Age and socioeconomic inequalities in health: Examining the role of lifestyle choices

Advances in Life Course Research 19 (2014) 1–13

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Age and socioeconomic inequalities in health: Examining the role of lifestyle choices Arnstein Øvrum a,b,*, Geir Wæhler Gustavsen a, Kyrre Rickertsen a,b a b

Norwegian Agricultural Economics Research Institute, P.O. Box 8024 Dep, NO-0030 Oslo, Norway UMB School of Economics and Business, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 A˚s, Norway

A R T I C L E I N F O

A B S T R A C T

Article history: Received 27 June 2013 Received in revised form 24 October 2013 Accepted 31 October 2013

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 in both important lifestyle choices and self-assessed health (SAH) vary with age. Repeated cross-sectional data from Norway (n = 25,016) and logistic regression models are 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 the education gradient in physical activity among males 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 variation could be important in explaining corresponding age patterns of inequality in health. ß 2013 Elsevier Ltd. All rights reserved.

Keywords: Socioeconomic status Inequality Life course Lifestyles Health 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, LlerasMuney, & 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 this important 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.

* 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). 1040-2608/$ – see front matter ß 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.alcr.2013.10.002

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 deterioration of 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, & Skalicka´, 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 particularly, biological factors become increasingly important

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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 selected health and socioeconomic status indicators, health differences 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 adverse health 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 characterized by cumulative, long-processes (Kuh & Shlomo, 2004), which highlights the importance of taking a life course perspective 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 when they 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 same individuals 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 ageas-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 representative and repeated cross-sectional survey of adults aged 15–95 years. The survey has been conducted every second year 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 file that 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 ages of 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 significantly 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

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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 agesquared 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 statistically 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-linearly in 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 age and by using 5-year birth cohort dummies, while period effects are accounted 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 study period 1997–2011 that should affect our results. We also

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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 for assessing 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 representative 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 conclusions 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 is that 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 25,016 observations.

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Table 1 Variable descriptions and summary statistics. Variablea

Description

Percentage/mean

Physical activity Non-smoking Fruit and vegetables Self-assessed health Lower secondary education Upper secondary education Some university University degree Income quartile 1 Income quartile 2 Income quartile 3 Income quartile 4 Age Female

Undertake physical activity at least twice per week Not a daily smoker Eat fruit, berries and/or vegetables at least twice per day Self-assessed health is ‘‘good’’ or ‘‘very good’’ Completed lower secondary education (9 years) or less Completed upper secondary education Attended some university or college Obtained a university or college degree Age-group survey-year specific income quartile 1 Age-group survey-year specific income quartile 2 Age-group survey-year specific income quartile 3 Age-group survey-year specific income quartile 4 Respondent ageb Respondent is female

54% 72% 50% 69% 15% 35% 20% 29% 26% 25% 25% 24% 48.07 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.

Approximately 54% of the respondents exercise at least twice 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 small income 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 the corresponding 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 consumption 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 and 3 suggest that SAH is significantly associated with all three

lifestyle choices, and in particular physical activity and nonsmoking. 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 smaller once 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 socioeconomic 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 income quartiles. 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

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 < 0.01), and the income and education gradients in all three lifestyle choices become smaller once we control for SAH.

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Non−smoking

.7 .6

Mean

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Mean

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First income quartile

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Fig. 1. Sample means split by 5-year age groups and age-group survey-year specific income quartiles.

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 age variation (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 in age, 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 < 0.05) and increasing linearly in age among females (P < 0.10). Figs. A1–A4 in Appendix A are based on these gender specific models and show how the predicted income and education gradients vary with age separately for males and females. Finally, while Fig. 3 suggests that the income gradient in non-smoking is decreasing somewhat in age, Table 2 shows that this age variation is not statistically significant. Table 3 shows that the education gradient in nonsmoking decreases significantly and linearly in age, and Fig. 4 shows that it moves from being very steep at young ages to almost zero at older ages. According to our model predictions, at 25 years of age those with a university or college degree are thirty-eight percentage points more likely than those who have only completed lower secondary education or less to be non-smokers, while at 79 years of age this difference in predicted probabilities is reduced to only two percentage points. The education gradients in physical activity, consumption of fruit and vegetables and SAH do not vary significantly with age, as shown in Table 2 and reflected

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

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Non−smoking

.7 .6

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.4 .2

.3

.3 .2

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Lower secondary education

Upper secondary education

Some university/college

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Fig. 2. Sample means split by 5-year age groups and the four education groups.

