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8/13/2019 Age and socioeconomic inequalities in health
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Age and socioeconomic inequalities in health: Examining therole of lifestyle choices
Arnstein Øvrum a,b,*, Geir Wæhler Gustavsen a, Kyrre Rickertsen a,b
aNorwegian Agricultural Economics Research Institute, P.O. Box 8024 Dep, NO-0030 Oslo, NorwaybUMB School of Economics and Business, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Å s, Norway
1. Introduction
A large and growing body of literature seeks to improve
our understanding of why indicators of socioeconomic
status and health are so strongly associated (Cutler, Lleras-
Muney, & Vogl, 2011; Marmot, Friel, Bell, Houweling, &
Taylor, 2008). Acknowledging the dynamic nature of
health production, this literature has partly focused on
how socioeconomic inequalities in health evolve over the
adult life course. The current empirical evidence on thisimportant issue is mixed, in part because different
indicators of socioeconomic status and health have been
investigated (Kim & Durden, 2007). However, three main
patterns of results stand out.
In some studies, health differences by socioeconomic
status are found to be increasing in age throughout the
adult life course (Benzeval, Green, & Leyland, 2011; Kim &
Durden, 2007; Ross & Wu, 1996; Wilson, Shuey, & Elder,
2007). Such results correspond with the cumulative
advantage hypothesis. This hypothesis asserts that
throughout the adult life course, socioeconomic status is
closely associated with our daily investments into the
production of poor and good health. Gradually, these
investments result in a relatively more rapid deteriorationof health among lower than higher socioeconomic status
groups.
In other studies, health differences by socioeconomic
status are found to be increasing in age until late midlife, or
pre-retirement (50–60 years of age), after which they level
off or begin to decrease (Beckett, 2000; Huijts, Eikemo, &
Skalická, 2010; van Kippersluis, O’Donnell, van Doorslaer,
& van Ourti, 2010). Such results are in line with the
cumulative advantage hypothesis until late midlife, but
with an age-as-leveler hypothesis thereafter. More partic-
ularly, biological factors become increasingly important
Advances in Life Course Research 19 (2014) 1–13
A
R
T
I
C
L
E
I
N
F
O
Article history:
Received 27 June 2013
Received in revised form 24 October 2013
Accepted 31 October 2013
Keywords:
Socioeconomic status
Inequality
Life course
Lifestyles
Health
Norway
A
B
S
T
R
A
C
T
The role of lifestyle choices in explaining how socioeconomic inequalities in health vary
with age has received little attention. This study explores how the income and education
gradients inboth important lifestyle choices andself-assessed health (SAH) vary with age.
Repeatedcross-sectionaldatafromNorway (n = 25,016)andlogistic regressionmodelsare
used to track the income and education gradients in physical activity, smoking,
consumption of fruit and vegetables and SAH over the age range 25–79 years. The
education gradient in smoking, the income gradient in consumption of fruit and
vegetables and theeducationgradient inphysical activityamongmales become smaller at
older ages. Physical activity among females is the only lifestyle indicator in which the
income and education gradients grow stronger at older ages. In conclusion, this study
shows that income and education gradients in lifestyle choices may not remain constant,
but vary with age, and such variationcould be important in explaining corresponding age
patterns of inequality in health. 2013 Elsevier Ltd. All rights reserved.
* Corresponding author at: Norwegian Agricultural Economics
Research Institute, P.O. Box 8024. Dep, N-0030 Oslo, Norway.
Tel.: +47 22367200; fax: +47 22367299.
E-mail addresses: [email protected] (A. Øvrum),
[email protected] (G.W. Gustavsen), [email protected]
(K. Rickertsen).
Contents lists available at ScienceDirect
Advances in Life Course Research
jo u rn al h omepage: ww w.e ls ev ier .co m/locat e /a lcr
1040-2608/$ – see front matter 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.alcr.2013.10.002
http://dx.doi.org/10.1016/j.alcr.2013.10.002http://dx.doi.org/10.1016/j.alcr.2013.10.002http://dx.doi.org/10.1016/j.alcr.2013.10.002http://dx.doi.org/10.1016/j.alcr.2013.10.002http://dx.doi.org/10.1016/j.alcr.2013.10.002http://dx.doi.org/10.1016/j.alcr.2013.10.002http://dx.doi.org/10.1016/j.alcr.2013.10.002http://dx.doi.org/10.1016/j.alcr.2013.10.002http://dx.doi.org/10.1016/j.alcr.2013.10.002http://dx.doi.org/10.1016/j.alcr.2013.10.002http://dx.doi.org/10.1016/j.alcr.2013.10.002http://dx.doi.org/10.1016/j.alcr.2013.10.002http://dx.doi.org/10.1016/j.alcr.2013.10.002http://dx.doi.org/10.1016/j.alcr.2013.10.002http://dx.doi.org/10.1016/j.alcr.2013.10.002mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]://www.sciencedirect.com/science/journal/10402608http://dx.doi.org/10.1016/j.alcr.2013.10.002http://dx.doi.org/10.1016/j.alcr.2013.10.002http://www.sciencedirect.com/science/journal/10402608mailto:[email protected]:[email protected]:[email protected]://dx.doi.org/10.1016/j.alcr.2013.10.002http://crossmark.crossref.org/dialog/?doi=10.1016/j.alcr.2013.10.002&domain=pdfhttp://crossmark.crossref.org/dialog/?doi=10.1016/j.alcr.2013.10.002&domain=pdf
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with older age in determining health, thus downplaying
the role of socioeconomic status (Herd, 2006). Also other
factors have been found to contribute to age-as-leveler
effects in health. These factors include the effects of
mortality selection (Kim & Durden, 2007), cohort effects
(Lynch, 2003) and labor market participation status (Case
& Deaton, 2005; van Kippersluis et al., 2010).
