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(Stroke. 2004;35:432.)
© 2004 American Heart Association, Inc.

Original Contributions

Educational Level and Stroke Mortality

A Comparison of 10 European Populations During the 1990s

Mauricio Avendaño, MPH; Anton E. Kunst, PhD; Martijn Huisman, MSc; Frank van Lenthe, PhD; Matthias Bopp, PhD; Carme Borrell, PhD; Tapani Valkonen, PhD; Enrique Regidor, PhD; Giuseppe Costa, PhD; Angela Donkin, PhD; Jens-Kristian Borgan, PhD; Patrick Deboosere, MSc; Sylvie Gadeyne, MSc; Teresa Spadea, MPhil; Otto Andersen, PhD Johan P. Mackenbach, PhD

From the Department of Public Health, Erasmus Medical Center, Rotterdam, Netherlands (M.A., A.E.K., M.H., F. van L., J.P.M.); Institute for Social and Preventive Medicine, University of Zurich, Zurich, Switzerland (M.B.); Municipal Health Service, Barcelona, Spain (C.B.); Department of Sociology, University of Helsinki, Helsinki, Finland (T.V.); Department of Preventive Medicine and Public Health, University of Madrid, Madrid, Spain (E.R.); Department of Public Health and Microbiology, University of Turin, Turin, Italy (G.C., T.S.); Health and Care Division, Office for National Statistics, London, UK (A.D.); Division for Health Statistics, Statistics Norway, Oslo, Norway (J-K.B.); Interface Demography, Free University Brussels, Brussels, Belgium (P.D., S.G.); and Research and Methodology Division, Statistics Denmark, Copenhagen, Denmark (O.A.).

Reprint requests to Mauricio Avendaño, MPH, Department of Public Health, Erasmus Medical Center, PO Box 1738, 3000 DR Rotterdam, Netherlands. E-mail m.avendanopabon{at}erasmusmc.nl

	Abstract

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Background and Purpose— Variations between countries in occupational differences in stroke mortality were observed among men during the 1980s. This study estimates the magnitude of differences in stroke mortality by educational level among men and women aged 30 years in 10 European populations during the 1990s.

Methods— Longitudinal data from mortality registries were obtained for 10 European populations, namely Finland, Norway, Denmark, England/Wales, Belgium, Switzerland, Austria, Turin (Italy), Barcelona (Spain), and Madrid (Spain). Rate ratios (RRs) were calculated to assess the association between educational level and stroke mortality. The life table method was used to estimate the impact of stroke mortality on educational differences in life expectancy.

Results— Differences in stroke mortality according to educational level were of a similar magnitude in most populations. However, larger educational differences were observed in Austria. Overall, educational differences in stroke mortality were of similar size among men (RR, 1.27; 95% CI, 1.24 to 1.30) and women (RR, 1.29; 95% CI, 1.27 to 1.32). Educational differences in stroke mortality persisted at all ages in all populations, although they generally decreased with age. Eliminating these differences would on average reduce educational differences in life expectancy by 7% among men and 14% among women.

Conclusions— Educational differences in stroke mortality were observed across Europe during the 1990s. Risk factors such as hypertension and smoking may explain part of these differences in several countries. Other factors, such as socioeconomic differences in healthcare utilization and childhood socioeconomic conditions, may have contributed to educational differences in stroke mortality across Europe.

Key Words: epidemiology • Europe • mortality • social class • stroke

	Introduction

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Research has shown that individuals with a low socioeconomic status experience higher stroke mortality than those with a high socioeconomic status.^1–8 During the 1980s, relatively small occupational differences in stroke mortality were observed in most Nordic countries (Sweden, Norway, and Denmark), Italy, and Spain, whereas larger differences were reported in England/Wales, Ireland, and Finland.¹ However, it is not known whether these international variations persisted into the 1990s.

Welfare and healthcare systems vary across European countries.^9,10 Similarly, the magnitude of socioeconomic differences in the prevalence of risk factors for stroke such as hypertension,¹¹ smoking,¹² and diet¹³ varies by country. Research on intercountry variation can contribute to the identification of risk factors and policies through which the excess stroke mortality among lower socioeconomic groups can be reduced. This would in turn contribute to an overall reduction in stroke mortality across Europe.

The aim of this report is to assess stroke mortality differences according to educational level across Europe during the 1990s. Previous studies were limited to middle-aged men and based on cross-sectional data for some countries.¹ This is the first longitudinal study of the association between educational level and stroke mortality in 10 European populations among men and women aged 30 years. This is also the first study to assess the impact of stroke mortality on educational differences in life expectancy, providing a broader public health perspective.

