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Ivana Kulhánová, Gwenn Menvielle, Rasmus Hoffmann, Terje A Eikemo, Margarete C Kulik, Marlen Toch-Marquardt, Patrick Deboosere, Mall Leinsalu, Olle Lundberg, Enrique Regidor, Caspar W N Looman, Johan P Mackenbach, for the EURO-GBD-SE Consortium, The role of three lifestyle risk factors in reducing educational differences in ischaemic heart disease mortality in Europe, European Journal of Public Health, Volume 27, Issue 2, April 2017, Pages 203–210, https://doi.org/10.1093/eurpub/ckw104
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Abstract
Background: Ischaemic heart disease (IHD) is one of the leading causes of death worldwide with a higher risk of dying among people with a lower socioeconomic status. We investigated the potential for reducing educational differences in IHD mortality in 21 European populations based on two counterfactual scenarios—the upward levelling scenario and the more realistic best practice country scenario. Methods: We used a method based on the population attributable fraction to estimate the impact of a modified educational distribution of smoking, overweight/obesity, and physical inactivity on educational inequalities in IHD mortality among people aged 30–79. Risk factor prevalence was collected around the year 2000 and mortality data covered the early 2000s. Results: The potential reduction of educational inequalities in IHD mortality differed by country, sex, risk factor and scenario. Smoking was the most important risk factor among men in Nordic and eastern European populations, whereas overweight and obesity was the most important risk factor among women in the South of Europe. The effect of physical inactivity on the reduction of inequalities in IHD mortality was smaller compared with smoking and overweight/obesity. Although the reduction in inequalities in IHD mortality may seem modest, substantial reduction in IHD mortality among the least educated can be achieved under the scenarios investigated. Conclusion: Population wide strategies to reduce the prevalence of risk factors such as smoking, and overweight/obesity targeted at the lower socioeconomic groups are likely to substantially contribute to the reduction of IHD mortality and inequalities in IHD mortality in Europe.
Introduction
Despite a considerable decline in ischaemic heart disease (IHD) mortality over the past decades, IHD remains one of the leading causes of mortality worldwide.1 Socioeconomic inequalities in IHD mortality have been consistently reported with important variations in their magnitude between European countries.2 In addition, the decrease in IHD mortality was more pronounced among people with high socioeconomic position, which resulted in widening socioeconomic inequalities in IHD mortality in many European countries during the 1980s, 1990s and 2000s.3,4
The major risk factors of IHD mortality include behavioural risk factors, such as tobacco consumption, physical inactivity, unhealthy diet or harmful use of alcohol; intermediate risk factors, such as overweight and obesity, hypertension and diabetes; and insufficient medical care.5 Most of these risk factors are amenable to change and could be reduced by suitable public health interventions. There are considerable health benefits at all ages, for men and women, in stopping smoking, reducing cholesterol or blood pressure, adopting a healthy diet or increasing physical activity.6,7 As these risk factors are more prevalent in lower socioeconomic groups, except smoking among women in southern Europe,8 their redistribution has a potential for reducing inequalities in IHD mortality.
However, although a few studies have quantified the impact of eliminating socioeconomic inequalities in risk factors distribution on socioeconomic inequalities in IHD mortality, the majority of studies is limited to a single country, especially to the United Kingdom.9,10 Because the socioeconomic distribution of risk factors and the average IHD mortality rates vary by country, scientific evidence limited to selected European countries hinders an effective public health policy in other parts of Europe.
The main purpose of this study was therefore to estimate to what extent educational differences in IHD mortality can potentially be reduced in 21 European populations by modifying the distribution of three lifestyle risk factors: smoking, overweight/obesity, and physical inactivity. We quantified the potential reduction in educational inequalities in IHD mortality by applying two scenarios in which the risk factor distribution according to educational level was modified.
Methods
Data
We collected and harmonized mortality data by sex, age, cause of death and level of education for 21 European populations. The mortality data came from both longitudinal and cross-sectional country-specific datasets with one to seven years of follow-up at the beginning of the 2000s covering national, regional or urban populations (Table S1, supplementary material). Additionally, we collected risk factors prevalence from representative national health surveys conducted around the year 2000 in the same populations (Table S2, supplementary material).
The socioeconomic status was measured by completed education. Educational level was classified according to the International Standard Classification of Education (ISCED) and split into three categories: less than secondary education (ISCED 0, 1, 2; ‘low’), secondary education (ISCED 3, 4; ‘mid’) and tertiary education (ISCED 5, 6; ‘high’). Individuals with unknown education were excluded from the analysis (on average less than 5%), except in Finland, Denmark and Lithuania where unknown education was classified together with low education. IHD was coded according to the International Classification of Diseases and defined as codes 410–414 in the ninth revision (Austria, Turin, Tuscany) and I20–I25 in the tenth revision (other countries).
Based on sufficient evidence on an association between the risk factor and IHD mortality and on data availability, current smoking, overweight/obesity, and physical inactivity were selected as important modifiable risk factors for IHD mortality. A detailed description of the variables used as well as the relative risks for these risk factors is presented in the Appendix (supplementary material).
Methods
We calculated age-standardized IHD mortality rates (ASMR) for the ages 30–79 and their 95% confidence intervals (CI) directly standardized to the European Standard Population. We estimated the impact of counterfactual distributions of smoking, overweight/obesity and physical inactivity on educational differences in IHD mortality by using the measure of the Population Attributable Fraction (PAF). The detailed description of the calculations can be found in the Appendix (supplementary material).
Based on the PAF methodology, we modelled two counterfactual scenarios in which the distribution of the risk factor changed. In the first scenario called upward levelling (UL) scenario, we set the prevalence of the risk factor to the level currently observed among the high educated. In rare cases where the scenario would lead to a mortality increase in lower educated groups (because of reverse social gradient of risk factor prevalence) we set the PAF to zero because it is implausible that a policy intervention would aim at deteriorating health outcomes. The second scenario called best practice country (BPC) scenario modelled a currently optimal situation, applying the risk factor distribution of a country that combined (one of) the lowest average risk factors prevalence with (one of) the smallest relative inequalities in the specific risk factor (without reverse social gradient in the risk factor distribution) (Tables S3 and S4, supplementary material). After careful evaluation, the countries that fulfilled best the above mentioned criteria were France (men) and Finland (women) for smoking, Norway (both men and women) for overweight/obesity, and Denmark (men) and Finland (women) for physical inactivity. The prevalence of the risk factor among high educated individuals always changes in the BPC scenario. It is therefore possible that by using the risk factor distribution of the ‘best country’ in other countries the mortality rate is reduced more among high educated than among low or mid educated and as a consequence, the relative inequalities increase. It is also possible that the mortality rate increases in a specific educational group if the risk factor distribution in this group is less ‘favourable’ in the BPC than in the specific country. This will lead to a negative PAF value. Although this is not what we aim for, we accepted negative values so that our results always show how the mortality would change if we applied the risk factor distribution of the BPC, regardless of whether this is an improvement or a deterioration.
