, Swiss Paraplegic
Research, Nottwil, Lucerne,
Switzerland
2
Department of Health
Sciences and Health Policy,
University of Lucerne, Lucerne,
Switzerland
3
International Centre for Life
Course Studies in Society and
Health, Department of Primary
Care and Public Health,
Imperial College London,
London, UK
4
Department of Medical
Sociology, Heinrich Heine
University of Duesseldorf,
Duesseldorf, Germany
Correspondence to
Dr Jan D Reinhardt,
Epidemiology of Functioning
and Disability, Swiss Paraplegic
Research, Guido A Zäch Str 4,
Nottwil, Lucerne 6207,
Switzerland;
jan.reinhardt@paranet.ch
Received 16 May 2012
Revised 5 October 2012
Accepted 12 November 2012
Published Online First
13 December 2012
ABSTRACT
Objectives Prevention of disability in the ageing
workforce is essential for sustaining economic growth in
Europe. In order to provide information on entry points
for preventive measures, it is important to better
understand sociodemographic, socioeconomic and workrelated determinants of disability in older employees. We
aimed to test the hypothesis that low socioeconomic
position and exposure to a stressful psychosocial work
environment at baseline contribute to later disability. We
further assumed that the association of socioeconomic
position with disability is partly mediated by exposure to
adverse working conditions.
Methods We studied longitudinal data from the first
two waves of the Survey on Health, Ageing and
Retirement in Europe comprising 11 European countries.
Sociodemographic, socioeconomic and work-related
factors (low control, effort-reward imbalance) and
baseline disability of 2665 male and 2209 female
employees aged between 50 and 64 years were used to
predict disability 2 years later. Following the International
Classification of Functioning (ICF), disability was
subdivided into the components ‘impairment’ and
‘restriction in activities and participation’. Two multilevel
Poisson regressions were fitted to the data.
Results After adjusting for baseline disability and
relevant confounding variables, low socioeconomic
position and chronic stress at work exerted significant
effects on disability scores 2 years later. We found some
support for the hypothesis that the association of
socioeconomic position with disability is partly mediated
by work stress.
Conclusions Investing in reduction of work stress and
reducing social inequalities in health functioning are
relevant entry points of policies that aim at maintaining
work ability in early old age.
INTRODUCTION
To cite: Reinhardt JD,
Wahrendorf M, Siegrist J.
Occup Environ Med
2013;70:156–163.
156
European populations are ageing faster than populations in many other parts of the world.1 Despite a
continued increase in healthy life expectancy, a substantial proportion of ‘people in early old age’
(approx. 50–64 years) in Europe suffer from some
degree of impairment which often limits their work
capability.2 In fact, reduced health functioning and
disability are major determinants of involuntary
early exit from the labour market.3 4 In view of the
strong economic need for extending labour market
What the paper adds
▸ Previous investigations demonstrated a social
gradient of disability in older populations, but
were restricted to single countries or cohorts,
were mostly cross-sectional, or did not use a
comprehensive measure of disability.
▸ In this study, we followed the guidelines of the
International Classification of Functioning and
used multiple variables to construct and
validate two different disability indices
(1. impairment; 2. restrictions in activity and
participation) in the frame of a longitudinal
cross-country study.
▸ Our findings based on employees aged 50–64
years from 11 European countries demonstrate
a social gradient of both indices of disability
which is in part mediated by adverse
psychosocial working conditions.
▸ Investment into reduction of psychosocial work
stress, in particular among employees in lower
socioeconomic positions, is a relevant entry
point of policies that aim at maintaining work
ability in the ageing workforce.
participation of older people in European countries,2 it is important to know more precisely the
sociodemographic, socioeconomic and workrelated characteristics of older men and women
whose work capability is limited due to impairment
or disability. Such knowledge may provide useful
information on entry points for preventive measures within and beyond occupational settings.
Previous investigations documented a social gradient of disability prevalence in early old age, leaving
men and women in lower socioeconomic positions
at higher risk.5–10 However, given the bidirectional
pathways between health functioning and socioeconomic position, including employment status, in
a life course perspective,3 a clear-cut interpretation
of this association is difficult. Moreover, measures
of socioeconomic position in terms of occupational
position or earnings are often confounded by distinct occupational exposures (eg, a high prevalence
of jobs with heavy physical demands among men
with low occupational position), which may trigger
early onset of disability.11 It is therefore important
Reinhardt JD, et al. Occup Environ Med 2013;70:156–163. doi:10.1136/oemed-2012-100924
Occup Environ Med: first published as 10.1136/oemed-2012-100924 on 13 December 2012. Downloaded from http://oem.bmj.com/ on 27 April 2019 by guest. Protected by copyright.
Workplace
to disentangle these effects and to test their robustness in a large
dataset which offers opportunities of adjusting for relevant
confounders.
