The European Journal of Public Health Advance Access originally published online on October 9, 2007
The European Journal of Public Health 2008 18(2):131-137; doi:10.1093/eurpub/ckm096
Health Inequalities |
Do risk factors explain more of the social gradient in self-reported health when adjusting for baseline health?
Jacob Nielsen Arendt and Jørgen LauridsenInstitute of Public Health – Health Economics, University of Southern Denmark, Denmark
Correspondence: Jacob Nielsen Arendt, J B Winsløws vej 9B, 5000, Odense C, tel: +45 6550 3843, fax: +45 6550 3880, e-mail: jna{at}sam.sdu.dk
Received December 20, 2006, accepted September 4, 2007
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Abstract |
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Background: It has often been debated the extent to which known risk factors explain socio-economic differences in health. While common in mortality studies, few studies of morbidity adjust for baseline health. In this study, we argue that there are sound reasons to do so, and examine whether a set of risk factors explain a larger part of social gradients in men and women's self-rated health (SRH) in Denmark when controlling for previous health. Methods: We use interval regression models on longitudinal survey data from 1990 and 1995 separately for Danish male and female workers aged 18–59. Results: Large social gradients are found in SRH for both men and women. The included risk factors (smoking, body mass index, high blood pressure and job satisfaction) reduce the educational gradient in SRH by 40% (based on highest versus no education), the wage gradient by 18% and leaves occupational gradients (based on no employment versus white collar workers) unaltered for men. For women, similar gradients are altered by 6 and 22 and 14% in cross-sectional models. Controlling for baseline health 5 years earlier, the risk factors reduce the education, occupation and wage gradients by 45, –15 and 17% for men and by 5, 25 and 15% for women. Conclusion: The findings suggest that common risk factors do not explain a larger fraction of social health inequalities in dynamic than in static models of self-reported health.
Keywords: dynamics, health, risk factors, socio-economic status
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Introduction |
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During the last couple of decades, a considerable amount of attention, academic as well as public, has been directed towards differences in health and mortality that are related to social status. In European countries, several trans-national working groups have addressed the issue and many public authorities have set as a main health goal to reduce social inequalities in health.1–4 This study adds to our knowledge on how large a fraction of social inequalities is explained by a given set of risk factors using Danish data.
Some evidence suggests that social inequalities in morbidity and mortality are quite substantial by international standards in Nordic European countries in spite of their egalitarian policies.5–7 This has though been critized.8 Moreover, in many western countries, including Denmark, there has been a rise in social inequality in mortality and morbidity over recent decades.9–11 There are therefore great incentives to try to understand these relationships further to help improve policy.
It is often debated how a large fraction of social inequalities in health is explained by known risk factors. There is now general consensus that the causes of social inequalities in health are multi-dimensional, emphasizing both material (e.g. poverty, financial problems), behavioural (e.g. smoking, alcohol consumption, diet and physical activity), biological [blood pressure, cholesterol, body mass index (BMI)] and psychosocial pathways (e.g. stress, lack of social support, control).12–20 Health knowledge and health-related behaviour often explain a quarter and sometimes more than half of social inequalities in health,3,13,16,17,21–27 while a combination of various sets of risk factors may explain all social inequalities in health.14,18,28
Studies that decompose inequalities into mediating pathways provide important insights on which pathways that seem to be the most important mediators from socio-economic status (SES) to health. Comparison of the explanatory power of given risk factors in different studies is, however, often complicated by the use of different study populations, health measures and risk factors. Moreover, results should be interpreted carefully in the sense that ‘explaining inequalities’ is not tantamount to being able to manipulate inequalities nor is it equivalent to knowing why inequalities arise.