Finally,
some
studies
have
found
that,
for
selectedhealth and socioeconomic status indicators, health differ-
ences by socioeconomic status do not vary significantly
with age (Beckett, 2000; Kim & Durden, 2007). We refer to
such patterns of results as being in line with the persistent
health inequality hypothesis (Ferraro & Farmer, 1996).
To the best of our knowledge, no studies have yet
explicitly examined the potential role of healthy lifestyle
choices in explaining these competing hypotheses for the
dynamics of socioeconomic inequalities in health. This is
surprising for at least three reasons. First, there is
convincing evidence for the protective effect of certain
lifestyle choices, including physical activity, not smoking
and
consumption
of
fruit
and
vegetables,
against
adversehealth outcomes such as type 2 diabetes, cardiovascular
disease and certain types of cancer (Gandini et al., 2008;
He, Nowson, Lucas, & MacGregor, 2007; Jeon, Lokken, Hu, &
Van Dam, 2007; Sofi, Capalbo, Cesari, Abbate, & Gensini,
2008; World Health Organization, 2003). Second, similar to
most health outcomes, the probability of making healthy
lifestyle choices is closely associated with socioeconomic
status indicators such as education and income (Cutler &
Lleras-Muney, 2010; Pampel, Krueger, & Denney, 2010).
Third, the effects of healthy lifestyle choices on the
incidence of adverse health outcomes are often character-
ized by cumulative, long-processes (Kuh & Shlomo, 2004),
which
highlights
the
importance
of
taking
a
life
courseperspective with respect to the dynamic relationship
between socioeconomic status, lifestyle choices and
health.
As noted, we often implicitly assume that lifestyle
choices differ systematically by socioeconomic status and
thereby contribute to patterns of cumulative advantage
effects in health. This is a reasonable assumption to the
extent that the socioeconomic gradients in lifestyle choices
remain stable or increase over the adult life course. But
what if the socioeconomic gradients in lifestyle choices
become smaller with older age? For example, people of
lower socioeconomic status may grow more health
conscious
and
thus
engage
in
healthier
lifestyles
whenthey reach late midlife and realize that good health
investments are important for longevity.
We use repeated cross-sectional data from Norway
from 1997 to 2011 to explore how the income and
education gradients in both important lifestyle choices and
SAH vary with age. Repeated cross-sectional data are often
referred to as pseudo-panel data because although not
tracking the same individuals as they age, such data allow
for tracking the average age patterns for groups of
individuals as they age while controlling for possibly
confounding cohort and period effects (Deaton, 1997).
However, note that our study is not a pure ‘life course’
study
in
the
sense
that
we
do
not
follow
the
sameindividuals as they age.
Our lifestyle indicators are physical activity, smoking
and consumption of fruit and vegetables. We use these
lifestyle indicators because they are different in nature and
because of their close association with both socioeconomic
status indicators and the risk of major health outcomes, as
described above. Our research questions are as follows.
First, to what extent are the observed age patterns of
inequality
in
lifestyle
choices
consistent
with
(i)
the
age-as-leveler, (ii) the persistent health inequality, and (iii) the
cumulative advantage hypothesis in health? Second, to
what extent do age patterns of inequality vary across
different lifestyle choices, education and income, and
gender?
2. Methods
2.1. Data source
The Norwegian Monitor Survey is a nationally represen-
tative and repeated cross-sectional survey of adults aged
15–95
years.
The
survey
has
been
conducted
every
secondyear since 1985 and is one of Norway’s most comprehensive
consumer and opinion surveys. The institution behind the
survey (Ipsos Norway) recruits respondents through a short
telephone interview, and those who accept to participate
receive a paper-based questionnaire by mail. Ethical
approval was not required for this research; we represent
a third party user of the data in question, and we only have
access to a data filethat contains anonymous data, i.e., we do
not have access to any information that can be used to
identify specific individuals.
