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Socioeconomic Inequalities in Health in 22 European Countries

Authors:
Johan Mackenbach at Erasmus MC
Johan Mackenbach
Irina Stirbu at Royal Tropical Institute
Irina Stirbu
Rita Ruby Albert at Christian Medical College
Rita Ruby Albert
Maartje M Schaap
Maartje M Schaap
Maartje M Schaap
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Abstract

Comparisons among countries can help to identify opportunities for the reduction of inequalities in health. We compared the magnitude of inequalities in mortality and self-assessed health among 22 countries in all parts of Europe. We obtained data on mortality according to education level and occupational class from census-based mortality studies. Deaths were classified according to cause, including common causes, such as cardiovascular disease and cancer; causes related to smoking; causes related to alcohol use; and causes amenable to medical intervention, such as tuberculosis and hypertension. Data on self-assessed health, smoking, and obesity according to education and income were obtained from health or multipurpose surveys. For each country, the association between socioeconomic status and health outcomes was measured with the use of regression-based inequality indexes. In almost all countries, the rates of death and poorer self-assessments of health were substantially higher in groups of lower socioeconomic status, but the magnitude of the inequalities between groups of higher and lower socioeconomic status was much larger in some countries than in others. Inequalities in mortality were small in some southern European countries and very large in most countries in the eastern and Baltic regions. These variations among countries appeared to be attributable in part to causes of death related to smoking or alcohol use or amenable to medical intervention. The magnitude of inequalities in self-assessed health also varied substantially among countries, but in a different pattern. We observed variation across Europe in the magnitude of inequalities in health associated with socioeconomic status. These inequalities might be reduced by improving educational opportunities, income distribution, health-related behavior, or access to health care.
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Supplementary Appendix
This appendix has been provided by the authors to give readers additional information about their work.
Supplement to: Mackenbach JP, Stirbu I, Roskam A-JR, et al. Socioeconomic inequalities in health
in 22 European countries. N Engl J Med 2008;358:2468-81.
(This PDF was updated September 17, 2008. The change was to Web Table 2.)
Web table 1. List of causes of death
Cause of death ICD 9 codes ICD 10 codes
1. Tuberculosis 010-018,137 A15-19, B90
2. Other infectious and parasitic
diseases Rest (001-139) Rest (A00-B99)
3. Cancer of buccal cavity, pharynx and
oesophagus 140-150 C00-C15
4. Cancer of stomach 151 C16
5. Cancer of colorectum 153-154 C18-C21
6. Cancer of liver 155 C22.0, C22.1, C22.9
7. Cancer of pancreas 157 C25
8. Cancer of larynx 161 C30-32
9. Cancer of trachea, bronchus, lung 162-163; 165 C33-C34; C39
10. Cancer of breast 174-175 C50
11. Cancer of cervix uteri 180 C53
12. Cancer of prostate 185 C61
13. Cancer of testis 186 C62
14. Cancer of kidney and bladder 188-189 C64-C68
15. Hodgkin’s disease and leukemia 201, 204-208 C81, C91-C95
16. Other neoplasms Rest (140-239) Rest (C00-D48)
17. Diabetes Mellitus 250 E10-E14
18. Alcoholic psychosis, dependence,
abuse 291, 303, 305.0 F10
19. Epilepsy 345 G40-G41
20. Hypertension 401-405 I10-I15
21. Ischaemic heart disease 410-414 I20-I25
22. Alcoholic cardiomyopathy 425.5 I42.6
23. Chronic rheumatic heart disease 390-398 I00-I09
24. Other heart disease 416; 420-429 I26-I52; I98
25. Cerebrovascular disease 430-438 I60-I69
26. Other circulatory diseases Rest (390-459) Rest (I00-I99)
27. Pneumonia/influenza 487; 480-486 J10-J18
28. Asthma 493 J45-J46
29. Other COPD 490-494; 496 J40-J44; J47
30. Appendicitis, hernia, and peptic ulcer 531-534, 540-543,
550-553, 560 K25-K28, K35-K38;
K40-K46; K56
31. Alcoholic cirrhosis of liver and
pancreas 571.0-571.3, 577.0-577.1 K70, K85-K86.0
32. Cholecystitis and lithiasis 574-576 K80-K83
33. Other liver and gall bladder diseases Rest (570-577) Rest (K70-K87)
34. Prostate hyperplasia 600 N40
35. Maternal deaths 630-677 O 00-99
36. Symptoms and ill defined conditions 780-799 R00-R99
37. Road traffic accidents E800-E829 V01-V89, Y85
38. Other traffic accidents E830-E848 V80-V99
39. Accidental poisoning by alcohol E860 X45
40. Accidental fall E880-888 W00-W19
41. Suicide E950-959 X60-X84, Y87.0
42. Homicide E960-E969 X85-Y09, Y87.1
43. Injuries, unknown whether intentional E980-989 Y10-Y24
44. Other external causes Rest (E800-999) Rest (V01-Y98)
Web table 2. Distribution of the study populations by educational level (men and
women, 30-74 years).
