Is Cardiovascular Disease Slowing Improvements in Life Expectancy?

5. Socio-economic inequalities in CVD mortality: an overview of patterns, secular changes and their determinants

Professor Anton E. Kunst

Abstract

Numerous studies have documented large differences in cardiovascular risk between people with higher and lower socio-economic status. Typically, there is an about two‑fold difference in cardiovascular mortality between those at the upper and lower ends of the social hierarchy – a difference that is much larger than for cancer mortality. With much of the population burden of cardiovascular disease concentrated in lower socio-economic groups, we should take the perspectives of these groups when searching for explanations and policy solutions when cardiovascular mortality rates fail to decline. In this search for explanations and solutions, we should look beyond individual-level factors and preventive actions focussed on “life styles”, and consider the role of wider policy areas such as urban renewal, employment and social welfare.

The secular decline in mortality from cardiovascular disease (CVD) mortality has been one of the greatest successes in modern medicine and public health. Between 1980 and 2005, CVD mortality rates have halved in several European countries. The persistence and omnipresence of these declines suggested that further substantial decreases may be expected in forthcoming decades. Yet, recent reports of stagnation in mortality declines have cast doubts on this optimistic view and prompted us to identify the causes of these unexpected trends.

Trends in CVD mortality can be understood as resulting from two components: changes in the incidence of CVD and changes in their case-fatality. Whereas the latter is driven by changes in medical technology and improvement in the organisation and quality of health care services, the former is driven by changing prevalence of a variety of risk factors. These include biological risk factors (e.g. BMI, hypertension, blood composition) and risk factors related to behaviour and psychosocial stress (e.g. smoking, diet, sleep patterns).

At the population level, changes in the prevalence of risk factors are not just driven by individual-level drivers such as values, attitudes and self-efficacy, they also reflect changes in the social and physical environments in which people live. Commercial exposures and novel fashions may change people’s taste for specific foods and activities. Environmental stressors and economic uncertainty may increase the occurrence of unhealthy habits such as smoking and excessive alcohol use, in as much as these constitute ways to cope with increased stress.

Not all people are influenced in the same way by their social and physical environments. This influence may vary according to, for example, a person’s age and gender. Another characteristic with strong moderating capacity is a person’s socio-economic position or ‘socio-economic status’ i.e. the position of a person on the social hierarchy or ladder, with higher positions implying greater access to scarce societal resources. ‘Socio-economic status’ is a multidimensional concept, as it depends on the educational level, employment history and position at work, income from gainful employment, wealth and housing conditions, and less tangible resources such as power and prestige.

Inequalities in CVD mortality: patterns and trends

Numerous studies have documented large differences in CVD risk between people with higher and lower socio-economic status. Typically, there is an about two‑fold difference in CVD mortality risk between those at the upper and lower ends of the social hierarchy – a difference that is much larger than for cancer mortality risk. Notably, CVD risk is often found to increase in a linear fashion when moving from higher towards lower socio-economic positions. This linear gradient is observed in relationship to educational level, occupational class position, income level and area-level deprivation. Commonly, about the same gradient is observed among women and men.

Once, ischemic heart disease (IHD) was regarded as a “manager’s disease”. This view was supported by reports up to the 1950s and 1960 that IHD incidence and mortality was higher among men with higher educational levels or occupational position. However, the gradient has reversed since then, first in the United States, and later in northern Europe. The reversal occurred latest, in the 1980s or 1990s, in southern parts of Europe. As a result, IHD has now become a “disease of poverty”, like so many other diseases. In contrast to IHD, the incidence and mortality of cerebrovascular disease has always been higher in socio-economically disadvantaged population groups.

A recent overview of socio-economic inequalities in CVD mortality in 12 European countries by Di Girolamo et al (2019[1]) analysed changes in CVD mortality between the 1990s and the early 2010s by gender, educational level and occupational class. CVD mortality rates were found to be higher in those with low education as compared to those with high education in all 12 countries included in the analysis. The differences were relatively small in southern European countries, possibly as a consequence of a relatively late reversal of IHD from a manager’s disease towards a disease of poverty. Inequalities in CVD mortality were large, in both relative and absolute terms, in Central and Eastern European countries – a pattern that was already seen in the 1990s.

For the same European countries, Di Girolamo et al (2019[1]) also documented trends in inequalities in CVD mortality between about 1990 and 2014. During this period, CVD mortality declined considerably in all 12 countries. This decline occurred in those with lower education as well as those with higher education, for men and women alike. Generally, in both genders, absolute declines were larger among the low educated, while relative declines were larger among the high educated. There were only a few situations where lower educated groups fared worse than those with high education – Estonia in the early 1990s and Lithuania in the early 2000s. In both genders, absolute inequalities in CVD mortality mostly decreased since the 1990s, while relative inequalities generally increased. The results were similar for the different measures used (IHD, cerebrovascular disease and total CVD mortality) and for analysis by occupational group. In the early 2010s, inequalities in CVD mortality were smallest in southern Europe, of intermediate magnitude in Northern and Western Europe, and largest in Central-Eastern European and Baltic countries.

Factors that operate as mediators of inequalities

Many studies and commentaries have addressed the question of how we can understand the persistent nature of socio-economic inequalities in health outcomes, including CVD risk. This understanding is commonly sought for by identifying factors that ‘mediate’ the causal relationship between socio-economic status and CVD risk. In a typical explanatory study, such ‘mediators’ are included in statistical models that aim to quantify their contribution to the observed association between socio-economic status and CVD outcomes.