The question about SAH was not included in the survey
before 1997, and therefore data from 1997 to 2011 are
used.
For
two
reasons,
only
respondents
between
the
agesof 25 and 79 years were included. First, we want to study
individuals who have completed most of their education
and started earning their own income. Second, the sample
includes relatively few respondents between the ages of 80
and 95 years. After deleting observations with missing
information for any of the variables included in this study
(3066 observations), we obtain our sample of 25,016
observations. Based on statistical tests comparing group
means, the deleted respondents were on average signifi-
cantly older, more likely female, less educated and had
lower incomes than the respondents that are included in
the sample.
2.2. Outcome variables
The survey questions related to physical activity,
smoking, consumption of fruit and vegetables and SAH
are based on various types of categorical scales. The
respondents were asked to indicate their frequency of
intake for nine different fruit and vegetables on the
following scale; ‘‘daily’’; ‘‘3–5 times per week’’; ‘‘1–2 times
per week’’; ‘‘2–3 times per month’’; ‘‘about once per
month’’; ‘‘3–11 times per year’’; ‘‘rarer’’; or ‘‘never’’.
Similarly, physical activity has an 8-point frequency scale
ranging from ‘‘never’’ to ‘‘once or more per day’’. The
respondents
also
indicated
if
they
smoked
tobacco
‘‘daily’’,‘‘sometimes’’ or ‘‘never’’ at the time of the survey, whereas
A. Øvrum et al. / Advances in Life Course Research 19 (2014) 1–132
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SAH is based on the typical 5-point scale ranging from
‘‘very poor’’ to ‘‘very good’’ health. To facilitate the
comparison of how income and education gradients vary
with age, we have dichotomized each of these categorical
variables. We define being physically active at least twice
per week, not a daily smoker (non-smoking), eating fruit
and vegetables at least twice per day and reporting one’s
SAH
to
be
‘‘good’’
or
‘‘very
good’’
as
binary
indicators
of healthy lifestyles and good health.
2.3. Explanatory variables
We categorize education into four groups using dummy
indicators, ranging from having completed only lower
secondary education (9 years of education) or less, to
having obtained a university or college degree. We divide
household income into age-group survey-year specific
income quartiles, with each age group comprising a 5-year
interval (e.g., people aged 25–29 years). The original survey
question on household income included nine response
alternatives,
each
representing
a
specific
income
interval.Before dividing income into age-group survey-year specific
quartiles, we (i) set household income to the midpoint value
of each income interval, and (ii) adjusted for household size
by dividing the resulting income measure by the square root
of household size (OECD, 2008).
We define age as a continuous variable, but center it at
age 30 to reduce multicollinearity between age and age-
squared in the later statistical analyses (Kim & Durden,
2007). Dichotomous indicators for gender, survey years
and 5-year birth cohorts are also included in the statistical
analyses, which we describe next.
2.4.
Statistical
analyses
We employ multivariate logistic regression models to
predict how the income and education gradients in
lifestyles and SAH vary with age and to assess whether
such age variation is statistically significant. The income
models control for age, age-squared, the second, third and
fourth income quartiles, interactions between each of
these age and income indicators, as well as education,
gender, survey years and 5-year birth cohorts. In cases
where no age-squared income interactions are statisti-
cally significant at the 95% level, the model is simplified by
removing these interactions to allow for the income
gradient
to
possibly
change
linearly
instead
of
non-linearlyin age (Beckett, 2000). The corresponding education
models are obtained by replacing the second, third and
fourth income quartiles with the education dummies for
having completed upper secondary education, some
university and university with a degree, respectively.
In our models, we treat age, period and cohort effects as
fixed effects. The linear dependence between a respondent’s
age, birth year and the survey year (Deaton, 1997) is handled
by allowing for non-linear effects in ageand by using 5-year
birth cohort dummies, while period effects areaccounted for
by including dummy indicators for each survey year except
the first (reference year) (Sarma, Thind, & Chu, 2011). There
were
no
major
changes
in
health
policy
during
the
studyperiod 1997–2011 that should affect our results. We also
tested alternative strategies for estimating age, period and
cohort effects, including the random intercept model
(O’Brien, Hudson, & Stockard, 2008) and the cross-classified
model (Reither, Hauser, & Yang, 2009). The estimated age
effects, which are the focus of this study, were very similar
across these alternative model specifications.
We also estimate SAH models in which we add the three
lifestyle
indicators
as
explanatory
variables.