Country Educational level (%)
Primary or lower
secondary Upper
secondary Tertiary
Finland 48,9 29.9 21.1
Sweden 39,6 42.3 18.1
Norway 32,2 48.3 19.5
Denmark 44,7 35.1 20.2
England 81,4 10,9 7,7
Belgium 64,0 20.5 15.5
Switzerland 29,7 54.9 15.5
France 55,4 32.8 11.8
Turin 71,4 19.8 8.7
Barcelona 69,1 14.7 16.1
Madrid region 70,3 14.8 14.9
Basque country 69,9 16.8 13.3
Slovenia 46.5 42.6 11.0
Hungary 64,3 23.3 12.4
Czech Republic 63,5 26.1 10.5
Poland 57,1 32.0 10.9
Lithuania 31,7 51.6 16.6
Estonia 30,6 51.9 17.5
Source: National or regional population census
Web table 3. Distribution of the study populations by occupational class (men 30-59 years).
Occupational class (%) Country
Manual Non-manual Other
Finland 34.6 46.6 18.8
Sweden 39.4 38.2 22.4
Norway 48.4 40.7 12.9
Belgium 38.0 33.3 28.7
Switzerland 53.3 24.9 21.8
France 46.2 34.5 19.3
Turin 37.3 42.8 19.9
Basque country 36.5 56.2 7.3
Source: National or regional population census
Web table 4. Distribution of the study populations by income level (both genders, 30-
69 years).
Country Income quintile Number of respondents % Average income*)
Sweden Poorest 1205 12,1 n/a
2 2092 21,1 n/a
3 2160 21,8 n/a
4 2196 22,1 n/a
Richest 2265 22,8 n/a
Missing 0 0,0 n/a
9918
Norway Poorest 1180 19,9 1355
2 1181 20,0 1968
3 1181 20,0 2398
4 1181 20,0 2888
Richest 1181 20,0 4503
Missing 14 0,2 -
5918
Denmark Poorest 2629 18,1 1130
2 2535 17,5 1945
3 2789 19,2 2665
4 2463 17,0 3411
Richest 2571 17,7 5247
Missing 1516 10,5 -
14503
Ireland Poorest 1208 22,8 n/a
2 1085 20,5 n/a
3 1037 19,6 n/a
4 985 18,6 n/a
Richest 979 18,5 n/a
Missing 0 0,0 n/a
5294
England/W Poorest 2233 16,0 610
2 2320 16,6 1132
3 2271 16,3 1777
4 2307 16,5 2735
Richest 2239 16,0 5794
Missing 2590 18,6 -
13960
Belgium Poorest 3208 19,7 303
2 3255 20,0 771
3 3131 19,2 1007
4 3240 19,9 1282
Richest 3207 19,7 1977
Missing 227 1,4 -
16268
Germany Poorest 1002 15,6 556
2 991 15,5 882
3 1003 15,7 1095
4 1033 16,1 1382
Richest 1022 16,0 2226
Missing 1352 21,1 -
6403
France Poorest 2078 15,1 582
2 2098 15,2 1009
3 2059 15,0 1318
4 2091 15,2 1698
Richest 2066 15,0 3185
Missing 3379 24,5 -
13771
Portugal Poorest 5323 15,3 129
2 8358 24,0 320
3 7619 21,9 546
4 6332 18,2 815
Richest 7208 20,7 1545
Missing 0 0,0 -
34840
Hungary Poorest 1597 17,4 101
2 1584 17,3 153
3 1643 17,9 197
4 1547 16,9 258
Richest 1572 17,1 461
Missing 1236 13,5 -
Czech Rep. Poorest 386 19,0 53
2 409 20,2 94
3 369 18,2 121
4 389 19,2 160
Richest 370 18,2 262
Missing 105 5,2 -
2028
Estonia Poorest 401 11,4 n/a
2 501 14,2 n/a
3 452 12,8 n/a
4 522 14,8 n/a
Richest 422 12,0 n/a
Missing 1227 34,8 n/a
3525
Note (*) Average monthly household income (in euros), including all income components received by
any household member, after subtraction of taxes and social premiums. Total household income was
corrected for household size through division by the number of household members raised to the
power 0.36.
n/a = only percentile
or categorical date were available in the original data source, which permitted a classification
in approximate quintiles but not a calculation of mean income per quintile.
Source: National health or multipurpose survey (see table 1)
... Lifestyle patterns and their impacts on health outcomes are also influenced by sociodemographic factors such as sex and socioeconomic position (SEP) [4][5][6][7][8]. Women generally engage in healthier lifestyle behaviours than men [7,9], while individuals with lower SEP are more likely to adopt riskier behaviours compared to those with higher SEP [10][11][12]. Additionally, sex disparities have been observed in the association between lifestyle and colon cancer risk, especially by anatomical subsite, with men more likely to develop distal colon cancer, and women at higher risk for proximal colon cancer [7,[13][14][15]. SEP may influence biological functioning through differential environmental, behavioural, and psychosocial exposures that may influence stress responses [16]. ...