In this study, we set out to test associations of sociodemographic, socioeconomic and work-related factors with disability
in a prospective study design, using strictly comparable indicators, based on a large sample of male and female employees in
early old age from 11 European countries. More specifically, we
test the hypothesis that low socioeconomic position and exposure to a stressful psychosocial work environment (assessed at
wave 1) contribute to later disability (assessed 2 years later at
wave 2). Furthermore, we claim that the association of socioeconomic position with disability is partly mediated by exposure
to a stressful psychosocial work environment, as measured by
core components of two established theoretical models: the
demand-control12 and the effort-reward imbalance (ERI)13
models.
The demand-control (or ‘job strain’) model defines work
stress in terms of a distinct job task profile where jobs defined
by high quantitative demands in combination with low decision
latitude or low task control are stressful.12 A complementary
model, ERI, focuses on the work contract and the principle of
social reciprocity lying at its core.13 Rewards received in return
for efforts spent at work include money, esteem and career
opportunities ( promotion prospects, job security). The model
asserts that lack of reciprocity (high effort in combination with
low reward) occurs frequently in modern economies and generates strong negative emotions and psychobiological stress
responses with adverse long-term effects on health.
A second aim of this study concerns the development of
indices of core dimensions of disability, taking note of a more
comprehensive approach to this notion, as documented in basic
work resulting in the International Classification of Functioning,
Disability and Health (ICF).14 Disability is no longer interpreted
as a fixed attribute of an individual, but rather as a dynamic
continuum of experiences at different levels (bodily impairment,
restrictions in activity and social participation). As disability
results from an interaction of vulnerable individuals with a
broad range of environmental factors including working conditions,15 16 it seems mandatory to assess this condition in a
multidimensional, quantitative frame of analysis. At least two
crucial components need to be distinguished, the level of bodily
impairment and the level of restriction in activities and social
participation, since ‘people with the same impairment can
experience very different types and degrees of restriction,
depending on the context’.16 A person with an amputation, may
for instance, not be restricted in activities and participation
when she is provided with appropriate prostheses and supports.
Another reason for this distinction is related to their potentially
different role in predicting morbidity and mortality. In a recent
study of older British women, it was observed that restrictions
in participation, and restrictions in complex activities, were
associated with increased risk of mortality, but no such effect
was found for impairment in the fully adjusted models.17
Therefore, embedded in the framework of ICF, we aim at developing a comprehensive continuous measure of core dimensions
of disability, based on available data.
METHODS
Data source
Data were obtained from the first two waves of the ‘Survey of
Health, Ageing and Retirement in Europe’ (SHARE, Release
2.5). SHARE is the first longitudinal research project comparing
data on working conditions, retirement, health and well-being,
and social position among people aged 50 years and older in a
variety of European countries.18 Data from the two first waves
(wave 1: 2004–2005; wave 2: 2006–2007) include 11 European
countries (Sweden, SE; Denmark, DK; Germany, DE; The
Netherlands, NL; Belgium, BE; France, FR; Switzerland, CH;
Austria, AT; Italy, IT; Spain, ES: Greece, GR). Data collection is
based on probability household samples where all people above
50 years of age, plus their ( possibly younger) partners, were
interviewed using Computer Assisted Personal Interviews. Due to
different institutional settings in the participating countries, the
sampling was either drawn as a stratified simple random selection
from the national population register (Denmark and Sweden), or
as a multistage sampling using regional and local registers
(Germany, Italy, Spain, France, The Netherlands) or telephone
directories (Greece, Austria, Switzerland). In SHARE, information is available for 28 517 respondents in wave 1, where the
country average of household response rate is 60.6% for the total
sample, ranging from 38.8% in Switzerland to 79.2% in France
(rates above 50% in eight countries). In wave 2, 18 742 of these
individuals remain in the sample, with an attrition rate of
27.9%.35 Because we were interested in associations between
working conditions in late midlife in wave 1 and disability in
wave2, we excluded people not aged 50–64 years at wave 1
(n=8196), and those reporting no employment at baseline
(n=12 485) from the longitudinal sample. Moreover, we conducted a complete case analyses, thus excluding respondents with
missing data on any of the variables (n=2636). This results in a
final sample size of 4864 respondents. In order to compensate
for unit non-response, and for attrition between the first and the
second waves, calibrated longitudinal weights were applied.
These weights are defined for the longitudinal sample only and
are calculated for each country separately (see ref. 19 for details
on methodological issues in SHARE).
Measurement
Disability
Based on ICF and its definition of disability, we divided between
two indices of disability: one measuring impairment and
another measuring restriction in activity and participation
(A&P).20 Accordingly, all SHARE modules were screened for
questions related to disability, and respective items were linked
to the two dimensions using established linking rules.20 21 Since
most original items were dichotomous, we dichotomised the
remaining items, such that one category was indicative of having
at least a moderate problem in its respective domain. As a next
step, we applied a principal component analysis to evaluate the
chosen items based on wave 1 data (see table 1). In the results,
two factors with eigenvalues above 2.0 were extracted confirming the assumed dimensions of disability. However, two candidate items for the A&P factor did not load as expected (‘using a
map in a strange place’ had a loading below 0.1 and the overall
activity limitation item loaded on the impairment factor) and
were excluded from further analysis. Not considering the
dropped items, the two factors explained around 34% of
the variance of all items, and 45% of the A&P, and 23% of the
impairment items were explained by the two-factor solution.