The outset of this study is hypotheses of the social health gradient that take the dynamic nature of health into account. This holds for the various selection hypotheses as well as the literature emphasizing a life course perspective. Selection may occur directly, i.e. when adult health affects adult SES (when SES is measured by employment status, this is referred to as the healthy worker effect) or indirectly, when unobserved factors influence both adult SES and adult health, creating a spurious relation among the two. When health selection arises in adulthood, it is often called intragenerational selection, as opposed to intergenerational selection, arising when childhood health prevents upward mobility in SES. 29 While there is certainly evidence that all types of selection occurs, there is less consensus on the size of its contribution to social inequalities in health. Most evidence suggests that direct selection on occupation plays a minor role for occupation-related health inequalities,30–32 whereas evidence on selection with respect to education33,34 and particularly income is more mixed.35–38 Indirect selection has been suggested as a process by which unfavourable health events accumulate over life.30 This is exactly the dynamic aspect stressed by the accumulation model, which is one out of several models with a life course perspective, that has received growing interest during the recent decade.1,39–45 Related models include the biological programming model, where conditions in utero or early childhood ‘programme’ the individual risk for later adult diseases39 and the pathway model (or chain-of-risk model), where exposure to adverse health events increases the risk of later adverse exposures.46 Within the health economic literature, it has been pointed out how a version of the life course hypothesis can be captured by an autoregressive dynamic model of health,47 capturing the idea that on average, individuals recover from illnesses, but some events have lasting negative health effects. Social status may matter for two reasons: either because recovery from illnesses differs across social groups or because of different exposures to health threats across social groups. A final strand of literature that emphasizes the dynamic nature of health determinations is also found within health economics. This literature stresses health as a capital good and therefore, health today depends on previous health and health investments.34,48
The main message from this literature is that health is determined over potentially long time periods. This has consequences when trying to explain health differences. Our a priori expectation is that current risk factors may explain a larger fraction of inequalities in adult health when adjusting for baseline health some years ago, than unadjusted health levels where inequalities may be accumulated over a life time. Of course, it is possible that controlling for baseline health reduces the explanatory power of risk factors, if they primarily explain baseline rather than current health. It is also possible that adjusting for baseline health may alter which pathways act as the most important mediators from social position to health as well as the quantitative importance of each pathway. These issues cannot be determined a priori and remain empirical questions.
The goal of this study is therefore to compare to what extent a given set of risk factors explain social health gradients, with and without adjustment for baseline health. Various strands of research are related to this purpose. Numerous studies have been conducted that examine the life course perspective. Most of this research examines the impact of socio-economic position during the life course of adult health.1,40–43 This perspective differs from the present study, where we aim at controlling for previous health rather than previous socio-economic conditions. Other types of studies more closely related to the present are studies that control for general measures of baseline health. This includes numerous studies of mortality.16,18,25,49–54 However, even though many of these studies confirm the importance of baseline health, only few of these allow us to infer the extent to which risk factors explain more of the gradient when controlling for baseline health.
There is much more limited empirical work on morbidity that control for baseline health. One study, which employs the same data as the present study, examines whether obesity, smoking and work environment can account for SES differences in self-reported health using probit models given either previous poor or good SRH.55 As opposed to this study, they use an indicator of social class (based on employment grade, job title and education) as a measure of SES. They show that obesity, smoking and work environmental risk factors reduce the social class differences in SRH by 57% when controlling for baseline health. No comparison with static models is provided. A British cohort study found that adjusting for social class and self-rated health (SRH) at age 23, social class differences in SRH at age 33 vanished.56 A few US studies have applied two- or three-wave panels to deal with the dynamics in health.57,58 Indeed, it has been found that social gradients in a range of health conditions are weakened when controlling for both health risk factors and past health conditions.58 However, it is not possible to infer to what extent the risk factors or previous health conditions account for the gradient.
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Methods |
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The statistical analyses are conducted using an interval regression model, which is an alternative to ordered probit/logit in the case where the threshold parameters among health categories are known from an external source. Using such information, the estimates of the coefficients for the individual characteristics are more efficient59 and are directly interpretable as marginal effects on a health scale that is bounded between zero (dead) and one (perfect health). We use two sets of external estimates of the thresholds between health categories to obtain robustness. The first set was derived from applying the HUI-3 health instrument in a 1994 Canadian survey.60,61 The second set was derived applying the 15D health instrument to a Finnish 1995–1996 survey.62 These thresholds of the health categories are summarized in table 1. Notice that the HUI index is multiplied by 100 so that it ranges from 0 to 100 rather than from 0 to 1, in order to avoid unnecessarily small coefficients in the interval regressions to follow. As the two sets of thresholds yielded quite similar results, only those based upon HUI thresholds are shown. The results based on 15D are available from the authors upon request.