Results
Country . | Men . | Women . | ||||
---|---|---|---|---|---|---|
Smoking . | Overweight/Obesity . | Physical inactivity . | Smoking . | Overweight/Obesity . | Physical inactivity . | |
Finland | 5.6 (3.9–7.5) | 1.4 (1.0–1.8) | 0.7 (0.5–1.2) | 2.4 (1.7–4.1) | 5.3 (3.1–7.5) | 0.1 (0.0–1.5) |
Sweden | 6.4 (4.6–8.1) | 3.7 (1.9–5.5) | n.a. | 4.8 (3.1–6.6) | 6.0 (4.3–7.7) | n.a. |
Norway | 11.5 (9.2–13.5) | 5.0 (2.7–7.3) | 3.0 (2.0–3.6) | 9.6 (7.4–11.6) | 7.3 (5.0–9.6) | 2.2 (1.7–3.0) |
Denmark | 6.3 (4.7–7.8) | 5.4 (3.6–6.9) | 2.9 (2.1–3.8) | 3.6 (2.6–4.9) | 6.3 (4.8–7.8) | 4.4 (3.4–5.3) |
England & Wales | 8.9 (7.8–10.1) | 3.3 (2.1–4.5) | n.a. | 6.5 (5.2–7.8) | 6.8 (5.6–8.0) | n.a. |
Scotland | 9.5 (7.8–11.1) | 3.8 (2.1–5.5) | n.a. | 12.0 (10.4–13.7) | 2.7 (1.2–4.2) | n.a. |
Netherlands | 5.2 (3.7–6.6) | 5.5 (4.1–6.9) | 1.2 (0.5–2.0) | 3.0 (2.4–3.8) | 6.8 (5.5–8.1) | 3.8 (3.0–4.5) |
Belgium | 3.0 (2.2–4.0) | 4.7 (3.6–5.8) | 3.1 (2.5–3.8) | 0.8 (0.7–1.1) | 8.3 (7.2–9.4) | 6.8 (6.1–7.5) |
France | 3.4 (2.8–4.7) | 6.7 (5.4–8.0) | n.a. | 1.8 (1.4–2.1) | 9.8 (8.4–11.2) | n.a. |
Switzerland | 3.7 (1.8–5.8) | 6.3 (4.4–8.2) | 4.8 (4.0–5.8) | 0.7 (0.4–1.4) | 10.3 (9.0–11.6) | 5.7 (5.0–6.6) |
Austria | 3.5 (1.9–6.2) | 7.7 (5.1–10.3) | n.a. | 0.5 (0.2–1.0) | 10.3 (8.5–12.1) | n.a. |
Barcelona | 2.6 (2.3–3.3) | 3.9 (3.1–4.7) | 3.1 (3.0–3.3) | 0.2 (0.0–0.3) | 9.2 (8.2–10.2) | 2.2 (2.1–2.4) |
Basque Country | 3.1 (2.3–4.5) | 2.1 (0.9–3.3) | 3.2 (3.1–3.3) | 0.7 (0.3–1.0) | 9.7 (8.4–11.0) | 2.4 (2.3–2.6) |
Madrid | 2.3 (2.1–2.9) | 3.9 (3.1–4.7) | 3.0 (2.9–3.2) | 0.3 (0.2–0.4) | 8.9 (7.9–9.9) | 2.1 (1.9–2.2) |
Turin | 1.5 (1.3–1.9) | 5.1 (4.7–5.5) | 1.3 (1.1–1.4) | 0.1 (0.1–0.2) | 8.3 (7.9–8.7) | 1.5 (1.4–1.7) |
Tuscany | 1.5 (1.3–1.9) | 5.0 (4.6–5.4) | 1.3 (1.2–1.5) | 0.1 (0.0–0.2) | 8.4 (8.0–8.8) | 1.6 (1.4–1.7) |
Hungary | n.a. | 2.3 (0.6–4.0) | n.a. | n.a. | 6.2 (4.7–7.7) | n.a. |
Czech Republic | 9.3 (6.4–12.1) | 5.8 (3.1–8.5) | 0.0 (0.0–0.6) | 3.0 (2.1–4.2) | 8.0 (5.4–10.6) | 2.0 (1.5–2.7) |
Poland | 10.9 (9.8–11.9) | 1.4 (0.6–2.2) | n.a. | 2.6 (2.0–3.2) | 8.6 (7.7–9.5) | n.a. |
Lithuania | 6.0 (3.6–7.2) | 2.4 (0.7–4.1) | 4.9 (4.0–5.9) | 1.4 (0.2–2.0) | 1.9 (0.1–3.7) | 10.5 (9.0–11.1) |
Estonia | 5.9 (4.1–9.2) | 0.1 (0.0–2.9) | 0.9 (0.5–1.2) | 2.4 (2.2–6.6) | 8.5 (5.4–11.6) | 2.9 (2.6–6.4) |
EU average | 6.1 | 4.1 | 2.4 | 3.3 | 7.3 | 3.8 |
Country . | Men . | Women . | ||||
---|---|---|---|---|---|---|
Smoking . | Overweight/Obesity . | Physical inactivity . | Smoking . | Overweight/Obesity . | Physical inactivity . | |
Finland | 5.6 (3.9–7.5) | 1.4 (1.0–1.8) | 0.7 (0.5–1.2) | 2.4 (1.7–4.1) | 5.3 (3.1–7.5) | 0.1 (0.0–1.5) |
Sweden | 6.4 (4.6–8.1) | 3.7 (1.9–5.5) | n.a. | 4.8 (3.1–6.6) | 6.0 (4.3–7.7) | n.a. |
Norway | 11.5 (9.2–13.5) | 5.0 (2.7–7.3) | 3.0 (2.0–3.6) | 9.6 (7.4–11.6) | 7.3 (5.0–9.6) | 2.2 (1.7–3.0) |
Denmark | 6.3 (4.7–7.8) | 5.4 (3.6–6.9) | 2.9 (2.1–3.8) | 3.6 (2.6–4.9) | 6.3 (4.8–7.8) | 4.4 (3.4–5.3) |
England & Wales | 8.9 (7.8–10.1) | 3.3 (2.1–4.5) | n.a. | 6.5 (5.2–7.8) | 6.8 (5.6–8.0) | n.a. |
Scotland | 9.5 (7.8–11.1) | 3.8 (2.1–5.5) | n.a. | 12.0 (10.4–13.7) | 2.7 (1.2–4.2) | n.a. |
Netherlands | 5.2 (3.7–6.6) | 5.5 (4.1–6.9) | 1.2 (0.5–2.0) | 3.0 (2.4–3.8) | 6.8 (5.5–8.1) | 3.8 (3.0–4.5) |
Belgium | 3.0 (2.2–4.0) | 4.7 (3.6–5.8) | 3.1 (2.5–3.8) | 0.8 (0.7–1.1) | 8.3 (7.2–9.4) | 6.8 (6.1–7.5) |
France | 3.4 (2.8–4.7) | 6.7 (5.4–8.0) | n.a. | 1.8 (1.4–2.1) | 9.8 (8.4–11.2) | n.a. |
Switzerland | 3.7 (1.8–5.8) | 6.3 (4.4–8.2) | 4.8 (4.0–5.8) | 0.7 (0.4–1.4) | 10.3 (9.0–11.6) | 5.7 (5.0–6.6) |
Austria | 3.5 (1.9–6.2) | 7.7 (5.1–10.3) | n.a. | 0.5 (0.2–1.0) | 10.3 (8.5–12.1) | n.a. |
Barcelona | 2.6 (2.3–3.3) | 3.9 (3.1–4.7) | 3.1 (3.0–3.3) | 0.2 (0.0–0.3) | 9.2 (8.2–10.2) | 2.2 (2.1–2.4) |
Basque Country | 3.1 (2.3–4.5) | 2.1 (0.9–3.3) | 3.2 (3.1–3.3) | 0.7 (0.3–1.0) | 9.7 (8.4–11.0) | 2.4 (2.3–2.6) |
Madrid | 2.3 (2.1–2.9) | 3.9 (3.1–4.7) | 3.0 (2.9–3.2) | 0.3 (0.2–0.4) | 8.9 (7.9–9.9) | 2.1 (1.9–2.2) |
Turin | 1.5 (1.3–1.9) | 5.1 (4.7–5.5) | 1.3 (1.1–1.4) | 0.1 (0.1–0.2) | 8.3 (7.9–8.7) | 1.5 (1.4–1.7) |
Tuscany | 1.5 (1.3–1.9) | 5.0 (4.6–5.4) | 1.3 (1.2–1.5) | 0.1 (0.0–0.2) | 8.4 (8.0–8.8) | 1.6 (1.4–1.7) |
Hungary | n.a. | 2.3 (0.6–4.0) | n.a. | n.a. | 6.2 (4.7–7.7) | n.a. |
Czech Republic | 9.3 (6.4–12.1) | 5.8 (3.1–8.5) | 0.0 (0.0–0.6) | 3.0 (2.1–4.2) | 8.0 (5.4–10.6) | 2.0 (1.5–2.7) |
Poland | 10.9 (9.8–11.9) | 1.4 (0.6–2.2) | n.a. | 2.6 (2.0–3.2) | 8.6 (7.7–9.5) | n.a. |
Lithuania | 6.0 (3.6–7.2) | 2.4 (0.7–4.1) | 4.9 (4.0–5.9) | 1.4 (0.2–2.0) | 1.9 (0.1–3.7) | 10.5 (9.0–11.1) |
Estonia | 5.9 (4.1–9.2) | 0.1 (0.0–2.9) | 0.9 (0.5–1.2) | 2.4 (2.2–6.6) | 8.5 (5.4–11.6) | 2.9 (2.6–6.4) |
EU average | 6.1 | 4.1 | 2.4 | 3.3 | 7.3 | 3.8 |
n.a. = not available
Note: The population attributable fraction was calculated according to the following formula:
where n is the number of exposure categories of the risk factor, Pi is the proportion of population currently in the ith exposure category, P’i is the proportion of population in the ith exposure category in the scenario, and RRi is the relative mortality risk for the ith exposure category obtained from the literature.