Next, we performed a confirmatory factor analysis for item
responses at wave 2 (2006) (table 1) using the STATA module
confa.22 The first item of the respective index was used as scalar
(loading set to 1). Results show that our index solution meets
the statistical criteria. However, one item on the impairment
factor and two items on the A&P restrictions factor displayed
marginal loadings only, probably due to their low prevalence,
but were, nevertheless, included in the final index solution. In
Reinhardt JD, et al. Occup Environ Med 2013;70:156–163. doi:10.1136/oemed-2012-100924
157
Occup Environ Med: first published as 10.1136/oemed-2012-100924 on 13 December 2012. Downloaded from http://oem.bmj.com/ on 27 April 2019 by guest. Protected by copyright.
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Table 1
Construction of sum indices for impairment and A&P restrictions
Construct
Variable
Measurement
Coding for ‘yes’
PCA*
CFA†
Impairment
Symptoms load
Problems with biting
Problems with seeing
Problems with hearing
Mobility
Two or more symptoms
Unable to bite on hard food
Seeing is fair or worse (either distance or reading)
Hearing is fair or worse
One or more mobility, arm function and
fine motor limitations
Less than good
1
1
1
1
1
0.16
0.12
0.14
0.10
0.18
1
0.15
0.22
0.21
0.9
1
0.15
0.07
Score 1 or 2 (range 1–5)
Male: <37/female: <21
Eurodcat
Less than 15
Three or less
Two or less
1
1
1
1
1
1
0.18
0.15
0.16
0.23
0.24
0.25
0.15
0.19
0.53
0.11
0.12
0.15
Dressing, including putting on shoes and socks
Walking across a room
Bathing or showering
Eating, such as cutting up your food
Getting in and out of bed
Using the toilet, including getting up or down
Preparing a hot meal
Shopping for groceries
Making telephone calls
Taking medications
Doing work around the house or garden
Managing money, such as paying bills and
keeping track of expenses
1
1
1
1
1
1
1
1
1
1
1
1
0.24
0.34
0.29
0.29
0.27
0.30
0.22
0.26
0.19
0.26
0.24
0.27
1
0.19
0.40
0.07
0.54
0.22
0.19
0.63
0.06
0.11
1.14
0.09
Orientation to date, month,
year and day of week
Mathematical performance
Grip strength
Depressive symptoms
Verbal fluency score
Ten words list
Delay ten words list
Range sum index
A&P restrictions
Reported problems with…
Range sum index
0–12
*Principal component analysis (PCA) with 8615 cases from the selected countries for 2004; numbers are loadings on respective components, two components impairment and A&P
restriction with eigenvalues >2 extracted.
†Confirmatory factor analysis (CFA) for the final sample (n=4864). Numbers indicate loadings on component impairment and A&P restriction; the first items were used as scalars.
case of A&P restrictions, it should be noted that the included
items turned out to be part of two other existing scales
(ADL-scale, IADL-scale). Yet, not all items of these two latter
scales were included, and instead of dichotomising both scales
into ‘No limitations’ versus ‘One or more limitations’ (as usually
done), we created one single scale with additional information
on the number of A&P limitations. We constructed sum indices
for impairment and A&P restrictions21 (table 1). Each of those
is a count variable ranging from 0 (no reported impairment or
A&P restrictions) to 12 (12 reported impairments or A&P
restrictions). We plotted both indices against a Poisson distribution which was confirmed in both cases. Expectedly, the impairment scale showed a high correlation with subjective health
(Pearsons r=0.40), while the A&P scale showed a lower correlation of r=0.17. Both scales were correlated with r=0.2. This
demonstrates convergent as well as divergent validity, that is, the
scales seem to measure different constructs.
Working conditions
Quality of work was assessed by a short battery of items derived
from (a) the Job Content Questionnaire measuring the demandcontrol model23 and (b) from the ERI model questionnaire24
(see online supplementary appendix 1). The psychometric properties of both questionnaires were previously tested.24 Given the
constraints of a multidisciplinary approach in SHARE project,
the inclusion of the full questionnaires was not possible. Thus,
items with best psychometric properties in their respective scales
158
were selected. With regard to the first model, the measurement
was restricted to the control dimension. This decision was based
on evidence that the predictive power of ‘control’ by far
exceeded the power of ‘demand’, and that tests of the interaction term ‘demand’בcontrol’ had produced inconsistent
results.25 Low control at work was measured by the sum score
of two Likert-scale items ranging from 2 to 8, with higher
scores indicating lower control at work. Scores in the upper
tertile of each country were defined as representing poor quality
of work in terms of low control.
