580120
research-article2015
JAHXXX10.1177/0898264315580120Journal of Aging and HealthDarin-Mattsson et al.
Article
Are Occupational
Complexity and
Socioeconomic Position
Related to Psychological
Distress 20 Years Later?
Journal of Aging and Health
1–20
© The Author(s) 2015
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DOI: 10.1177/0898264315580120
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Alexander Darin-Mattsson, MSc1,2, Ross Andel, PhD3,4,
Stefan Fors, PhD1,2, and Ingemar Kåreholt, PhD1,2,5
Abstract
Objective: To assess occupational complexity in midlife in relation to
psychological distress in older adulthood (69+ years) and explore the role
of socioeconomic position. Method: Baseline data from the Swedish Level
of Living Survey and follow-up data from the Swedish Longitudinal Study of
Living Conditions of the Oldest Old were combined, resulting in 20+ years of
follow-up. Data were analyzed using ordered logistic regressions. Results:
Higher occupational complexity was associated with less psychological
distress 20 years later adjusted for age, sex, follow-up year, hours worked
the year before baseline, and psychological distress at baseline. Higher
socioeconomic position yielded the same pattern of results. Socioeconomic
position partially accounted for the association between occupational
complexity and psychological distress. Discussion: With social gradient not
easily amenable to modification, efforts to increase engagement at work
may offer a viable option to attenuate the influence of work environment on
psychological distress later in life.
1Karolinska
Institutet, Solna, Sweden
University, Sweden
3School af Aging Studies, University of South Florida, Tampa, USA
4International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
5Jönköping University, Sweden
2Stockholm
Corresponding Author:
Alexander Darin-Mattsson, Aging Research Center, Gävlegatan 16, 113 30 Stockholm,
Sweden.
Email: alexander.darin.mattsson@ki.se
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2
Journal of Aging and Health
Keywords
psychological distress, occupational complexity, socioeconomic position, old
age, population based
Introduction
Earlier studies show that mental health in old age is dependent on experiences during the life course (Gruenewald et al., 2012; Mirowsky & Ross,
2005). Most people spend a large part of their lives at work, so the work
environment is probably one of the most important sources of health-related
exposures. Findings from the Whitehall II study suggest that adverse socioeconomic conditions and working conditions in midlife are strong predictors
of post-retirement depressive symptoms (Virtanen et al., 2015).
Intellectually challenging occupations have been associated with better
cognitive abilities in older adulthood (Andel, Kåreholt, Parker, Thorslund, &
Gatz, 2007; Gow, Avlund, & Mortensen, 2012). In addition, the influence of
work characteristics on cognitive abilities appears not to be attenuated by
retirement (Coe, von Gaudecker, Lindeboom, & Maurer, 2012). In turn, cognitive abilities have been related to a number health-related outcomes in old
age (Small, Dixon, & McArdle, 2011; Verhaegen, Borchelt, & Smith, 2003),
suggesting that the established association between occupational characteristics on cognitive abilities may have a more widespread influence on health
and aging.
Kohn and Schooler (1983) formulated the environmental complexity
hypothesis based on the idea that environmental demands posed by complex
environments are related to favorable mental health outcomes. More complex
work constantly allows or demands that persons do challenging tasks that
engage the person cognitively but may also increase psychological wellbeing. Occupational complexity has also been associated with multiple positive psychological outcomes in people of working age (Adelmann, 1987; J.
Miller, Schooler, Kohn, & Miller, 1979). Thus, complex occupations may be
associated with better psychological well-being even in older adulthood.
Social engagement, measured as social activity and as paid or unpaid
work, has been associated with fewer depressive symptoms in old age in both
cross-sectional and longitudinal studies (Glass, de Leon, Bassuk, & Berkman,
2006). In the current study, we focus specifically on the association between
intellectual engagement measured as occupational complexity of paid work
at midlife and self-reported psychological distress in older adulthood.
Occupational complexity is positively correlated with socioeconomic
position (SEP). SEP is conventionally assessed using education, social class,
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Darin-Mattsson et al.
3
and financial conditions (e.g., income, wealth, or cash margin), all of which
are also related to occupational complexity (le Grand & Tåhlin, 2013;
Mirowsky & Ross, 2005; Tåhlin, 2007). A substantial body of research has
also shown associations between SEP and psychological problems; individuals with lower SEP are more likely to report psychological distress than those
with higher SEP (Mirowsky & Ross, 2003). Thus, any assessment of the
associations between occupational complexity and psychological distress
must take differences in SEP into consideration.
