Publicamos un ensayo escrito en inglés por el odontólogo Luis Enrique Cam en el 2007 sobre la inequidad social en salud como parte del curso de Sociology as applied to medicine and dentistry (MSc in Dental Public Health King’s College-University of London) . El ensayo confronta la idea de que la magnitud de la inequidad social en salud que se suele reportar no es real sino que es producto de la manera de recolectar la información.
Social inequalities in health are an artifact of the manner in which the data is collected. They are no real. Discussion by Luis Enrique Cam
In Western countries, it is evident that social class differences exist. The term “social stratification” generally refers to this kind of socially structured inequality (Scrambler and Blane 2003). It applies to several dimensions such as education, lifestyles, housing, leisure activities, career prospects, cultural background and so on. These differences tend to come together, so that someone who has an advantage in any dimension is likely to also be advantaged in all others.
When these differences produce a large gap between the haves and have-nots, grave inequalities arise. Some commentators have suggested that there is a strong evidence of a relationship between social class and some health status indicators. On the other hand, it is argued that such inequalities in health are an artifact and not more than a result of data collection errors. This essay examines the arguments that support this stance and those which do not.
Equity in health should be a essential goal for any society. Investment in health has a high social return since it strengthens human resources. Healthy people are called to be agents of development. Without good health, no society can achieve some legitimate goals such as good education and labour competence. Moreover, and beyond this instrumental value, health is a basic human right in the first place.
To begin with, it is important to find an accurate definition of what equity in health is. In the case of health care, Whitehead (1990) spoke of equal access to available care for equal need, equal utilization of the health service for equal need, and equal quality of care for all. Turning to equity in health, he argues that it implies that ideally everyone should have a fair opportunity to attain her full health potential and, more pragmatically, that no one should be disadvantaged from achieving this potential, if it can be avoided. On the other hand, Whitehead states that Governments should not aim to eliminate all health differences so that everyone has the same level and quality of health, but rather to reduce or eliminate those which result from factors which are considered both avoidable and unfair. At this point, it is important to set that the the term social inequality depends on what is considered normal or aceptable in a particular society.
As for the determinants of health inequality, Wilkinson (1996) argues that the level of income inequality is crucial in more affluent societies like Britain. He argued that once certain levels of gross national product (GNP) per capita has been reached, the main determinant of health status within a country is the degree of income inequality. Furthermore, he asserted that the populations of rich ‘equal’ countries have better health profiles than the populations of rich ‘unequal’ countries. Finally, he argued that there is evidence that where income inequalities are more marked, social divisions tend to be exacerbated; levels of trust and strength of community life tend to be lower; rates of social anxiety and chronic stress tend to be higher, as well as rates of hostility, violence and murder.
Against Wilkinson´s thesis, Coburn (2000) put the blame on ‘neo-liberalism’ for income inequality and low social cohesion. He presumed with caution that neo-liberalism (or market dominance) causes low health status as well as a loss of solidarity values.
Some researchers have argued that inequalities are the result of wrong resources management. Sen (1999) reported that some countries which have lower income per capita than others have better health indicators. This is the case of Sri Lanka, Costa Rica and Kerala State in India that have rapid reductions in mortality rates in spite of the fact that exhibit slow economic growth. These results could have obtained because of good management and an egalitarian distribution of resources as compared with other countries, even with better gross domestic product (GDP).
In this context, this essay intends to explore whether health inequalities today can be said to be primarily due to the combined effect of class differences in exposure to factors that promote health or cause disease, or whether their relation with economic circumstances are no more than a statistical artefact. In the past, both the Black and the Acheson Report argued that income, education and employment were main determinants of ill health, as well as material environments and lifestyles.
Interpretation of the relationship between social class and health
The empirical association between social class and health shows that death and disease are socially structured, as opposed to randomly distributed throughout the population, and that they vary in line with differences in living standards (Scrambler 2003).
This empirical evidence is not beyond counterarguments. To begin with, social class and health relationship should be examined in order to determine whether there is causation. The Black Report (DHSS 1980) suggested four types of explanation of social class differences in health, the first of which accepted the possibility that they were a result of a statistical artefact. The other three explanations were potential social selection, behavioural/cultural causes and an argument based on access to material means. Next, the artefact explanation is discussed.
