Socioeconomic Inequalities in Health in 22 European Countries
Johan P. Mackenbach, Ph.D., Irina Stirbu, M.Sc., Albert-Jan R. Roskam, M.Sc., Maartje M. Schaap, M.Sc., Gwenn Menvielle, Ph.D., Mall Leinsalu, Ph.D., Anton E. Kunst, Ph.D., for the European Union Working Group on Socioeconomic Inequalities in Health
Background Comparisons among countries can help to identifyopportunities for the reduction of inequalities in health. Wecompared the magnitude of inequalities in mortality and self-assessedhealth among 22 countries in all parts of Europe.
Methods We obtained data on mortality according to educationlevel and occupational class from census-based mortality studies.Deaths were classified according to cause, including commoncauses, such as cardiovascular disease and cancer; causes relatedto smoking; causes related to alcohol use; and causes amenableto medical intervention, such as tuberculosis and hypertension.Data on self-assessed health, smoking, and obesity accordingto education and income were obtained from health or multipurposesurveys. For each country, the association between socioeconomicstatus and health outcomes was measured with the use of regression-basedinequality indexes.
Results In almost all countries, the rates of death and poorerself-assessments of health were substantially higher in groupsof lower socioeconomic status, but the magnitude of the inequalitiesbetween groups of higher and lower socioeconomic status wasmuch larger in some countries than in others. Inequalities inmortality were small in some southern European countries andvery large in most countries in the eastern and Baltic regions.These variations among countries appeared to be attributablein part to causes of death related to smoking or alcohol useor amenable to medical intervention. The magnitude of inequalitiesin self-assessed health also varied substantially among countries,but in a different pattern.
Conclusions We observed variation across Europe in the magnitudeof inequalities in health associated with socioeconomic status.These inequalities might be reduced by improving educationalopportunities, income distribution, health-related behavior,or access to health care.
Inequalities in health among groups of various socioeconomicstatus (as measured by education, occupation, and income) constituteone of the main challenges for public health,1 but it is unknownto what extent such inequalities are modifiable. Because internationalcomparative studies can help identify opportunities for reducinginequalities in health, we conducted a study aimed at measuringvariations in the magnitude of inequalities in health among22 European countries and at identifying some of the immediatedeterminants of these variations.
Europe offers excellent opportunities for this type of researchbecause of the intercountry variety of political, cultural,economic, and epidemiologic histories and because good dataon inequalities in health are often available.2 In a previousstudy, we compared socioeconomically based inequalities in mortalityand morbidity among 10 countries in western Europe during the1980s.3,4,5,6,7 We now report a study of the magnitude of inequalitiesin health in a much larger number of countries in both westernand eastern Europe during the 1990s and early 2000s. The inclusionof eastern Europe allows us to determine whether countries thathave gone through a turbulent period of political, economic,and health care reform8,9,10,11,12 have larger inequalitiesin health than countries elsewhere in Europe.
Methods
We obtained data on mortality according to age, sex, cause ofdeath, and indicators of socioeconomic status from mortalityregistries (Table 1). The data were based on 3.5 million deathsin 16 countries among more than 54 million persons ranging inage from 30 to 74 years at the beginning of the study. The datawere drawn from national populations, except for the UnitedKingdom, with data from England and Wales only; Italy, withdata from Turin only; and Spain, with data from Madrid, Barcelona,and the Basque country only. With regard to the mortality datafrom England and Wales, this article has received clearancefrom the Office for National Statistics Longitudinal Study (referencenumber 20037C). We performed two analyses of the data on deathaccording to cause; one analysis focused on common causes ofdeath (cancer, cardiovascular disease, and injuries), and theother focused on more specific causes of death (smoking-relatedcauses, alcohol-related causes, and causes amenable to medicalintervention, such as tuberculosis and hypertension13,14). Codenumbers of the causes of death according to the ninth and tenthrevisions of the International Classification of Diseases, ClinicalModification (ICD-9-CM and ICD-10-CM) are given in Table 1 inthe Supplementary Appendix, available with the full text ofthis article at www.nejm.org.
Table 1. Countries Included in the Analysis and Sources of Data.