Prospective studies have typically focused either on working conditions or
on socioeconomic conditions (Hoven & Siegrist, 2013), but we were able to
study both. Moreover, studies rarely use more than one or two indicators of
SEP (Hoven & Siegrist, 2013), whereas we had the opportunity to use multiple indicators, which helped capture the multidimensionality of this complex variable.
Aims
The overarching aim of this study was to assess whether occupational complexity would be associated with psychological distress in older adulthood
(69+ years). We also set out to study whether any such associations would be
explained by midlife SEP. Our hypotheses were as follows:
Hypothesis 1: Higher occupational complexity in midlife would be associated with less psychological distress in older adulthood.
Hypothesis 2: Higher SEP in midlife would be associated with less psychological distress in older adulthood.
Hypothesis 3: Higher occupational complexity in midlife would be associated with less psychological distress in older adulthood even after
adjustment for midlife SEP.
Method
Data
Data from the Swedish Level of Living Surveys (LNUs), collected in 1968,
1981, and 1991, were used as baseline assessment data. Data from the
Swedish Longitudinal Study of Living Conditions of the Oldest Old
(SWEOLD), collected in 1992, 2002, 2004, and 2011, were used as followup data (see Table 1). The linkages represent observations from two waves of
data collection separated by 20+ years (baseline and follow-up for the different years), specifically 1968 to 1992, 1981 to 2002, 1981 to 2004, and 1991
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Journal of Aging and Health
Table 1. Analytical Population.
Linkage
1
2
3
4
Total/M
Baseline Age at
years
baseline
1968
1981
1981
1991
53-66
56-64
46-64
57-64
M=
57.8
Followup years
1992
2002
2004
2011
Age at
Follow-up
Linked
Analytical
follow-up time (years) observations population
77-91
77-86
69-88
76-85
M = 79.9
24
21
23
20
535
617
1,166
663
2,981
316
330
820
343
1,809
to 2010. We found small or no differences in the associations between the
independent variables and the outcomes for the different linkages. Therefore,
the linkages were combined and analyzed as one data set by retaining a
covariate with separate value specified for each linkage.
Both LNU (response rates 78.3%-90.8%) and SWEOLD (response rates
84.4%-95.4%) are nationally representative of the Swedish population; new
respondents are added every survey year to maintain national representativeness. SWEOLD is a continuation of LNU: LNU includes persons aged 15 to
75 years, whereas SWEOLD includes persons older than 75 who participated
in LNU. SWEOLD 2004 was an exception; it included people 69 years and
older.
Because of the sampling procedure (using 1981 data collection as baseline
for two different linkages), 282 persons (from baseline 1981) were linked
within two follow-ups, 2002 and 2004. The small time difference between
the 2002 and 2004 follow-ups made it problematic to treat them as independent observations, as this could lead to artificially low standard errors. To
control for this, cluster-correlated robust estimate of variance was used in the
analyses (Hardin & Hilbe, 2012).
Participants without a gainful occupation at baseline (mostly housewives)
were excluded (Linkage 1 = 24.5% of the linked observations, Linkage 2 =
32.9%, Linkage 3 = 13.9%, and Linkage 4 = 23.8%; overall = 21.9%).
Persons who had passed retirement age at baseline were also excluded
(Linkage 1 = 9.2% of the linked observations, Linkage 2 = 12.0%, Linkage
3 = 7.0%, and Linkage 4 = 26.4%; overall = 12.7%). Retirement age was 67
in 1968 and 65 in all other baseline years. The oldest person in the 1968
LNU baseline data collection wave was therefore 66 years, whereas the oldest person in the other baseline data collection waves was 64 years.
Observations with item non-response for any of the independent variables,
or the covariates, were excluded (Linkage 1 = 7.2% of the linked
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Darin-Mattsson et al.
5
observations, Linkage 2 = 1.8%, Linkage 3 = 2.9%, and Linkage 4 = 2.5%;
overall = 3.5%). The linkages were analyzed separately. The results showed
small differences between the linkages; the linkages were therefore merged
and analyzed as one data set.
Measures
Dependent Variables
In both LNU and SWEOLD, respondents were asked about many different
outcomes, including outcomes pertaining to psychological distress, such as
fatigue, anxiety, and depression. The question was, “Have you had any of the
following diseases or disorders during the last 12 months?” and the response
alternatives were “no,” “yes, slight,” and “yes, severe.” Answers were coded
0, 1, and 2. Fatigue, anxiety, and depression were examined separately and in
a summarized index of psychological distress. Fatigue may be considered a
less common measure of psychological distress. However, previous research
suggests that fatigue may tap into the psychological distress construct well
(Mänty, Rantanen, Era, & Avlund, 2014)
In the summarized index of psychological distress, all the items were
given equal weight. The index ranged from 0 to 6. A rating of 0 equaled no
problems, and a rating of 6 equaled severe problems in all three items (fatigue,
anxiety, and depression).
