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Background: In 2011, WHO member states signed up to the 25 × 25 initiative, a plan to cut mortality due to non-communicable diseases by 25% by 2025. However, socioeconomic factors influencing non-communicable diseases have not been included in the plan. In this study, we aimed to compare the contribution of socioeconomic status to mortality and years-of-life-lost with that of the 25 × 25 conventional risk factors. Methods: We did a multicohort study and meta-analysis with individual-level data from 48 independent prospective cohort studies with information about socioeconomic status, indexed by occupational position, 25 × 25 risk factors (high alcohol intake, physical inactivity, current smoking, hypertension, diabetes, and obesity), and mortality, for a total population of 1 751 479 (54% women) from seven high-income WHO member countries. We estimated the association of socioeconomic status and the 25 × 25 risk factors with all-cause mortality and cause-specific mortality by calculating minimally adjusted and mutually adjusted hazard ratios [HR] and 95% CIs. We also estimated the population attributable fraction and the years of life lost due to suboptimal risk factors. Findings: During 26·6 million person-years at risk (mean follow-up 13·3 years [SD 6·4 years]), 310 277 participants died. HR for the 25 × 25 risk factors and mortality varied between 1·04 (95% CI 0·98–1·11) for obesity in men and 2 ·17 (2·06–2·29) for current smoking in men. Participants with low socioeconomic status had greater mortality compared with those with high socioeconomic status (HR 1·42, 95% CI 1·38–1·45 for men; 1·34, 1·28–1·39 for women); this association remained significant in mutually adjusted models that included the 25 × 25 factors (HR 1·26, 1·21–1·32, men and women combined). The population attributable fraction was highest for smoking, followed by physical inactivity then socioeconomic status. Low socioeconomic status was associated with a 2·1-year reduction in life expectancy between ages 40 and 85 years, the corresponding years-of-life-lost were 0·5 years for high alcohol intake, 0·7 years for obesity, 3·9 years for diabetes, 1·6 years for hypertension, 2·4 years for physical inactivity, and 4·8 years for current smoking. Interpretation: Socioeconomic circumstances, in addition to the 25 × 25 factors, should be targeted by local and global health strategies and health risk surveillance to reduce mortality. European Commission, Swiss State Secretariat for Education, Swiss National Science Foundation, the Medical Research Council, NordForsk, Portuguese Foundation for Science and Technology.

Background: In 2011, WHO member states signed up to the 25 × 25 initiative, a plan to cut mortality due to non-communicable diseases by 25% by 2025. However, socioeconomic factors influencing non-communicable diseases have not been included in the plan. In this study, we aimed to compare the contribution of socioeconomic status to mortality and years-of-life-lost with that of the 25 × 25 conventional risk factors. Methods: We did a multicohort study and meta-analysis with individual-level data from 48 independent prospective cohort studies with information about socioeconomic status, indexed by occupational position, 25 × 25 risk factors (high alcohol intake, physical inactivity, current smoking, hypertension, diabetes, and obesity), and mortality, for a total population of 1 751 479 (54% women) from seven high-income WHO member countries. We estimated the association of socioeconomic status and the 25 × 25 risk factors with all-cause mortality and cause-specific mortality by calculating minimally adjusted and mutually adjusted hazard ratios [HR] and 95% CIs. We also estimated the population attributable fraction and the years of life lost due to suboptimal risk factors. Findings: During 26·6 million person-years at risk (mean follow-up 13·3 years [SD 6·4 years]), 310 277 participants died. HR for the 25 × 25 risk factors and mortality varied between 1·04 (95% CI 0·98–1·11) for obesity in men and 2 ·17 (2·06–2·29) for current smoking in men. Participants with low socioeconomic status had greater mortality compared with those with high socioeconomic status (HR 1·42, 95% CI 1·38–1·45 for men; 1·34, 1·28–1·39 for women); this association remained significant in mutually adjusted models that included the 25 × 25 factors (HR 1·26, 1·21–1·32, men and women combined). The population attributable fraction was highest for smoking, followed by physical inactivity then socioeconomic status. Low socioeconomic status was associated with a 2·1-year reduction in life expectancy between ages 40 and 85 years, the corresponding years-of-life-lost were 0·5 years for high alcohol intake, 0·7 years for obesity, 3·9 years for diabetes, 1·6 years for hypertension, 2·4 years for physical inactivity, and 4·8 years for current smoking. Interpretation: Socioeconomic circumstances, in addition to the 25 × 25 factors, should be targeted by local and global health strategies and health risk surveillance to reduce mortality.