This
allows
forassessing whether the lifestyle indicators are significantly
associated with SAH, and whether the income and
education gradients in SAH become smaller once we
control for lifestyle choices. Age patterns of health
inequalities may differ by gender (Corna, 2013), and
therefore we also estimate our models separately by
gender. We comment on the results of gender specific
models when they are relevant. All the statistical models in
this study are estimated using survey weights and robust
standard errors. The survey weights are provided by the
institution behind the survey and account for sampling
differences with respect to age, gender and geographic
region,
such
that
the
statistical
results
are
made
represen-tative of the overall population within each survey year.
Our four outcome variables are binary, but three of
them contain more information. As a robustness check, we
have estimated ordered logit models with alternative
variable definitions for physical activity (frequency scale
1–8), consumption of fruit and vegetables (frequency scale
1–9) and SAH (likert scale 1–5). The results of these
alternative model specifications suggest that the conclu-
sions of this study are not sensitive to how we define the
dependent variables in our models.
Finally, as described above, in this study we decided to
delete observations with missing values rather than use
imputation
techniques.
The
main
reason
for
this
decision
isthat nearly seventy percent of the 3066 observations with
missing values are due to missing information on one or
several of the four outcome variables. However, as a
robustness check, we have re-estimated the models for
each lifestyle indicator and SAH after adding observations
for which we have data on all explanatory variables and
the outcome variable in question, but missing information
on at least one of the remaining three outcome variables
that are not part of the model.1 The results from these
models with additional observations were nearly identical
to the results of the models to follow in the results section
below. Therefore, we believe that the results of this study
are
not
sensitive
to
left-out
observations.
3. Results
3.1. Descriptive statistics
Table 1 provides the descriptions and sample means for
the outcome and explanatory variables of this study.
1 We thereby add 1804 observations to the physical activity model,
1381 observations to the non-smoking model, 619 observations to the
fruit and vegetables model and 1679 observations to the SAH model
compared
to
the
models
in
the
results
section,
which
all
include
25,016observations.
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Approximately
54%
of
the
respondents
exercise
at
leasttwice per week, 72% are non-smokers, 50% eat fruit and
vegetables at least twice per day and 69% report their
health status as either ‘‘good’’ or ‘‘very good’’.
Figs. 1 and 2 depict age variation in lifestyles and SAH
by income and education, respectively. The figures
illustrate the development in sample means for physical
activity, non-smoking, consumption of fruit and vegetables
and SAH for each income quartile and each education
group at each 5-year age interval. The figures indicate that
lifestyle habits become healthier with increasing age until
at least late midlife, while SAH is decreasing in age. There
are clear income and education gradients in lifestyles and
SAH
in
most
age
groups.
The
main
exceptions
are
the
smallincome gradients in lifestyles at age 25–29 years and the
small income and education gradients in non-smoking at
age 75–79 years. Age variation in the gradients are most
evident in the case of income and SAH, with the gradient
clearly peaking at age 55–59 years, and in the case of
education and non-smoking, with the gradient clearly
declining with higher age.
3.2. Logistic regression models
Table 2 reports the results of the income models for
physical activity, non-smoking, consumption of fruit and
vegetables
and
SAH,
and
Table
3
reports
the
results
of
thecorresponding education models. The tables show odds
ratios (ORs) and indicate the significance of different ORs
using asterisks.
Table 2 shows that at 30 years of age, there are clear
income gradients in all outcome variables except con-
sumption of fruit and vegetables, and Table 3 shows that
there are clear education gradients in all outcome variables
– and in particular non-smoking – at this age (recall that
the age variable is centered at 30 years of age). Thus, the
results in Tables 2 and 3 confirm the patterns observed in
Figs. 1 and 2 with respect to the income and education
gradients in lifestyles and SAH in young adulthood.
The
SAH
models
in
the
rightmost
column
of
Tables
2
and3 suggest that SAH is significantly associated with all three
lifestyle
choices,
and
in
particular
physical
activity
and
non-smoking. Furthermore, comparing the two SAH models in
Table 2, the education gradient in SAH becomes smaller
once we control for lifestyles (e.g., the OR of university
degree is reduced from 1.88 to 1.61 when we add lifestyles
as control variables). Similarly, Table 3 shows that also the
income gradient in SAH becomes smalleronce we control for
lifestyles.2 The cross-sectional nature of our data do not
allow for any casual inference. However, these results
indicate, at least, that our lifestyle indicators might be
important in affecting health (World Health Organization,
2003), and in mediating the relationship between socioeco-
nomic status and health (Cutler et al., 2011).
3.3. Predicted income and education gradients
Our main interest is to explore how the income and
education gradients in lifestyles and health vary with age.
To facilitate interpretation, we will in the following focus
mainly on comparing results across the lowest and the
highest income and education groups, and focus less on
results for the two intermediate income and education
groups.
Fig. 3 is based on the results of the first four income
models in Table 2 and shows how the predicted
probabilities for healthy lifestyles and good health vary
with
age
for
people
in
the
first
and
the
fourth
incomequartiles. The figure also shows the absolute differences in
predicted probabilities between these two income groups,
which we refer to as the income gradient. The predictions
were calculated at the mean values of the other
explanatory variables that are included in the models
(i.e., variables that do not involve age and income).