Independent Variables
The main independent variables were occupational complexity and SEP.
We measured occupational complexity as substantive complexity, complexity of work with data, and complexity of work with people. The measures of occupational complexity build on research in functional job
analysis (Fine, 1968), which focuses on complexity of work with data,
people, and things. Note that complexity of work with things was not used
in this study because of its low reliability and predictive ability (Andel
et al., 2005; Cain & Treiman, 1981).
To generate these scores, qualified job analysts observed workers and
classified jobs on the basis of work tasks and skills needed to carry out the
tasks specific to each occupation. Complexity scores for each of the three
dimensions (data, people, and things) are included among the 46 worker
characteristics obtained via the observations and presented in the U.S.
Dictionary of Occupational Titles (DOT; Cain & Treiman, 1981). Specifically,
complexity in work with data refers to the level at which persons handle
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6
Journal of Aging and Health
information in their work (see the appendix). For example, it is considered
more complex to synthesize information or knowledge than to compile it.
Complexity of work with people refers to the demands imposed by working
with others. For example, therefore, it is considered more complex to negotiate than to supervise. With respect to specific occupations, a secretary (the
most common occupation in the analyzed population) would score 2.2 in
complexity of work with data (range 0-6) and 1.8 in complexity of work with
people (range 0-7). This means their work mainly includes “computing” data
and “speaking/signaling” with people to exchange information. Teachers
would score 4.00 in complexity of work with data and 6.00 in complexity of
work with people, because they “analyze” data and “instruct” people. Being
a teacher is more complex and engaging, because the work is less routine;
requires more initiative, thought, and independent judgment; and involves
more freedom from supervision than the work of a secretary. See the appendix for precise definitions of “data” and “people.”
Besides the complexity of work with data and people, we also assessed
substantive complexity using a measure previously developed by Roos and
Treiman (1980). Roos and Treiman used a principal components analysis to
reduce all 46 worker characteristics included as part of job descriptions in the
DOT (see A. R. Miller, Treiman, Cain, & Roos, 1980). The principal component included 8 of the 46 worker characteristics, namely, general educational
development, specific vocational preparation, complexity of work with data,
intelligence aptitude, verbal aptitude, numerical aptitude, abstract interest in
the job, and temperament for repetitive and continuous processes. According
to Roos and Treiman, an index of these eight worker characteristics represents substantive, or overall, complexity. All measures of work complexity
were standardized as z scores in the main analyses.
The scores from the approximately 12,000 occupations listed in the DOT
were averaged and assigned to the occupational categories in the 1970 U.S.
Census. Occupational codes from the 1980 Swedish Population and Housing
Census were matched with the U.S. occupational categories and assigned
complexity scores. The matching procedure has been described previously
(Andel et al., 2005).
SEP. Geyer, Hemström, Peter, and Vågerö (2006) have concluded that the
most commonly used indicators of SEP (education, income, and social class)
are not interchangeable, because they measure different social dimensions
that are associated with different health outcomes and tap into different
mechanisms. Prospective studies rarely use more than one or two indicators
of SEP (Hoven & Siegrist, 2013), which may create bias. Given the availability of relevant data, we were able to create an index that comprises more
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Darin-Mattsson et al.
7
than one dimension of SEP as suggested by Geyer et al. (2006)—social class,
education, income, and cash margin.
Years of education was included as a continuous variable.
Occupation-based social class was divided into four social classes: (a)
unskilled blue-collar workers; (b) skilled blue-collar workers (those who normally need 2 years of formal training), small farmers (less than 10 hectare
arable land), and entrepreneurs without employees; (c) lower white-collar
workers, large-scale farmers (at least 100 hectares arable land), and entrepreneurs with 1 to 19 employees; and (d) intermediate and upper white-collar
workers, entrepreneurs with at least 20 employees, and academic professionals (Kåreholt, Lennartsson, Gatz, & Parker, 2011).
The SEP index also included log transformed individual income.
