Results

At first the distribution of the three indicators for SES are considered. According to the first research question it will be analysed to what extent they do overlap.

In terms of Spearman rank correlations, the strongest association appears between qualification and occupational position (r=0.58) while the correlations between each of these and income are rather weak (r=0.13 and r=0.11, respectively). If the distributions are considered in detail, it emerges that 71.6% of the unskilled and semi-skilled do not have a qualification. Considering this relation from the other side, 86.5% of the insured without a qualification hold unskilled jobs. We find similar, but no identical results for the more privileged segment of the distribution: 37.3% of the higher positions are occupied by people with a high qualification.

Comparing positions of income and occupational status yields an interpretable, but smaller association, the rank correlation is onlyr=0.11. If the distributions are considered in detail (table 3), it turns out that this is because the extreme ends as 71% in the lowest income group hold unskilled or semi-skilled positions, but having a lower occupational status does not necessarily go together with low incomes. For the opposite part of the distributions this holds to a smaller degree as only 32.9% of the intermediates or executives receive the highest salaries (within our statutory health insurance population). If this is extended to the upper 40% of the income distribution, 52.2% of insured in the highest positions are covered.

Considering qualification in intersection (table 4) with income gives similar results, but the rank correlation between the two characteristics is low (r=0.13). In the lowest income group the proportion of insured without a formal qualification is 48.3%., Summing up the personnel with 13 years of school and a university degree covers 97% of the highest income group, but only 11.4% of the insured with an academic degree fall into the highest income group. These low correlations come about by the small correspondences between the positions in intermediate categories of the variables considered.

In the second step we deal with multivariate relations between social status indicators and mortality with the dependent variable being the age of death or surviving the age of 65, respectively. The results of the Cox regression are displayed in terms of relative risks. These indicate how much more likely the case of death is for a person of a certain category as compared with the highest category (that is, people with the highest income, the highest occupational position or the highest qualification).

The main research question is related to the contributions of the three indicators under study, predominantly concerning the relative importance of income. In all the analyses to follow gender was introduced as a control variable. This procedure was chosen because a mortality gradient (in terms of relative risks) for all SES indicators emerged likewise for men and women if only one SES indicator was introduced. If only men were considered, the results did not change. However, the confidence intervals for the female study population were large, thus not permitting firm conclusions for women. The reasons are the smaller proportion of women in our population, and the higher life expectancy in women further reduced the number of death cases available.

At first the SES indicators are analysed separately (table 5). Regardless of the indicator considered, compared with the highest income or status category all the remaining ones show increased relative mortality risks. In contrast with qualification and occupational position, the income related mortalities do not show a linear increase with falling income as not the lowest, but the second lowest percentile shows the highest relative risk. In contrast with income, qualification and mortality show an inverse pattern. For occupational position also an inverse relation emerges, but the difference between unskilled/semi-skilled and the skilled manuals is fairly small.

Considering income and qualification (table 6), each indicator still yields increased mortality risks. For income, the size of the relative risks persists, but for qualification it decreases, and the confidence intervals widens. A cross tabulation (not shown here) performed separately for deaths and censored cases shows that this is because of small cell frequencies in the subtables displaying the death cases.

If relative risks for income and occupational position are estimated, a similar result emerges (table 7). Again, the relative risks of income remain relatively unchanged while the effects of occupational position decrease, again accompanied by widening confidence intervals.

In the beginning of the methods section we have pointed out that the population eligible for analysis was smaller than the total number of the employed insured. This was because of missing values on at least one SES indicator. To estimate an eventual bias resulting out of this considerable loss of cases, we estimated the mortality risk for people with complete data on all three indicators in comparison with those with at least one missing value. A Cox regression with again controlling for gender yielded a relative risk of RR=1.08 (95% CI: 1.03, 1.12)—that is, subjects with complete data had a 8% higher mortality risk.

Discussion

Our first research question was related to examining empirical overlaps between income, qualification, and occupational position. The starting point was the question whether the shift in the debate on health inequalities is related to a different conceptual content of SES indicators or whether the same is discussed under a different heading.