Data on self-assessed health and risk factors for disease (e.g.,smoking and obesity) according to age, sex, and indicators ofsocioeconomic status were obtained from national health or multipurposesurveys that also included self-reported socioeconomic data(Table 1). The data came from 19 countries and almost 350,000respondents who ranged in age from 30 to 64 years in some surveysand from 30 to 69 years in others. All data are nationally representative.For self-reported illness, our study focused on the single-itemquestion on self-assessed health ("How is your health in general?"),which has five possible answers, ranging from "very good" to"bad." In order to make use of the full range of levels of self-assessedhealth, we gave quantitative weights to each level (i.e., amultiplicative factor of 1.85 for each level worse than "verygood") that were derived from the average number of chronicconditions in each level15 (details of the calculation are givenin the legend to Figure 2). The only risk factors for diseasefor which data were available in a form that enabled them tobe compared across countries were current tobacco smoking andobesity, defined as a body-mass index (the weight in kilogramsdivided by the square of the height in meters) greater than30.
Socioeconomic status was measured by education, occupation,and income. Education levels were categorized as no educationor primary education (up to approximately 6 years of education),lower secondary education (up to approximately 9 years), highersecondary education (up to approximately 11 years), and tertiaryeducation (bachelor's degree or higher). Data on education levelwere available in a comparable form for most countries fromboth mortality registries and health interviews or multipurposesurveys. Occupations were classified as "manual" (consideredthe lower level) or "nonmanual." Data on occupation were availablefrom mortality registries for middle-aged men in a limited numberof countries only. Income was categorized in approximate quintilesof equivalent net household income. The self-reported after-taxincomes of all household members, including benefits, were added,and the total was corrected for household size by dividing itby the total number of persons in the household to the powerof 0.36. Income data were available from surveys in a limitednumber of countries only. Tables 2, 3, and 4 in the Supplementary Appendixshow the distribution of study populations according to educationlevel, occupational classification, and income level. The proportionof the population with less education tended to be large inthe southern and eastern regions, whereas inequalities in incomewere large in England and Wales and in Portugal.
All measures were adjusted for age. Because both relative andabsolute measures of inequalities in health are important, wehave presented both the relative index of inequality and theslope index of inequality16,17 for each country separately.Both indexes are regression-based measures that take into accountthe whole socioeconomic distribution and that remove variabilityin the size of socioeconomic groups as a source of variationin the magnitude of inequalities in health.17 In the regressionanalysis, mortality, morbidity, or risk-factor prevalence wasrelated to a measure of the rank of education, occupation, orincome, in which the rank was calculated as the mean proportionof the population having a higher level of education, occupation,or income.
The relative index of inequality is the ratio between the estimatedmortality, morbidity, or risk-factor prevalence among personsat rank 1 (the lowest education, occupation, or income level)and rank 0 (the highest level). The relative index of inequalitywas calculated with the use of Poisson regression analysis,which also generated 95% confidence intervals. The slope indexof inequality measures absolute differences in rates (e.g.,in deaths per 100,000 person-years) between the lowest and thehighest ends of the socioeconomic scale. The slope index ofinequality is derived from the relative index of inequalityand the age-adjusted overall mortality rate according to thefollowing formula: slope index of inequality=2xmortality ratex(relativeindex of inequality–1)÷(relative index of inequality+1).16Because the slope index of inequality depends on the overallmortality rate in the population, we have presented these overallmortality rates together with the slope indexes of inequality.
Results
Figure 1A and 1B show relative inequalities in the rate of deathfrom any cause according to education level. The relative indexof inequality is greater than 1 for both men and women in allcountries, indicating that, throughout Europe, mortality ishigher among those with less education. The magnitude of theseinequalities varies substantially among countries. For example,in Sweden, the relative index of inequality for men is lessthan 2, indicating that mortality among those with the leasteducation is less than twice that among those with the mosteducation; on the other hand, in Hungary, the Czech Republic,and Poland, the relative index of inequality for men is 4 orhigher, indicating that mortality differs by a factor of morethan 4 between the lower and upper ends of the education scale.The smallest inequalities for both men and women are found inthe Basque country of Spain, whereas the largest inequalitiesare found in the Czech Republic and Lithuania. Education-relatedinequalities in mortality are smaller than the average for Europein all southern European populations included in this analysisand larger than average in most countries in the eastern andBaltic regions. Data on occupation-related inequalities in mortalityamong middle-aged men (Figure 1C) confirm that relative inequalitiesin mortality tend to be smaller in southern European populations.