Finally, a less traditional indicator of SEP, cash margin, was also included
in the SEP index. In 1968, cash margin was assessed with the question, “Can
you raise 2000 SEK in a week?” After 1968, the amount was adjusted to have
the same purchase value at each baseline wave of interviews as 2,000 Swedish
krona (SEK) had in 1968. Cash margin was divided into three categories:
“Yes, from own savings or borrowing from someone in the family”; “Yes, by
borrowing from someone else or raising the money in some other way” (e.g.,
by selling things); and “No.”
All indicators of SEP had approximate linear associations with the outcome.
All SEP items were standardized as z scores and summarized in the SEP index.
The index was created to account for as much variation in psychological
distress associated with SEP as possible without multicollinearity. All variables were also tested separately against the outcome, and overall, the SEP
index was more strongly associated with the outcomes compared with the
separate items included in the index. The exceptions were the association
between income and fatigue (odds ratio [OR] = .76, p = .002; SEP index and
fatigue: OR = .86, p = .014) and between cash margin and anxiety (OR = .77,
p = .003; SEP index and anxiety: OR = .83, p = .005). The SEP index was
then divided into three groups with ranges of equal size on the SEP index
scale (0-2.14, >2.14-4.28, >4.28-6.42).
Covariates
Covariates in all models presented were age, sex, family status, interaction of
sex and family status, follow-up year, hours worked the year before baseline,
childhood conditions, and psychological distress at baseline. Family status
can affect psychological distress and therefore was controlled in the analyses.
Family status was measured as married, divorced, widowed, or cohabitating.
Childhood conditions were included to adjust for potential bias by pre-selection
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8
Journal of Aging and Health
to occupations with varying levels of complexity. Childhood conditions were
measured with retrospective questions about fathers’ social class and education, family conflicts (yes/no), financial hardship (yes/no), and whether some
family member had severe or long-lasting sickness. Adjusting for follow-up
year adjusts for period effects and, in combination with adjusting for age, is
a simple way of adjusting for cohort effects. Adjusting for hours worked the
year before the survey year is a simple way of controlling for how much
individuals work, so associations with psychological distress will not be due
to differing amounts of work. Niedhammer, Chastang, David, and Kelleher
(2008) argue that adjusting for working hours could limit selection bias
caused by the healthy worker effect. We also adjusted for psychological distress (the index) at baseline.
Statistical Method
All analyses were conducted with StataMP 12. The main analyses were conducted with ordered logistic regressions. The OR of an ordered logistic
regression corresponds to the weighted OR of a series of binary logistic
regressions. The final OR is the OR of the dependent variable when the independent variable changes by one unit and all other variables in the model are
held constant.
All models (1-8) included all covariates. Independent variables of interest
were tested separately. In Models 5 to 7, occupational complexity measures
were also adjusted for SEP, and in Model 8, the association between SEP and
the outcomes were adjusted for substantive complexity.
Results
As shown in Table 2, approximately half the respondents reported at least one
slight problem with fatigue, anxiety, or depression during the last 12 months
(values > 0 in the index of psychological distress). The most common kind of
distress was fatigue, and the least common was depression. About 1% experienced severe problems in all three areas, and 2.3% had at least two severe
and one slight problems. Women reported more distress than men in all the
indicators. Women experienced both more slight problems and more severe
problems than men.
Table 2 also shows the mean values of occupational complexity measures
and the SEP index. Men’s complexity of work with data was typically one
unit higher than women’s. Thus, men’s occupations, on average, included
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Darin-Mattsson et al.
Table 2. Prevalence of Self-Reported Psychological Distressa and Descriptive
Information on the Independent Variables.b
Women %
Fatigue
No
58.69
Yes, slight
32.35
Yes, severe
8.96
Total
100 (949)
Anxiety
No
67.93
Yes, slight
24.89
Yes, severe
7.17
Total
100 (948)
Depression
No
85.76
Yes, slight
10.55
Yes, severe
3.69
Total
100 (948)
Psychological distressa
0
43.70
1
29.21
2
13.86
3
6.67
4
3.81
5
1.69
6
1.06
Total
100 (945)
Men %
Total %
67.62
25.15
7.24
100 (843)
62.89
28.96
8.15
100 (1,792)
81.55
14.76
3.69
100 (840)
74.33
20.13
5.54
100 (1,788)
90.25
7.02
2.73
100 (841)
87.87
8.89
3.24
100 (1,789)
58.73
23.68
10.65
2.87
2.27
0.96
0.84
100 (836)
50.76
26.61
12.35
4.88
3.09
1.35
0.95
100 (1,781)
Range and mean for independent variables divided by sex
Range
Datac
Peopled
Substantive
complexity
SEP index
Women (M)
Men (M)
Total (M)
0-6
0-7
0-10
2.59
1.91
3.68
3.45
1.85
4.70
3.00
1.88
4.16
0-6.42
2.50
2.93
2.70
(continued)
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Journal of Aging and Health
Table 2. (continued)
Means of occupational complexity divided by SEP indexe and sex
Low (31.4%)
Middle (61.75%)
High (6.85%)
Women Men Total Women Men Total Women Men
Datac
Peopled
Substantive
complexity
Total
Total
2.10
1.31
2.71
2.68 2.29
0.97 1.20
3.74 3.04
2.82
2.13
4.13
3.52 3.18
1.83 1.98
4.70 4.43
4.43
5.12
7.34
4.61
3.80
6.64
4.56
4.18
6.84
385
183 568
539
578 1,117
36
88
124
Note. SEP = socioeconomic position.