Several mediators have been found to play a role. These include factors that are related to relevant exposure in early life (e.g. intra-uterine growth rate), housing conditions and the living environment (e.g. neighbourhood social disorder), working conditions (e.g. lack of autonomy and control), and health care (e.g. poor access to adequate services). Each of these factors has been found to play a role. Their exact role is found to vary between settings (periods, countries, regions) and population groups (generation, sex, ethnic group). There is no single set of explanations that applies everywhere in the same way.

Particular attention is given to the role of health-related behaviours such as smoking, alcohol abuse, lack of physical activity, and diet. Many descriptive studies have documented that socio-economic differences in the prevalence of such factors are commonly large. Dozens of mediation studies have demonstrated that these factors contribute to inequalities in CVD risk, with explained percentages up to about 50%. Smoking has been found to be the largest single contributor in many studies, but its role varies strongly according to sex (larger for men), time (diminishing role) and country (smaller in Southern Europe).

Smoking, drinking, diet or other behavioural factors are often labelled as “lifestyle factors”, with the suggestion that they are a matter of “lifestyle” that is voluntary and consciously adopted in different ways by people from different socio-economic groups. However, several studies have contested this view, by showing that people’s behaviour is influenced by their socio-economic status in several ways, and often involuntary and unconsciously. Increasingly more studies have tried to understand these influences, in order to identify how those in socio-economically disadvantaged positions can be protected or helped so that healthy choices are easier to make.

Take the example of smoking. The higher prevalence of smoking among those with low education largely stems from higher risks to start smoking by adolescents who have problems at school and at home. They are more likely to be tempted to smoke, to persist smoking and to become addicted because of pro-smoking norms in their families, greater peer pressure to smoke, need for “a break” to be able to cope with their problems, and the social stigmatisation of those who had become smokers. Similarly, when adults, they face greater difficulty in quitting smoking and remaining smoke-free due to external factors such as the many problems and stressors that they face in daily life, pro-smoking norms in their social networks, and poorer access to smoking services attuned to their needs.

Policies for reducing inequality

Preventive policies can exert an important influence, for better or for worse. In tobacco control strategies, early policies commonly brought most benefits to the rich and more educated. Early publicity campaigns were designed from a middle-class perspective, early smoke-free policies were mostly implemented in white-collar work settings on a voluntary basis, and smoking services were affordable only to those with sufficient income. However, not all preventive policies widen inequalities. In tobacco control strategies, more equitable impacts were achieved in policies implemented later, e.g. through publicity campaigns focussed on less educated groups, comprehensive and compulsory smoke-free policies, and cessation services affordable and acceptable to the socio-economically disadvantaged.

It is widely recognised that socio-economic inequalities in health, including CVD risk, cannot only be addressed by preventive policies focussed on single risk factors, and that we also need to consider the wider ‘structural’ policies and systems. It has often been discussed whether socio-economic inequalities in health are smaller in countries with smaller income inequalities and/or generous welfare systems. There is no clear verdict on this issue, other than the recognition that welfare systems and income do play a role (see e.g. the large inequalities in Central and Eastern European countries) but that this role may be more limited than once hoped for (see e.g. the persistent inequalities in Nordic countries).

There is however increasing evidence about the important role of specific structural policies in fields such as employment, housing and urban renewal, protection of youth, and integration of ethnic minorities. In the EU-funded SOPHIE project, the available evidence regarding the potential impact of such policies has been evaluated and complemented with novel studies from different European countries. For example, systematic reviews of urban renewal programs have outlined the many ways in which these could benefit health behaviour (e.g. physical activity) and psychosocial wellbeing of residents of deprived neighbourhoods. Some of these positive effects were identified in additional evaluations of new ‘natural experiments’ in Barcelona, Turin and the Netherlands.

Conclusion

How could the available evidence on socio-economic inequalities in CVD risk, as summarised above, be used in our attempt to understand recent national-level trends in CVD mortality? In countries where CVD mortality is stagnating, how does such a trend relate to the socio-economic position of the residents? We should recognise that a clear link might not be established. Determinants of time trends in population health (including a stagnation in CVD mortality decline) may be quite different from determinants of cross-sectional differences (including inequalities in CVD mortality). The two phenomena may not be influenced by the same factors. For example, while inequalities in CVD mortality are attributable in part to higher smoking prevalence in lower socio-economic groups, it is unlikely that a stagnation in CVD mortality decline could be attributed to a secular increase in smoking rates.

Yet, at a more general level, we may draw two messages. First, given that much of the population burden of CVD is concentrated in lower socio-economic groups, we should take the perspectives of these groups when searching for explanations and policy solutions when CVD mortality rates fail to decline. Second, in this search for explanations and solutions, we should look beyond individual-level factors and preventive actions focussed on “life styles”, and consider the role of wider policy areas such as urban renewal, employment and social welfare.

References

[1] Di Girolamo, C. et al. (2019), “Progress in reducing inequalities in cardiovascular disease mortality in Europe”, Heart, Vol. 106/1, pp. 40-49, http://dx.doi.org/10.1136/heartjnl-2019-315129.