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

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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.

A. Øvrum et al. / Advances in Life Course Research 19 (2014) 1–13 3


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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%




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


6/13

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.



7/13

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


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


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


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.



9/13

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


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


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


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


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.



10/13

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.



11/13

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


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


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


25 30 35 40 45 50 55 60 65 70 75 80

Age

Self−assessed health (SAH) male



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


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


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


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


25 30 35 40 45 50 55 60 65 70 75 80

Age

Self−assessed health (SAH) female



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.



12/13

0

. 1

. 2

. 3

. 4

. 5

. 6

. 7

. 8

. 9

1

P r e


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


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


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


25 30 35 40 45 50 55 60 65 70 75 80

Age

Self−assessed health (SAH) male



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


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


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


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


25 30 35 40 45 50 55 60 65 70 75 80

Age

Self−assessed health (SAH) female



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.



13/13

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age and socioeconomic inequalities in health

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