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Joint analyses of wage, occupation and education gradients are conducted. Four sets of estimates are presented for each gender: one with only SES variables, one where we add the risk factors and then these are repeated in models with baseline health. All estimations control for age. The overall results are robust to different functional forms in age or wages. References to significance levels will be at a 5% level, unless otherwise mentioned.
Data
The present study uses data from the Work Environment Cohort Study (WECS), which is a survey of Danish employees, aged 18–59 in 1990 with a follow up in 1995. The survey was collected by the Danish National Institute of Occupational Health (AMI) and the National Institute of Social Research (SFI). Further details are found in the appendix.
The data set includes SRH (very poor, poor, fair, good and very good), which will be our main health measure. SRH has been found to be a good overall summary measure of health, related e.g. to risks of functional disability and mortality.63,64 Some studies have been concerned that self-reported health measures are reported with error which, if related to SES, may create a spurious gradient.65–68
We use three indicators of SES: education, occupation and wages. Occupation is divided into white collar workers, skilled and unskilled blue collar workers and a residual group, consisting mostly of people out of employment or self-employed. The following educational levels are used: 9th form or less, semi-skilled, short education, vocational, short advanced, medium advanced and long advanced degree. Short education covers those with 10th form, another type of education beyond 9th form of at most a year of length and those with high school. The vocational and the three advanced studies constitute the main types of educations directly qualifying for the labour market. Finally, we use pre-tax hourly wages as SES measure. Hourly wages are typically viewed as a measure of employee productivity in the economic literature and may in broader terms reflect social status derived on the labour market. Besides, using hourly wages rather than gross income cleans the measure for differences in hours of work. The final data set consists of 1985 observations for men and 1702 observations for women.
Risk factors
The following binomial risk indicators are included. Physiological: being fat (BMI between 25 and 30) or obese (BMI above 30). Biological: whether a doctor has reported having high blood pressure. Behavioural: number of years having been smoking. We also include a binomial indicator of whether being satisfied with ones’ job, to account for risk factors related to psychosocial work environment. Job satisfaction has been shown to be closely related to sickness absenteeism and to be related to psychosocial problems such as burnout, low self-esteem, depression and anxiety.69,70 More typical measures of psychosocial risk factors include locus of control, job strain or stress;21,51 see also.15,71 We refer broadly to the set of variables describing BMI, blood pressure, smoking and job satisfaction as risk factors.
Table 2 contains descriptive statistics; distributions of explanatory variables and the share with poor SRH (the original three worst outcomes fair, poor or very poor health). The table shows that SRH varies with type of education, age, occupation, wage level, the risk factors and baseline health. Finally, to apply health in 1990 to the regression, the categories of health for this year were scaled by replacing with the mid-point of the intervals of the health categories according to the HUI and 15D thresholds.
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Social gradients for men
Table 3 contains results with HUI thresholds for men. The results show that on average, all men with any type of schooling beyond 9th form, besides the semi-skilled, report better health than those with at most 9th form. However, only the semi-skilled education group is significant, making the education variables jointly significant (see bottom of the table). It should also be noted that if the dummies for semi-skilled and short education are included in the reference group, all higher educations are significant. Therefore, it makes sense to evaluate the size of the education gradient. Those with an advanced degree have the best health [table 3, Model (1)], on average reporting higher health corresponding to 0.004–0.0075 higher health index. To put this into a perspective, the standard deviation of the HUI-based health index is 0.054 in 1990.
