Recent empirical work conducted, however, seems to cast doubt on the validity of the assumption that SAH can be compared directly across members of different groups. In light of this concern, and the challenge which it poses to the interpretation of the results of much current South African empirical work, this article explorespossible systematic differences with respect to the perception and reporting of SAH between members of different race groups in South Africa.

Using data drawn from a nationally representative survey of approximately 3000 respondents, this study analysed racial differences in SAH alongside domain-specific SAH and attitudes to a number of health-related areas (e.g. reference groups, perceptions of main influences on health, etc.). The analysis revealed a number of differences when compared to the usual racialised pattern observed. It is held that these differences suggest that there might be a role for race to play in the assessment and reporting of SAH independently of its continued association with socio-economic status.

Onlangse empiriese werk werp egter twyfel op die geldigheid van die veronderstelling dat SAH van lede van verskillende groepe direk met mekaar vergelyk kan word. In die lig hiervan en die uitdaging wat dit inhou vir die interpretasie van die resultate van ‘n baie onlangse Suid-Afrikaanse empiriese studie, ondersoek hierdieartikel moontlike sistematiese verskille met betrekking tot die persepsie en rapportering van SAH tussen mense van verskillende rassegroepe in Suid-Afrika.

Data vanuit ‘n nasionale verteenwoordigende opname van ongeveer 3000 respondente is in hierdie studie gebruik. Die studie ontleed hierdie rasseverskille in SAHtesame met die domein-spesifieke SAH asook die gesindhede ten opsigte van ’n aantal van die gesondheidsverwante gebiede (bv. verwysingsgroepe, die persepsies van die belangrikste invloede op gesondheid ens.) Die analise het ‘n aantal verskille getoon in vergelyking met die gewone patroon wat op ras gebaseer is. Daar is van mening dat hierdie verskille daarop dui dat ras dalk tog ‘n rol speel in die evaluering en rapportering van SAH, ongeag die voortgesette assosiasie met sosiaalekonomiese status.

The many studies which have investigated the distribution of SAH in South Africa exhibit the same strong relationship between race and SAH that was describedearlier. Typically, this pattern is attributed to the continued association between race and socio-economic status, which is a product of South Africa’s long history of inequality and official discrimination on the basis of race, or the psychosocial effects attached to the persistence of attitudes (perceived or real) fostered thereunder. Charasse-Pouèlè and Fournier (2006) and Williams et al. (2008) elaborate on these effects. Evidence of the long-ranging effects of these policies is shown in studies ofSouth Africa’s national income distribution which is still notably skewed in favour of White persons (Hoogeveen & Özler 2005; Statistics South Africa 2007). Studies of access to healthcare reveal similar racial differences (Health Systems Trust 2004; Statistics South Africa 2004). Race, class and socio-economic status in South Africa have an intertwined relationship. Consequently, it becomes relatively straightforward to explain current racial differences in health in terms of the racially stratifieddistribution of income and the previous government’s policy of systematic under-investment in infrastructure in, and provision of public services to members of, certain communities. According to this view, the relationship between race and health is an artefact of the relationship between race and socio-economic status. Race per se is not driving this relationship.

Recent studies in this area have also raised several questions about the validity of the implicit assumption that SAH is directly comparable across members of different groups. In particular, several concerns have been raised about the effects of ‘reference groups’ and ‘cultural factors’ on respondents’ assessment and on the reportingof SAH. For instance, Kuhn, Rahman and Menken (2004) contend that SAH may not be internationally comparable, citing especially the divide between developed and developing countries. Jürges (2004) concludes that SAH is subject to reference group effects, where reference groups are delineated along age, sex and income lines. Based on these findings, he argues that systematic differences in the reporting of SAH across groups may markedly bias the conclusions that can be drawn from studies that investigate the distribution of SAH across different groups of respondents. He even proceeds to recommend that SAH be compared only withinspecific subsamples where reporting styles are similar across respondents (Jürges 2004:11). Jürges (2007) supports this assertion with cross-national data from a survey run across 10 European countries. The data illustrate how explicit control for cross-cultural differences in reporting styles affects the size of the estimated average inter-country health differences, and the relative ranking of countries according to the reported health levels of citizens (Jürges 2007). The conclusions withregard to ‘reference groups’ are supported by Ahn (2003), who contends that culture and the social environment play an important role in the formation of responses to questions about SAH. The net effect of this work is that the appropriateness of intra-group comparisons of SAH has been questioned in many instances.

