In this study of three age cohorts, covering 60 years of the lifecourse, social class inequalities in self-assessed health vary considerably as people age and are dependent on the measurement of health and class. Without including decedents or employing proximal measures of social class, those in manual households appear to become ill earlier than those in non-manual households, who have a low probability of reporting poor health until the age of 65 years when the probability of reporting poor health steepens making inequalities in health appear to narrow. However, including death as part of the health outcome changes the pattern. Convergence of inequalities is still evident when the outcome is a simple comparison between reporting excellent/good health and fair/poor health or death, but inequalities persist to 75 for the continuous measure, which ranges from 1 (excellent) to 5 (dead) and hence better captures severity. Replacing baseline social class with a time-varying measure shows smaller inequalities in health in late middle age, and taking account of this suggests that inequalities continue into old age without narrowing. We find only modest evidence of cohort effects on the prevalence of poor health but these do not explain the pattern of inequalities with age. We found only one statistically significant gender interaction with SES as people aged (from 16 models), which might suggest that inequalities in women's health widened more than men's. However, given the number of interactions tested for, this may be due to chance.
A number of other studies also find that disadvantaged groups become ill at younger ages in relation to physical health [12
], disease conditions [9
] and self-assessed health,[19
] but that having widened in middle age inequalities narrow again at older ages [9
]. A few studies [7
] have contradictory results depending on the health outcome and socioeconomic measure being considered. The studies that explicitly considered mortality and other selection effects [8
] generally found that inequalities widened until older ages, although they did begin to narrow again. The only other study to investigate the effect of using time-varying class also found that this reduced inequalities in working age [19
This paper has extended the age range and time period over which the issue of widening or converging inequalities in health has been considered in a single study. It directly examined the shape of age-health trajectories and unlike much of the existing literature, which has used linear or quadratic functions, found a cubic shape performed best, consistent with the only other study that tested for this [16
]. Employing a cubic function allows greater flexibility in the shape of the health trajectory as people age. This suggests that there are improvements in self-assessed health in adolescence and early adulthood and steep increases in reporting poor health at older ages. The changes in prevalence with age may reflect both changes in actual health status and changes in respondents' perception of their health. For example, it has been shown that younger people associate being healthy with physical fitness and having healthy behaviours while older people tend to focus on physical functioning and mental well-being [33
While many studies have speculated about the effects of survival bias, only a few have investigated it directly. We go further by including the available data on people who died and investigating what the pattern of social inequalities in health would look like if decedents were not excluded from the analyses after their death. We also use a measure of time-varying SES. This is the first study to take both issues into account at the same time.
There are a number of limitations to this analysis. First, while the Twenty-07 Study covers ages 15/6 to 75/6, it cannot investigate inequalities at very old ages; this is a limitation in much of the literature, reflecting the length and scope of many longitudinal studies. Secondly, the study was originally set in a predominantly urban area with a wide range of health experiences but generally poor health [24
], and as such the results may not be generalisable to other kinds of places. For example, in the late 1980s, when the study began, there were relatively few people of minority ethnic backgrounds living in Clydeside. Whilst the situation in Clydeside is different today, the study may not reflect the experiences of a multicultural society. Similarly, there were few rural areas in the study and so it is difficult to know whether results would generalise to such places. Thirdly, this paper has made modest attempts to explore cohort effects on health inequalities as people age, but the three cohorts only had partial overlap in their ages so these conclusions are limited. Fourthly, we have employed head of household social class based on occupation for our SES measure. However, using occupation to categorise people can be more problematic for some groups of the population than others. For example, the last occupation of those who have retired or are not working for other reasons may not reflect their current circumstances [34
]. Also, while for some respondents their social class reflects both their household socioeconomic circumstances and their own occupational exposures, for others it is purely a measure of household socioeconomic status. Repeating these analyses with other measures of socioeconomic status would help to validate the results beyond this specific measure. Finally, only 15% of all respondents, and 37% of the 1930s cohort, had died by the final wave. This is a small proportion of the sample as a whole, but a significant proportion of the oldest cohort. Nevertheless, the effects of survival bias may change as more respondents die. However, since dying young is more prevalent among those from manual classes this analysis is likely to have incorporated the most significant biases caused by unequal mortality at younger ages. Noymer [35
] criticises Beckett [11
] and other studies for imputing health data for decedents arguing that they are likely to differ significantly from survivors and hence the approach is invalid. However, although we appreciate the crudeness of this approach, we have not tried to estimate the health of decedents as if they had stayed alive, but to extend our health measures to include the ultimate poor health state - death [23
There are two ways in which this analysis might underestimate the extent of social inequalities in health. First, it is based on self-assessed health, which has been shown to be a good predicator of mortality [23
] and morbidity, [36
] but the way people answer the question may change with age and period [37
], and there is conflicting evidence about whether people's answers vary by their socioeconomic background. Some studies have found no [38
], others small [39
] and others significant [40
] differences in the association between self-assessed health and mortality by socioeconomic status. In addition, there is some evidence that more affluent groups over-reported poor self-assessed health when compared to more objective measures of health status [41
]. Overall, therefore, these studies suggest that investigating health inequalities based on self-assessed health measures may under report their magnitude. Secondly, those respondents who are still alive but dropped out of the study are only included until they drop out. Such people may have different health trajectories to those who remain in the study, however, as Table shows they tend to be in poorer health and from manual social classes, which suggests excluding them will again underestimate the inequalities gap between manual and non-manual classes.
Further research is required to investigate the trajectories for different socioeconomic and health measures and how inequalities in them change across the widest age ranges available in longitudinal data. Using more proximal measures of social class suggests that social mobility may be important and this also need further investigation. There was a suggestion that there may be gender differences in these associations which have also been found in cross-sectional research, [42
] and these require further exploration. These associations need to be investigated with multiple cohorts to develop a better understanding of the interactions between cohort, period, age and socioeconomic effects.