Country . | Men . | Women . | ||||
---|---|---|---|---|---|---|
Smoking . | Overweight/Obesity . | Physical inactivity . | Smoking . | Overweight/Obesity . | Physical inactivity . | |
Finland | 5.6 (3.9–7.5) | 1.4 (1.0–1.8) | 0.7 (0.5–1.2) | 2.4 (1.7–4.1) | 5.3 (3.1–7.5) | 0.1 (0.0–1.5) |
Sweden | 6.4 (4.6–8.1) | 3.7 (1.9–5.5) | n.a. | 4.8 (3.1–6.6) | 6.0 (4.3–7.7) | n.a. |
Norway | 11.5 (9.2–13.5) | 5.0 (2.7–7.3) | 3.0 (2.0–3.6) | 9.6 (7.4–11.6) | 7.3 (5.0–9.6) | 2.2 (1.7–3.0) |
Denmark | 6.3 (4.7–7.8) | 5.4 (3.6–6.9) | 2.9 (2.1–3.8) | 3.6 (2.6–4.9) | 6.3 (4.8–7.8) | 4.4 (3.4–5.3) |
England & Wales | 8.9 (7.8–10.1) | 3.3 (2.1–4.5) | n.a. | 6.5 (5.2–7.8) | 6.8 (5.6–8.0) | n.a. |
Scotland | 9.5 (7.8–11.1) | 3.8 (2.1–5.5) | n.a. | 12.0 (10.4–13.7) | 2.7 (1.2–4.2) | n.a. |
Netherlands | 5.2 (3.7–6.6) | 5.5 (4.1–6.9) | 1.2 (0.5–2.0) | 3.0 (2.4–3.8) | 6.8 (5.5–8.1) | 3.8 (3.0–4.5) |
Belgium | 3.0 (2.2–4.0) | 4.7 (3.6–5.8) | 3.1 (2.5–3.8) | 0.8 (0.7–1.1) | 8.3 (7.2–9.4) | 6.8 (6.1–7.5) |
France | 3.4 (2.8–4.7) | 6.7 (5.4–8.0) | n.a. | 1.8 (1.4–2.1) | 9.8 (8.4–11.2) | n.a. |
Switzerland | 3.7 (1.8–5.8) | 6.3 (4.4–8.2) | 4.8 (4.0–5.8) | 0.7 (0.4–1.4) | 10.3 (9.0–11.6) | 5.7 (5.0–6.6) |
Austria | 3.5 (1.9–6.2) | 7.7 (5.1–10.3) | n.a. | 0.5 (0.2–1.0) | 10.3 (8.5–12.1) | n.a. |
Barcelona | 2.6 (2.3–3.3) | 3.9 (3.1–4.7) | 3.1 (3.0–3.3) | 0.2 (0.0–0.3) | 9.2 (8.2–10.2) | 2.2 (2.1–2.4) |
Basque Country | 3.1 (2.3–4.5) | 2.1 (0.9–3.3) | 3.2 (3.1–3.3) | 0.7 (0.3–1.0) | 9.7 (8.4–11.0) | 2.4 (2.3–2.6) |
Madrid | 2.3 (2.1–2.9) | 3.9 (3.1–4.7) | 3.0 (2.9–3.2) | 0.3 (0.2–0.4) | 8.9 (7.9–9.9) | 2.1 (1.9–2.2) |
Turin | 1.5 (1.3–1.9) | 5.1 (4.7–5.5) | 1.3 (1.1–1.4) | 0.1 (0.1–0.2) | 8.3 (7.9–8.7) | 1.5 (1.4–1.7) |
Tuscany | 1.5 (1.3–1.9) | 5.0 (4.6–5.4) | 1.3 (1.2–1.5) | 0.1 (0.0–0.2) | 8.4 (8.0–8.8) | 1.6 (1.4–1.7) |
Hungary | n.a. | 2.3 (0.6–4.0) | n.a. | n.a. | 6.2 (4.7–7.7) | n.a. |
Czech Republic | 9.3 (6.4–12.1) | 5.8 (3.1–8.5) | 0.0 (0.0–0.6) | 3.0 (2.1–4.2) | 8.0 (5.4–10.6) | 2.0 (1.5–2.7) |
Poland | 10.9 (9.8–11.9) | 1.4 (0.6–2.2) | n.a. | 2.6 (2.0–3.2) | 8.6 (7.7–9.5) | n.a. |
Lithuania | 6.0 (3.6–7.2) | 2.4 (0.7–4.1) | 4.9 (4.0–5.9) | 1.4 (0.2–2.0) | 1.9 (0.1–3.7) | 10.5 (9.0–11.1) |
Estonia | 5.9 (4.1–9.2) | 0.1 (0.0–2.9) | 0.9 (0.5–1.2) | 2.4 (2.2–6.6) | 8.5 (5.4–11.6) | 2.9 (2.6–6.4) |
EU average | 6.1 | 4.1 | 2.4 | 3.3 | 7.3 | 3.8 |
Country . | Men . | Women . | ||||
---|---|---|---|---|---|---|
Smoking . | Overweight/Obesity . | Physical inactivity . | Smoking . | Overweight/Obesity . | Physical inactivity . | |
Finland | 5.6 (3.9–7.5) | 1.4 (1.0–1.8) | 0.7 (0.5–1.2) | 2.4 (1.7–4.1) | 5.3 (3.1–7.5) | 0.1 (0.0–1.5) |
Sweden | 6.4 (4.6–8.1) | 3.7 (1.9–5.5) | n.a. | 4.8 (3.1–6.6) | 6.0 (4.3–7.7) | n.a. |
Norway | 11.5 (9.2–13.5) | 5.0 (2.7–7.3) | 3.0 (2.0–3.6) | 9.6 (7.4–11.6) | 7.3 (5.0–9.6) | 2.2 (1.7–3.0) |
Denmark | 6.3 (4.7–7.8) | 5.4 (3.6–6.9) | 2.9 (2.1–3.8) | 3.6 (2.6–4.9) | 6.3 (4.8–7.8) | 4.4 (3.4–5.3) |
England & Wales | 8.9 (7.8–10.1) | 3.3 (2.1–4.5) | n.a. | 6.5 (5.2–7.8) | 6.8 (5.6–8.0) | n.a. |
Scotland | 9.5 (7.8–11.1) | 3.8 (2.1–5.5) | n.a. | 12.0 (10.4–13.7) | 2.7 (1.2–4.2) | n.a. |
Netherlands | 5.2 (3.7–6.6) | 5.5 (4.1–6.9) | 1.2 (0.5–2.0) | 3.0 (2.4–3.8) | 6.8 (5.5–8.1) | 3.8 (3.0–4.5) |
Belgium | 3.0 (2.2–4.0) | 4.7 (3.6–5.8) | 3.1 (2.5–3.8) | 0.8 (0.7–1.1) | 8.3 (7.2–9.4) | 6.8 (6.1–7.5) |
France | 3.4 (2.8–4.7) | 6.7 (5.4–8.0) | n.a. | 1.8 (1.4–2.1) | 9.8 (8.4–11.2) | n.a. |
Switzerland | 3.7 (1.8–5.8) | 6.3 (4.4–8.2) | 4.8 (4.0–5.8) | 0.7 (0.4–1.4) | 10.3 (9.0–11.6) | 5.7 (5.0–6.6) |
Austria | 3.5 (1.9–6.2) | 7.7 (5.1–10.3) | n.a. | 0.5 (0.2–1.0) | 10.3 (8.5–12.1) | n.a. |
Barcelona | 2.6 (2.3–3.3) | 3.9 (3.1–4.7) | 3.1 (3.0–3.3) | 0.2 (0.0–0.3) | 9.2 (8.2–10.2) | 2.2 (2.1–2.4) |
Basque Country | 3.1 (2.3–4.5) | 2.1 (0.9–3.3) | 3.2 (3.1–3.3) | 0.7 (0.3–1.0) | 9.7 (8.4–11.0) | 2.4 (2.3–2.6) |
Madrid | 2.3 (2.1–2.9) | 3.9 (3.1–4.7) | 3.0 (2.9–3.2) | 0.3 (0.2–0.4) | 8.9 (7.9–9.9) | 2.1 (1.9–2.2) |
Turin | 1.5 (1.3–1.9) | 5.1 (4.7–5.5) | 1.3 (1.1–1.4) | 0.1 (0.1–0.2) | 8.3 (7.9–8.7) | 1.5 (1.4–1.7) |
Tuscany | 1.5 (1.3–1.9) | 5.0 (4.6–5.4) | 1.3 (1.2–1.5) | 0.1 (0.0–0.2) | 8.4 (8.0–8.8) | 1.6 (1.4–1.7) |
Hungary | n.a. | 2.3 (0.6–4.0) | n.a. | n.a. | 6.2 (4.7–7.7) | n.a. |
Czech Republic | 9.3 (6.4–12.1) | 5.8 (3.1–8.5) | 0.0 (0.0–0.6) | 3.0 (2.1–4.2) | 8.0 (5.4–10.6) | 2.0 (1.5–2.7) |
Poland | 10.9 (9.8–11.9) | 1.4 (0.6–2.2) | n.a. | 2.6 (2.0–3.2) | 8.6 (7.7–9.5) | n.a. |
Lithuania | 6.0 (3.6–7.2) | 2.4 (0.7–4.1) | 4.9 (4.0–5.9) | 1.4 (0.2–2.0) | 1.9 (0.1–3.7) | 10.5 (9.0–11.1) |
Estonia | 5.9 (4.1–9.2) | 0.1 (0.0–2.9) | 0.9 (0.5–1.2) | 2.4 (2.2–6.6) | 8.5 (5.4–11.6) | 2.9 (2.6–6.4) |
EU average | 6.1 | 4.1 | 2.4 | 3.3 | 7.3 | 3.8 |
n.a. = not available
Note: The population attributable fraction was calculated according to the following formula:
where n is the number of exposure categories of the risk factor, Pi is the proportion of population currently in the ith exposure category, P’i is the proportion of population in the ith exposure category in the scenario, and RRi is the relative mortality risk for the ith exposure category obtained from the literature.