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. We find that in Norway, there are clear income and education gradients in the probability of being physically active, smoking and eating fruit and vegetables 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 advantage 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 the production 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 socioeconomic 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

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Table 2 Logistic regressions for lifestyle choices and health–income models.

Agea Age2 Income quartile 2b Income quartile 2  age Income quartile 2  age2 Income quartile 3 Income quartile 3  age Income quartile 3  age2 Income quartile 4 Income quartile 4  age Income quartile 4  age2 Female Upper secondary educationb Some university University degree Physical activity Non-smoking Fruit and vegetables

Physical activity

Non-smoking

Fruit and vegetables

Self-assessed health (SAH)

Self-assessed health (SAH)

OR

OR

OR

OR

OR

1.17 0.96** 1.19** 0.85* 1.05** 1.39*** 0.92 1.02 1.68*** 0.77*** 1.07*** 1.33*** 1.21*** 1.57*** 1.71***

0.98 1.05*** 1.26*** 1.02 –c 1.38*** 1.00 –c 1.49*** 0.95 –c 0.93** 1.26*** 1.97*** 3.09***

1.47*** 0.95*** 1.05 1.15 0.97 1.11 1.19* 0.97 1.07 1.34*** 0.95** 2.73*** 1.20*** 1.65*** 1.86***

0.73** 1.03 1.60*** 0.98 1.00 1.83*** 1.11 0.98 2.01*** 1.29** 0.95** 1.03 1.18*** 1.54*** 1.88***

0.69*** 1.03 1.55*** 1.00 1.00 1.74*** 1.10 0.98 1.85*** 1.31** 0.95** 0.97 1.13** 1.37*** 1.61*** 1.63*** 1.57*** 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 < 0.05 and were removed from the final model. * P < 0.10. ** P < 0.05. *** P < 0.01. Table 3 Logistic regressions for lifestyle choices and health–education models.

Agea Age2 Upper secondary educationb Upper secondary education  age Upper secondary education  age2 Some university Some university  age Some university  age2 University degree University degree  age University degree  age2 Female Income quartile 2b Income quartile 3 Income quartile 4 Physical activity Non-smoking Fruit and vegetables

Physical activity

Non-smoking

Fruit and vegetables

Self-assessed health (SAH)

Self-assessed health (SAH)

OR

OR

OR

OR

OR

1.27 0.95* 1.31* 0.84 1.05* 1.90*** 0.74*** 1.07** 1.94*** 0.82 1.05 1.33*** 1.19*** 1.31*** 1.48***

1.12 1.05*** 1.71*** 0.90** –c 3.39*** 0.78*** –c 5.77*** 0.72*** –c 0.91*** 1.31*** 1.39*** 1.39***

1.60*** 0.92*** 1.04 1.07* –c 1.50*** 1.04 –c 1.71*** 1.03 –c 2.73*** 1.16**** 1.30*** 1.41***

0.79** 1.01 1.18 1.01 –c 1.78*** 0.94 –c 2.15*** 0.94 –c 1.02 1.57*** 1.95*** 2.49***

0.74*** 1.02 1.09 1.02 –c 1.49*** 0.96 –c 1.72*** 0.97 –c 0.97 1.51*** 1.86*** 2.35*** 1.63*** 1.56*** 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  education-interactions had P < 0.05 and were removed from the final model. * P < 0.10. ** P < 0.05. *** P < 0.01.

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 become smaller once we control for these lifestyle indicators.

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

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.9 .8 .7 .6 .5 .4 .3

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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.

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 moves from 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 health outcomes (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, Humphries, & 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 adult life 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 higher socioeconomic status women, than at older ages, when

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

.9 .8 .7 .6 .5 .4 .3

Predicted probability

.1 0

35

40

45

50

55

60

65

70

75

80

25

30

35

40

45

50

55

60

65

Age

Age

Fruit and vegetables

Self−assessed health (SAH)

70

75

80

70

75

80

.9 .8 .7 .6 .5 .4 .3

Predicted probability

.7 .6 .5 .4 .3

0

.1

.2 0

.1

.2

.8

.9

1

30

1

25

Predicted probability

.2

.8 .7 .6 .5 .4 .3 .2 0

.1

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.9

1

Non−smoking

1

Physical activity

9

25

30

35

40

45

50

55

60

65

70

75

80

25

30

35

40

45

50

Age

55

60

65

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.