Figure 1. Relative Inequalities in the Rate of Death from Any Cause.
Panel A shows inequalities between men with the lowest level of education and those with the highest, and Panel B shows education-related inequalities for women. Panel C shows inequalities between men in the lower and higher occupational classes. Economically inactive men whose last occupation was unknown were excluded from the analysis. Because exclusion of these men may lead to underestimation of mortality differences between occupational classes, we applied an adjustment procedure that was developed and tested in a previous European comparative study of inequalities in mortality; the procedure is based on national estimates of the proportion of economically inactive men in each occupational class and of the mortality rate ratio of inactive as compared with active men in each occupational class.18
Table 2 shows that the international pattern observed for relativeeducation-related inequalities in mortality also generally appliesto absolute education-related inequalities in mortality, asindicated by the slope index of inequality. In Europe as a whole,persons with less education have higher rates of death fromall causes except breast cancer, as indicated by a negativeslope index of inequality for this cause of death. Inequalitiesin the rate of death from cardiovascular disease account for34% of education-related inequalities in the rate of death fromany cause among men (451 of 1333 deaths per 100,000 person-years)and 51% of those among women (251 of 492 deaths per 100,000person-years). Although death from almost any cause is morefrequent among those with less education than among those withmore education, the range of variation for a single cause ofdeath sometimes includes both "reverse" inequalities (highermortality in groups with higher education) and "regular" inequalities(higher mortality in groups with lower education).
Table 2. Absolute Inequalities in Overall and Cause-Specific Mortality Rates between Persons with the Lowest and Those with the Highest Level of Education.
These data help to explain how smaller education-related inequalitiesin the rate of death from any cause in southern European populationsand larger inequalities in the eastern and Baltic regions arise.Among men and women, smaller inequalities in the rate of deathfrom any cause in the southern region are due mainly to smallerinequalities in the rate of death from cardiovascular disease.For example, among men in the Basque country, where the education-relatedinequality in the rate of death from any cause is below theEuropean average, death from cardiovascular disease accountsfor 46% of this difference (i.e., [451–16 deaths per 100,000person-years]÷[1333–384 deaths per 100,000 person-years]).Larger inequalities in the rate of death from cardiovasculardisease make an important contribution to larger inequalitiesin the rate of death from any cause in the eastern and Balticregions as well; however, important contributions are also madeby cancer in the eastern region and injuries in the Baltic region.
In Europe as a whole, inequalities in mortality from smoking-relatedconditions account for 22% of the inequalities in the rate ofdeath from any cause among men and 6% of those among women (Table 2).Inequalities in smoking-related mortality tend to be largerin the eastern and Baltic regions (among men only) and smaller(or even "reverse") in the southern region. In Europe as a whole,inequalities in alcohol-related mortality account for 11% ofinequalities in the rate of death from any cause among men and6% of those among women. Larger inequalities in alcohol-relatedmortality contribute to larger inequalities in the rate of deathfrom any cause in Hungary (among men and women) and the Balticregion (among men only). In Europe as a whole, deaths from conditionsamenable to medical intervention account for 5% of inequalitiesin the rate of death from any cause. However, these inequalitiesare larger than the European average in Lithuania and Estonia,where they contribute to the larger inequalities in the rateof death from any cause (among men only).
Figure 2 shows the relative inequalities in the prevalence ofpoorer self-assessed health (weighted on the basis of the burdenof chronic disease) according to education and income level.The relative index of inequality is greater than 1 in all countries,indicating worse health in groups of lower socioeconomic statusthroughout Europe. The variation of this measure among countriesis considerably less than that of inequalities in the rate ofdeath from any cause, and the international pattern also tendsto be different from that of death from any cause. In Italyand Spain, education-related inequalities in self-assessed healthare smaller than average, a finding that mirrors the smallereducation-related inequalities in the rate of death from anycause observed in Turin, Barcelona, Madrid, and the Basque country.In the Baltic region, on the other hand, education-related inequalitiesin self-assessed health are smaller than average, whereas education-relatedinequalities in death from any cause are larger. Income-relatedinequalities in self-assessed health are not larger in the easternand Baltic regions than in other parts of Europe and are remarkablylarge in the northern and western regions, particularly Englandand Wales, where income inequalities are also large (see Table4 in the Supplementary Appendix).