Table 2 describes the potential reduction of relative and absolute inequalities in IHD mortality. Among men, eliminating educational inequalities in smoking reduced relative inequalities in IHD mortality by more than 10% in most European populations, whereas the elimination of educational inequalities in overweight/obesity and in physical inactivity had considerable impact on relative inequalities only in southern Europe. Among women, the elimination of educational inequalities in smoking and in physical inactivity would lead to a small reduction in relative inequalities in IHD mortality in most populations, except Norway, England and Wales, and Scotland, where the reduction exceeded 10%. By contrast, eliminating inequalities in overweight/obesity could reduce relative inequalities in IHD mortality by 10%–50% in most European populations. Although the largest percentage reductions were achieved in southern European regions, these reductions applied to low mortality rate ratio and/or to low mortality rate and did not lead to any considerable reductions in absolute terms. The reduction in absolute inequalities was largest in Eastern Europe, especially among men. A substantial reduction in absolute inequalities would also be observed with an elimination of inequalities in smoking in the Nordic countries (men), England and Wales (men) and Scotland (men and women).
Country . | Original . | Inequality decrease after implementation of the scenario . | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
MRR . | RD . | Smoking . | Overweight/Obesity . | Physical inactivity . | ||||||||||
UL scenario . | BPC scenario . | UL scenario . | BPC scenario . | UL scenario . | BPC scenario . | |||||||||
rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | |||
MEN | ||||||||||||||
Finland | 2.13 | 157 | 11 | 17 | 7 | 11 | 3 | 4 | −17 | −2 | 1 | 2 | −7 | −4 |
Sweden | 2.10 | 107 | 12 | 13 | 8 | 7 | 7 | 8 | −3 | 2 | n.a. | n.a. | n.a. | n.a. |
Norway | 2.51 | 114 | 19 | 22 | 15 | 14 | 8 | 10 | ref | ref | 5 | 6 | 0 | 16 |
Denmark | 1.87 | 76 | 14 | 10 | 10 | 13 | 12 | 9 | 0 | 4 | 6 | 5 | ref | ref |
England & Wales | 1.60 | 83 | 22 | 20 | 18 | 14 | 9 | 7 | −5 | 7 | n.a. | n.a. | n.a. | n.a. |
Scotland | 2.11 | 130 | 18 | 23 | 12 | 17 | 7 | 10 | −3 | 11 | n.a. | n.a. | n.a. | n.a. |
Netherlands | 2.08 | 75 | 10 | 8 | 6 | 10 | 11 | 8 | 1 | 3 | 2 | 2 | −4 | 7 |
Belgium | 1.80 | 56 | 7 | 4 | 2 | 5 | 11 | 6 | −1 | 3 | 7 | 4 | 0 | 3 |
France | 2.16 | 51 | 6 | 3 | ref | ref | 13 | 6 | 3 | 4 | n.a. | n.a. | n.a. | n.a. |
Switzerland | 1.95 | 68 | 8 | 5 | 4 | 6 | 13 | 9 | 2 | 4 | 10 | 7 | 4 | 2 |
Austria | 1.63 | 90 | 9 | 8 | 6 | 4 | 20 | 18 | 4 | 8 | n.a. | n.a. | n.a. | n.a. |
Barcelona | 1.33 | 24 | 10 | 3 | −1 | 2 | 16 | 4 | −7 | 1 | 12 | 3 | 0 | 5 |
Basque Country | 1.24 | 17 | 16 | 3 | 0 | 1 | 11 | 2 | −19 | −1 | 16 | 3 | 0 | 3 |
Madrid | 1.12 | 11 | 22 | 3 | −1 | 1 | 36 | 4 | −19 | −1 | 28 | 3 | −1 | 2 |
Turin | 1.35 | 26 | 6 | 2 | −3 | 0 | 20 | 5 | 0 | 1 | 5 | 1 | −7 | 3 |
Tuscany | 1.54 | 32 | 4 | 1 | −2 | 1 | 14 | 5 | 0 | 1 | 4 | 1 | −5 | 4 |
Hungary | 2.53 | 308 | n.a. | n.a. | n.a. | n.a. | 4 | 12 | −5 | 20 | n.a. | n.a. | n.a. | n.a. |
Czech Republic | 2.90 | 251 | 14 | 36 | 12 | 25 | 9 | 22 | 2 | 25 | 0 | 0 | −5 | 36 |
Poland | 2.04 | 143 | 21 | 31 | 17 | 26 | 3 | 4 | −9 | 6 | n.a. | n.a. | n.a. | n.a. |
Lithuania | 2.04 | 309 | 12 | 36 | 9 | 37 | 5 | 15 | −7 | 3 | 10 | 30 | 5 | 39 |
Estonia | 2.29 | 400 | 10 | 42 | 5 | 54 | 0 | 1 | −13 | 3 | 2 | 7 | −9 | 48 |
WOMEN | ||||||||||||||
Finland | 2.64 | 56 | 4 | 2 | ref | ref | 9 | 5 | −4 | 2 | 0 | 0 | ref | ref |
Sweden | 2.72 | 48 | 8 | 4 | 4 | 4 | 10 | 5 | −3 | 1 | n.a. | n.a. | n.a. | n.a. |
Norway | 2.91 | 42 | 15 | 6 | 12 | 6 | 11 | 5 | ref | ref | 3 | 1 | 3 | 10 |
Denmark | 2.28 | 35 | 6 | 2 | 2 | 6 | 11 | 4 | −2 | 0 | 8 | 3 | 9 | 2 |
England & Wales | 2.70 | 60 | 10 | 6 | 6 | 8 | 11 | 7 | −1 | 7 | n.a. | n.a. | n.a. | n.a. |
Scotland | 3.23 | 81 | 17 | 14 | 12 | 15 | 4 | 3 | −8 | 5 | n.a. | n.a. | n.a. | n.a. |
Netherlands | 2.44 | 33 | 5 | 2 | −2 | 3 | 12 | 4 | −2 | 1 | 7 | 2 | 8 | 6 |
Belgium | 2.20 | 23 | 2 | 0 | −8 | 0 | 15 | 4 | 2 | 1 | 12 | 3 | 14 | 4 |
France | 3.48 | 19 | 3 | 1 | −8 | −1 | 14 | 3 | 3 | 1 | n.a. | n.a. | n.a. | n.a. |
Switzerland | 2.21 | 23 | 1 | 0 | −11 | 0 | 19 | 4 | 5 | 1 | 10 | 2 | 12 | 2 |
Austria | 1.88 | 43 | 1 | 0 | −6 | −3 | 22 | 10 | 5 | 3 | n.a. | n.a. | n.a. | n.a. |
Barcelona | 1.62 | 9 | 1 | 0 | −26 | −2 | 24 | 2 | 5 | 1 | 6 | 1 | 10 | 3 |
Basque Country | 1.49 | 7 | 2 | 0 | −26 | −1 | 30 | 2 | 8 | 1 | 7 | 1 | 12 | 2 |
Madrid | 1.37 | 8 | 1 | 0 | −31 | −2 | 33 | 3 | 5 | 1 | 8 | 1 | 14 | 3 |
Turin | 1.21 | 5 | 0 | 0 | −57 | −2 | 48 | 3 | 2 | 0 | 9 | 0 | 13 | 2 |
Tuscany | 1.32 | 7 | 0 | 0 | −44 | −3 | 35 | 3 | 1 | 0 | 7 | 1 | 6 | 2 |