Arguably, the validity of the assumption that SAH of members of different groups can be compared, directly influences the inferences that can be made about the nature of the relationships between reported SAH, race and socio-economic status that exist in South Africa and were described earlier. Moreover, given the increasing use of this indicator in policy design, biases in its assessment and reporting are likely to complicate the targeting and evaluation of healthcare policy.There is thus a weakness in one of the tools available to policymakers to make decisions on the allocation of scarce resources to intended beneficiaries who are identified on the basis of need. The use of a flawed indicator, without taking steps to remedy its weaknesses, might compromise the achievement of greater health equity in future. The consequences thereof are likely to be magnified in a society as diverse as South Africa.

Importantly, these difficulties are present whether the state is aware of the incomparability of this measure across members of these two communities or not. By way of example, consider the situation of a population which consists of two communities, A and B, where members of Community A appeal to a higher level of idealisedhealth than members of Community B when constructing their notions of health, that is, members of Community B are likely to be satisfied with a lower level of health than members of Community A. Should perceptions of healthiness influence the health levels to which persons aspire, one would presume that the average (true or underlying) health of members of Community A would be better than those of Community B in future. This could occur through either a greater likelihoodof these citizens adopting more protective health behaviours, proactive health-seeking behaviour, their greater demands upon the state for better access to healthcare facilities and so forth. Regardless of the channels through which this occurs, SAH would mask differences in underlying health between members of these two communities if assessment and reporting patterns remain unchanged.

If the government is aware of the differential assessment of SAH, it is confronted with the dilemma of which level of optimal health, that of Community A or that of Community B, to set as a target for the average citizen by bearing in mind that equity considerations often prevent the state from providing services at differentiallevels to individuals which could be considered to be more consistent with the health perceptions of the groups to which they belong. Setting one level of health as a goal for both communities means that the state effectively under-supplies or over-supplies healthcare provision, depending on which community’s perception of optimal health or healthiness it uses as a benchmark for all citizens to achieve or maintain or both. Both the under-supply and the over-supply of healthcare, relativeto perceptions of healthiness, that is, hold implications for the state’s ability to achieve its other developmental goals.

On the other hand, if the state is unaware of differences in health perceptions across communities and their effect on the reporting of SAH, it risks the possibility offomenting health inequalities between groups. Returning to the stylised aforementioned example, consider that a person drawn from Community A, whose members generally appeal to a higher standard of health when reporting their SAH, will be more likely to report poorer health than a comparable person from Community B, that is, ceteris paribus. If this relationship held on average and if the state aimed to equalise the distribution of SAH between the two communities, the implementationof SAH as a guide to resource allocation would suggest that it ought to direct more efforts towards improving the health of members of Community A. If it does so, it would ultimately increase health inequalities within the country. Research objectives
In light of the biases associated with the use of SAH for comparative purposes and the potential dangers associated with these biases, this study attempts to explore systematic differences with respect to the reporting of SAH in South Africa between members of different racial groups. This will be conducted by investigating the racial distribution of SAH and a number of aspects related to it, such as SAH in a number of domains (e.g. vision and hearing) and attitudes to a number of health-related areas (e.g. the existence of reference groups relative to whose members respondents rate their SAH, perceptions of the main influences on health, etc.), which might provide insight into the way that respondents construct their notions of ‘health’ and thence influence their reporting behaviour when assessing and reporting their SAH. Where racial differences are found, possible reasons for their existence will be advanced wherever possible. Thereafter, implications for the validity of theassumption that SAH can be used as a measure when comparing interracial health levels in South Africa will be discussed.

Two inherent weaknesses in this approach are worth noting. Firstly, concentrating exclusively on interracial group differences in the reporting of SAH could beconsidered a shortcoming as, in practice, there are likely to be many other potential influences, for example, age group, sex, education and class, which could exert an even greater influence on respondents’ reporting behaviour. Another limitation is that differences between subgroups of respondents (e.g. differences in health assessments between black men and black women or White rural dwellers and White urban dwellers) will not be investigated. In defence of this decision, it shouldbe noted that small sample sizes frequently preclude this type of analysis in practice.

The analysis in this article is based on respondents’ answers to 17 of the 30 questions which make up the Health Module of the questionnaire. The module itself was designed specifically for the purposes of this study. Most questions relied on Likert-scale-type responses with five discrete response categories ranging from ‘excellent’ through to ‘very poor’ with a separate category for ‘Don’t know’. A few questions did not offer any response guidelines to respondents and answersconsisted solely of the information proffered by respondents. In these cases, responses were grouped to discern patterns in the raw data.