... We did not find the associations between the HLI and colon cancer risk to differ by socioeconomic position (SEP), despite SEP being a known determinant of lifestyle behaviours, particularly with respect to smoking habits, alcohol drinking, obesity, physical inactivity, and poor diet quality [10,11]. In our study, participants with lower SEP had on average lower and more variable HLI scores, higher smoking and obesity rates, and poorer dietary habits than participants with higher SEP. ...
... Additionally, our analysis on SEP may have been limited by using the highest education level attained to measure SEP. While education is a well-established proxy for SEP in epidemiological studies, other indicators, such as occupation and income, as well as parental education, may provide information to more accurately characterise participants' SEP and investigate its complex interplay with lifestyle factors and disease risk [11,43,44]. ...
Can serum metabolic signatures inform on the relationship between healthy lifestyle and colon cancer risk?
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... Socioeconomic factors also correlate with the supply structure: regions with good medical care are often socioeconomically advantaged [7] and tend to have lower morbidity rates [8]. Simultaneously, these regions are typically more urban, have higher population densities, and a younger age structure [9]. ...
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Introduction The German hospital reform introduces population-based planning to allocate hospital budgets, considering each hospital’s role in meeting regional care needs. However, current hospital case numbers may reflect supply-side factors, such as physician density and socioeconomic disparities, rather than actual morbidity. Ambulatory care utilization inversely correlates with hospital usage, emphasizing the need to integrate ambulatory sector data into hospital planning. This study examines factors influencing hospital and office-based case numbers at the district level. Methods Linking 2021 data from the Federal and State Statistical Offices, INKAR data and health insurance claims data in Germany at the district level, a multiple linear regression model assessed the association between case counts in hospitals or office-based practices per 10,000 residents and distance to the nearest general practitioner (GP), as well as hospital bed and GP density. The Global Moran’s I as well as a geographically weighted regression (GWR) analysis were conducted to assess regional differences. Results Multiple linear regression revealed that greater GP distance, fewer GPs and more hospital beds were linked to more hospital cases, while office-based cases rose with shorter GP distance. Global Moran’s I confirmed spatial clustering, and GWR revealed heterogeneous effects of primary-care access on hospital admissions, whereas bed capacity uniformly increased hospital cases and shorter GP distances consistently predicted more office visits across Germany. Discussion Our findings align with research showing supply-induced demand of hospital cases and emphasize the need for coordinated hospital and ambulatory care planning to improve access, reduce unnecessary hospital admissions, and optimize patient outcomes.
