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Page | Last updated: 06 Apr 2021

Health Inequalities: dietary and physical activity-related determinants

In the European Union, health inequalities exist both between and within countries

Health inequalities have been defined as 'differences in health status or in the distribution of health determinants between different population groups' ( WHO Health Inequality and Inequity ). When avoidable, these inequalities are termed health inequities ( WHO Social Determinants of Health ). In this Brief, the term 'health inequalities' will be used in its broader sense, which includes (but is not limited to) health inequities.

In the European Union, health inequalities exist both between and within countries. Within countries, vulnerable (and often socially excluded) population groups are characterized by one or more of the following conditions: low household income, low educational level, unemployment (especially long-term unemployment), low socioeconomic status (SES, which often includes one or more of the already mentioned conditions), poverty (or at risk of poverty), migrant background and ethnic minority background (EC 2009).

Health tends to worsen from society's richest to poorest, and this is often referred to as a social gradient in health. Age and gender differences can also worsen health inequalities.

Common indicators of health status - part of the European Core Health Indicators (ECHI) - are life expectancy at birth, healthy life expectancy at birth, infant mortality rate and cause-specific mortality rates. Table 1  and the maps below shows how these indicators perform in EU member states. It illustrates several differences between countries such as:

  • Life expectancy at birth - in 2014 life expectancy at birth differed by almost 9 years between countries with the highest and lowest records (ECHI Data tool, Eurostat 2017) (albeit life expectancy has increased in most EU countries since 1990) ( OECD 2016 ); similar inequalities (gaps of 10 years and more, e.g. 67.5 vs 81.7) can be seen within some countries between people with different education levels (ECHI Data tool).
  • Healthy life expectancy - in 2014, healthy life expectancy at birth in both men and women, ranged from approximately 50 to 70 years, meaning that depending on their country of birth Europeans could expect to have 20 more (or less) years of healthy life (ECHI Data tool, Eurostat 2017). Moreover, the mismatch between life expectancy at birth and healthy life expectancy varied across the EU, from the lowest difference of 10 years to the highest one of 23.6 years.
  • Infant mortality rate per 1,000 ranged from 1.4 to 8.4 in 2014, indicating a six fold variation across EU countries ( OECD 2016 ).
  • Mortality rates for ischemic heart disease, the leading cause of death in the EU, ranged from below 100/100,000 deaths in some Northern and Southern European countries to over 350/100,000 deaths in Eastern European ones in 2013 ( OECD 2016 ).

Table 1: Life expectancy at birth, infant mortality and standardised mortality rates

Life expectancy at birth in EU Member States in 2014 (both sexes) map 

Life expectancy at birth in EU Member States in 2014 (both sexes) chart 

Healthy Life Expectancy in EU Member States in 2014 map men

Healthy Life Expectancy in EU Member States in 2014 map women

Healthy Life Expectancy in EU Member States in 2014 chart

For most EU countries, Eurostat population data and ECHI offer estimates of the fraction and the number of people belonging to specific vulnerable groups. The available data is presented in Table 2, and can be summarised as follows:

  • Among an estimated 507 million people in the EU in 2014, around 41 million were unemployed (half of which were long-term unemployed), at least 74 million people had a low education level (ISCED 0, 1 or 2, year 2010), and around 124 million people lived in poverty or at risk of poverty, 34 million EU resident non-citizens (which does not include illegal migrants nor migrants with an EU citizenship already acquired, year 2013) and 6 million Roma (year 2012).
  • 124 million people defined as in poverty or at risk of poverty (mainly women, children, young people, people living in single-parent households, lower educated people and migrants (Eurostat 2015).

Table 2: Total population size and numbers and percentages of people in vulnerable categories 

Differences in several health determinants underlie the health inequalities observed within and between EU member states, such as in smoking, alcohol consumption, diets or physical activity (PA) levels. In what regards diets and PA, albeit limited data availability on the breakdown of the quality of diets and levels of physical activity per population group, there is a considerable amount of information contained in more aggregated data. Table 3 presents an example of data on fruit and vegetable intake and physical activity levels, stratified by educational level. Self-reported daily consumption of fruit and vegetables is higher in groups with higher education levels in northern and central European countries, but not necessarily in southern ones. In most countries, the self-reported absence of health-enhancing aerobic physical activity was highest in people with a low level of education.