Similarly, Fig. 4 is based on the results of the first four
Table 1
Variable descriptions and summary statistics.
Variablea Description Percentage/mean
Physical activity Undertake physical activity at least twice per week 54%
Non-smoking Not a daily smoker 72%
Fruit and vegetables Eat fruit, berries and/or vegetables at least twice per day 50%
Self-assessed health Self-assessed health is ‘‘good’’ or ‘‘very good’’ 69%
Lower secondary education Completed lower secondary education (9 years) or less 15%
Upper secondary education Completed upper secondary education 35%Some university Attended some university or college 20%
University degree Obtained a university or college degree 29%
Income quartile 1 Age-group survey-year specific income quartile 1 26%
Income quartile 2 Age-group survey-year specific income quartile 2 25%
Income quartile 3 Age-group survey-year specific income quartile 3 25%
Income quartile 4 Age-group survey-year specific income quartile 4 24%
Age Respondent ageb 48.07
Female Respondent is female 54%
Norwegian Monitor Survey (1997–2011). Summary statistics based on 25,016 observations.a All variables except age are dummy indicators taking a value of one if the response to the variable description is yes, and zero otherwise.b Age is centered at age 30 in the later statistical analyses to reduce multicollinearity between age and age-squared.
2 We find similar patterns when we instead estimate the lifestyle
models with current SAH added as explanatory variable. That is, all three
lifestyle choices are positively associated with SAH (P
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education models in Table 3 and shows how the predicted
probabilities for healthy lifestyles and good health vary
with age for people who have completed only lower
secondary education or less and for those with a university
degree, along with the absolute differences in predicted
probabilities between these two education groups, which
we refer to as the education gradient.
Fig. 3 shows that the income gradients in consumption
of fruit and vegetables and SAH are concave in age, i.e.,
income differences are stronger during late midlife – and at
their strongest at 60 and 61 years of age, respectively –
than
at
younger
and
older
ages.
Table
2
shows
that
this
agevariation (Age Income quartile 4 and Age2 Income
quartile 4) is statistically significant at the 95% level. The
strongest predicted income gradient across the four
outcome variables is found in SAH at 61 years of age,
where only 52.3% of those in the first income quartile are
predicted to report being in good or very good health,
compared with 75.0% of those in the fourth income
quartile. As discussed, the age pattern of cumulative
advantage effects in SAH by income until late midlife
followed by age-as-leveler effects at older ages have been
reported in several earlier studies (Beckett, 2000; Huijts
et al., 2010; van Kippersluis et al., 2010).
The
income
gradient
in
physical
activity
is
convex
inage, and this age variation is statistically significant, i.e.,
income differences in physical activity are smaller during
midlife than at younger and older ages. However, this
result seems to reflect gender differences; when we
estimate the models separately by gender, the income
gradient in physical activity is decreasing linearly in age
among males (P
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in Fig. 4. However, when we estimate the models
separately by gender, we find that while the education
gradients in physical activity and SAH do not vary
significantly with age among males, they are convex in
age among females, i.e., the education gradients in these
variables among females are smaller during late midlife –
and at their smallest at 51 and 58 years of age, respectively
– than at younger and older ages (see Fig. A4 in
Appendix A).
We summarize our results in Table 4. Based on the
above statistical and graphical analysis, we indicate how
the
income
and
education
gradients
in
physical
activity,non-smoking, consumption of fruit and vegetables and
SAH vary with age, including whether this age variation is
statistically significant. We separate the results by gender
where relevant.
4. Discussion
The relationship between socioeconomic status and
health is dynamic and may vary with age. Our analysis has
explored the potential role of lifestyle choices in explaining
some of these dynamics. Wefind that in Norway, there are
clear income and education gradients in the probability of
being
physically
active,
smoking
and
eating
fruit
andvegetables throughout most stages of the adult life course.
However, the predicted age patterns of inequality are
found to vary across different lifestyle choices, education
and income, and to some extent gender (see Table 4).
The income gradient in smoking, the education gradient
in consumption of fruit and vegetables and the education
gradient in physical activity among males do not vary
significantly with age. These results suggest that lifestyle
choices are expected to contribute to cumulative advan-
tage effects in health by socioeconomic status (Benzeval et
al., 2011; Kim & Durden, 2007; Ross & Wu,1996; Wilson et
al., 2007); throughout the life course, socioeconomic status
is
closely
associated
with
our
daily
investments
into
theproduction of poor and good health. Because many adverse
health outcomes are the result of long-term, cumulative
processes (Kuh & Shlomo, 2004), these daily health
investments eventually result in a relatively more rapid
deterioration of health among lower than higher socioeco-
nomic status groups.