To measure ERI, two out of six items measuring ‘effort’, and
five out of 11 items assessing ‘reward’ at work were included.
For the selected items, all-item total correlations were far
beyond the established threshold of 0.30,26 ranging from 0.93
to 0.81 (uncorrected), and from 0.67 to 0.42 (corrected).
‘Effort-reward imbalance’ was defined by a ratio of the sum
score of the ‘effort’ items (nominator) and of the sum score of
the ‘reward’ items (adjusted for number of items; denominator).
As previous analyses showed that quality of work in terms of
this model varies across countries under study in SHARE, tertiles of the ratio were calculated for each country separately.24
Participants scoring in the upper tertiles of this ratio of imbalance were considered experiencing poor quality of work.
Additional measures
Age, gender, income and education were included as additional
measures. Income information is based on the total annual
Reinhardt JD, et al. Occup Environ Med 2013;70:156–163. doi:10.1136/oemed-2012-100924
Occup Environ Med: first published as 10.1136/oemed-2012-100924 on 13 December 2012. Downloaded from http://oem.bmj.com/ on 27 April 2019 by guest. Protected by copyright.
Workplace
Table 2
Sample description and mean disability scores and SD according to covariates (n=4864)
Variables
Sex
Male
Female
p Value
Age group
50–55 years
55–59 years
60–65 years
p Value
Education
High
Medium
Low
p Value
Income
High
Medium
Low
p Value
Low control
Yes
No
p Value
Effort reward imbalance
Yes
No
p Value
Overall
Prevalence (%)
(unweighted)
n
W2* mean
impairment (SD)
W2* mean
A&P (SD)
54.6
45.4
2655
2209
1.72 (1.69)
2.01 (1.64)
<0.001
0.09 (0.51)
0.06 (0.33)
0.052
43.6
37.0
19.4
2123
1798
943
1.68 (1.52)
1.95 (1.76)
2.05 (1.84)
<0.001
0.08 (0.49)
0.07 (0.44)
0.07 (0.29)
0.571
31.0
36.4
32.6
1509
1771
1584
1.38 (1.38)
1.72 (1.50)
2.45 (1.95)
<0.001
0.05 (0.24)
0.10 (0.53)
0.08 (0.46)
0.004
54.1
30.5
15.4
2631
1482
751
1.62 (1.51)
1.95 (1.74)
2.35 (1.90)
<0.001
0.07 (0.45)
0.08 (0.38)
0.11 (0.54)
0.104
22.6
77.4
1099
3765
2.32 (1.96)
1.73 (1.58)
<0.001
0.11 (0.53)
0.07 (0.42)
0.017
31.3
68.7
1525
3339
100.0
4864
2.21 (1.82)
1.68 (1.58)
<0.001
1.84 (1.68)
0.12 (0.60)
0.06 (0.35)
<0.001
0.08 (0.44)
Note. Means are based on weighted data.
p Values are based on analysis of variance.
Source: Survey on Health, Ageing and Retirement in Europe (release 2.5.0).
*Wave 2 (W 2).
household income composed by the sum of different income
components that were assessed in the questionnaire. In case
income components were missing, information was obtained
through imputation.27 To adjust for household size, we divided
the value of income in accordance with the OECD equivalent
scale, and categorised it into country-specific tertiles based on
all available cases in each country (low, medium, high).
Education was measured according to the International
Standard Classification of Educational Degrees (ISCED-97) that
was categorised into ‘low education’ ( preprimary, primary or
lower secondary education), ‘medium education’ (secondary or
postsecondary education), and ‘high education’ (first and second
stage of tertiary education). These two indicators are introduced
as proxy measures of socioeconomic position, but are analysed
separately as they point to different dimensions of social
inequality.28
Analyses
As a first step, table 2 gives an overview of the study sample and
the core variables. Additionally, associations between disability
and all covariates are analysed using analysis of variance.
Thereafter, we calculated Poisson regression models to predict
disability20 in wave 2. Given the multilevel structure of the data,
we estimated a random intercept multilevel model with individuals (level 1) nested within countries (level 2).29 This model
contains two components: a so-called fixed component that
explains systematic variability in the data, and a random component which accounts for unobserved variability between countries (random intercepts for each country). This procedure
allows for accurate adjustment for country affiliation. Maximum
likelihood estimation is used for parameter estimation. In the
Results section, for each one of the two disability outcomes, we
display findings based on three consecutive models, all adjusted
for age, gender and level of impairment at wave 1. This procedure allows for exploring to what extent the predicting variables
are associated with change in disability between wave 1 and
wave 2.30 Model 1 investigates the joint effect of income and
education on disability. In model 2, the joint effects of the two
work-stress measures are presented. Model 3 includes all variables of the first two models, with the aim of examining potential mediation effects. In respective tables, we present incidence
rates ratios together with the level of statistical significance and
CIs. Calculations were done using the ‘xtmepoisson’ procedure
in STATA 11.