Table 3: Proportion of people reporting daily consumption of vegetables or fruit and reporting no time in health enhancing aerobic physical activity stratified by ISCED education level

Table 4 summarises the evidence reported so far on these and other nutrition and PA-related health determinants and their relation to health inequalities.

Table 4: Nutrition- and Physical activity-related health behaviours and inequalities 

A recent analysis ( UNICEF 2016 (pdf) ) that explored how inequality trends are changing over time in children and adolescents of various countries, including EU countries, reports that inequality in unhealthy eating (based on 2002 – 2014 data on self-reported consumption of 'sweets (candy or chocolate)' and 'coke or other soft drinks that contain sugar', was reduced in most EU countries (with the exception of Hungary, Poland, Estonia and Lithuania with no change, and Romania, Slovakia and Belgium, with increased inequalities). This reduction appears to be driven by improvements on the unhealthy eating behaviours of the lower SES population groups. Similarly, inequality in physical activity decreased in the majority of countries, where the lower SES improved more than others (with the exception of Poland and Romania with no change, and Italy with increased inequalities).

Among various minority groups living in the EU, it appears that dietary and PA habits have been recorded systematically only for Roma (EC 2014 (pdf)). The data indicate that Roma minorities tend to have a diet characterized by fewer vegetables and more fats, compared to the general population. As for physical activity, 60% of the Roma population does not do any exercise during free time, probably because of segregation (and reduced access to recreational services) and cultural factors (which prioritize short-term rather than long-term health impacts).

Obesity is the most described health outcome associated with health inequalities related to diets and physical activity. Data from 2014 (or latest year available) indicate that the prevalence of self-reported adult obesity in the EU varies from 9% in Romania to 26% in Malta ( OECD 2016 ). In the EU, measured overweight (including obesity) among children at various ages is about 23% for boys and 21% for girls (2010 or latest year available, evidence from 22 countries) ( OECD 2016 ); self-reported overweight (including obesity) among 15-year-olds is about 22% in boys and 13% in girls (2013-14, evidence from 27 countries) ( OECD 2016 ). Many socio-economic variables are associated with obesity and these are summarised in Table 5.

Table 5: Proposed relations between overweight/obesity, and socioeconomic status, educational level and ethnicity. 

A strong gender gap, defined as the difference between women and men in attainments and attitudes, is present in the distribution of obesity according to socio-economic variables. In most countries, obesity rates have grown more rapidly in low SES groups than in high SES groups ( OECD 2014 (pdf) ), thus widening health inequalities. The link between obesity and socio-economic disadvantage appears to be perpetuated in a vicious cycle ( ENHR 2009 ), and, over the life course, different types of inequality can contribute to deteriorate the nutritional and PA status, thus maintaining the cycle. For instance, obesity in women, especially during pregnancy and lactation, contributes to the health risks of their children, amplifying health inequities across generations. Impacts can be long-lasting; for example the association between the BMIs of parents and their children has been shown to persist from birth up to 45 years of age.

SES effects on health can also be seen in medical conditions other than obesity (EU 2012 (pdf)). National data from across Europe show an inverse association between the level of education and the proportion of population reporting any long-standing chronic illness or long-standing health problem (i.e. the higher the education level, the fewer the reported long-standing health concern). In fact, obesity itself often clusters with metabolic risk factors, such as high blood pressure, high fasting glucose and dyslipidaemia in the so-called metabolic syndrome, and acts as a risk factor for the development of other non-communicable diseases. Data on reported BMI and reported recent diagnosis of diabetes and high blood pressure, stratified by education level, are presented in Table 6. BMI and education level data is also presented in the map below.

Table 6: Proportion of people reporting BMI above 30, new diagnosis of diabetes or high blood pressure stratified by ISCED educational level

Proportion (%) of people reporting BMI above 30 stratified by ISCED in EU Member States in 2014 chart

Proportion (%) of people reporting BMI above 30 stratified by ISCED in EU Member States in 2014 ISCED class 0-2 map 

Proportion (%) of people reporting BMI above 30 stratified by ISCED in EU Member States in 2014 ISCED class 3-4 map

Proportion (%) of people reporting BMI above 30 stratified by ISCED in EU Member States in 2014 ISCED class 5-8 map

The European Commission regards health inequalities as a challenge to the EU's commitment to solidarity, social and economic cohesion, human rights and equal opportunities (EU 2010). Reducing them is one of the greatest public health challenges in Europe ( WHO 2008 (pdf) ). The concept of proportionate universalism must be considered in this regard, i.e. 'to reduce the steepness of the social gradient in health, actions must be universal, but with a scale and intensity that is proportionate to the level of disadvantage' ( WHO 2014b (pdf) ).