The education gradient in smoking, the income gradient
in consumption of fruit and vegetables and the income
gradient in physical activity among males become smaller
as people grow older. These results suggest that, in some
cases, the income and education gradients in lifestyle
choices may not be constant, but vary with age. To the
extent
that
lifestyle
habits
are
converging
with
older
age,as found in these examples, this may contribute to patterns
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
M
e a n
25 30 35 40 45 50 55 60 65 70 75
Age group
Physical activity
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
M
e a n
25 30 35 40 45 50 55 60 65 70 75
Age group
Non−smoking
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
M e a
n
25 30 35 40 45 50 55 60 65 70 75
Age group
Fruit and vegetables
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
M e a
n
25 30 35 40 45 50 55 60 65 70 75
Age group
Self−assessed health (SAH)
Lower secondary education Upper secondary education
Some university/college University/college degree
Fig. 2. Sample means split by 5-year age groups and the four education groups.
A. Øvrum et al. / Advances in Life Course Research 19 (2014) 1–136
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of age-as-leveler effects in health (Beckett, 2000; Huijts
et al., 2010; van Kippersluis et al., 2010), persistent health
inequalities (Ferraro & Farmer, 1996), or a slowing down of
cumulative advantage effects in health by socioeconomic
status at older ages.
Our
analysis
is
based
on
repeated
cross-sectional
data,and thus we are not able to directly assess whether
converging lifestyle habits in age contribute to a slowing
down of cumulative advantage effects in health by
socioeconomic status. We find that current lifestyle
choices are significantly associated with the probability
of reporting good health, as represented by SAH, and that
the
income
and
education
gradients
in
SAH
becomesmaller once we control for these lifestyle indicators.
Table 2
Logistic regressions for lifestyle choices and health–income models.
Physical activity Non-smoking Fruit and vegetables Self-assessed
health (SAH)
Self-assessed
health (SAH)
OR OR OR OR OR
Agea 1.17 0.98 1.47*** 0.73** 0.69***
Age2 0.96** 1.05*** 0.95*** 1.03 1.03
Income quartile 2b 1.19** 1.26*** 1.05 1.60*** 1.55***
Income quartile 2 age 0.85* 1.02 1.15 0.98 1.00
Income quartile 2 age2 1.05** –c 0.97 1.00 1.00
Income quartile 3 1.39*** 1.38*** 1.11 1.83*** 1.74***
Income quartile 3 age 0.92 1.00 1.19* 1.11 1.10
Income quartile 3 age2 1.02 –c 0.97 0.98 0.98
Income quartile 4 1.68*** 1.49*** 1.07 2.01*** 1.85***
Income quartile 4 age 0.77*** 0.95 1.34*** 1.29** 1.31**
Income quartile 4 age2 1.07*** –c 0.95** 0.95** 0.95**
Female 1.33*** 0.93** 2.73*** 1.03 0.97
Upper secondary educationb 1.21*** 1.26*** 1.20*** 1.18*** 1.13**
Some university 1.57*** 1.97*** 1.65*** 1.54*** 1.37***
University degree 1.71*** 3.09*** 1.86*** 1.88*** 1.61***
Physical activity 1.63***
Non-smoking 1.57***
Fruit and vegetables 1.11***
Norwegian Monitor Survey (1997–2011). All models based on 25,016 observations. OR, odds ratio.a Age and Age2 have been centered at age 30 and divided by 10 and 102, respectively.b Income quartile 1 and Lower secondary education are the reference groups.c No Age2 income-interactions had P
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We further find patterns of age-as-leveler effects in SAH by
income, persistent inequalities in SAH by education among
males, and after decreasing until late midlife, cumulative
advantage effects in SAH by education among females after
58 years of age.
As noted, our results are relatively mixed across
different lifestyle choices, education and income, and to
some extent gender. For example, while the education
gradient in physical activity and consumption of fruit and
vegetables for the total sample do not vary significantly
with
age,
the
education
gradient
in
non-smoking
movesfrom being very strong at younger ages, to almost zero at
older ages. This age pattern in smoking appears too
pronounced to be explained fully by sample selection
because of high mortality rates among people in the lower
education groups (Beckett, 2000). Instead, different age
patterns for the above education gradients might in part
reflect systematic variation across different lifestyle
choices in terms of perceived health risks. That is, people
with low levels of formal education quit smoking at faster
rates as they grow older because they learn that not doing
so can seriously damage their health (Gandini et al., 2008).
While eating fruit and vegetables and being physically
active
are
also
clearly
associated
with
good
healthoutcomes (He et al., 2007; Jeon et al., 2007; World Health
Organization, 2003), this evidence may be less accessible
or perceived as less striking than the corresponding
evidence on smoking (Sanderson, Waller, Jarvis, Humph-
ries, & Wardle, 2009).