Link and Phelan have proposed to explain the persistence of health inequalities from the fact that socioeconomic status is a "fundamental cause" which embodies an array of resources that can be used to avoid disease risks no matter what mechanisms are relevant at any given time. To test this theory we compared the magnitude of inequalities in mortality between more and less preventable causes of death in 19 European populations, and assessed whether inequalities in mortality from preventable causes are larger in countries with larger resource inequalities. We collected and harmonized mortality data by educational level on 19 national and regional populations from 16 European countries in the first decade of the 21st century. We calculated age-adjusted Relative Risks of mortality among men and women aged 30-79 for 24 causes of death, which were classified into four groups: amenable to behavior change, amenable to medical intervention, amenable to injury prevention, and non-preventable. Although an overwhelming majority of Relative Risks indicate higher mortality risks among the lower educated, the strength of the education mortality relation is highly variable between causes of death and populations. Inequalities in mortality are generally larger for causes amenable to behavior change, medical intervention and injury prevention than for non-preventable causes. The contrast between preventable and non-preventable causes is large for causes amenable to behavior change, but absent for causes amenable to injury prevention among women. The contrast between preventable and nonpreventable causes is larger in Central & Eastern Europe, where resource inequalities are substantial, than in the Nordic countries and continental Europe, where resource inequalities are relatively small, but they are absent or small in Southern Europe, where resource inequalities are also large. In conclusion, our results provide some further support for the theory of "fundamental causes". However, the absence of larger inequalities for preventable causes in Southern Europe and for injury mortality among women indicate that further empirical and theoretical analysis is necessary to understand when and why the additional resources that a higher socioeconomic status provides, do and do not protect against prevailing health risks. (C) 2014 Elsevier Ltd. All rights reserved.

Smoking is an important determinant of socioeconomic inequalities in mortality in many countries. As the smoking epidemic progresses, updates on the development of mortality inequalities attributable to smoking are needed. We provide estimates of relative and absolute educational inequalities in mortality from lung cancer, aerodigestive cancers, and chronic obstructive pulmonary disease (COPD)/asthma in Europe and assess the contribution of these smoking-related diseases to inequalities in all-cause mortality. We use data from 18 European populations covering the time period 19982007. We present age-adjusted mortality rates, relative indices of inequality, and slope indices of inequality. We also calculate the contribution of inequalities in smoking-related mortality to inequalities in overall mortality. Among men, relative inequalities in mortality from the 3 smoking-related causes of death combined are largest in the Czech Republic and Hungary and smallest in Spain, Sweden, and Denmark. Among women, these inequalities are largest in Scotland and Norway and smallest in Italy and Spain. They are often larger among men and tend to be larger for COPD/asthma than for lung and aerodigestive cancers. Relative inequalities in mortality from these conditions are often larger in younger age groups, particularly among women, suggesting a possible further widening of inequalities in mortality in the coming decades. The combined contribution of these diseases to inequality in all-cause mortality varies between 13% and 32% among men and between 5% and 30% among women. Our results underline the continuing need for tobacco control policies, which take into account socioeconomic position.