	Subjects and Methods

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Data and Subjects
Longitudinal data on mortality by educational level, sex, and 5-year age group were obtained for 10 European populations. Participants were enumerated during a census in the early 1990s and followed up for different periods (Table 1). Most studies covered the entire national population, except Madrid (regional), Barcelona and Turin (urban), Switzerland (population living in German-speaking areas), and England/Wales (1% representative sample of the population). Studies included individuals aged 30 years (age specified at the start of follow-up), except in Denmark, where data on education were not available for those aged 70 years.

View this table: [in this window] [in a new window]   TABLE 1. Follow-Up Period, Number of Person-Years at Risk, and Stroke Deaths Among Men and Women Aged 30 Years

Educational level was used as an indicator of socioeconomic status. In contrast to occupational class, this indicator can be applied equally to both men and women and is more comparable between age groups.^14,15 Education is considered a more reliable indicator of socioeconomic status among the elderly because it includes the large proportion of economically inactive individuals that would be excluded when occupational class is used.¹⁵ Educational level is also more comparable between European countries and more stable over time than other indicators such as occupational class and income.^14,15

Educational level was first coded according to national classification schemes. To enhance comparability between countries, education was subsequently reclassified into 3 equivalent categories so that the proportion of individuals with a low educational level was similar across populations. These groups approximately corresponded to levels 0 to 2 (preprimary, primary, and lower secondary education), 3 (upper secondary education), and 4 to 6 (postsecondary education) of the United Nations Education, Scientific, and Cultural Organization (UNESCO) Standard Classification scheme. In most countries, approximately 65% to 80% of the population had a low educational level, 15% to 30% had a middle level, and 10% to 15% had a high educational level.

Stroke (cerebrovascular disease) was defined as code numbers 430 to 438 of the International Classification of Diseases, Ninth Revision, except in Denmark and Switzerland, where both International Classification of Diseases, Eighth Revision (430 to 438) and International Statistical Classification of Diseases, 10th Revision (I60 to I69) codes were used.

Methods of Analyses
Age-standardized stroke mortality rates were calculated for sex and education strata distinguishing 3 age groups: 30 to 59, 60 to 74, and 75 years. Rates were standardized by 5-year age groups by the direct method with the European population of 1995 as the standard.¹⁶ Age-adjusted rate ratios (RRs) were calculated by Poisson regression. This summary index compared the mortality rate of the low education group with the combined rate of the middle/high education group, with the latter used as the reference category. These 2 upper levels were combined to obtain more precise estimates, given the small size of these education groups. Analyses were performed with the use of SAS, version 6.12.

To estimate the impact of eliminating educational differences in stroke mortality on educational differences in life expectancy at age 30 years, the cause-elimination life table method was applied. First, the difference in life expectancy between the low and middle/high education groups was calculated. This was compared with predicted differences in life expectancy, as they would have been if the low education group had the same stroke mortality rate of the middle/high education group.

Analyses were performed separately for each population and for a pooled data set weighted on the size of each cohort. This was done to take into account differences in population size in the pooled analysis.

	Results

Top Abstract Introduction Subjects and Methods Results Discussion References

 
The age-standardized stroke mortality rates among men and women with low education were higher than the mortality rates among those with a middle or high educational level in all populations (Tables 2 and 3, respectively). This pattern was consistent in all age groups. A clear educational gradient was generally observed among middle-aged men and women, although this was less consistent among the younger and older age groups in some populations.

View this table: [in this window] [in a new window]   TABLE 2. Age-Standardized Stroke Mortality Rates (per 100 000 Person-Years) by Educational Level Among Men Aged 30–59, 60–74, and 75 Years

View this table: [in this window] [in a new window]   TABLE 3. Age-Standardized Stroke Mortality Rates (per 100 000 Person-Years) by Educational Level Among Women Aged 30–59, 60–74, and 75 Years

Results shown in Figure 1 indicate that men and women with a low educational level had significantly higher mortality rates than those with a middle/high educational level. Educational differences in stroke mortality were of a similar magnitude in most populations. However, in Austria, differences were larger than the European average among both men and women, although this difference was only statistically significant among women. Overall, men and women with a low educational level had approximately a 26% to 28% higher risk of dying from stroke than those with a middle/high educational level.