RESULTS
Descriptive findings
In table 2, sample characteristics are given, and mean scores of
the two disability factors, impairment and A&P restrictions, are
displayed according to covariates. A total of 2655 men and
Reinhardt JD, et al. Occup Environ Med 2013;70:156–163. doi:10.1136/oemed-2012-100924
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Figure 1 Mean disability scores
(in wave 2) according to income
(in wave 1, top) and effort-reward
imbalance (in wave 1, bottom) across
Survey on Health, Ageing and
Retirement in Europe countries
(n=4864). Note: means are based on
weighted data.
2209 women provided full data and were included in the
analyses. The age distribution was as follows: 43.6% were
50–54 years, 37% were 55–59 years, and 19.4% were
60–64 years old. A majority of the participants had medium or
low education, and more than half had a high income. With
regard to work stress, 22.6% experienced low control at work
and 31.3% experienced an imbalance between high efforts and
low rewards according to our classification.
Concerning the scores of impairment and A&P restrictions,
we first observe that overall levels of A&P restrictions are much
lower than those of impairment scores. Impairment scores
varied according to gender (significantly higher values for
women), age (higher values for older participants) and socioeconomic position (higher values for people with lower education or lower income). As shown in figure 1 (top), this social
gradient is consistent across all countries, using income as an
indicator of socioeconomic position. Less clear findings are
obvious in case of A&P restrictions.
160
As expected, we observe higher scores of the two disability
factors among men and women experiencing chronic stress
at work in terms of the two work-stress models. Again, relatively consistent trends are observed across the countries under
study, as exemplified by measuring work stress in terms of ERI
(figure 1, bottom).
Multivariate findings
In tables 3 and 4, results of multivariate analyses are presented.
In both tables, we present findings of the three regression
models described in the Methods section, first for impairment,
and second for A&P restrictions.
Significant effects of gender (women), age (older), socioeconomic position (lower), and work stress (both models) on
impairment scores were confirmed in multivariate analysis
(models 1 and 2). If the indicators of socioeconomic position
and the two work-stress models are included simultaneously
(model 3), we observe an attenuated effect of the two indicators
Reinhardt JD, et al. Occup Environ Med 2013;70:156–163. doi:10.1136/oemed-2012-100924
Occup Environ Med: first published as 10.1136/oemed-2012-100924 on 13 December 2012. Downloaded from http://oem.bmj.com/ on 27 April 2019 by guest. Protected by copyright.
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Table 3
Predictors of impairment: results of multilevel Poisson regression models: incidence rate ratios and significance level (n=4864)
Model
1
Variables
Sex
Male
Female
Age group
<55 years
55–59 years
60–65 years
Education
High
Medium
Low
Income
High
Medium
Low
Low control
No
Yes
Effort reward imbalance
No
Yes
Prior impairment state
2
3
IRR
CI (95%)
IRR
CI (95%)
IRR
CI (95%)
1.06*
1.01 to 1.11
1.06*
1.01 to 1.10
1.06†
1.01 to 1.11
1.07†
1.11‡
1.02 to 1.12
1.05 to 1.18
1.07*
1.13‡
1.01 to 1.12
1.06 to 1.20
1.07†
1.12‡
1.02 to 1.12
1.05 to 1.19
1.08†
1.19‡
1.02 to 1.15
1.12 to 1.27
1.07*
1.16‡
1.01 to 1.13
1.09 to 1.24
1.07†
1.11‡
1.02 to 1.12
1.05 to 1.18
1.06*
1.10†
1.01 to 1.12
1.03 to 1.17
1.24‡
1.22 to 1.25
1.10‡
1.04 to 1.15
1.07†
1.02 to 1.13
1.12‡
1.25‡
1.07 to 1.17
1.23 to 1.26
1.10‡
1.23‡
1.05 to 1.15
1.22 to 1.25
Source: Survey on Health, Ageing and Retirement in Europe (release 2.5.0).
*p<0.05.
†p<0.01.
‡p<0.001.
of socioeconomic position on impairment, pointing to a partial
mediation of the association between socioeconomic position
and impairment by a high level of work stress. Results are not as
consistent in case of A&P restrictions (table 4). Effects of
gender and age are not significant in the multivariate model,
whereas the remaining effects correspond to those of
impairment.