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 preretirement (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

Table 4 Lifestyle choices and SAH – summary of age variation in income and education gradients. Age variation in income gradienta

Physical activity Non-smoking Fruit and vegetables SAH

Age variation in education gradienta

Total sample

Male

Female

Total sample

Male

Female

Convex Constant Concave Concave

Decreasing

Increasingb

Constantc Decreasing Constant Constant

Constant

Convex

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

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

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, wellfunded 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 several other 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, experience 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 address this 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 socioeconomic 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 socioeconomic 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 that track 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 inequalities 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 levels of income and formal education. Health information policies aimed toward 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 comments and suggestions.

Appendix A Figs. A1–A4.

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

.9 .8 .7 .6 .5 .4 .3

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80

75

80

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.7 .6 .5 .4 .3

0

.1

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.1

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.8

.9

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.9

1

Non−smoking male

1

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11

25

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75

80

25

30

35

40

45

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50

55

60

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Age

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.

.9 .8 .7 .6 .5 .4 .3

Predicted probability

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.7 .6 .5 .4 .3 .2

0

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80

Age

First income quartile

25

30

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40

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50

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70

Age

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 regression models 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

12

.9 .8 .7 .6 .5 .4 .3

Predicted probability

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75

80

25

30

35

40

45

50

Age

55

60

65

70

Age

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.

.9 .8 .7 .6 .5 .4 .3

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Physical activity female

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75

80

Age

Lower secondary education

25

30

35

40

45

50

55

60

65

70

Age

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 regression models 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) 1–13

References Aguiar, M., & Hurst, E. (2007). Measuring trends in leisure: The allocation of time over five decades. The Quarterly Journal of Economics, 122, 969– 1006. Beckett, M. (2000). Converging health inequalities in later life: An artifact of mortality selection? Journal of Health and Social Behavior, 41, 106–119. Benzeval, M., Green, M. J., & Leyland, A. H. (2011). Do social inequalities in health widen or converge with age? Longitudinal evidence from three cohorts in the West of Scotland. BMC Public Health, 11, 947. Case, A., & Deaton, A. (2005). Broken down by work and sex: How our health declines. In D. A. Wise (Ed.), Analyses in the economics of aging (pp. 185– 205). Chicago: Chicago University Press. Corna, L. M. (2013). A life course perspective on socioeconomic inequalities in health: A critical review of conceptual frameworks. Advances in Life Course Research, 18, 150–159. Cutler, D. M., & Lleras-Muney, A. (2010). Understanding differences in health behaviors by education. Journal of Health Economics, 29, 1–28. Cutler, D. M., Lleras-Muney, A., & Vogl, T. (2011). Socioeconomic status and health: Dimensions and mechanisms. In S. Glies & P. C. Smith (Eds.), The Oxford handbook of health economics (pp. 124–163). Oxford: Oxford University Press. Deaton, A. (1997). The analysis of household surveys: A microeconometric approach to development policy (World Bank). Baltimore: The Johns Hopkins University Press. Ferraro, K. F., & Farmer, M. M. (1996). Double jeopardy, aging as leveller, or persistent health inequality? A longitudinal analysis of white and black Americans. Journal of Gerontology, 51B, S319–S328. Gandini, S., Botteri, E., Iodice, S., Boniol, M., Lowenfels, A. B., Maisonneuve, P., et al. (2008). Tobacco smoking and cancer: A meta-analysis. International Journal of Cancer, 122, 155–164. Guryan, J., Hurst, E., & Kearney, M. (2008). Parental education and parental time with children. The Journal of Economic Perspectives, 22, 23–46. He, F. J., Nowson, C. A., Lucas, M., & MacGregor, G. A. (2007). Increased consumption of fruit and vegetables is related to a reduced risk of coronary heart disease: Meta-analysis of cohort studies. Journal of Human Hypertension, 21, 717–728. Herd, P. (2006). Do functional health inequalities decrease in old age? Educational status and functional decline among the 1931–1941 birth cohort. Research on Aging, 28, 375–392. Huijts, T., Eikemo, T. A., & Skalicka´, V. (2010). Income-related health inequalities in the Nordic countries: Examining the role of education, occupational class and age. Social Science & Medicine, 71, 1964–1972. Idler, E. L., & Benyamini, Y. (1997). Self-rated health and mortality: A review of twenty-seven community studies. Journal of Health and Social Behavior, 38, 21–37. Jeon, C. Y., Lokken, R. P., Hu, F. B., & Van Dam, R. M. (2007). Physical activity of moderate intensity and risk of type 2 diabetes: A systematic review. Diabetes Care, 30, 744–752. Kim, J., & Durden, K. (2007). Socioeconomic status and age trajectories of health. Social Science & Medicine, 65, 2489–2502. D. Kuh & Shlomo, Y. B. (Eds.), A life course approach to chronic disease epidemiology (Vol. 2). Oxford: Oxford University Press. Lundberg, O., Yngwe, M. A˚. , Stja¨rne, M. K., Elstad, J. I., Ferrarini, T., Kangas, O., et al. (2008). The role of welfare state principles and generosity in social