a0 = no problems, 1 = one slight problem, 2 = two slight problems or one slight and one severe
problem, 3 = three slight problems or one severe problem plus one slight problem, 4 = two severe
problems or one severe and two slight problems, 5 = two severe problems and one slight problem,
and 6 = severe problems in all three items.
bNumber of observations differs by dependent variable because of internal non-response.
cComplexity of work with data.
dComplexity of work with people.
eThe SEP index was divided into three groups with equal range on the SEP index scale (0-2.14,
>2.14-4.28, >4.28-6.42).
compiling and analyzing data, and women’s included computing and some
compiling (see the appendix). Women, on average, worked in occupations
with higher complexity of work with people, although the differences
between men and women with regard to this kind of complexity were small.
This finding indicates that most individuals, on average, had jobs that
included speaking with people to exchange information, such as giving
assignments and directions. Men had occupations that were about one unit
higher in substantive complexity than women’s. Table 2 also presents the
mean scores in the SEP index. These scores cannot be interpreted directly,
because they are purely relative. In general, men had higher SEP than women.
Men also reported more education, a higher social class, higher income, and
less economic hardship at baseline. Finally, Table 2 also shows mean scores
for occupational complexity divided by SEP and sex.
Table 3 presents the associations between occupational complexity and
SEP at midlife and late-life psychological distress. Two sets of models are
presented. Both sets of models were adjusted for all the covariates. In addition, Models 5 to 7 were adjusted for the SEP index, and Model 8 was additionally adjusted for substantive complexity.
As shown in Table 3, Models 1 to 4, all measures of occupational complexity and the SEP index were separately associated with psychological
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11
Darin-Mattsson et al.
Table 3. Associations Between Work Complexity and Socioeconomic Position in
Midlife and Late-Life Psychological Distress.
Fatigue
OR
Models
Datab
Peoplec
Substantive
complexity
Model 4
SEP index
High
Medium
Low
Models 5-7
Datab,d
Peoplec,d
Substantive
complexityd
Model 8
SEP indexe
High
Medium
Low
Anxiety
CI
OR
CI
Depression
OR
Psychological distress
CI
OR
CI
1-3a
0.86* [0.77, 0.96] 0.87* [0.77, 0.98]
0.92
[0.82, 1.02] 0.87* [0.76, 0.99]
0.80*** [0.71, 0.89] 0.85* [0.75, 0.96]
0.79** [0.67, 0.93]
0.82*
[0.68, 0.99]
0.71*** [0.60, 0.83]
0.87**
0.90*
0.81***
[0.78, 0.96]
[0.81, 0.99]
[0.73, 0.90]
Ref
Ref
2.12** [1.29, 3.48] 2.30* [1.15, 4.60]
2.58** [1.50, 4.44] 2.59* [1.24, 5.39]
Ref
9.01**
9.15**
Ref
2.46***
2.82***
[1.60, 3.85]
[1.73, 4.62]
0.90† [0.80, 1.01] 0.90† [0.79, 1.02]
0.98
[0.87, 1.11] 0.91 [0.80, 1.05]
0.83** [0.74, 0.93] 0.88† [0.77, 1.00]
0.82** [0.70, 0.97]
0.88
[0.73, 1.07]
0.73*** [0.62, 0.87]
0.91†
0.96
0.85**
[0.81, 1.01]
[0.86, 1.07]
[0.76, 0.95]
Ref
1.67*
2.01*
Ref
6.89*
5.99*
Ref
2.13**
2.27**
[1.35, 3.37]
[1.37, 3.77]
Ref
[1.09, 2.98] 2.03* [1.01, 4.01]
[1.15, 3.52] 2.01* [1.02, 4.54]
[1.95, 42.17]
[1.93, 43.41]
[1.46, 32.48]
[1.46, 28.82]
Note. All models adjusted for age, sex, family status, interaction of sex and family status, childhood
conditions, follow-up year, hours worked the year before baseline, and psychological distress at baseline.