DISCUSSION
Based on two summary indices of impairment and restrictions
of activities and participation, we tested sociodemographic,
socioeconomic and work-related predictors of disability among
working men and women of early old age across 11 European
countries, using data from two waves of SHARE study. After
adjusting for baseline disability and relevant confounding variables, low socioeconomic position and chronic stress at work
(low control, ERI) exerted significant effects on disability
scores 2 years later. Effects were more consistent for impairment than for A&P restrictions. In addition, we found some
support of the hypothesis that the association of socioeconomic position with disability is partly mediated by work
stress (tables 3 and 4; model 3). To our knowledge, no former
study analysed these associations in a prospective design comparing data across a variety of European countries. A further
innovative aspect of this investigation concerns the construction of linear indices of two core components of a comprehensive concept of human functioning that was developed in the
frame of ICF.14
Previous studies found similar associations of socioeconomic
position with measures of disability, but were restricted to single
cohorts or single countries.5–10 Huisman,5 for instance, found
that inequalities were lowest in the oldest age group, particularly
among women. However, due to the cross-sectional nature of
this study, it cannot be determined if this is an ageing or cohort
effect. Moreover, it is unclear if the reported socioeconomic differences in long-term disability are in part due to differential
working conditions. Similarly, a few investigations tested prospective associations of job strain31 32 or ERI31–33 with reduced
functioning, often measured by the SF-36 or short versions.34
Stansfeld31 found, for example, that high demands and ERI and
negative aspects of close relationships were independent predictors of poor SF-36 functioning. However, other indicators of
socioeconomic position, such as income and education, were
not analysed. In this study, we add information on the combined
effect of socioeconomic position and work strain on two core
indicators of disability, and further on a partial mediating role
of work stress in this former association.
Several limitations need to be mentioned. First, most items
we used for construction of the disability indices were based on
self-reported data rather than on functional testing. Therefore,
the validity of our indices may be limited, even more so due to
potential reporting bias (eg, social desirability). However,
graded associations with age and, in case of impairment, consistent variations with gender and socioeconomic position point to
a valid assessment. As reporting bias is assumed to vary across
countries it is unlikely that it inflates the findings of the total
sample. Nevertheless, future studies should aim at integrating
more ‘objective’ functional measures and determining differences between self-reported and observational data. A second
limitation concerns the assessment of stressful work with
Reinhardt JD, et al. Occup Environ Med 2013;70:156–163. doi:10.1136/oemed-2012-100924
161
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Table 4
Predictors of A&P restrictions: results of multilevel Poisson regression models: incidence rate ratios and significance level (n=4864)
Model
1
Variables
Sex
Male
Female
Age group
<55 years
55–59 years
60–65 years
Education
High
Medium
Low
Income
High
Medium
Low
Low control
No
Yes
Effort reward imbalance
No
Yes
Prior A&P restrictions
2
3
IRR
CI (95%)
IRR
CI (95%)
IRR
CI (95%)
0.97
0.78 to 1.21
0.96
0.77 to 1.19
0.97
0.77 to 1.20
1.07
0.99
0.84 to 1.36
0.73 to 1.33
1.04
1.01
0.82 to 1.32
0.75 to 1.36
1.06
1.00
0.83 to 1.35
0.74 to 1.35
1.93‡
2.02‡
1.42 to 2.62
1.45 to 2.80
1.83‡
1.87‡
1.34 to 2.50
1.34 to 2.60
1.34*
1.36*
1.05 to 1.72
1.01 to 1.85
1.30*
1.31
1.01 to 1.67
0.97 to 1.78
1.53‡
1.44 to 1.63
1.43†
1.12 to 1.82
1.30*
1.02 to 1.66
1.40†
1.55‡
1.11 to 1.75
1.45 to 1.64
1.31*
1.52‡
1.04 to 1.64
1.43 to 1.62
Source: Survey on Health, Ageing and Retirement in Europe (release 2.5.0).
*p<0.05.
†p<0.01.
‡p<0.001.
abbreviated scales. A test of the full models using original measures of all respective scales (including ‘demand’ and ‘social
support’ in the job strain model, and including ‘overcommitment’ in the ERI model) may reduce the risk of underestimating their effects on prospective disability. Third, we
restricted our analyses to the assessment of work stress at wave
1. Additional information on the duration of exposure available
from the assessment of work stress at wave 2 could enrich the
current analyses. Also, we did not consider data on
non-work-related or extracurricular exposures or information
on the cause of impairment, for example, injury versus disease.
This will be possible with data from SHARE wave 3 that was
focused on retrospective assessment of the participants life
courses.
Since previous analyses indicate that healthier people were
more likely to participate in wave 2,35 we also cannot rule out
some selection bias, where people with low levels of disability
are more likely to participate in survey research. However, this
may rather lead to an underestimation of disability levels and its
association with work stress due to health selection.
Furthermore, while our analyses focussed on individual predictors, future research may also explore the role of distinct
national disability policies and their interactions with working
conditions.2 36 Similarly, albeit the conducted multivariate analyses account for country affiliation, future research may invest
in more detailed differences between countries and measurement equivalence of our two indices. In this regard, additional
explorative principal component analysis by country showed
that A&P items mostly loaded on the same factor which did not
hold true for several impairment items.
162
These limitations are balanced by several strengths. First, the
SHARE study applied strictly comparable procedures in data collection based on identical measures and study designs across all
countries.19 Second, by applying multilevel statistical modelling
in the analysis of data from two measurement waves, by including
relevant confounding factors in multivariate analysis, and by providing quantitative indices of two relevant dimensions of disability, our results can be considered rather robust, even more so as
they are based on a large sample representative of working men
and women aged 50–64 years within the respective countries.