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Only the SRH of the residual occupation group (mainly out of employment) differs significantly from the other occupation groups. The occupation variables are however jointly insignificant. Finally, hourly wages are strongly and significantly related to SRH. If wages increase by 1 SD, the health index increases by 0.005, corresponding to the impact of 7.5 years of age.
Controlling for risk factors and baseline health
All the risk factors are significant and particularly low job satisfaction and high blood pressure is associated with poor health. When the risk factors are included, both the wage and the educational gradient are reduced [table 3, Model (1) versus Model (2)], but significant wage and education differences persist. Measured by the difference in coefficients for long-advanced education versus no schooling, the reduction is 40%. The differences between white collar workers and the residual group are unaltered. The wage gradient is reduced by 18%. In Model (3), we see that when baseline health status in 1990 is controlled for, but the risk factors are excluded, there is a significant wage gradient similar to the one in Model (2). Controlling for the risk factors reduces this gradient in a similar manner as in the static model, namely by 45% for the educational differences and by 17% for the wage gradient, although widening occupational differences by 15%. The results reveal that SRH shows a very large degree of persistence over the 5-year period.
Social gradients for women
The education-related differences in SRH are substantial for women [table 4, Model (1)]. Women with a vocational education have better health than those with a medium or long advanced degree, but the best health is reported by those with a short advanced degree as was the case for men (controlling for wages, occupation and age). They report better health corresponding to a 0.0037 higher health index. For women, there are significant health differences between white collar and unskilled blue collar workers. Finally, wages are significant, although the association is weaker than for men. One standard deviation of wages alters the health index by 0.002 corresponding to an effect of 3.9 years of age.
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Controlling for risk factors and baseline health
Three risk factors are highly significant: obesity, high blood pressure and job satisfaction. Controlling for the risk factors only alter health advantages for the educated slightly and not in a significant manner [table 4, Model (1) versus Model (2)].The occupational health difference is reduced by 24% and the wage gradient by 22%. The latter gradient becomes insignificant. Again, when controlling for baseline health, but excluding the risk factors, the gradients are reduced compared with the static model, particularly because of reductions in SRH differences between people with different wages [table 4, Model (1) versus Model (3)], but educational and occupational differences persist. Finally, adding controls for the risk factors again does not significantly alter the educational gradient [table 4, Model (4)], whereas both wage and occupational gradients are reduced and both become insignificant. The persistence over time in SRH is substantial but less than the persistence for men.
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Discussion |
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The present study examines socio-economic inequalities in health measured by education, occupation and wage-related differences in self-reported health, using a Danish survey of individuals who have been working within 2 months of an interview in 1990, with a follow-up interview in 1995. Besides providing new evidence on social gradients in SRH, the main contribution of this study is to infer the extent to which adjustment for baseline health affects the explanatory power of given risk factors. Adjustment for baseline health can be advocated theoretically, because it limits gradients due to social selection and also because health is accumulated over time. Thus, as stressed by the literature, taking a life course perspective, a large part of the variation in health has early determinants. This may alter the scope for explaining social inequalities as well as which pathways are the predominant ones.
In general, a sample of employees would be expected to be a group where health inequalities are lower than in the total population.11 Nevertheless, substantial social differences in SRH among both men and women are found in this sample. As opposed to several other studies, including Danish findings on mortality, education-related differences in SRH are larger for women than for men.24,65,67,72 For both gender, non-monotone relationships between length of education and SRH are found which correspond roughly to findings on mortality in Denmark.73
For men, even for this sample, we see large differences in health between employed and a residual group consisting of individuals mainly out of work as well as large wage-related health differences that persist after controlling for baseline health and risk factors. For women, occupational and wage-related health differences vanish when controlling for baseline health and risk factors. It should be stressed that particularly the findings with respect to wage effects should be interpreted with care, as wages are only observed in 1995 and changes in health from 1990 to 1995 may affect wages in 1995.