Hungary | 2.08 | 102 | n.a. | n.a. | n.a. | n.a. | 12 | 12 | −3 | 8 | n.a. | n.a. | n.a. | n.a. |
Czech Republic | 3.06 | 96 | 5 | 4 | −5 | 0 | 12 | 12 | 1 | 11 | 3 | 3 | 4 | 24 |
Poland | 2.35 | 57 | 5 | 3 | −10 | −1 | 15 | 9 | 1 | 6 | n.a. | n.a. | n.a. | n.a. |
Lithuania | 2.48 | 138 | 2 | 3 | −10 | −20 | 3 | 4 | −19 | 5 | 18 | 24 | 3 | 33 |
Estonia | 2.52 | 174 | 4 | 7 | 1 | 0 | 14 | 25 | −1 | 25 | 5 | 8 | 8 | 33 |
Country . | Original . | Inequality decrease after implementation of the scenario . | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
MRR . | RD . | Smoking . | Overweight/Obesity . | Physical inactivity . | ||||||||||
UL scenario . | BPC scenario . | UL scenario . | BPC scenario . | UL scenario . | BPC scenario . | |||||||||
rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | |||
MEN | ||||||||||||||
Finland | 2.13 | 157 | 11 | 17 | 7 | 11 | 3 | 4 | −17 | −2 | 1 | 2 | −7 | −4 |
Sweden | 2.10 | 107 | 12 | 13 | 8 | 7 | 7 | 8 | −3 | 2 | n.a. | n.a. | n.a. | n.a. |
Norway | 2.51 | 114 | 19 | 22 | 15 | 14 | 8 | 10 | ref | ref | 5 | 6 | 0 | 16 |
Denmark | 1.87 | 76 | 14 | 10 | 10 | 13 | 12 | 9 | 0 | 4 | 6 | 5 | ref | ref |
England & Wales | 1.60 | 83 | 22 | 20 | 18 | 14 | 9 | 7 | −5 | 7 | n.a. | n.a. | n.a. | n.a. |
Scotland | 2.11 | 130 | 18 | 23 | 12 | 17 | 7 | 10 | −3 | 11 | n.a. | n.a. | n.a. | n.a. |
Netherlands | 2.08 | 75 | 10 | 8 | 6 | 10 | 11 | 8 | 1 | 3 | 2 | 2 | −4 | 7 |
Belgium | 1.80 | 56 | 7 | 4 | 2 | 5 | 11 | 6 | −1 | 3 | 7 | 4 | 0 | 3 |
France | 2.16 | 51 | 6 | 3 | ref | ref | 13 | 6 | 3 | 4 | n.a. | n.a. | n.a. | n.a. |
Switzerland | 1.95 | 68 | 8 | 5 | 4 | 6 | 13 | 9 | 2 | 4 | 10 | 7 | 4 | 2 |
Austria | 1.63 | 90 | 9 | 8 | 6 | 4 | 20 | 18 | 4 | 8 | n.a. | n.a. | n.a. | n.a. |
Barcelona | 1.33 | 24 | 10 | 3 | −1 | 2 | 16 | 4 | −7 | 1 | 12 | 3 | 0 | 5 |
Basque Country | 1.24 | 17 | 16 | 3 | 0 | 1 | 11 | 2 | −19 | −1 | 16 | 3 | 0 | 3 |
Madrid | 1.12 | 11 | 22 | 3 | −1 | 1 | 36 | 4 | −19 | −1 | 28 | 3 | −1 | 2 |
Turin | 1.35 | 26 | 6 | 2 | −3 | 0 | 20 | 5 | 0 | 1 | 5 | 1 | −7 | 3 |
Tuscany | 1.54 | 32 | 4 | 1 | −2 | 1 | 14 | 5 | 0 | 1 | 4 | 1 | −5 | 4 |
Hungary | 2.53 | 308 | n.a. | n.a. | n.a. | n.a. | 4 | 12 | −5 | 20 | n.a. | n.a. | n.a. | n.a. |
Czech Republic | 2.90 | 251 | 14 | 36 | 12 | 25 | 9 | 22 | 2 | 25 | 0 | 0 | −5 | 36 |
Poland | 2.04 | 143 | 21 | 31 | 17 | 26 | 3 | 4 | −9 | 6 | n.a. | n.a. | n.a. | n.a. |
Lithuania | 2.04 | 309 | 12 | 36 | 9 | 37 | 5 | 15 | −7 | 3 | 10 | 30 | 5 | 39 |
Estonia | 2.29 | 400 | 10 | 42 | 5 | 54 | 0 | 1 | −13 | 3 | 2 | 7 | −9 | 48 |
WOMEN | ||||||||||||||
Finland | 2.64 | 56 | 4 | 2 | ref | ref | 9 | 5 | −4 | 2 | 0 | 0 | ref | ref |
Sweden | 2.72 | 48 | 8 | 4 | 4 | 4 | 10 | 5 | −3 | 1 | n.a. | n.a. | n.a. | n.a. |
Norway | 2.91 | 42 | 15 | 6 | 12 | 6 | 11 | 5 | ref | ref | 3 | 1 | 3 | 10 |
Denmark | 2.28 | 35 | 6 | 2 | 2 | 6 | 11 | 4 | −2 | 0 | 8 | 3 | 9 | 2 |
England & Wales | 2.70 | 60 | 10 | 6 | 6 | 8 | 11 | 7 | −1 | 7 | n.a. | n.a. | n.a. | n.a. |
Scotland | 3.23 | 81 | 17 | 14 | 12 | 15 | 4 | 3 | −8 | 5 | n.a. | n.a. | n.a. | n.a. |
Netherlands | 2.44 | 33 | 5 | 2 | −2 | 3 | 12 | 4 | −2 | 1 | 7 | 2 | 8 | 6 |
Belgium | 2.20 | 23 | 2 | 0 | −8 | 0 | 15 | 4 | 2 | 1 | 12 | 3 | 14 | 4 |
France | 3.48 | 19 | 3 | 1 | −8 | −1 | 14 | 3 | 3 | 1 | n.a. | n.a. | n.a. | n.a. |
Switzerland | 2.21 | 23 | 1 | 0 | −11 | 0 | 19 | 4 | 5 | 1 | 10 | 2 | 12 | 2 |
Austria | 1.88 | 43 | 1 | 0 | −6 | −3 | 22 | 10 | 5 | 3 | n.a. | n.a. | n.a. | n.a. |
Barcelona | 1.62 | 9 | 1 | 0 | −26 | −2 | 24 | 2 | 5 | 1 | 6 | 1 | 10 | 3 |
Basque Country | 1.49 | 7 | 2 | 0 | −26 | −1 | 30 | 2 | 8 | 1 | 7 | 1 | 12 | 2 |
Madrid | 1.37 | 8 | 1 | 0 | −31 | −2 | 33 | 3 | 5 | 1 | 8 | 1 | 14 | 3 |
Turin | 1.21 | 5 | 0 | 0 | −57 | −2 | 48 | 3 | 2 | 0 | 9 | 0 | 13 | 2 |
Tuscany | 1.32 | 7 | 0 | 0 | −44 | −3 | 35 | 3 | 1 | 0 | 7 | 1 | 6 | 2 |
Hungary | 2.08 | 102 | n.a. | n.a. | n.a. | n.a. | 12 | 12 | −3 | 8 | n.a. | n.a. | n.a. | n.a. |
Czech Republic | 3.06 | 96 | 5 | 4 | −5 | 0 | 12 | 12 | 1 | 11 | 3 | 3 | 4 | 24 |
Poland | 2.35 | 57 | 5 | 3 | −10 | −1 | 15 | 9 | 1 | 6 | n.a. | n.a. | n.a. | n.a. |
Lithuania | 2.48 | 138 | 2 | 3 | −10 | −20 | 3 | 4 | −19 | 5 | 18 | 24 | 3 | 33 |
Estonia | 2.52 | 174 | 4 | 7 | 1 | 0 | 14 | 25 | −1 | 25 | 5 | 8 | 8 | 33 |
rel red = relative reduction; abs red = absolute reduction; ref = reference country; n.a. = not available
Note: The potential reduction in relative inequalities was expressed as a percentage change in excess mortality using following formula (for low educated):
, where MRR is the mortality rate ratio.
The potential reduction in absolute inequalities was expressed as a number of deaths per 100 000 person-years and calculated as the difference in rate difference before and after the implementation of the counterfactual scenario using the following formula (for low educated):
, where ASMR is the age-standardized mortality rate.