View larger version (22K): [in this window] [in a new window]   Figure 1. Stroke mortality: RR between low and middle/high educational levels among men and women aged 30 years. Fin indicates Finland; Nor, Norway; Den, Denmark; Eng, England/Wales; Bel, Belgium; Swi, Switzerland; Aus, Austria; Tur, Turin (Italy); Bar, Barcelona (Spain); and Mad, Madrid (Spain). *Data available for ages 30 to 69 years only.

Differences in stroke mortality according to educational level were similar among men (RR, 1.27; 95% CI, 1.24 to 1.30) and women (RR, 1.29; 95% CI, 1.27 to 1.32) (Figure 1). Although in most populations educational differences in stroke mortality were somewhat larger among women than among men, these variations were generally small and not significant.

Figure 2 shows the RRs for stroke mortality rates in low compared with middle/high education groups for each age category. Because results were similar for men and women, RRs were calculated for both groups combined. Differences in stroke mortality between education groups were generally present in all age groups (Figure 2). However, differences decreased with age in most populations and in Europe as a whole. RRs in the pooled analysis were 1.52 (95% CI, 1.45 to 1.59) among those aged 30 to 59 years, 1.37 (95% CI, 1.33 to 1.41) among those aged 60 to 74 years, and 1.19 (95% CI, 1.17 to 1.21) among those aged 75 years. A sharper decline with age was observed in Norway, Austria, and Barcelona. In contrast, in England/Wales, Turin, and Madrid, educational differences did not decrease consistently with age. However, in all populations, absolute rate differences increased with age and were largest among those aged 75 years (Tables 2 and 3, respectively).

View larger version (20K): [in this window] [in a new window]   Figure 2. Stroke mortality: RR between low and middle/high educational levels among both men and women aged 30 to 59, 60 to 74, and 75 years. Fin indicates Finland; Nor, Norway; Den, Denmark; Eng, England/Wales; Bel, Belgium; Swi, Switzerland; Aus, Austria; Tur, Turin (Italy); Bar, Barcelona (Spain); and Mad, Madrid (Spain). *Data available for ages 30 to 69 years only.

Differences in life expectancy at age 30 years between low and middle/high education groups were on average 3.22 years among men and 2.18 years among women (Table 4). Eliminating educational differences in stroke mortality would on average reduce educational differences in life expectancy by 7% (0.24 years) among men and 14% (0.31 years) among women. The largest reduction would be achieved among both men and women in Turin (9% and 18%, respectively) and Austria (7% and 18%, respectively), as well as among men in Norway (7%) and England/Wales (7%).

View this table: [in this window] [in a new window]   TABLE 4. Impact of Eliminating Educational Differences in Stroke Mortality on Educational Differences in Life Expectancy at Age 30 Among Men and Women

	Discussion

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Differences in stroke mortality between education groups were of a similar magnitude across populations. Larger disparities were, however, observed in Austria. Educational differences in stroke mortality were similar among men and women and generally decreased with age. Eliminating differences in stroke mortality between education groups would on average reduce disparities in life expectancy by 7% among men and 14% among women.

Evaluation of Data
Some limitations of this study should be considered. First, countries might differ in the practices and accuracy of death registrations, which might have influenced results for some countries. However, our results would only be biased if misclassification of stroke occurred differentially across educational levels. There is no evidence to suggest that this has occurred in any of the countries. Therefore, any bias caused by this problem is likely to be small.

Second, national education levels were reclassified so that the proportion of participants with low education was similar across countries. This was not possible in Switzerland, where a larger proportion of participants had a high educational level. However, as this was possible in most populations, educational levels were generally comparable across countries. Nevertheless, low education groups were relatively large in each country. We do not know whether similar results would be observed if this group was divided into more specific levels.

Finally, we assessed whether data problems might explain the results for Austria. However, such problems were not identified. Although the follow-up period was shorter in Austria and Madrid, previous evaluations indicate that mortality differences by education are unrelated to follow-up time.¹⁷ Instead, the pattern for Austria might resemble that of Middle European countries such as the Czech Republic and Hungary, where socioeconomic differences in stroke mortality were larger than in Western Europe.^7,17

Comparison With Previous Studies
In contrast to previous research during the 1980s among men,¹ we found similar educational differences in stroke mortality across Europe. Populations with small occupational differences during the 1980s, such as Turin, Spain, and most Nordic countries, had educational disparities similar to those of other populations during the 1990s. Similarly, whereas the largest disparities were observed in Finland during the 1980s, these were around the European average in our study. These discrepancies might be explained by improvements in the quality of our data, which comprised broader age groups. Previous studies in some countries used cross-sectional data in which the number of deaths was not directly linked to data on the population at risk. These studies might also have been biased by the use of occupational class as a result of the exclusion of economically inactive men.^15,17

Furthermore, different mechanisms might account for the effect of educational level and occupational class.^14,15 Whereas education might influence mortality primarily through factors such as health behavior, occupation might also represent exposure to the psychosocial and physical dimensions of work arrangements.¹⁴ Finally, widening socioeconomic differences in stroke mortality in some southern European countries³ may have contributed to a more homogeneous international pattern during the 1990s.