Controlling for the risk factors measured by being fat, obese, having high blood pressure, years of smoking and job satisfaction reduces education, occupation and wage gradients in static models by 0–40%, which is a commonly found explanatory power for such a limited set of risk factors. Turning to dynamic models that control for baseline health shows that SRH is very persistent over the 5-year period from 1990 to 1995. When the risk factors are controlled for in addition to baseline health status, most education-related differences in health are insignificant for men, whereas this is not the case for women. In addition, the impact of the risk factors on SES gradients is quantitatively alike in static and dynamic models. Therefore, we do not find that the risk factors explain a larger share of the social gradients in health in dynamic models than in static models. The converse could be expected because differences across individuals may be accumulated over long time periods thus to a lesser degree affected by current behaviour.
Limitations
The empirical investigation is hampered by several data limitations. The study was conducted on a rather small sample with only one follow-up period and few risk factors. The limited number of risk factors limits comparison with previous studies. It also limits to what extend we may expect the risk factors to explain social inequalities in health. On the other hand, the limited number of risk factors may imply an overstatement of the impact of a single risk factor. Moreover, the use of a sample of employees might introduce the healthy worker effect, which is likely to downward bias the impact from the risk factors. The use of self-reported health and self-reported risk factors might also introduce biases. As mentioned earlier, several studies have found measurement error in self-reported health to be limited and that it has many advantages as a summary health measure. Clearly, the limited time period used in this study, the high correlation between baseline and current health and the fact that few previous studies have looked at the explanatory power of the risk factors in dynamic health models, warrants caution in extrapolating the results to other populations.
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Conclusions |
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When evaluating whether a set of risk factors ‘explain’ socio-economic inequalities in health, it is important be realistic about what to expect. In this study, we hypothesized that common risk factors may explain a larger part of health inequalities when adjusting for baseline health, than differences in unadjusted health. We found this not to be the case. The study showed the existence of particularly educational gradients in health for women and wage or occupational gradients for men, which were present whether or not adjusting for baseline health.
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Appendix |
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Data construction
In 1990, 9653 persons aged 18–59 were randomly chosen and 8 664 were interviewed (90% response rate).74 In 1995, 7532 were re-interviewed (86.9% response rate), 300 migrated or died and 1 821 were not interviewed. We end up with a smaller sample mainly for two reasons. First of all, self-reported health was only asked to people who were employed within the last 2 months of the interview. Second, a number of individuals were deliberately only interviewed once. These are deleted. Furthermore, observations with missing information on education in both 1990 and 1995 are deleted, as well as the remaining individuals who are still under education in 1995, since focus is on the effect of completed education. Ten individuals reported their age as more than 6 or less than 4 years in 1995 from the reporting in 1990, so they are deleted. Among the remaining observations, 2096 are deleted due to missing information on SRH in 1990 or 1995. Finally, 68 observations contained missing information on wages are also deleted. The sample construction is illustrated in table A1, showing the number of missing information for various variables and the remaining sample size. We see that the largest relative drop occurs when removing missing SRH. Comparing to the original sample, only 4976 were employed among the 7532 re-interviewed in 1995. Since only employed individuals were asked about SRH, this corresponds to the drop in the original sample.
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The sample does not differ much with respect to age, education and occupational distribution from the overall national representative sample of employed individuals. There is though a significant geographical variation in attrition from the 1990 to the 1995 survey,74 but including controls for region does not alter the results.
The questions used to construct the hourly wage are: ‘What is your usual wage payment before tax and deductions’ (answer a1 in Danish kroner), ‘How is your wage usually paid’ (answer a2 = 1, 2, 3, 4, 5 or 6 for respective pay per hour, day, week, second week, month, or other) and ‘How many hours do you usually work per week in your main job’ (answer a3 in hours). The following rules are used to convert the answers into one variable containing hourly wages: w = a1 if a2 = 1, w = 5a1/a3 if a2 = 2, w = a1/a3 if a2 = 3, w = a1/2a3 if a2 = 4, w = a1/4a3 if a2 = 5 and w = missing if a2 = 6.
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Acknowledgement |
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The authors wish to thank participants at the Symposium in Applied Statistics in Odense 2005 and two anonymous referees.
Conflicts of interest: None declared.
Key points
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