Whereas policies that aim to improve income distribution and raise income of the poorest groups (e.g. social protection, minimum wage, equal pay legislation, and redistributive taxation) are of great relevance to decreasing the inequality gap, the discussion that follows is focused on diet and physical activity-related policies that health inequalities. Policies can reduce inequalities in absolute or relative terms. In the current European context, where baseline levels of obesity are greater in low- than high-SES groups ( LSE 2009 ) , member states may wish to directly those more vulnerable. Table 7 summarises several recommendations to address these.

Table 7: Examples of policy recommendations addressing inequalities in diet and physical activity 

The European policy-making process is increasingly sensitive to inequalities. For example, ex-ante impact assessments should address health and inequalities. This means that, an assessment of potential direct and indirect effects on specific population sub-groups should be presented for any new initiative including whether these sub-groups can be affected differently and disproportionately by the initiative (CDC 2009).

Many of the implemented policies reviewed in other chapters that diets, nutrients or specific food and drink products have the potential to reduce health inequalities related to these. As highlighted in the recommendations above, they can for example be delivered specifically to more vulnerable groups to reduce the inequality gap. Also population measures that attempt a universal coverage have the potential to reduce inequalities as they will effectively limit exposure to or intake of unhealthier messages or food products ( WHO 2014a (pdf) ). Some examples are the limitation on advertising to minors (see Food and non-alcoholic beverage marketing to children and adolescents in this series), or on the use of industrial trans-fatty acids in foods as seen in Austria, Denmark, and Hungary (see also Dietary Fats, table 5.7, in this series) (EC 2015). Food improvement and reformulation initiatives that aim to decrease overall intake of nutrients of concern by directly improving the food products in the market also have the potential to reach all consumers as long as prices are not affected. The EU salt reduction framework (EU Salt reduction framework (pdf)), or other efforts framed by the EU framework for national initiatives on selected nutrients (EU Nutrient framework (pdf)) or the Roadmap for Action on Food Product Improvement ( Dutch EU presidency ) are good examples, provided that reformulation efforts cover all market segments.

School initiatives are also crucial in targeting population from all SES groups. Interventions at vocational schools, as carried out in Denmark, specifically reach young people with a lower education.

One other mechanism that deserves to be highlighted in the context of inequalities is the Fund for European Aid to the Most Deprived (FEAD) and similar national schemes. FEAD can be used to support vulnerable groups by providing, among others, food aid. For example, a total of 228 707 tonnes of food were distributed in 2014. With a health sensitive selection of food products, the measure has the potential to increase the healthiness of the recipients' diets. The BE food package is a case in point (EC 2016). Nutrient criteria such as those applied in school food policies or marketing restrictions could be considered to ensure the nutritional quality of these baskets.

A recurring issue in addressing health inequalities and identifying measures and policies that effectively diminish them is lack of data. Monitoring schemes and data collection initiatives that allow for data stratification on the basis of socio-economic status, education levels, minorities or religious and migrant backgrounds, gender and age will not only allow for a better assessment of the health gap between different population groups but also allow for a better evaluation of policies and measures that try to address them. The Global Observatory for Physical Activity ( GoPA ) and the UK National Child Measurement Programme ( HSCIC 2015 ) are two examples of surveillance schemes that are sensitive to these issues.

In an attempt to sensitise and involve other stakeholders in reducing nutrition and physical activity-related inequalities, the European Commission has also urged the members of the EU Platform for Action on Diet, Physical Activity and Health to consider health inequalities in their commitments (EU Platform 2013 (pdf)). Some stakeholders have built on this call for action and the 2016 annual monitoring report, whose evaluation is limited to what is declared by the commitment proponents, flags 13 out of the 109 commitments active in 2015 as relevant to health inequalities (EU Platform 2016 (pdf)). Some are listed in Table 8.

Table 8: Examples of implemented programmes (EU Platform commitments) with relevance to inequalities in nutrition and physical activity 


Overview of the references