Country . | Original . | Inequality decrease after implementation of the scenario . | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
MRR . | RD . | Smoking . | Overweight/Obesity . | Physical inactivity . | ||||||||||
UL scenario . | BPC scenario . | UL scenario . | BPC scenario . | UL scenario . | BPC scenario . | |||||||||
rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | |||
MEN | ||||||||||||||
Finland | 2.13 | 157 | 11 | 17 | 7 | 11 | 3 | 4 | −17 | −2 | 1 | 2 | −7 | −4 |
Sweden | 2.10 | 107 | 12 | 13 | 8 | 7 | 7 | 8 | −3 | 2 | n.a. | n.a. | n.a. | n.a. |
Norway | 2.51 | 114 | 19 | 22 | 15 | 14 | 8 | 10 | ref | ref | 5 | 6 | 0 | 16 |
Denmark | 1.87 | 76 | 14 | 10 | 10 | 13 | 12 | 9 | 0 | 4 | 6 | 5 | ref | ref |
England & Wales | 1.60 | 83 | 22 | 20 | 18 | 14 | 9 | 7 | −5 | 7 | n.a. | n.a. | n.a. | n.a. |
Scotland | 2.11 | 130 | 18 | 23 | 12 | 17 | 7 | 10 | −3 | 11 | n.a. | n.a. | n.a. | n.a. |
Netherlands | 2.08 | 75 | 10 | 8 | 6 | 10 | 11 | 8 | 1 | 3 | 2 | 2 | −4 | 7 |
Belgium | 1.80 | 56 | 7 | 4 | 2 | 5 | 11 | 6 | −1 | 3 | 7 | 4 | 0 | 3 |
France | 2.16 | 51 | 6 | 3 | ref | ref | 13 | 6 | 3 | 4 | n.a. | n.a. | n.a. | n.a. |
Switzerland | 1.95 | 68 | 8 | 5 | 4 | 6 | 13 | 9 | 2 | 4 | 10 | 7 | 4 | 2 |
Austria | 1.63 | 90 | 9 | 8 | 6 | 4 | 20 | 18 | 4 | 8 | n.a. | n.a. | n.a. | n.a. |
Barcelona | 1.33 | 24 | 10 | 3 | −1 | 2 | 16 | 4 | −7 | 1 | 12 | 3 | 0 | 5 |
Basque Country | 1.24 | 17 | 16 | 3 | 0 | 1 | 11 | 2 | −19 | −1 | 16 | 3 | 0 | 3 |
Madrid | 1.12 | 11 | 22 | 3 | −1 | 1 | 36 | 4 | −19 | −1 | 28 | 3 | −1 | 2 |
Turin | 1.35 | 26 | 6 | 2 | −3 | 0 | 20 | 5 | 0 | 1 | 5 | 1 | −7 | 3 |
Tuscany | 1.54 | 32 | 4 | 1 | −2 | 1 | 14 | 5 | 0 | 1 | 4 | 1 | −5 | 4 |
Hungary | 2.53 | 308 | n.a. | n.a. | n.a. | n.a. | 4 | 12 | −5 | 20 | n.a. | n.a. | n.a. | n.a. |
Czech Republic | 2.90 | 251 | 14 | 36 | 12 | 25 | 9 | 22 | 2 | 25 | 0 | 0 | −5 | 36 |
Poland | 2.04 | 143 | 21 | 31 | 17 | 26 | 3 | 4 | −9 | 6 | n.a. | n.a. | n.a. | n.a. |
Lithuania | 2.04 | 309 | 12 | 36 | 9 | 37 | 5 | 15 | −7 | 3 | 10 | 30 | 5 | 39 |
Estonia | 2.29 | 400 | 10 | 42 | 5 | 54 | 0 | 1 | −13 | 3 | 2 | 7 | −9 | 48 |
WOMEN | ||||||||||||||
Finland | 2.64 | 56 | 4 | 2 | ref | ref | 9 | 5 | −4 | 2 | 0 | 0 | ref | ref |
Sweden | 2.72 | 48 | 8 | 4 | 4 | 4 | 10 | 5 | −3 | 1 | n.a. | n.a. | n.a. | n.a. |
Norway | 2.91 | 42 | 15 | 6 | 12 | 6 | 11 | 5 | ref | ref | 3 | 1 | 3 | 10 |
Denmark | 2.28 | 35 | 6 | 2 | 2 | 6 | 11 | 4 | −2 | 0 | 8 | 3 | 9 | 2 |
England & Wales | 2.70 | 60 | 10 | 6 | 6 | 8 | 11 | 7 | −1 | 7 | n.a. | n.a. | n.a. | n.a. |
Scotland | 3.23 | 81 | 17 | 14 | 12 | 15 | 4 | 3 | −8 | 5 | n.a. | n.a. | n.a. | n.a. |
Netherlands | 2.44 | 33 | 5 | 2 | −2 | 3 | 12 | 4 | −2 | 1 | 7 | 2 | 8 | 6 |
Belgium | 2.20 | 23 | 2 | 0 | −8 | 0 | 15 | 4 | 2 | 1 | 12 | 3 | 14 | 4 |
France | 3.48 | 19 | 3 | 1 | −8 | −1 | 14 | 3 | 3 | 1 | n.a. | n.a. | n.a. | n.a. |
Switzerland | 2.21 | 23 | 1 | 0 | −11 | 0 | 19 | 4 | 5 | 1 | 10 | 2 | 12 | 2 |
Austria | 1.88 | 43 | 1 | 0 | −6 | −3 | 22 | 10 | 5 | 3 | n.a. | n.a. | n.a. | n.a. |
Barcelona | 1.62 | 9 | 1 | 0 | −26 | −2 | 24 | 2 | 5 | 1 | 6 | 1 | 10 | 3 |
Basque Country | 1.49 | 7 | 2 | 0 | −26 | −1 | 30 | 2 | 8 | 1 | 7 | 1 | 12 | 2 |
Madrid | 1.37 | 8 | 1 | 0 | −31 | −2 | 33 | 3 | 5 | 1 | 8 | 1 | 14 | 3 |
Turin | 1.21 | 5 | 0 | 0 | −57 | −2 | 48 | 3 | 2 | 0 | 9 | 0 | 13 | 2 |
Tuscany | 1.32 | 7 | 0 | 0 | −44 | −3 | 35 | 3 | 1 | 0 | 7 | 1 | 6 | 2 |
Hungary | 2.08 | 102 | n.a. | n.a. | n.a. | n.a. | 12 | 12 | −3 | 8 | n.a. | n.a. | n.a. | n.a. |
Czech Republic | 3.06 | 96 | 5 | 4 | −5 | 0 | 12 | 12 | 1 | 11 | 3 | 3 | 4 | 24 |
Poland | 2.35 | 57 | 5 | 3 | −10 | −1 | 15 | 9 | 1 | 6 | n.a. | n.a. | n.a. | n.a. |
Lithuania | 2.48 | 138 | 2 | 3 | −10 | −20 | 3 | 4 | −19 | 5 | 18 | 24 | 3 | 33 |
Estonia | 2.52 | 174 | 4 | 7 | 1 | 0 | 14 | 25 | −1 | 25 | 5 | 8 | 8 | 33 |
Country . | Original . | Inequality decrease after implementation of the scenario . | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
MRR . | RD . | Smoking . | Overweight/Obesity . | Physical inactivity . | ||||||||||
UL scenario . | BPC scenario . | UL scenario . | BPC scenario . | UL scenario . | BPC scenario . | |||||||||
rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | rel red . | abs red . | |||
MEN | ||||||||||||||
Finland | 2.13 | 157 | 11 | 17 | 7 | 11 | 3 | 4 | −17 | −2 | 1 | 2 | −7 | −4 |
Sweden | 2.10 | 107 | 12 | 13 | 8 | 7 | 7 | 8 | −3 | 2 | n.a. | n.a. | n.a. | n.a. |
Norway | 2.51 | 114 | 19 | 22 | 15 | 14 | 8 | 10 | ref | ref | 5 | 6 | 0 | 16 |
Denmark | 1.87 | 76 | 14 | 10 | 10 | 13 | 12 | 9 | 0 | 4 | 6 | 5 | ref | ref |
England & Wales | 1.60 | 83 | 22 | 20 | 18 | 14 | 9 | 7 | −5 | 7 | n.a. | n.a. | n.a. | n.a. |
Scotland | 2.11 | 130 | 18 | 23 | 12 | 17 | 7 | 10 | −3 | 11 | n.a. | n.a. | n.a. | n.a. |
Netherlands | 2.08 | 75 | 10 | 8 | 6 | 10 | 11 | 8 | 1 | 3 | 2 | 2 | −4 | 7 |
Belgium | 1.80 | 56 | 7 | 4 | 2 | 5 | 11 | 6 | −1 | 3 | 7 | 4 | 0 | 3 |
France | 2.16 | 51 | 6 | 3 | ref | ref | 13 | 6 | 3 | 4 | n.a. | n.a. | n.a. | n.a. |
Switzerland | 1.95 | 68 | 8 | 5 | 4 | 6 | 13 | 9 | 2 | 4 | 10 | 7 | 4 | 2 |
Austria | 1.63 | 90 | 9 | 8 | 6 | 4 | 20 | 18 | 4 | 8 | n.a. | n.a. | n.a. | n.a. |
Barcelona | 1.33 | 24 | 10 | 3 | −1 | 2 | 16 | 4 | −7 | 1 | 12 | 3 | 0 | 5 |
Basque Country | 1.24 | 17 | 16 | 3 | 0 | 1 | 11 | 2 | −19 | −1 | 16 | 3 | 0 | 3 |
Madrid | 1.12 | 11 | 22 | 3 | −1 | 1 | 36 | 4 | −19 | −1 | 28 | 3 | −1 | 2 |
Turin | 1.35 | 26 | 6 | 2 | −3 | 0 | 20 | 5 | 0 | 1 | 5 | 1 | −7 | 3 |
Tuscany | 1.54 | 32 | 4 | 1 | −2 | 1 | 14 | 5 | 0 | 1 | 4 | 1 | −5 | 4 |
Hungary | 2.53 | 308 | n.a. | n.a. | n.a. | n.a. | 4 | 12 | −5 | 20 | n.a. | n.a. | n.a. | n.a. |
Czech Republic | 2.90 | 251 | 14 | 36 | 12 | 25 | 9 | 22 | 2 | 25 | 0 | 0 | −5 | 36 |
Poland | 2.04 | 143 | 21 | 31 | 17 | 26 | 3 | 4 | −9 | 6 | n.a. | n.a. | n.a. | n.a. |
Lithuania | 2.04 | 309 | 12 | 36 | 9 | 37 | 5 | 15 | −7 | 3 | 10 | 30 | 5 | 39 |
Estonia | 2.29 | 400 | 10 | 42 | 5 | 54 | 0 | 1 | −13 | 3 | 2 | 7 | −9 | 48 |
WOMEN | ||||||||||||||
Finland | 2.64 | 56 | 4 | 2 | ref | ref | 9 | 5 | −4 | 2 | 0 | 0 | ref | ref |
Sweden | 2.72 | 48 | 8 | 4 | 4 | 4 | 10 | 5 | −3 | 1 | n.a. | n.a. | n.a. | n.a. |
Norway | 2.91 | 42 | 15 | 6 | 12 | 6 | 11 | 5 | ref | ref | 3 | 1 | 3 | 10 |