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... The clinical utility of lipid ratios-such as total cholesterol to HDL-c (TC/HDL-c), LDL-c to HDL-c (LDL-c/HDL-c), and triglycerides to HDL-c (TG/HDL-c)-has garnered increasing attention as surrogate markers for assessing atherogenic risk beyond traditional lipid parameters [4][5][6]. These composite indices integrate pro-atherogenic and antiatherogenic lipid fractions, thus providing a more robust estimate of residual cardiovascular risk, particularly in populations with metabolic disturbances such as obesity, insulin resistance, and type 2 diabetes [7][8][9]. Among these, the TG/HDL-c ratio has been widely recognized as a sensitive marker of insulin resistance and a predictor of cardiovascular events, even in normolipidemic individuals [10,11]. ...
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Atherogenic dyslipidemia is a major contributor to cardiovascular disease, the leading cause of morbidity and mortality worldwide. While lipid abnormalities are well-established clinical risk factors, growing evidence highlights the influence of sociodemo-graphic and lifestyle determinants on lipid profiles. However, large-scale epidemiological data addressing these associations within occupational settings remain limited. Objective: To assess the cross-sectional and longitudinal associations between atherogenic risk-measured through TC/HDL-c, LDL-c/HDL-c, TG/HDL-c ratios, and atherogenic dyslipidemia (AD)-and sociodemographic variables, health behaviors, and alcohol consumption in a large cohort of Spanish workers. Methods: A dual-phase study was conducted. The first phase was a cross-sectional analysis of 139,634 workers (83,282 men; 56,352 women) from multiple employment sectors undergoing routine occupational health assessments. The second phase was a longitudinal study of a subsample (n = 40,431) with complete data from 2009 and 2019. Clinical, anthropometric, and biochemical data were collected using standardized protocols. Lifestyle factors (smoking, physical activity, Mediterranean diet adherence, alcohol intake) and socioeconomic indicators (education, occupational class) were recorded. Multinomial logistic regression was used to determine independent associations with high-risk atherogenic profiles. Results: Higher atherogenic indices and prevalence of AD were associated with advancing age, lower educational level, lower social class, smoking, physical inactivity, poor diet quality, and alcohol consumption. Men exhibited higher TG/HDL-c and AD values, whereas women had higher TC/HDL-c and LDL-c/HDL-c. Physical inactivity showed the strongest association with TG/HDL-c (OR: 36.23; 95% CI: 32.12-40.35) and AD (OR: 16.86; 95% CI: 14.80-18.93). Alcohol intake also independently predicted higher TG/HDL-c (OR: 1.60) and AD (OR: 1.79). Over the decade, a general increase in atherogenic risk was observed, especially among older adults, socially disadvantaged groups, and those with unhealthy behaviors. Conclusions: Sociodemographic and lifestyle factors, particularly physical inactivity and alcohol consumption, are strongly associated with adverse atherogenic profiles in the working population. The observed rise in lipid-related cardiovascular risk over the past decade emphasizes the urgent need for workplace-based health promotion strategies targeting modifiable behaviors and structural health inequalities. Citation: Obrador de Hevia, J.; López-González, Á.A.; Ramírez-Manent, J.I.; Busquets-Cortes, C.; Tárraga López, P.J.; García Samuelsson, M.; Riutord-Sbert, P. Atherogenic Risk and Its Association with Alcohol Consumption, Lifestyle Habits, and Sociodemographic Factors in a Population of Spanish Workers. Life 2025, 15, 923. https://doi.