Explanation of Results
Hypertension is the most important risk factor for stroke. A higher hypertension prevalence has been observed in Northern countries such as Finland and England compared with Mediterranean states.¹⁸ However, socioeconomic differences in hypertension prevalence have been reported both in Northern¹¹ and Southern European populations.^19,20 Despite differences between countries in treatment guidelines, a recent study found small variations in hypertension treatment rates, with somewhat higher rates in Italy and lower rates in Finland and England.¹⁸ On average, only 8% of hypertensive individuals had their condition controlled in Europe.¹⁸ Furthermore, a higher prevalence of poor hypertension treatment has been observed among low socioeconomic groups.²⁰ These findings suggest that educational differences in hypertension prevalence and treatment may partly explain the association between educational level and stroke mortality observed in our study.

Relatively high smoking prevalence rates have been observed among men in Denmark, Spain, and Italy.¹² However, socioeconomic differences in smoking prevalence have only been observed in Northern States, whereas small or no disparities have been reported in Southern European countries.¹² Dietary factors such as salt and fat intake are also important risk factors for stroke. Socioeconomic differences in dietary patterns have been reported in Nordic countries,^21,22 the United Kingdom,¹³ Austria,²³ and Switzerland.²⁴ However, no socioeconomic differences in diet have been observed in Southern Europe.¹³ Although a higher prevalence of obesity has been reported in Italy and Spain compared with other countries,²⁵ consistent socioeconomic differences in obesity prevalence have been observed among women across Europe.^13,19 This may have contributed to educational differences in stroke mortality among women, although this pattern is less consistent among men.^13,19 In summary, these classic risk factors may have contributed to educational differences in stroke mortality in several countries.

However, research indicates that classic risk factors explain less than half of socioeconomic differences in stroke.^6,26 This suggests that other factors, such as access to healthcare, may have played a role. Socioeconomic differences in healthcare utilization among stroke patients have been observed even in countries with universal healthcare systems such as Finland⁴ and Sweden.⁸ A recent study found similar socioeconomic disparities in access to specialist care in most European countries,⁹ including Italy, Spain, Belgium, the United Kingdom, and Denmark. Inequity in the utilization of both general practice and specialist care has been observed in Finland¹⁰ and Austria.⁹ Thus, socioeconomic differences in healthcare access existed in most countries during the 1990s, which possibly contributed to educational differences in stroke mortality across Europe.

Finally, research indicates that childhood socioeconomic status is related to stroke.^2,5 Education is typically completed early in life and can therefore be considered a marker of childhood socioeconomic conditions.^14,15,27 Thus, early life circumstances may have contributed to educational differences in stroke mortality during adulthood across Europe. However, the extent to which this applies to all populations remains uncertain.

Implications
This study supports previous research and provides further evidence of higher stroke mortality rates among individuals with a low level of education. Improving the unfavorable risk profile among these groups can therefore lead to an overall reduction in stroke mortality. However, the contribution of factors such as smoking and diet may differ across populations, and therefore different interventions may be required for each country. Furthermore, policies that tackle inequity in healthcare access and hypertension treatment may also be necessary to reduce stroke mortality differences between education groups. Such a reduction could have an important public health impact by diminishing educational disparities in life expectancy in Europe.

	Acknowledgments

 
This study was funded by a grant (contract QLK6-CT-1999-02161) from the European Union Fifth Framework Program on Quality of Life and Management of Living Resources. The Swiss National Science Foundation (grants 32-5884.98 and 32-63625.00) supported the construction of the Swiss National Cohort. Dr van Lenthe is supported by a grant from the Netherlands Organization for Scientific Research (NWO grant 904-66-104). The authors would like to thank Professor G.A.M. Van den Bos, Dr Hendriek Boshuizen, Dr Anna Peeters, and Dr Wilma Nusselder for their valuable comments.

Received July 14, 2003; revision received September 13, 2003; accepted October 1, 2003.

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