Figure 2. Relative Inequalities in the Prevalence of Poorer Self-Assessed Health.
Panels A and B show inequalities between persons with the lowest and those with the highest level of education for men and women, respectively. Panels C and D show inequalities between persons with the lowest and those with the highest level of income for men and women, respectively. In order to make use of the full range of levels of self-assessed health, we calculated the estimated burden of disease associated with each level on the basis of the number of chronic conditions reported by respondents to these surveys. Relative differences in self-reported chronic conditions between answer categories of the self-assessed health question were remarkably similar between countries and varied only marginally around a multiplicative factor of 1.85 (i.e., each step down on the self-assessed health scale was found to be associated with 1.85 times more chronic conditions). On the basis of this analysis, we assigned a weight for burden of disease to each category of answer to the question "How is your health in general?" "Very good" was assigned a weight of 1.850=1, "good" a weight of 1.851=1.85, "fair" a weight of 1.852=3.42, and "poor" or "very poor" a weight of 1.853=6.33. Sensitivity analyses showed that the ranking of countries according to the magnitude of inequalities in self-assessed health did not change when these weights were varied within the range of observed values.15
In Europe as a whole, both smoking and obesity are more commonamong people of lower education level; education-related inequalitiesin smoking are larger among men, and education-related inequalitiesin obesity are larger among women (Figure 3). There are strikingdifferences among countries in the magnitude and even the directionof these inequalities, however. Large education-related inequalitiesin smoking are seen in the northern, western, and continentalregions; small inequalities (and, among women, even reverseinequalities, in which smoking rates are higher in groups withmore education) are seen in the southern region. In the easternand Baltic regions, the pattern is unclear. Large education-relatedinequalities in obesity are seen in the southern region, particularlyamong women, for whom the relative indexes of inequality areabove 4, indicating that the prevalence of obesity among thosewith the least education is more than four times higher thanthat among those with the most education. By contrast, education-relatedinequalities in obesity tend to be smaller than average in theeastern and Baltic regions.
Figure 3. Relative Inequalities in the Prevalence of Current Smoking (Panel A) and Obesity (Panel B) between Persons with the Lowest and Those with the Highest Level of Education, According to Sex.
Discussion
As compared with our study of inequalities in mortality andmorbidity related to socioeconomic status in 10 western Europeancountries during the 1980s,3 the present, more extensive studyof the situation during the 1990s and early 2000s found muchlarger among-country variability in the magnitude of inequalitiesin health. Inequalities in mortality from selected causes suggestthat some variations may be attributable to socioeconomic differencesin smoking, excessive alcohol consumption, and access to healthcare. We also found among-country variations in the magnitudeof inequalities in self-assessed health, but in a differentpattern, precluding a generalization from inequalities in mortalityto inequalities in overall health.
Our study had several limitations. International comparabilityof data on socioeconomic inequalities in health is still imperfect,and the degree of comparability is likely to decline with increasinggeographical coverage. There are differences among countriesin various aspects of data collection, and some of these mightaffect the size of inequalities in health, as we have shownpreviously.18 We found smaller inequalities in mortality insome urban, relatively prosperous southern European populationsthat are not necessarily representative of the whole of Italyor Spain. Some studies have shown, however, that inequalitiesin health tend to be larger in urban than in rural areas.19Our previous study in the 1980s, which used national data forItaly and Spain from methodologically less-refined sources,also showed smaller inequalities in mortality in these countries.4,5We found larger inequalities in mortality in the eastern andBaltic regions. All these countries except Slovenia, which hassmaller inequalities in mortality, provided data from cross-sectional,non-census–linked studies. Although this may suggest bias,20it is also possible that Slovenia, which is close to Italy,shares some of the favorable characteristics of the southernregion.