13

policy programmes for public health: An international comparative study. The Lancet, 372, 1633–1640. Lynch, S. M. (2003). Cohort and life course patterns in the relationship between education and health: A hierarchical approach. Demography, 40, 309–331. Mackenbach, J. P., Stirbu, I., Roskam, A. J. R., Schaap, M. M., Menvielle, G., Leinsalu, M., et al. (2008). Socioeconomic inequalities in health in 22 European countries. New England Journal of Medicine, 358, 2468–2481. Marmot, M., Friel, S., Bell, R., Houweling, T. A., & Taylor, S. (2008). Closing the gap in a generation: Health equity through action on the social determinants of health. The Lancet, 372, 1661–1669. O’Brien, R. M., Hudson, K., & Stockard, J. (2008). A mixed model estimation of age, period, and cohort effects. Sociological Methods Research, 36, 402–442. OECD. (2008). Growing unequal? Income distribution and poverty in OECD countries Paris: OECD Publishing. OECD. (2011). Society at a glance 2011 – OECD social indicators. Paris: OECD Publishing. Pampel, F. C., Krueger, P. M., & Denney, J. T. (2010). Socioeconomic disparities in health behaviors. Annual Review of Sociology, 36, 349. Reither, E. N., Hauser, R. M., & Yang, Y. (2009). Do birth cohorts matter? Age– period–cohort analyses of the obesity epidemic in the United States. Social Science & Medicine, 69, 1439–1448. Ross, C. E., & Wu, C. L. (1996). Education, age, and the cumulative advantage in health. Journal of Health and Social Behavior, 37, 104–120. Sanderson, S. C., Waller, J., Jarvis, M. J., Humphries, S. E., & Wardle, J. (2009). Awareness of lifestyle risk factors for cancer and heart disease among adults in the UK. Patient Education and Counseling, 74, 221–227. Sarma, S., Thind, A., & Chu, M. (2011). Do new cohorts of family physicians work less compared to their older predecessors? The evidence from Canada. Social Science & Medicine, 72, 2049–2058. Shkolnikov, V. M., Andreev, E. M., Jdanov, D. A., Jasilionis, D., Kravdal, Ø. , Va˚gero¨, D., et al. (2012). Increasing absolute mortality disparities by education in Finland, Norway and Sweden, 1971–2000. Journal of Epidemiology and Community Health, 66, 372–378. Sofi, F., Capalbo, A., Cesari, F., Abbate, R., & Gensini, G. F. (2008). Physical activity during leisure time and primary prevention of coronary heart disease: An updated meta-analysis of cohort studies. European Journal of Cardiovascular Prevention & Rehabilitation, 15, 247–257. Smith, J. P. (2004). Unraveling the SES: Health connection. Population and Development Review, 30, 108–132. Sørensen, M., & Gill, D. L. (2008). Perceived barriers to physical activity across Norwegian adult age groups, gender and stages of change. Scandinavian Journal of Medicine & Science in Sports, 18, 651–663. van Kippersluis, H., O’Donnell, O., van Doorslaer, E., & van Ourti, T. (2010). Socioeconomic differences in health over the life cycle in an egalitarian country. Social Science & Medicine, 70, 428–438. Welch, N., McNaughton, S. A., Hunter, W., Hume, C., & Crawford, D. (2009). Is the perception of time pressure a barrier to healthy eating and physical activity among women? Public Health Nutrition, 12, 888–895. Wilson, A., Shuey, K., & Elder, G. (2007). Cumulative advantage processes as mechanisms of inequality in life course health. American Journal of Sociology, 112, 1886–1924. World Health Organization. (2003). Diet, nutrition and the prevention of chronic diseases. WHO Technical Report Series No. 916. Geneva, Switzerland.