In addition, respondents were asked whether they experienced any health conditions that either impeded their enjoyment or quality of life, or interfered with theirmovement or functioning in daily activities. A minority of respondents reported that they did experience these problems (Table 3).

The percentage of respondents who rated their health in the following domains as either good or excellent broken down by race, were recorded (Table 4). It should be pointed out that no information was gathered on whether respondents had any aids to assist them in their functioning in these areas. It is thus possible thatthe responses reflect a combination of respondents’ perceptions of health in this domain as well as their perceptions of any actual limitations imposed on their functioning or quality of life as a result of any difficulties. This latter effect may well be mitigated by the employment of certain aids such as spectacles, wheelchairs and so forth. It is also possible that the availability of these aids may vary across different groups in the population.

In contrast to the distribution of overall SAH, a statistically significant difference at the 1% level is only reported in one of the domains: mobility. The only other significant difference reported, is for vision, which is significant at the 10% level.

Results show significant racial differences amongst those respondents who reported knowing to whom they compared their health (Table 6). Differences appearto be driven by the responses to whether respondents compared their health to those of friends and family, or those with whom respondents have some contact and interact with in some way (neighbours, co-workers or other gymnasium members). Whilst family and friends were by far the most popular choice amongst all respondents, White and Indian respondents were much more inclined to choose them. It should be noted that reporting similar reference groups does not implythat the underlying populations against which respondents compare their health are the same. For instance, the population of family members will differ between respondents and with it the health of members of this group. Finally, only respondents’ first responses to the question about reference groups were recorded. This means that only simply-defined or one-dimensional reference groups are described. Such simple reference groups are unlikely to apply in practice (Deaton 2002; Ahn2003).

Two striking observations about the data (Table 7) presented should be pointed out. One is the low percentage of respondents (roughly between 0.5% and 4% for individual statements) who reported Don’t Know as their answer to any of the questions below. The second is the low percentage (roughly 9% of all responses including those who ‘Don’t know’) of respondents who reported that they neither agreed nor disagreed with each of these statements. This high degree of certaintywas unexpected given the attitudinal nature of the questions.

For the statement ‘You can never be too thin’, White persons’ responses were significantly different from those of other race groups with roughly 60% of White respondents strongly disagreeing with this statement. Conversely, black respondents tended to support the notion that one can never be too thin.

The only other statistically significant difference was in the percentage of respondents who agreed or strongly agreed that richer people are healthier people. Thepercentage of respondents that agreed that richer people are healthier was 31%. Black respondents were more likely to feel that richer people were healthier people than respondents of other races. Their responses were significantly different from those of other races. The responses of Indian respondents were also significantly different from those of respondents of other races. Just 4% of Indian respondents agreed or strongly agreed with this statement. The responses of Coloured (20%) andWhite (11%) respondents were not significantly different from each other.

The distribution of the overall indicator is broken down by race (Table 8). Overall, it can be seen that respondents thought predominantly about conditions that required a medical diagnosis when thinking about their health (Table 8). This was the most popular category after a ‘combination of factors’ (see Column 6). Visual inspection was the next most popular category. Although therewas some variation in the responses of members of different race groups, these differences were not statistically significant at traditional testing levels.

In terms of internal validity, it is pointed out that although SAH has been found to be a good predictor of later health outcomes such as mortality and morbidity (Idler & Kasl 1991; Idler & Benyamini 1997), the components of SAH have been debated since it first began to be widely used in survey research, as seen in an early summary by Okun et al. (1984), and scholars probably will continue to investigate in the foreseeable future.

External validity of results is ensured through SASAS’s research design. As mentioned, SASAS 2008 is a nationally representative survey of a sample of approximately 3000 randomly selected South Africans aged 16 and older. All analyses make use of weights that take stratification (by province) and clustering (bymunicipal ward) effects into account.