We used a large data set representing the population of a German statutory health insurance. The associations between qualification and occupational position were strong, the correlations between these two indicators and income were much smaller. A considerable part of unskilled/semi-skilled had a relatively high income level, pointing to the phenomenon of status inconsistency.39 40 Two explanations may apply: A substantial part of the insured from lower occupational positions might have been on shift work or they got overtime premiums. Both results in a classification into higher income categories. The second explanation concerns part time employees holding jobs according to their qualification, but their wages are lower than for full timers, and so they fall into lower income categories. Unfortunately, in our data details concerning individual employment conditions were unavailable, the same applies for overtime or shift work, thus separate analyses could not be performed.

In the regression analyses for every status indicator a gradient with respect to mortality could be demonstrated, a finding that is already known from the inequality literature. A deviation from the pattern of an inverse relation of mortality risks and SES was income as the lowest 20% of wage earners show a lower risk than the 20% above. We assume this (at least partly) to be attributable to part time employment.41 In the higher income groups there might have been people from lower ranks of the social scale (in terms of qualification or occupational position) with increased health risks. An earlier study25 has likewise demonstrated a non-linear relation between household income and chronic health conditions (page 595). This suggests that a straightforward interpretation of income (or the material situation) may hardly be possible. If material conditions are major determinants for health inequalites, income levels should be associated with differential mortality risks regardless whether subjects are part timers or full time employed. This is again challenged by our lack of information on household budgets or about the amount of money available per person. People may have other sources of income or part timers may be member of a household with more than one wage earner. Both have been found to be important determinants of health inequalities.4 42 It is not unambiguously known whether the income we were dealing with was the only money available, whether an insured was the only person in a household, or whether and how many children were present.43

In comparison with qualification and occupational position, income has turned out to be the strongest predictor of mortality. There were still mortality gradients for qualification and occupational position, but the corresponding confidence intervals were large, thus not permitting substantial interpretations. At first this supports a materialistic explanation for health inequalities, but by several reasons, this should not be the last word on this issue. In a recent study, Stronkset al 5 concluded that in addition to effects of material conditions, psychological and behavioural factors may determine mortality risks. Empirically, these were subsumed under the general heading of “confounders”, but the related odds ratios were considerable. Another reason for concentrating on income is the temporal order of the three SES indicators. Qualification, especially school education, has a placement function for or it precedes the first occupational position, and it may also determine income opportunities. In a recent publication, Stronkset al 25 concluded that the effect of material conditions on health my be attributable to the worse health of unemployed who, after having lost the job, also experience a reduction of incomes. Although this is an interesting conclusion, it does not apply to our data as the income classification is based on wages attributable to employment.

Earlier comparative studies were not concerned with mortality, but with different measures of morbidity, and their results are not consistent. Blaxter23 reported stronger effects for income than for social class, and Dahl44 found occupational status to be a stronger predictor than income, Winkleby et al 24 emphasised education, and a study published by Stronks et al 25 does not permit a clear conclusion.

In contrast with the comparative studies above, our very large data set may lend credibility to the results, but the material is not without problems. The relatively small proportion of women and the lower mortality rate in women did not permit separate analyses for men and women. Thus the results are much more convincing for the male insured while for women the results are not clear. Another problem is the reduced case number because of inconsistencies in the income data. The advantage of having the exact income figures becomes a problem as we often could not compute a yearly income if unusually high or low payments for short periods were recorded. In survey studies, the income information may be less accurate, but the respondents may be able to give the rough amount of a regular income.

The consequences of the large number of income missings for the interpretation of our data are hard to evaluate. It cannot be assessed whether it may result in an overestimation or an underestimation of the social gradients as we do not know the “true” income distribution of our population. One might only conclude that, taking mortality as a criterion, in subjects with income missing values the distribution may be skewed towards the higher income groups as they have a somewhat lower mortality than those with complete income data.

A last problem germane to information from a statutory health insurance may affect the size of the social gradient. The data do not depict the upper 10% of the population and likewise the subjects from the lowest positions of the social scale, for example, the majority of people on social security or the homeless. Although these groups are comparatively small, omitting them should lead to an underestimation of mortality gradients. To reduce this inaccuracy, for the upper 10% the inclusion of data from private insurers is a solution while the lowest social groups can hardly be covered appropriately.

Our results emphasise the present discussion on the consequences of material living conditions. Income on the one hand and qualification and occupational position on the other turned out to be largely independent. Mortality related effects of income were more pronounced than those of the other SES indicators. However, seen in a time perspective, qualification has still a placement function at least for the first occupational position.