Some commentators think that the social inequalities in health are an artefact of the manner in which the data is collected. Certainly, there are several reasons that support their position.
Firstly, poor people dislike to openly recognize their poverty. Hence, surveys determining the number of poor people usually underestimate this figure. However, in the reckoning of death cases, social classes are more easily identified, with no such downward bias. The result is that mortality ratios among the poor (deaths/poor population) are overestimated (Black Report).
Likewise, other reasons exist why measurement errors may boost morbidity ratios upwards. For instance, Butler, Burkhauser et al. (1987) report empirical evidence showing that the unemployed are inclined to overplay their diseases, due to their need to justify their idleness in the face of social pressure.
However, measurement errors can also cause the opposite effect. To put it simply, data collection on illness prevalence may face greater difficulties and be less complete among the poor, especially when such data is inferred from services actually provided by health centres. Hence, morbidity in lower social groups would be underestimated. In this sense, a common situation occurs in Peruvian highlands area around 4000m above sea level, where the poorest Peruvian communities are located. Despite the fact that small health centres do offer health care (even though they are admittedly thinly spread across the region), nearby communities often refuse to request attention from them. Andean people have ancient beliefs in health matters which are preferred above “city doctors”. Commonly, they turn to traditional medicine, usually plants, if they get sick. In other words, health providers do not serve these poor strata and hence they are a misleading source of information about illnesses among them.
Similarly, illness reports may fall short of actual prevalence because health centres are too distant from poor populations. In fact, this means that illness data based on registries of the existing centres will not fully capture the extent of the problem. This is a common case among the Peruvian Amazonian communities, where health centres are absent or very scant (UNICEF 2001).
Secondly, and more importantly, poor health may well be more prevalent among the poor, but not due to their poverty, but to some other characteristics which happens to be more common among the poor. For instance, since young people have greater chances to get better-paid jobs, the group of low social classes may be on average older. Thus, it would be old age, and not low incomes, what underlies high reports of illness (Gibson 2003).
Several other variants of the same argument can be imagined, i.e. instead of old age, some other hidden characteristic can be found to cause illness, with low income being ‘rashly’ blamed for it. Take education. Health and education are deeply intertwined. Family and system education are the two main sources of basic education. Being born within a high level education family brings along access to up-to-date health information right from the very early years of life. Often, the education level is closely related to income levels, for instance in Peru (World Bank, 1993). On the other hand, schooling allows the formation and maintenance of health-preventive attitudes. People with secondary education show less risk factors than uneducated people. In this sense, according to World Bank (1993), in Peru, 72% of educated parents were still alive when their children were aged between 25 and 29, whereas this proportion reduces to 55% among uneducated parents. To sum up, the inequalities observed in access to education have strong consequences on health habits and on ultimately health status.
However, the argument is again double-edged and can act to the contrary. That unseen characteristic of the lower classes (in the examples above, old age and poor education) may be conducive to better health, and not to illness. For instance, take the peacefulness and lack of distress typical of rural life. In rural India, low-income families are found to freely decide not to migrate to cities, for the sake of preserving the protection and solidarity values they enjoy in their local villages (Das Gupta 1987).
On balance, we have found that as far as empirical and measurement-related arguments go, the risk of overplaying the link between health and wealth is as strong as the risk of underplaying it. Hence, in this particular case, we should not allow mere statistical figures to drive the main bulk of our conclusions. At any rate, we should not fail to complement our discussion with the other arguments underlying the relation between health and socioeconomic conditions, as suggested by the Black Report (Scrambler, 2003).
1. Social Selection: Health determines social class through a process of health-related social mobility.
2. Behavioural/cultural: social class determines health through social class differences in health-damaging or health-promoting behaviours.
3. Materialist: social class determines health through social class differences in the material circumstances of life.
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Butler, J., Burkhauser, R. and et al (1987). Measurement Error in Self-Reported Health Variables. The Review of Economics and Statistics, Vol. 69, No. 4 pp. 644-650
Department of Health and Social Security (1980). Inequalities in Health: Report of a research working group (The Black report) HMSO. London
Das Gupta, M. (1987). Informal Security Mechanisms and Population Retention in Rural India. Economic Development and Cultural Change. vol. 36-1, pp. 101-20
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