OR = odds ratio; CI = confidence interval; SEP = socioeconomic position.
aEach variable was entered into a separate model.
bComplexity of work with data.
cComplexity of work with people.
dAlso adjusted for SEP index.
eAlso adjusted for substantive complexity.
†p < .10. *p < .05. **p < .01. ***p < .001.
distress. Higher level of occupational complexity and SEP in midlife were
associated with less psychological distress 20 years later. Complexity of
work with data was significantly associated with all outcomes. For example,
the OR of 0.86 (complexity of work with data in relation to fatigue) shows
that complexity of work with data (a z score) that is one standard deviation
unit higher was associated with 14% lower odds of fatigue. The findings
indicate that occupational complexity contributes to the understanding of
psychological distress in older adulthood. Medium and low SEPs were associated with more psychological distress than high SEP. For example, medium
SEP was associated with 2.30 greater odds of anxiety, indicating that those
with medium SEP had 130% greater odds of reporting anxiety than those
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12
Journal of Aging and Health
with high SEP. In Models 5 to 7, the associations between occupational complexity and psychological distress were additionally adjusted for SEP. Most
associations were attenuated. This indicates that the association between
occupational complexity and psychological distress was partially captured by
SEP. The significant association between complexity of work with data at
midlife and late-life depression remained, independent of SEP. The associations between substantive complexity and psychological distress were also
attenuated when we adjusted for SEP. However, these associations were still
significant.
In Model 8, the associations between SEP and psychological distress were
adjusted for substantive complexity, which attenuated the ORs. The associations between SEP and psychological distress were also adjusted for complexity of work with data, complexity of work with people, and both
dimensions at the same time. Substantive complexity had the greatest impact
on the association between SEP and psychological distress.
In addition to the above-mentioned results, we checked for interaction
effects between (a) sex and occupational complexity, (b) SEP and occupational complexity, (c) sex and SEP, (d) the different linkage years and occupational complexity, and (e) the different linkage years and SEP. None of the
results were significant.
Discussion
We found that higher occupational complexity was associated with less psychological distress 20 years later, even after adjustment for age, sex, family
status, interaction of sex and family status, childhood conditions, follow-up
year, hours worked the year before baseline, and psychological distress at
baseline. Higher SEP yielded a similar pattern of results. Adjustment for
SEP reduced the associations between complexity of work with data and
psychological distress and between complexity of work with people and
psychological distress to non-significant, suggesting that these associations
were mostly a function of differences in SEP. However, substantive complexity seems to have long-term associations with psychological distress
that are independent of SEP. Our findings also support the notion that there
is a social gradient in psychological distress in older adulthood. Results
from Model 8 (Table 3) indicate that occupational complexity might play a
role in the social gradient, because substantive complexity attenuates the
association between midlife SEP and psychological distress in older adulthood. This is important for intervention as substantive complexity, which
reflects intellectual engagement at work, may be more easily modified in the
workplace than SEP.
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Darin-Mattsson et al.
13
Several mechanisms may lie behind these findings: (a) Higher occupational complexity may build a reserve of psychological resources and coping
strategies; (b) people whose occupations are more complex may, out of habit,
stay more socially engaged and productive in older adulthood; and (c) there
may be a selection effect; that is, specific characteristics of people and society may influence individuals’ occupational pathways.
First, occupational complexity might influence psychological distress in
older adulthood by increasing cognitive and psychological resource reserves,
including self-esteem, self-efficacy, sense of control over one’s own life, and
self-worth. These psychological resources might protect against mental
health problems by influencing physiological pathways related to stress
(Berkman, Glass, Brissette, & Seeman, 2000). Jonker, Comijs, Knipscheer,
and Deeg (2009) found that changes in coping resources indicating feelings
of control, self-esteem, and self-efficacy protected against decreasing life satisfaction and promoted positive affect in people with persistent health decline.
Research shows that occupational complexity acts as a buffer against both
cognitive decline (Andel et al., 2007) and dementia (Andel et al., 2005; Karp
et al., 2009) and reduces mortality risk in men (Moore & Hayward, 1990).
More complex occupations that demand more engagement might also build
up a reserve that protects against psychological distress via psychological
pathways and by stimulating multiple bodily systems.