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... Socioeconomic inequalities in many health outcomes are large and persistent (1). Individuals with low socioeconomic position (SEP) often have a more unhealthy lifestyle than their higher SEP counterparts; they more often smoke, have an unhealthier diet, have worse sleeping habits, and are less physically active during leisure time (2,3). ...
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  • AGING CLIN EXP RES
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  • Jan 1994
Socio-economic differentials in mortality are examined using the explanatory framework advanced by the Black Report. The evidence reviewed largely relates to studies carried out in Britain, although data from other European countries and the United States have also been considered. The many possible forms of artefactual distortion of associations between socio-economic status and mortality risk are examined and judged to have little effect; if anything, artefactual factors mean that conventional ideas about the magnitude of socio-economic differentials in mortality are an underestimate. Social selection is also considered to have little effect. Neither intragenerational downward drift nor direct intergenerational selection contribute significantly to the size of the differentials. Indirect selection has yet to be evaluated, although whether ft should be considered as a form of health selection rather than one form of accumulated disadvantage is unclear. Behavioural factors, while making important contributions to mortality risk, do not adequately account for the differences between social groups. Materialist factors, that is differential exposure to physical hazards determined by the distribution of income and opportunity, are an attractive explanatory category, since the persisting (or growing) socio-economic and geographical mortality differentials, seen during a period of broad secular declines in mortality rates, would be the anticipated outcome of their action. However, neither the social processes determining risk exposure nor the mechanisms by which exposures produce disease are well understood. Progress jn this area will depend upon studies which can examine how exposures interact and accumulate over the course of life to produce the observed pattern of mortality risk.
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Occupational class and cause specific mortality in middle aged men in 11 European countries: comparison of population based studies
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  • Br Med J
Objectives: To compare countries in western Europe with respect to class differences in mortality from specific causes of death and to assess the contributions these causes make to class differences in total mortality. Design: Comparison of cause of death in manual and non-manual classes, using data on mortality from national studies. Setting: Eleven western European countries in the period 1980-9. Subjects: Men aged 45-59 years at death. Results: A north-south gradient was observed: mortality from ischaemic heart disease was strongly related to occupational class in England and Wales, Ireland, Finland, Sweden, Norway, and Denmark, but not in France, Switzerland, and Mediterranean countries. In the latter countries, cancers other than lung cancer and gastrointestinal diseases made a large contribution to class differences in total mortality. Inequalities in lung cancer, cerebrovascular disease, and external causes of death also varied greatly between countries. Conclusions: These variations in cause specific mortality indicate large differences between countries in the contribution that disease specific risk factors like smoking and alcohol consumption make to socioeconomic inequalities in mortality. The mortality advantage of people in higher occupational classes is independent of the precise diseases and risk factors involved.
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Welfare state and health inequalities
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  • Jun 2006
This chapter begins by re-evaluating the reasons one might have to expect that some welfare states regimes, and especially the Nordic ones, can be expected to influence inequalities in population health. It then examines whether the expected effects of welfare state regimes are observed in recent research on inequalities in health. It reviews the empirical evidence brought forward by recent comparative studies, paying special attention to comparative studies on health inequalities that include both Nordic countries and countries with different welfare state regimes. Available evidence shows that healthinequalities are not consistently, significantly, and systematically smaller in the social democratic countries than in countries belonging to the other welfare regimes, i.e., the conservative and the liberal. However, for inequalities in mortality, except for Finland, the other Scandinavian countries do perform rather well, in relative terms (Denmark and Norway) or in absolute terms (Norway and Sweden).