Physical activity among females is the only lifestyle
indicator for which income and education differences are
increasing in age; the income gradient increases linearly in
age and the education gradient is convex in age and at its
smallest at 51 years of age. This result could reflect the
effect of time constraints as a result of combining a career
with
raising
children
during
the
earlier
stages
of
the
adultlife course (Sørensen & Gill, 2008). These time constraints
may be particularly pronounced among women in the
highest socioeconomic status groups. For example, studies
from the USA find that both number of working hours in
the labor market and time spent with the children
increases markedly with length of education (Aguiar &
Hurst, 2007; Guryan, Hurst, & Kearney, 2008), which leaves
less hours available for time-consuming leisure activities
such as physical activity (Welch, McNaughton, Hunter,
Hume, & Crawford, 2009). Thus, income and education
differences in physical activity among females may be
smaller until about 50 years of age, when time constraints
are
likely
to
be
important,
particularly
among
highersocioeconomic status women, than at older ages, when
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Physical activity
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Non−smoking
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p
r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Fruit and vegetables
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p
r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Self−assessed health (SAH)
First income quartile Fourth income quartile
Absolute difference (Income gradient)
Fig. 3. Predicted age variation in the income gradients in lifestyles and self-assessed health (SAH). Predicted probabilities based on results of the models in
Table
2
and
calculated
at
the
mean
values
of
the
additional
explanatory
variables
that
are
included
in
these
models.
A. Øvrum et al. / Advances in Life Course Research 19 (2014) 1–138
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8/13/2019 Age and socioeconomic inequalities in health
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time constraints are likely to become increasingly less
important.
To some extent, our results are sensitive to choice of
education or income as socioeconomic status indicator.
While education and income are usually highly correlated,
previous life course studies have shed light on some of the
fundamental differences between these two leading
socioeconomic status indicators (Cutler et al., 2011). For
example, while education is more or less fixed at an early
stage of the adult life course, income may be affected by
many factors throughout the adult life course, including
health shocks and the gradual deterioration of health in
age (Smith, 2004). We find, for example, that while the
income gradient in SAH is clearly peaking around pre-
retirement (50–60 years of age), this is not the case for the
education gradient in SAH. According to previous studies
that find similar patterns of results, the income gradient in
SAH peaks around pre-retirement mostly because of the
effect of poor health on premature exit from the labor
force, which in turn negatively affect incomes because of
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Physical activity
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Non−smoking
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p
r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Fruit and vegetables
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p
r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Self−assessed health (SAH)
Lower secondary education University/college degree
Absolute difference (Education gradient)
Fig. 4. Predicted age variation in the education gradients in lifestyles and self-assessed health (SAH). Predicted probabilities based on results of the models in
Table
3
and
calculated
at
the
mean
values
of
the
additional
explanatory
variables
that
are
included
in
these
models.
Table 4
Lifestyle choices and SAH – summary of age variation in income and education gradients.
Age variation in income gradienta Age variation in education gradienta
Total sample Male Female Total sample Male Female
Physical activity Convex Decreasing Increasingb Constantc Constant Convex
Non-smoking Constant Decreasing
Fruit and vegetables Concave Constant
SAH Concave Constant Constant Convex
The table summarizes the results in Tables 2 and 3 and Figs. 3 and 4 and corresponding results by gender (Figs. A1–A4) where relevant.a The income gradient refers to absolute differences in predicted probabilities for lifestyles and SAH between people in the first and fourth income
quartiles, while the education gradient refers to such differences between people with lower secondary education (or less) and people with a university or
college degree.b
P
<
0.10.
Other
age
variation
in
income
and
education
gradients
that
is
not
‘‘Constant’’
has
P
<
0.05.c ‘‘Constant’’ refers to linear or non-linear age variation in the income or education gradient that is not statistically significant.
A. Øvrum et al. / Advances in Life Course Research 19 (2014) 1–13 9
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the shift from wage earning to a reliance on social security
payments (van Kippersluis et al., 2010).
We find that there are strong education and income
gradients in lifestyles and health in Norway, which is
considered an egalitarian country, with a strong, well-
funded welfare state and a low level of income inequality
(OECD, 2011). However, this result is not surprising
considering
that
similar
results
have
been
found
in
severalother studies from Norway and other Scandinavian
countries (e.g., Huijts et al., 2010; Mackenbach et al.,
2008; Shkolnikov et al., 2012).While strong welfare states
may not be sufficient to avoid socioeconomic inequalities
in health, it may influence the way in which such
inequalities evolve over the life course. For example,
Lundberg et al. (2008) found that countries with generous
basic security pension systems, including Norway, experi-
ence lower rates of excess mortality among elderly people
than other countries. However, in general, the evidence on
the role of social policies and different types of welfare
states in shaping life course patterns of health inequalities
is
scarce
(Corna,
2013), and
thus
more
studies
that
addressthis issue are needed.