Second, there is path dependency. People are “creatures of habit,” and
having a demanding, challenging, and engaging occupation might set them
on a path of continuing high engagement that are protective against mental ill
health in old age (Glass et al., 2006; Glass, de Leon, Marottoli, & Berkman,
1999). Agahi, Ahacic, and Parker (2006) found that leisure activities in old
age are dependent on earlier life activities, which suggests that occupational
complexity might play a role in the continuation of one’s activities and social
engagement in old age.
Third, the strong association between occupational complexity and SEP
shows that SEP is associated with people’s occupational pathways, which in
turn might affect the associations between midlife occupational complexity
and late-life psychological distress. Selection into different occupations (e.g.,
because of “the healthy worker effect” or societal structures) may mean that
people with low levels of sickness absence or those with specific characteristics (e.g., intelligence or certain personality traits) end up in more complex
occupations. For example, previous studies have shown that higher intelligence is associated with higher occupational complexity (Ganzach, 1998)
and that specific personality traits are associated with structural characteristics of occupations (Bihagen, Nermo, & Stern, 2013), which could include
occupational complexity. Selection might have affected the population in the
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Journal of Aging and Health
current analyses. However, the association between substantive complexity
and psychological distress, independent of SEP, suggests that occupational
complexity plays a role in psychological distress in older adulthood.
The findings are relevant to debates about working conditions and mental
health, retirement age and working conditions, and the social gradient in
health. The information about long-term associations provided by this study
contributes to the discourse about working conditions and mental health. The
ongoing debate about retirement age in many affluent democracies that has
arisen from the need to finance retirement benefits for the growing population of older people often focuses on who will be able to continue to work at
older ages (Swedish Government Report, Swedish Social Ministry, 2013; van
Rijn, Robroek, Brouwer, & Burdorf, 2014). To increase the age of the workforce, investments in favorable working conditions have been proposed
(Swedish Government Report, Swedish Social Ministry, 2013; Wahrendorf,
Blane, & Siegrist, 2011). Long-term effects of working conditions are infrequently discussed but would also affect health in older adulthood and could
be a prerequisite for policy making regarding the workforce. The results
show that working conditions, such as occupational complexity, may have
long-term effects on mental health into older adulthood.
It may be difficult to implement changes that modify socioeconomic hierarchy, as redistribution of resources is a complex and politically difficult
issue. Therefore, modifications to work environment might be a more readily
available area for policy considerations with respect to improving population
health in older adulthood.
Furthermore, it is important to note that because this study only included
participants who held a gainful occupation, it is only representative of individuals in the workforce. This is particularly important to the generalizability
of the results to women. Specifically, women in the workforce may be more
career-oriented or, conversely, may seek income as a means of survival, leading to greater disruptions to work–life balance. Therefore, they may differ
substantially, and in many respects, from women not in the workforce. Some
other limitations should be mentioned. The results might have been affected
by selective survival (Markides & Machalek, 1984); individuals with better
mental health and those with higher SEP were more likely to be included in
the study. However, our intention was to study those who survived into old
age, and we used data from nationally representative surveys. In SWEOLD,
non-responders had a higher mortality rate than responders. However, individuals living in institutions were included, and proxy interviews were used
to increase response rate and facilitate representativeness of the sample
(Kelfve, Thorslund, & Lennartsson, 2013). Another bias might come from
the “healthy worker effect,” whereby less healthy people might not have been
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Darin-Mattsson et al.
15
employed at baseline and therefore might not be included in the study. A
meta-analysis by van Rijn, Robroek, Brouwer, & Burdorf (2014) showed that
poor health increased the risk of exiting the labor market through disability
pension, early retirement, or unemployment. Most plausibly, the healthy
worker effect leads to underestimation of the associations found.
Furthermore, it is possible that personality or intelligence played a role in
job selection (Bihagen, Nermo, & Stern, 2013; Roberts, Kuncel, Shiner,
Caspi, & Goldberg, 2007), thus affecting study outcomes above and beyond
work environment itself. To reduce this bias, we added control over childhood conditions including fathers’ SEP. Fathers’ SEP is known to be associated with intelligence (Neisser et al., 1996). In addition, occupational
complexity was previously associated with late-life cognitive outcomes irrespective of familial, predisposing factors (Andel et al., 2005). Yet, pre-selection into occupations based on inherent characteristics remains a concern and
may deem intervention to modify work environment based on our findings
somewhat less effective.
Fatigue in old age could be related to many different sources, for example,
medication. Hence, the associations between occupational complexity in
midlife and fatigue 20 years later should be interpreted with caution. Yet,
fatigue is known to be related to depressive symptoms (Mänty, Rantanen,
Era, & Avlund, 2014), and the results for fatigue, anxiety, and depression
were very similar.