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Mortality by occupational class among men 30–64 years in 11 European countries
Article
  • Jan 1998
This study compares eleven countries with respect to the magnitude of mortality differences by occupational class, paying particular attention to problems with the reliability and comparability of the data that are available for different countries. Nationally representative data on mortality by occupational class among men 30–64years at death were obtained from longitudinal and cross-sectional studies. A common social class scheme was applied to most data sets. The magnitude of mortality differences was quantified by three summary indices. Three major data problems were identified and their potential effect on inequality estimates was quantified for each country individually. For men 45–59years, the mortality rate ratio comparing manual classes to non-manual classes was about equally large for four Nordic countries, England and Wales, Ireland, Switzerland, Italy, Spain and Portugal. Relatively large ratios were only observed for France. The same applied to men 60–64years (data for only 5 countries, including France). For men 30–44years, there was evidence for smaller mortality differences in Italy and larger differences in Norway, Sweden and especially Finland (no data for France and Spain). Application of other summary indices to men 45–59years showed slightly different patterns. When the population distribution over occupational classes was taken into account, relatively small differences were observed for Switzerland, Italy and Spain. When national mortality levels were taken into account, relatively large differences were observed for Finland and Ireland. For each summary index, however, France leads the international league table. Data problems were found to have the potential to bias inequality estimates, substantially especially those for Ireland, Spain and Portugal. This study underlines the similarities rather than the dissimilarities between European countries. There is no evidence that mortality differences are smaller in countries with more egalitarian socio-economic and other policies.
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The 'Southern Model' of Welfare in Social Europe
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  • Feb 1996
  • J EUR SOC POLICY
This article tries to identify some common traits of the welfare states of Italy, Spain, Portugal and Greece, with special attention to institutional and political aspects. The main traits identified are: (1) a highly fragmented and 'corporatist' income maintenance system, displaying a marked internal polarization: peaks of generosity (e.g. as regards pensions) accompanied by macroscopic gaps of protection; (2) the departure from corporatist traditions in the field of health care and the establishment (at least partially) of National Health Services based on universalistic principles; (3) a low degree of state penetration of the welfare sphere and a highly collusive mix between public and non public actors and institutions; (4) the persistence of clientelism and the formation - in some cases - of fairly elaborated 'patronage machines' for the selective distribution of cash subsidies. A number of factors are then discussed to explain these peculiarities of the Southern model. Among these, the historical weakness of the state apparatus in this area of Europe; the preminence of parties as main actors for interest articulation and aggregation; ideological polarizations and, in particular, the presence of a maximalist and divided Left. In the last section, the article addresses the severe problems which are currently confronting - in various degrees - the four southern European welfare states. Both the exogenous challenges, connected with market globalization and EMU, and the endogenous challenges (which as rapid ageing, mass unemployment, etc.) are discussed. It is concluded that the adaptation of the southern model to these challenges will be a very difficult process in the years ahead, in both social and political terms.
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Patterns of cigarette sales and lung cancer mortality in some Central and Eastern European countries, 1960-1989
Article
Background: Remarkable increases in lung cancer risk recently have been observed in the Central and Eastern European (CEE) area. This study examines the patterns of lung cancer mortality rates and cigarette sales in 1960-1989 in seven CEE countries with a total population of 97.5 million and 43,000 deaths from lung cancer in the last year under study. Methods: Trends in cigarette sales and mortality rates from lung cancer in seven CEE countries were compared for the years 1960-1989. Results: Among males, recent lung cancer death rates were the highest in Europe, and trends by country largely reflected the varied prevalence and duration of smoking in previous decades. For females, lung cancer mortality rates were much lower, although there were exponential rate increases. In the more recent birth cohorts, there were some declines in mortality rates among males, but not among females. Conclusions: The rising cigarette consumption through the 1960s, 1970s, and, in some countries, the 1980s is accompanied in most of the countries by rising lung cancer mortality rates for young adults. This increasing cigarette consumption will determine future trends in lung cancer, which will increase well beyond the turn of the century and will continue longer for females than for males. This outlook underlines the urgent need for comprehensive lung cancer prevention with the concerted control of smoking as a priority. The role of cofactors and their interaction with smoking deserve further exploration.
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