Relying on conjecture alone, a number of explanations of these results could be put forward. The most simple of these is that, in spite of the ample evidence availablewhich highlights the disproportionate disease burden borne by the poor (Feinstein 1993; Judge & Paterson 2001; Deaton 2002), no association between socio-economic status and subjective health could exist in certain health domains. From the close relationship between race and socio-economic status, it follows that there are no significant racial differences in these domains. Allowing for the possibility that SAH is subject to cultural or racial influences is another possible explanation. Morespecifically, the possibility that race and reported SAH in South Africa might be related via channels other than socio-economic status or the lasting effects of past racist policies might help to reconcile the two sets of analyses mentioned earlier. In particular, the absence of marked racial effects in the reporting of health in certain domains might allude to the possibility that respondents of different races appeal to different standards in such reporting. For instance, if the underlying distributionof good or excellent vision (objectively measured) was the same across the population but Black persons appealed to lower standards and White persons to higher standards in the self-reporting of their vision, then Black persons’ self-reported responses will be biased upwards and White persons’ responses downwards. If the situation described earlier was applicable, racial differences in the reporting of assessments in this area would either be reduced or possibly eliminated in the casewhere true underlying vision (objectively measured) was higher for White persons than for Black persons. Notwithstanding this speculation, it should be clear that no firm conclusions can be reached in the absence of information on exactly how respondents of different races form their benchmarks or reference points when assessing and reporting upon their domain-specific health.

Disregarding the situation where differences in the average health of black respondents relative to that of White respondents only arose during the previous five year period, how might results be influenced by differences, if any, in race-based reporting styles? Firstly, it could reflect some influence of state dependence onrecall by respondents. Assume that those who are in relatively poorer health states at present tend to over-estimate their health previous status. As those who are in poorer health now are predominantly black, this could account for black responses of prior health being ‘inflated’ relative to current health. Related to this account, differences might also be driven by the manner in which respondents recall and report upon prior events. For example, it has been argued that retrospectiveevaluations of health and well-being in general are frequently dominated by the recall of a usually severe reference point or bout of negative health status (Hillenet al. 2000; Siegrist 2003). Given observed racial patterns to morbidity and mortality levels in SA (Ijumba & Barron 2005), black respondents are more likely to have experienced a negative health event than White respondents during the past year. Therefore, even if current levels of health status between respondents of differentraces were the same, they might report relatively higher levels of retrospective SAH than White respondents on average given the greater likelihood that their assessments are being benchmarked against a negative event in the recent past.

Secondly, there may be a differential age effect at play amongst members of different race groups. The following contrived example illustrates how this differential age effect could lead to the observed pattern to responses. Assume that all respondents benchmark their assessments of current SAH relative to those of similar age, that is, there is an age-based reference group effect. Additionally, assume that black respondents, when recalling their previous health states, do so relative to theirhealth now or relative to the health of those who are now of similar age. By doing so, they are using the same standard to form current and past health assessments. Assume that White respondents, on the other hand, base their assessments of previous health states upon the health of those who are now of similar age to their younger selves. By doing so, they are allowing for the possibility that the target health levels or the standards by which health is measured ought to be different forpersons of different ages. Accounting for age effects in this way could possibly lead to a ‘softening’ of their health assessments as they age. Subject to the deterioration in health which is presumed to accompany the ageing process, it is easy to see how the differential manner in which respondents form their assessments of health may affect the reported responses of black versus White respondents in this situation. It is worthwhile to note that this can arise without having to make anyassumptions about true movements in the underlying distribution of health in each race group over the intervening period.

At first glance, the percentage of respondents that reported either agreeing or strongly agreeing with this statement (see Table 7, Row 5) appears to be somewhat lowrelative to the well-established link between income and health (Goldman 2001; Adler & Newman 2002). Tentatively, this result might suggest that the pathways through which respondents perceive wealth or socio-economic status to be associated with health status might not be as apparent as the evidence of the actual link between the two that has been gleaned from aggregated data.Breaking results down by race also masks some facts about the racialised distribution of national income that might contextualise respondents’ views on the link between health and income. Recall that the average Indian or White respondent is likely to enjoy a higher level of socio-economic status than the average Black respondent. Thus, finding that respondents from these groups are less likely than Black respondents to feel as strongly that income and health are related implies thatricher people are less likely to feel that they are healthier than others. Assuming that persons who enjoy different levels of socio-economic status subscribe to similar notions of health when responding (i.e. assessments are comparable), this could mean one of two things. Respondents might concentrate on the causation which runs from income to health when replying. If so, results could suggest that poorer respondents over-estimate the perceived benefits of increased income or richer peopletend to be more reserved in their assessment of the health benefits which income can confer. Alternatively, richer people might not want to believe that their wealth confers health benefits on them and instead concentrate on the causation that runs from health to income when responding. If so, perhaps responses to the statement ‘Healthier people are richer people’ would have allowed a truer assessment of their views on the health-income relationship than the statement ‘Richer people arehealthier people’ which was used in SASAS.

In light of this assertion, it is recommended that further detailed work be undertaken into testing the validity of the assumption that SAH can be compared acrossmembers of the four major race groups in South Africa.