Internationally comparable data on inequalities in specificdeterminants of mortality and morbidity are scarce, and we couldstudy only smoking and obesity. The contribution to inequalityof other factors, such as alcohol consumption, use of healthcare, working and housing conditions, and psychosocial stressors,could not be studied directly.
Both smoking and obesity have been shown to contribute to inequalitiesin health related to socioeconomic status in studies of individualpersons in some countries.21,22,23 Obesity, however, is unlikelyto be a major contributor to international variations in inequalitiesin health, because inequalities in obesity related to socioeconomicstatus are large where inequalities in mortality related tosocioeconomic status, particularly mortality from cardiovasculardisease, are small (i.e., in the southern region). Smoking,on the other hand, does appear to be a major explanatory factor.It has been well documented that countries in the southern regionare in an earlier stage of the smoking epidemic than countriesin the northern, western, and continental regions.24,25 We stillfound reverse inequalities in smoking among women and smallinequalities among men, findings that are consistent with thesmaller inequalities in mortality in the southern region, particularlyfrom conditions related to smoking. The history of the smokingepidemic is much less well documented for the eastern and Balticregions,26,27 and it is therefore difficult to determine whyinequalities in mortality from smoking-related conditions arelarge, whereas inequalities in smoking are often small.
The role of hazardous drinking (daily consumption of large amountsof alcohol-containing beverages, binge drinking, or consumptionof surrogate alcohols) in generating high mortality rates ineastern Europe, particularly among men, has been well documented.28,29,30We have not been able to find comparable survey data on inequalitiesin alcohol consumption related to socioeconomic status in easternEurope, but our analysis of cause-specific mortality suggeststhat rates of hazardous drinking are substantially higher inthe lower than in the higher socioeconomic groups, particularlyamong men. Low levels of social support, lack of control overone's life, and material hardship, combined with a culture thatapproves of excessive alcohol consumption, are likely to beinvolved.8,9
Although the role of deficiencies in health care in the highmortality rates of eastern Europe has been pointed out before,31,32our study demonstrates the magnitude of inequalities in mortalityrelated to socioeconomic status from conditions amenable tomedical intervention in this part of Europe. Our results suggestthat inequalities in access to good-quality health care havea role in generating inequalities in mortality. Inequalitiesin access to health care leading to inequalities in survivalfrom chronic conditions may also partly explain the discrepancybetween our results for mortality and those for self-assessedhealth. Inequalities in the prevalence of poorer self-assessedhealth are the result of inequalities in both the incidenceand the duration of health problems, which may be shortenedby lower survival rates among less-educated persons in easternEurope.
Smoking, obesity, excessive alcohol consumption, and deficienciesin health care represent only some of the immediate determinantsof inequalities in health, and both lifestyle choices and patternsof use of health care are likely to be constrained by inequalitiesin general living conditions, as structured by political, economic,social, and cultural forces. Within western Europe, there islittle evidence that among-country variations in the magnitudeof inequalities in health are related to variations in politicalfactors. For example, Italy and Spain have welfare policiesthat are less generous and less universal than those of northernEurope,33,34 but they appear to have substantially smaller inequalitiesin mortality, perhaps partly because of cultural factors, suchas the Mediterranean diet and the reluctance of women to takeup smoking.35,36 Cultural factors seem to have prevented differencesin access to material and other resources in these populationsfrom translating into inequalities in lifestyle-related riskfactors for mortality.
We also found no evidence for systematically smaller inequalitiesin health in countries in northern Europe. This is surprising,because these countries have long histories of egalitarian policies,reflected by, among other things, welfare policies. These policiesprovide a high level of social-security protection to all residentsof the country, resulting in smaller income inequalities andlower poverty rates.33,34,37 Our results suggest that althougha reasonable level of social security and public services maybe a necessary condition for smaller inequalities in health,it is not sufficient. Lifestyle-related risk factors have animportant role in premature death in high-income countries38and also appear to contribute to the persistence of inequalitiesin mortality in the northern region.39
Our study shows that although inequalities in health associatedwith socioeconomic status are present everywhere, their magnitudeis highly variable, particularly for inequalities in mortality.This result implies that there is opportunity to reduce inequalitiesin mortality. Developing policies and interventions that effectivelytarget the structural and immediate determinants of inequalitiesin health is an urgent priority for public health research.40
Supported by a grant (2003125) from the Health and ConsumerProtection Directorate-General of the European Union as a partof the Eurothine Project.