Furthermore, occupational complexity was measured at one point in time,
so changes in individuals’ occupational complexity were not taken into consideration. Changes in the type of industry people work in decrease with age
(Swedish Work Environment Authority, 2011), and we believe that the
respondents had probably reached their highest occupational complexity
level by baseline. To test this idea, a mean value of level of occupational
complexity was calculated using two points of measurement, but the differences between complexity scores at one and two points of measurement were
negligible. Overall, we believe the limitations might have attenuated the
observed associations, potentially leading to underestimation of the associations between midlife occupational complexity and psychological distress in
older adulthood.
Our results confirm earlier findings showing a social gradient in mental
health in older adulthood in the Swedish working population, and it seems as
if occupational complexity contributes to the understanding of the social gradient. However, more research is needed to clarify the relationship between
occupational complexity, SEP, and psychological distress. Research should
focus on using the life-course perspective to disentangle how occupational
complexity and SEP at different ages, during different periods of time, and in
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Journal of Aging and Health
different cohorts are related to mental health. In conclusion, occupational
complexity contributes to our understanding of differences in psychological
distress in older adulthood. With social gradient not readily amenable to
modification, it may be that efforts to increase engagement at work (i.e., substantive complexity) may offer a viable option to attenuate the influence of
work environment on psychological distress later in life.
Appendix
Description of complexity scores as presented in the Fourth Edition of the
Dictionary of Occupational Titles (U.S. Department of Labor, 4th ed.,
Revised, 1991, pp. 1005-1007). Complexity of work is rated along three
dimensions: data, people, and things. The scores were reversed to reflect
higher complexity with higher scores and lower complexity with lower
scores.
DATA
6
Synthesizing
5
Coordinating
4
Analyzing
3
Compiling
2
Computing
1
0
Copying
Comparing
PEOPLE
Information, knowledge, and conceptions, related to data, people,
or things, obtained by observation, investigation, interpretation,
visualization, and mental creation, data are intangible and
include numbers, words, symbols, ideas, concepts, and oral
verbalization.
Integrating analyses of data to discover facts and/or to
develop knowledge concepts or interpretations.
Determining time, place, and sequence of operations or
action to be taken on the vases of analysis of data; executing
determinations and/or reporting on events.
Examining and evaluating data. Presenting alternative actions
in relation to the evaluation is frequently involved.
Gathering, collating, or classifying information about
data, people, or things. Reporting and/or carrying out a
prescribed action in relation to the information is frequently
involved.
Performing arithmetic operations and reporting on and/or
carrying out a prescribed action in relation to them. Does
not include counting.
Transcribing, entering, or posting data.
Judging the readily observable functional, structural, or
compositional characteristics (whether similar to or
divergent from obvious standards) of data, people, or things.
Human beings; also animals dealt with on an individual basis as if
they were human beings.
(continued)
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Darin-Mattsson et al.
17
Appendix (continued)
8
Mentoring
7
Negotiating
6
Instructing
5
Supervising
4
Diverting
3
Persuading
2
Speaking–
Signaling
1
Serving
0
Taking
instructions–
Helping
Dealing with individuals in terms of their total personality to
advise, counsel, and/or guide them with regard to problems
that may be resolved by legal, scientific, clinical, spiritual,
and/or other professional principles.
Exchanging ideas, information, and opinions with others to
formulate policies and programs and/or arrive jointly at
decisions, conclusions, or solutions.
Teaching subject matter to others, or training others
(including animals) through explanation, demonstration, and
supervised practice; or making recommendations on the
basis of technical disciplines.
Determining or interpreting work procedures for a group
of workers, assigning specific duties to them, maintaining
harmonious relations among them, and promoting efficiency;
a variety of responsibilities is involved in this function.
Amusing others. (Usually accomplished through the medium
of stage, screen, television, or radio.)
Influencing others in favor of a product, service, or point of
view.
Talking with and/or signaling people to convey or exchange
information. Includes giving assignments and/or directions to
helpers or assistants.
Attending to the needs or requests of people or animals
or the expressed or implicit wishes of people. Immediate
response is involved.
Helping applies to “non-learning” helpers. No variety of
responsibility is involved in this function.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research,
authorship, and/or publication of this article: This research was funded by the
Marianne and Marcus Wallenberg (MMW) Foundation (Grant MMW 2011.0036)
and the Swedish Research Council for Health, Working Life, and Welfare (Grant
2012-0761 and 2012-1704).
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Journal of Aging and Health
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