The results of this study must be considered in light of
its limitations. In particular, our analysis employs repeated
cross-sectional data, and thus we are not able to fully
capture the dynamic nature of health production, nor are
we able to capture possible feedbacks between socioeco-
nomic status, lifestyle choices and health. Thus, the results
of this study are mainly of a descriptive nature, since our
data do not allow for any causal inference. Some of our key
variables may also include measurement error because of
incompleteness and the reliance on self-reported data,
although, for example, SAH has been shown to be highly
correlated
with
several
objective
health
measures
(Idler
&Benyamini, 1997). Biases may also arise from mortality
selection, as discussed, and from the fact that 10.9% of the
respondents were excluded from our final sample because
of missing information on one or more relevant variables.
Factors such as mortality selection (Beckett, 2000), the
increasing importance of biological factors relative to
socioeconomic status in determining health at older ages
(Herd, 2006), cohort effects (Lynch, 2003) and labor market
participation status (Case & Deaton, 2005) may all be
important in explaining why we sometimes observe that
socioeconomic inequalities do not continue to widen, or
accumulate, into older age. However, our results suggest
that also dynamics in the relationship between socioeco-
nomic status and health affecting lifestyle choices may be
important in explaining such patterns. Given the results
and limitations of this study, there is a need for more
similar
research.
Studies
based
on
long
panel
data
thattrack important lifestyle and health indicators as well as
socioeconomic status in the same individuals over most
stages of the adult life course would be particularly
relevant. Studies on other lifestyle indicators, such as
alcohol use and the consumption of unhealthy foods,
would also be interesting, as would further analyses of the
three lifestyle indicators used in this study, but possibly
using alternative variable definitions (e.g., physical activity
accounting for intensity level).
Our results suggest that, except for physical activity
among females, income and education gradients in lifestyle
choices either remain constant in age or become smaller
with
older
age.
While
policies
for
reducing
health
inequal-ities and its sources are important at all stages of the life
course, from birth to old age, policies for improved lifestyle
habits may benefit especially from targeting young people,
and particularly young people with low levelsof income and
formal education. Health information policies aimedtoward
making people more health consciousness at younger ages
may be efficient. This type of health information could focus
on the long-term, cumulative nature of health production
and thus the importance of making healthy lifestyle choices
already at younger ages.
Acknowledgements
Funding for this research was provided by the Research
Council of Norway, Grant Nos. 182289 and 184809. We
thank two anonymous reviewers for their helpful com-
ments and suggestions.
Appendix A
Figs. A1–A4.
A. Øvrum et al. / Advances in Life Course Research 19 (2014) 1–1310
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0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e
d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Physical activity male
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e
d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Non−smoking male
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Fruit and vegetables male
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Self−assessed health (SAH) male
First income quartile Fourth income quartile
Absolute difference (Income gradient)
Fig. A1. Predicted age variation in the income gradients in lifestyles and SAH among males. Predicted probabilities based on results of logistic regression
models that are equivalent to the models in Table 2, but estimated only for the male subsample.
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Physical activity female
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Non−smoking female
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Fruit and vegetables female
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Self−assessed health (SAH) female
First income quartile Fourth income quartile
Absolute difference (Income gradient)
Fig.
A2.
Predicted
age
variation
in
the
income
gradients
in
lifestyles
and
SAH
among
females.
Predicted
probabilities
based
on
results
of
logistic
regressionmodels that are equivalent to the models in Table 2, but estimated only for the female subsample.
A. Øvrum et al. / Advances in Life Course Research 19 (2014) 1–13 11
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0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e
d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Physical activity male
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e
d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Non−smoking male
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Fruit and vegetables male
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Self−assessed health (SAH) male
Lower secondary education University/college degree
Absolute difference (Education gradient)
Fig. A3. Predicted age variation in the education gradients in lifestyles and SAH among males. Predicted probabilities based on results of logistic regression
models that are equivalent to the models in Table 3, but estimated only for the male subsample.
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Physical activity female
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Non−smoking female
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Fruit and vegetables female
0
. 1
. 2
. 3
. 4
. 5
. 6
. 7
. 8
. 9
1
P r e d i c t e d p r o b a b i l i t y
25 30 35 40 45 50 55 60 65 70 75 80
Age
Self−assessed health (SAH) female
Lower secondary education University/college degree
Absolute difference (Education gradient)
Fig.
A4.
Predicted
age
variation
in
the
education
gradients
in
lifestyles
and
SAH
among
females
Predicted
probabilities
based
on
results
of
logistic
regressionmodels that are equivalent to the models in Table 3, but estimated only for the female subsample.
A. Øvrum et al. / Advances in Life Course Research 19 (2014) 1–1312
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