Third, our analysis is based on two established, theoretically
grounded notions of stressful work, lack of control and failed
reciprocity between effort and reward.37 Both models were previously tested in a range of prospective cohort studies and were
shown to contribute to the prediction of stress-related disorders,
such as coronary heart disease38 and depressive episodes.39
Available evidence on health-adverse effects of an adverse psychosocial work environment strengthens the significance of
current findings with regard to reduced functioning in terms of
impairment and restricted activities and social participation.
Furthermore, additional analyses based on SHARE data demonstrate that the experience of stressful work in terms of these
models increases the likelihood of intended early retirement.37
In conclusion, low socioeconomic position and stressful work
are prospectively associated with two core indices of disability
in a large cohort of working men and women of early old age
from 11 European countries. Investing in good quality of work
and reducing social inequalities in health and functioning are
relevant entry points of policies that aim at maintaining work
ability in early old age.
Reinhardt JD, et al. Occup Environ Med 2013;70:156–163. doi:10.1136/oemed-2012-100924
Occup Environ Med: first published as 10.1136/oemed-2012-100924 on 13 December 2012. Downloaded from http://oem.bmj.com/ on 27 April 2019 by guest. Protected by copyright.
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Acknowledgements This study was funded by the Swiss Paraplegic Research in
Nottwil, Switzerland. MW was supported by a grant from the German Research
Foundation ( project number: WA 3065/1-1).
This paper uses data from SHARE release 2.5.0, as of May 24, 2011. The SHARE
data collection has been primarily funded by the European Commission through the
5th framework programme ( project QLK6-CT-2001-00360 in the thematic
programme Quality of Life), through the 6th framework programme ( projects
SHARE-I3, RII-CT-2006-062193, COMPARE, CIT5-CT-2005-028857, and
SHARELIFE, CIT4-CT-2006-028812), and through the 7th framework programme
(SHARE-PREP, 211909 and SHARE-LEAP, 227822). Additional funding from the US
National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291,
P30 AG12815, Y1-AG-4553-01 and OGHA 04-064, IAG BSR06-11, R21
AG025169) as well as from various national sources is gratefully acknowledged
(see http://www.share-project.org for a full list of funding institutions).
We would like to thank Thorsten Lunau, MA and Natalia Wege, MD, MPH, from
the Department of Medical Sociology, University of Duesseldorf, Germany, for fruitful
discussions. Moreover, we thank Andrew ‘Randy’ Pennycott, PhD, London, for
English language corrections.
Contributors JDR wrote the first draft. JDR, MW and JS designed the data
analysis. MW performed the data analysis. JDR, MW and JS wrote the final draft.
15
16
17
18
19
20
21
22
23
Funding Swiss Paraplegic Research.
Competing interests None.
Patient consent Obtained.
Ethics approval Ethical commissions of the study countries.
24
25
Provenance and peer review Not commissioned; externally peer reviewed.
26
27
REFERENCES
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Vaupel JW. Biodemography of human ageing. Nature 2010;464:536–42.
OECD. Sickness, disability and work: breaking the barriers. A synthesis of findings
across OECD countries. Paris: OECD, 2010.
Avendano M, Mackenbach JP. Life course and labour market exit in 13 European
Countries: results from SHARELIFE. In: Börsch-Supan A, Brandt M, Hank K, Schröder M.
eds. The individual and the Welfare State. Berlin: Springer, 2011:203–13.
Haan P, Myck M. Dynamics of health and labor market risks. J Health Econ
2009;28:1116–25.
Huisman M, Kunst AE, Mackenbach JP. Socioeconomic inequalities in morbidity
among the elderly; a European overview. Soc Sci Med 2003;57:861–73.
Koukouli S, Vlachonikolis IG, Philalithis A. Socio-demographic factors and
self-reported functional status: the significance of social support. BMC Health Serv
Res 2002;2:20.
Rautio N, Heikkinen E, Ebrahim S. Socio-economic position and its relationship to
physical capacity among elderly people living in Jyvaskyla, Finland: five- and
ten-year follow-up studies. Soc Sci Med 2005;60:2405–16.
Schoeni RF, Martin LG, Andreski PM, et al. Persistent and growing socioeconomic
disparities in disability among the elderly: 1982–2002. Am J Public Health
2005;95:2065–70.
Minkler M, Fuller-Thomson E, Guralnik JM. Gradient of disability across the
socioeconomic spectrum in the United States. N Engl J Med 2006;355:695–703.
Ramsay SE, Whincup PH, Morris RW, et al. Extent of social inequalities in disability
in the elderly: results from a population-based study of British men. Ann Epidemiol
2008;18:896–903.
Blekesaune M, Solem PE. Working conditions and early retirement—a prospective
study of retirement behavior. Research on Aging 2005;27:3–30.
Karasek R, Theorell T. Healthy work. New York: Basic Books, 1990.
Siegrist J. Adverse health effects of high-effort/low-reward conditions. J Occup
Health Psychol 1996;1:27–41.