Denmark | 2.28 | 35 | 6 | 2 | 2 | 6 | 11 | 4 | −2 | 0 | 8 | 3 | 9 | 2 |
England & Wales | 2.70 | 60 | 10 | 6 | 6 | 8 | 11 | 7 | −1 | 7 | n.a. | n.a. | n.a. | n.a. |
Scotland | 3.23 | 81 | 17 | 14 | 12 | 15 | 4 | 3 | −8 | 5 | n.a. | n.a. | n.a. | n.a. |
Netherlands | 2.44 | 33 | 5 | 2 | −2 | 3 | 12 | 4 | −2 | 1 | 7 | 2 | 8 | 6 |
Belgium | 2.20 | 23 | 2 | 0 | −8 | 0 | 15 | 4 | 2 | 1 | 12 | 3 | 14 | 4 |
France | 3.48 | 19 | 3 | 1 | −8 | −1 | 14 | 3 | 3 | 1 | n.a. | n.a. | n.a. | n.a. |
Switzerland | 2.21 | 23 | 1 | 0 | −11 | 0 | 19 | 4 | 5 | 1 | 10 | 2 | 12 | 2 |
Austria | 1.88 | 43 | 1 | 0 | −6 | −3 | 22 | 10 | 5 | 3 | n.a. | n.a. | n.a. | n.a. |
Barcelona | 1.62 | 9 | 1 | 0 | −26 | −2 | 24 | 2 | 5 | 1 | 6 | 1 | 10 | 3 |
Basque Country | 1.49 | 7 | 2 | 0 | −26 | −1 | 30 | 2 | 8 | 1 | 7 | 1 | 12 | 2 |
Madrid | 1.37 | 8 | 1 | 0 | −31 | −2 | 33 | 3 | 5 | 1 | 8 | 1 | 14 | 3 |
Turin | 1.21 | 5 | 0 | 0 | −57 | −2 | 48 | 3 | 2 | 0 | 9 | 0 | 13 | 2 |
Tuscany | 1.32 | 7 | 0 | 0 | −44 | −3 | 35 | 3 | 1 | 0 | 7 | 1 | 6 | 2 |
Hungary | 2.08 | 102 | n.a. | n.a. | n.a. | n.a. | 12 | 12 | −3 | 8 | n.a. | n.a. | n.a. | n.a. |
Czech Republic | 3.06 | 96 | 5 | 4 | −5 | 0 | 12 | 12 | 1 | 11 | 3 | 3 | 4 | 24 |
Poland | 2.35 | 57 | 5 | 3 | −10 | −1 | 15 | 9 | 1 | 6 | n.a. | n.a. | n.a. | n.a. |
Lithuania | 2.48 | 138 | 2 | 3 | −10 | −20 | 3 | 4 | −19 | 5 | 18 | 24 | 3 | 33 |
Estonia | 2.52 | 174 | 4 | 7 | 1 | 0 | 14 | 25 | −1 | 25 | 5 | 8 | 8 | 33 |
rel red = relative reduction; abs red = absolute reduction; ref = reference country; n.a. = not available
Note: The potential reduction in relative inequalities was expressed as a percentage change in excess mortality using following formula (for low educated):
, where MRR is the mortality rate ratio.
The potential reduction in absolute inequalities was expressed as a number of deaths per 100 000 person-years and calculated as the difference in rate difference before and after the implementation of the counterfactual scenario using the following formula (for low educated):
, where ASMR is the age-standardized mortality rate.
The results did not differ considerably between the two scenarios. However, the reduction in inequalities was smaller under the BPC scenario than under the UL scenario, and inequalities even sometimes slightly increased, mostly for smoking among women in southern Europe.
Discussion
Our findings on educational inequalities in IHD mortality are in line with what has been reported previously.2 In absolute terms, IHD mortality was extremely large in Eastern European populations leading to large inequalities. We observed the north–south gradient with IHD mortality and inequalities in IHD mortality being higher in the North and lower in the South of Europe, which is believed to be at least partly attributable to the Mediterranean diet.11
Our findings regarding the potential for inequalities reduction revealed important country-specific and gender-specific patterns. The impact of smoking on IHD mortality and on educational differences in IHD mortality was larger in the North, West and East (men only) of Europe and the impact of overweight/obesity was larger in the South of Europe, especially among women. We found only a relatively small impact of physical inactivity in most European countries, except Lithuania, which may partly be due to imprecision in the measurement of this risk factor. Although these results are consistent with the available literature on socioeconomic inequalities in these risk factors in Europe,12–14 they cannot fully explain the large cross-country differences in IHD mortality.
The relatively small IHD mortality gains from the elimination or modification of the exposure to certain behavioural risk factors may be a consequence of the limited number of risk factors investigated. As we estimated a percentage reduction in inequalities for single risk factors, our findings indicate a smaller reduction compared with the literature where a combination of behavioural risk factors was studied. In the literature, behavioural risk factors explained 53% among men and 25% among women of the relative differences between low and high educated in IHD mortality15 and smoking and physical inactivity were reported to be the most important health behaviours explaining educational differences in mortality.16 Other IHD risk factors were not available in our study but may play a role in the explanation of educational inequalities in IHD mortality. High consumption of fruits and vegetables and high intake of dietary fibre and unsaturated fatty acids have a protective effect on IHD mortality.17,18 Air pollution is associated with IHD mortality 19 and may partly account for our findings in Eastern Europe as outdoor and indoor air pollution is higher in Eastern European countries than in other parts of Europe, with large social inequalities in the exposure to this risk factor.20,21 Physical and psychosocial working conditions or material circumstances, have also been found to be major sources of inequalities in IHD.22 Finally, socioeconomic differences in access to health care and treatment should be taken into account. A poorer access to revascularization operations and a higher risk of coronary mortality after revascularization operation or cardiac surgery was observed in persons with lower socioeconomic status in Finland.23
Despite differences in study populations, measurement of socioeconomic position and method of analysis, our results are consistent with the literature. Interventions based on smoking cessation in the UK have shown that relative inequalities in IHD mortality could be reduced by 28% among men,9 a value close to the 22%-reduction we found in England and Wales and the 18%-reduction in Scotland if all men had the same smoking prevalence as high educated men. In Denmark, among men, 8% and 21% of the excess mortality between low and high educated has been shown to be attributable to smoking and BMI, respectively, whereas these figures were 6% and 14% among women.24 We found that, when inequalities in smoking and BMI are eliminated in Denmark, educational inequalities in IHD mortality could be reduced by 14% and 12% among men and by 6% and 11% among women, respectively.