Background: Socioeconomic inequalities in mortality are one of the greatest challenges for health policy in all European countries, but the potential for reducing these inequalities is unclear. We therefore quantified the impact of equalizing the distribution of six risk factors for mortality: smoking, overweight, lack of physical exercise, lack of social participation, low income, and economic inactivity. Methods: We collected and harmonized data on mortality and risk factors by educational level for 21 European populations in the early 2000s. The impact of the risk factors on mortality in each educational group was determined using Population Attributable Fractions. We estimated the impact on inequalities in mortality of two scenarios: a theoretical upward levelling scenario in which inequalities in the risk factor were completely eliminated, and a more realistic best practice scenario, in which inequalities in the risk factor were reduced to those seen in the country with the smallest inequalities for that risk factor. Findings: In general, upward levelling of inequalities in smoking, low income and economic inactivity hold the greatest potential for reducing inequalities in mortality. While the importance of low income is similar across Europe, smoking is more important in the North and East, and overweight in the South. On the basis of best practice scenarios the potential for reducing inequalities in mortality is often smaller, but still substantial in many countries for smoking and physical inactivity. Interpretation: Theoretically, there is a great potential for reducing inequalities in mortality in most European countries, for example by equity-oriented tobacco control policies, income redistribution and employment policies. Although it is necessary to achieve substantial degrees of upward levelling to make a notable difference for inequalities in mortality, the existence of best practice countries with more favourable distributions for some of these risk factors suggests that this is feasible. © 2014 Eikemo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Healthy life expectancy is a composite measure of length and quality of life and an important indicator of health in aging populations. There are few cross-country comparisons of socioeconomic differences in healthy life expectancy. Most of the existing comparisons focus on Western Europe and the United States, often relying on older data. To address these deficiencies, we estimated educational differences in disability-free life expectancy for eight countries from all parts of Europe in the early 2000s. Long-standing severe disability was measured as a Global Activity Limitation Indicator (GALI) derived from the European Union Statistics on Income and Living Conditions (EU-SILC) survey. Census-linked mortality data were collected by a recent project comparing health inequalities between European countries (the EURO-GBD-SE project). We calculated sex-specific educational differences in disability-free life expectancy between the ages of 30 and 79 years using the Sullivan method. The lowest disability-free life expectancy was found among Lithuanian men and women (33.1 and 39.1 years, respectively) and the highest among Italian men and women (42.8 and 44.4 years, respectively). Life expectancy and disability-free life expectancy were directly related to the level of education, but the educational differences were much greater in the latter in all countries. The difference in the disability-free life expectancy between those with a primary or lower secondary education and those with a tertiary education was over 10 years for males in Lithuania and approximately 7 years for males in Austria, Finland and France, as well as for females in Lithuania. The difference was smallest in Italy (4 and 2 years among men and women, respectively). Highly educated Europeans can expect to live longer and spend more years in better health than those with lower education. The size of the educational difference in disability-free life expectancy varies significantly between countries. The smallest and largest differences appear to be in Southern Europe and in Eastern and Northern Europe, respectively. (C) 2013 Elsevier Ltd. All rights reserved.

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In west-europese landen is een lagere sociaal-economische status geassocieerd met een hogere frequentie van gezondheidsproblemen en met een hogere sterfte. In Nederland is deze associatie waargenomen voor diverse gezondheidsindicatoren, waaronder chronische aandoeningen, klachten en ervaren gezondheid. Gegevens over sterfte onder mannen tonen een omgekeerd evenredig verband met sociaal-economisch status. Een vermindering van de sterfte met 20-50% kan worden verwacht als het voorkomen van morbiditeit in de hoogste sociaal-economische klasse kan worden toegepast op de gehele bevolking. Er is weinig informatie over trends in sociaal-economische verschillen in gezondheid in Nederland. Er zijn aanwijzingen voor een toename in verschillen in sterfte sinds de vijftiger jaren, maar gegevens over verschillen in de lengte van kinderen wijzen juist in de andere richting. Een gedeelte van de sociaal-economische ongelijkheid is waarschijnlijk het gevolg van sociaal-economische verschillen in gezondheid (selectie mechanisme). Het grootste gedeelte van de sociaal-economische verschillen in gezondheid wordt echter veroorzaakt door een ongelijke verdeling van determinanten over de populatie, zoals roken, hoge bloeddruk, werk en woonomstandigheden, sociale steun. Er zijn niet veel studies die trachten vast te stellen welke bijdrage een bepaalde determinant heeft aan de waargenomen sociaal-economische gezondheidsverschillen. Er zouden meer gegevens moeten worden verzameld om de achtergrond van deze verschillen te verklaren en beleidsmaatregelen op te baseren.