WHO. International Classification of Functioning, Disability and Health (ICF).
Geneva: WHO Press, 2001.
28
29
30
31
32
33
34
35
36
37
38
39
Reinhardt JD, et al. Occup Environ Med 2013;70:156–163. doi:10.1136/oemed-2012-100924
United Nations. Convention on the rights of persons with disabilities. New York,
2006.
WHO, World Bank. World report on disability. Geneva: WHO Press, 2011.
Dale C, Prieto-Merino D, Kuper H, et al. Modelling the association of disability
according to the WHO International Classification of Functioning, Disability and
Health (ICF) with mortality in the British Women’s Heart and Health Study.
J Epidemiol Community Health 2012;66:170–5.
Börsch-Supan A, Hank K, Jürges H. A new comprehensive and international view on
ageing: introducing the ‘Survey of Health, Ageing and Retirement in Europe’. Eur J
Ageing 2005;2:245–53.
Börsch-Supan A, Jürges H. The survey of health, aging and retirement in Europe—
methodology. Mannheim: Mannheim Research Institute for the Economics of
Ageing, 2005.
Reinhardt J, von Elm E, Fekete C, et al. Social inequalities of functioning and
perceived health in Switzerland—a representative cross-sectional analysis. PLoS One
2012;7:e38782002E.
Cieza A, Geyh S, Chatterji S, et al. ICF linking rules: an update based on lessons
learned. J Rehabil Med 2005;37:212–18.
Kolenikov S. Confirmatory factor analysis using confa. The Stata Journal
2009;9:329–73.
Karasek R, Brisson C, Kawakami N, et al. The Job Content Questionnaire ( JCQ): an
instrument for internationally comparative assessments of psychosocial job
characteristics. J Occup Health Psychol 1998;3:322–55.
Siegrist J, Starke D, Chandola T, et al. The measurement of effort-reward imbalance
at work: European comparisons. Soc Sci Med 2004;58:1483–99.
Marmot M, Siegrist J, Theorell T. Health and the psychosocial environment at work.
In: Marmot M, Wilkinson RG. eds. Social determinants of health. 2nd edn. Oxford:
Oxford Univ. Press, 2006:97–130.
Nunnally JC, Bernstein IH. Psychometric theory. New York: McGraw-Hill, 1994.
Paccagnella O, Weber G. Income imputation. In: Börsch-Supan A, Brugiavini A,
Jürges H, Mackenbach JP, Siegrist J, Weber G. eds. Health, ageing and retirement in
Europe. First results from the survey of health, ageing and retirement in Europe.
Mannheim: Mannheim Research Institute for the Economics of Ageing,
2005:357–8.
Lynch J, Kaplan G. Socioeconomic factors. In: Kawachi I, Berkman L. eds. Social
epidemiology. New York: Oxford University Press, 2000:13–35.
Rabe-Hesketh S, Skrondal A. Multilevel and longitudinal modeling using Stata.
Lakeway Drive (Texas): Stata Press, 2005.
Finkel SE. Causal analysis with panel data. Thousand Oaks, CA: Sage, 1995.
Li J, Yang W, Cho SI. Gender differences in job strain, effort-reward imbalance,
and health functioning among Chinese physicians. Soc Sci Med 2006;
62:1066–77.
Stansfeld SA, Bosma H, Hemingway H, et al. Psychosocial work characteristics and
social support as predictors of SF-36 health functioning: the Whitehall II study.
Psychosom Med 1998;60:247–55.
Kuper H, Singh-Manoux A, Siegrist J, et al. When reciprocity fails: effort-reward
imbalance in relation to coronary heart disease and health functioning within the
Whitehall II study. Occup Environ Med 2002;59:777–84.
Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey
(SF-36). I. Conceptual framework and item selection. Med Care 1992;30:473–83.
Schröder M. Attrition. In: Börsch-Supan A, Brugiavini A, Jürges H, Mackenbach J,
Siegrist J, Weber G. eds. (Hrsg.): Health, ageing and retirement in Europe (2004–
2007). Starting the longitudinal dimension. Mannheim: Mannheim Research
Institute for the Economics of Ageing, 2008:S.325–30.
van Oorschot W, Hvinden B. Disability policies in European countries. Tilburg:
Kluwer Law International, 2001.
Siegrist J, Wahrendorf M. Quality of work, health, and retirement. Lancet
2009;374:1872–3.
Kivimaki M, Virtanen M, Elovainio M, et al. Work stress in the etiology of coronary
heart disease—a meta-analysis. Scand J Work Environ Health 2006;32:431–42.
Stansfeld S, Candy B. Psychosocial work environment and mental health—a
meta-analytic review. Scand J Work Environ Health 2006;32:443–62.
163
Occup Environ Med: first published as 10.1136/oemed-2012-100924 on 13 December 2012. Downloaded from http://oem.bmj.com/ on 27 April 2019 by guest. Protected by copyright.
Workplace