The reduction of relative and absolute inequalities in IHD mortality does not extensively differ between the two scenarios investigated, especially in countries with large inequalities in IHD mortality and/or high IHD mortality. This means that the UL scenario does not lead to a clear larger decrease in inequalities in IHD mortality. On the contrary, the BPC scenario often showed a larger decrease in mortality, sometimes more than double, among low educated than the UL scenario did. These results mean that low educated in the BPC have a lower prevalence of risk factors than high educated in the studied country. Our results point to countries where efforts should be made to decrease the risk factor prevalence among the whole population and suggest that focusing only on the reduction of inequalities in IHD mortality does not show the entire picture and may not be the best option to decrease health inequalities in some countries. For instance in England and Wales, the population health regarding IHD would be much more improved in all educational groups if we manage to lower the BMI to that observed in Norway, even though inequalities would remain, than if we would remove educational differences in BMI. Indeed, the mortality would decrease by 11.8% among low educated men (vs. 3.3%) and by 13.5% among high educated men (vs. no reduction). Under the BPC scenario a large mortality decrease among low educated does not necessarily imply a decrease in inequalities in IHD mortality as measured with mortality rate ratio if mortality also decreased among high educated. However, the considerable reduction in IHD mortality among low educated is a great achievement from a health inequalities perspective.
In this study we encountered several limitations. IHD is prone to misclassification with variation in coding practices across countries,25,26 which may affect comparability. While IHD may be overestimated in eastern European countries, a substantial underestimation of IHD in official mortality statistics has been reported in Belgium25 and in France, where IHD mortality could be underestimated by 27% for men and 35% for women.26 Although the coding practice unlikely differed by socioeconomic groups, we probably over- or underestimated IHD mortality in different parts of Europe, which may have affected the absolute differences in IHD mortality between low and high educated.
Differences in the measurement of exposure to risk factors between countries may hamper comparability. Physical inactivity is likely the risk factor with the largest heterogeneity due to variation in survey questions across European populations. Self-reporting bias may also be an issue. Although self-reports may underestimate the true smoking prevalence,27 most studies, including biochemical validations, reported that self-reported smoking status was reliable with no differences by socioeconomic groups.28,29 Participants recruited from introductory psychology courses at a large southern university in US for a health and nutrition project were moderately accurate in recalling their physical activity compared with the objective measures of their physical activity, with a tendency towards an underestimation of sedentary activities, especially among obese and an overestimation of aerobic activities, especially among men.30 Based on the US National Health and Nutrition Examination Study, self-reported physical activity was found to be more reliable among higher educated than in poorly educated individuals.31 In general, people usually underestimate their BMI, especially women, obese, elderly and higher educated.32 On the other hand, there is evidence that self-reported weight and height are remarkably accurate indicators of actual weight and height.33 Regardless of these limitations, we believe that the self-reporting bias is negligible.
Finally, despite a negative association between obesity and IHD mortality, several studies have demonstrated a phenomenon called the ‘obesity paradox’: overweight and slightly obese patients with established cardiovascular disease have better survival.34 As this is restricted to a subgroup of patients, this is not likely to strongly bias our estimates.
Other limitations concern the assumptions of the PAF methodology. These assumptions are: (i) RRs should reflect the causal effect of risk factor on IHD mortality; (ii) RRs were assumed to be the same for all countries; (iii) RRs were assumed to be the same for all educational groups. These assumptions and their limitations have been extensively described elsewhere.35,36 Furthermore, mortality and risk factor prevalence data were collected at the same period around the year 2000 and therefore we could not take into account any time-lag between risk factor exposure and IHD mortality. The implicit time frame is that we can only expect to see a decrease in mortality after persons that have been moved from one exposure group to another have also acquired the mortality risk of this new group.37 Regarding smoking, although one study found that the IHD risk among former smokers returned to the level of never smokers 10–14 years after smoking cessation,38 several other studies have shown that the biological effect of smoking on IHD was related to current use39 and that the risk of IHD therefore became similar to the risk of never smokers within two or three years after smoking cessation.40 Regular physical activity and weight loss are associated with several biological mechanisms, such as blood pressure reduction, improvements in glucose control, reduction of cholesterol level or reduction of stress, anxiety and depression.7 Although the time-lag is not known, the association of physical activity and weight loss with these biological mechanisms suggests an immediate beneficial effect on IHD mortality.
Conclusions
Drawing general conclusions from our analysis is challenging due to the diversity of country-specific situations. Although the UL scenario may not be achievable, the BPC scenario is realistic. Our analysis shows that even if a modest reduction of educational inequalities in IHD mortality may be achieved under this realistic scenario, substantial reduction in the IHD mortality level among all educational groups, especially among the least educated, can be achieved in many countries. To tackle health inequalities in IHD mortality, policy makers should learn from countries that managed to combine low exposure to the main risk factors and small inequalities in distribution of these risk factors.
Acknowledgements
We would like to thank the EURO-GBD-SE international project partners who supplied the mortality and morbidity data.
Mortality data: Pekka Martikainen (Finland), Olle Lundberg (Sweden), Bjørn Heine Strand (Norway), Anita Lange (Denmark), Lynsey Brown and Chris White (England and Wales), Chris Dibben (Scotland), Centraal Bureau voor de Statistiek (Netherlands), Patrick Deboosere (Belgium), Gwenn Menvielle (France), Matthias Bopp (Switzerland), Johannes Klotz (Austria), Carme Borrell and Maica Rodríguez-Sanz (Barcelona, Spain), Santiago Esnaola (Basque Country, Spain), Enrique Regidor (Madrid, Spain), Giuseppe Costa (Turin, Italy), Annibale Biggeri (Tuscany, Italy), Katalin Kovács (Hungary), Jitka Rychtaříková (Czech Republic), Bogdan Wojtyniak (Poland), Domantas Jasilionis (Lithuania), Mall Leinsalu (Estonia).
Risk factor data: Satu Helakorpi (Finland), Bo Burström (Sweden), Espen Dahl (Norway), Ola Ekholm (Denmark), Ken Judge (England), Chris Dibben (Scotland), J.J.M. Geurts (Netherlands), Herman van Oyen (Belgium), Frédérique Ruchon and Centre Maurice Halbwachs (France), Office Fédéral de la Statistique (Switzerland), Santiago Esnaola (Basque Country, Spain), Enrique Regidor (Spain), Giuseppe Costa (Italy), Ferenc Marton (Hungary), Dagmar Dzúrová (Czech Republic), Bogdan Wojtyniak (Poland), Jurate Klumbiene (Lithuania), Mare Tekkel (Estonia).
Funding
This study was financed by the Public Health Programme of the European Commission (grant number 20081309). Financial support was also provided by the Netherlands Organization for Health Research and Development (ZonMw, project number 121020026). The funding sources had no involvement in the study design, in the collection, analysis and interpretation of data, in the writing of the report and in the decision to submit the article for publication.
Conflicts of interest: None declared.
Preventive strategies to reduce the prevalence of smoking, and overweight and obesity among lower socioeconomic groups are likely to substantially contribute to the IHD mortality decline in Europe.
The impact of smoking on IHD mortality and on educational differences in IHD mortality was larger in the North, West and East of Europe, whereas the impact of overweight and obesity was larger in the South of Europe, especially among women.
Based on the upward levelling scenario, the socioeconomic inequalities in IHD mortality may be reduced up to 12% for smoking and up to 10% for overweight and obesity.
This study shows that by reducing the exposure to smoking, overweight and obesity, and physical inactivity a substantial reduction in IHD mortality can be achieved. This work may therefore aid planning and decision making related to IHD control strategy.
References
Author notes
Present Address: Margarete C. Kulik is currently affiliated with the Center for Tobacco Control Research and Education, University of California at San Francisco, San Francisco, CA, USA
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