No potential conflict of interest relevant to this article wasreported.
We thank the members of the Eurothine consortium for their commentsand suggestions on a previous version of this manuscript.
* Other investigators who participated in the study are listedin the Appendix.
Source Information
From the Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands (J.P.M., I.S., A.-J.R.R., M.M.S., G.M., A.E.K.); INSERM Unité 687, Saint-Maurice, France (G.M.); the Stockholm Center on Health of Societies in Transition, Södertorn University College, Södertorn, Sweden (M.L.); and the Department of Epidemiology and Biostatistics, National Institute for Health Development, Tallinn, Estonia (M.L.).
Address reprint requests to Dr. Mackenbach at the Department of Public Health, Erasmus University Medical Center, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands, or at j.mackenbach{at}erasmusmc.nl.
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Appendix
In addition to the authors, the following members of the EuropeanUnion Working Group on Socioeconomic Inequalities in Healthparticipated in this study: Scientific Institute of Public Health,Brussels — H. van Oyen, S. Demarest; Department of Demographyand Geography, Faculty of Science, Charles University in Prague,Prague, Czech Republic — J. Rychtarikova; Department ofSocial Geography and Regional Development, Faculty of Science,Charles University in Prague, Prague, Czech Republic —D. Dzurova; National Institute of Public Health, Copenhagen— O. Andersen; National Institute of Public Health, Universityof Southern Denmark, Copenhagen — O. Ekholm; School forHealth, University of Bath, Bath, England — K. Judge;National Institute for Health Development, Department of Epidemiologyand Biostatistics, Tallinn, Estonia — M. Tekkel; Departmentof Health Promotion and Chronic Disease Prevention, NationalPublic Health Institute, Helsinki — R. Prättäla;Department of Sociology, University of Helsinki, Helsinki —P. Martikainen; Institut National de la Statistique et des ÉtudesÉconomiques, Paris — G. Desplanques; Research andInformation Institute for Health Economics, Paris — F.Jusot; Center for Social Policy Research, University of Bremen,Bremen, Germany — U. Helmert; Demographic Research Institute,Hungarian Central Statistical Office, Budapest, Hungary —K. Kovacs; Hungarian National Center of Epidemiology, Budapest,Hungary — F. Marton; Economic and Social Research Institute,Dublin — R. Layte; Department of Public Health, Universityof Turin, Turin, Italy — G. Costa; Servizio di Epidemiologia,Grugliasco, Italy — F. Vannoni; Faculty of Public Health,Riga Stradins University, Riga, Latvia — A. Villerusa;Kaunas University of Medicine, Kaunas, Lithuania — R.Kalediene, J. Klumbiene; Centraal Bureau voor de Statistiek,Voorburg, the Netherlands — J.J.M. Geurts; Research ProgramCare, Health and Welfare, Oslo University College, Oslo —E. Dahl; Division of Epidemiology, Norwegian Institute of PublicHealth, Oslo — B.H. Strand; Department of Medical Statistics,National Institute of Hygiene, Warsaw, Poland — B. Wojtyniak;Centro de Estudos Geográficos, Universidade de Coimbra,Coimbra, Portugal — P. Santana; Koice Institute for Societyand Health, Pavol Josef Safarik University, Koice, Slovakia— A. Madarasova Geckova; Department of Public Health,Faculty of Medicine, Ljubljana, Slovenia — B. Artnik;Agencia de Salut Pública de Barcelona, Barcelona —C. Borrell; Research Unit, Department of Health, Basque Government,Vitoria-Gasteiz, Spain — S. Esnaola; Department of PreventiveMedicine and Public Health, Universidad Complutense de Madrid,Madrid — E. Regidor; Department of Public Health Sciences,Karolinska Institute, Stockholm — B. Burström; Centerfor Health Equity Studies Stockholm, Stockholm University, Stockholm— J. Fritzell, O. Lundberg; Institute of Social and PreventiveMedicine, University of Zurich, Zurich, Switzerland —M. Bopp; Office of National Statistics, Newport, United Kingdom— M. Glickman.
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