Substudy 1: Educational differences in disability-free life

Substudy 1: Educational differences in disability-free life expectancy:
a comparative study of long-standing activity limitation in eight European
countries
Netta Mäki1*, Pekka Martikainen1, Terje Eikemo2, Gwen Menvielle3, Olle Lundberg4 5, Olof
Östergren4, Domantas Jasilionis6, Johan Mackenbach2 & the EURO-GBD-SE consortium†
1 Department of Social Research
University of Helsinki
Finland
2 Department of Public Health
Erasmus MC
University Medical Center Rotterdam
Netherlands
3 l'Institut national de la santé et de la recherche médicale
France
4 CHESS | Centre for Health Equity Studies
Sweden
5 Department of Health Sciences, Mid Sweden University
Sweden
6 Max Planck Institute for Demographic Research
Germany
*Corresponding author: Department of Social Research, P.O.Box 18,
FIN-00014 University of Helsinki,
Finland
E-mail: [email protected]
† Carme Borrell, Maica Rodríguez-Sanz, Enrique Regidor
6
ABSTRACT
Background
Research comparing socioeconomic differences in health between countries has mostly been
limited to various measures of either morbidity or mortality. This study combines these data
to yield a summary measure of population health for a broad age-group. Educational
differences in disability-free life expectancy are studied for eight countries from all parts of
Europe in the early 2000s.
Methods
Long-standing severe disability was measured as a Global Activity Limitation Indicator
(GALI) derived from the European Union Statistics on Income and Living Conditions (EUSILC) survey. Census-linked mortality data were collected by the EURO-GBD-SE project.
We calculated sex-specific educational differences in partial disability-free life expectancy
between the ages 30 and 79 years using the Sullivan method.
Results
Disability-free life expectancy varied substantially between the European countries. The
lowest figures were found among Lithuanian men and women (33.1 and 39.1 years,
respectively), and the highest among Italian (42.8 and 44.4, respectively). Both life
expectancy and disability-free life expectancy were longer the higher the education, but
educational differences were much larger in the latter in all countries. The difference between
primary and lower secondary educated and tertiary educated was over 10 years for males in
Lithuania and about seven years for males in Austria, Finland, France and for females in
Lithuania. The difference was lowest in Italy (4 and 2 years among men and women,
respectively).
Conclusion
Highly educated Europeans can not only expect to live longer, but also to spend these years
in better health than those with lower education. However, the size of the educational
difference in disability-free life expectancy varies significantly between countries. The
smallest differences are found in Southern and the largest in Eastern and Northern Europe.
7
INTRODUCTION
Decreasing mortality and increasing life-expectancy in most Western-European countries
during the last decades is well documented.1 This, however, does not in itself mean a
healthier population. In addition to the length of the life span, the more recent research and
policy interest is concerned with how many years of life are lived with and without
morbidity, functional disability or activity limitations. Ageing populations pose a challenge to
the sustainability of the social protection system, and, especially, people with functional
limitations and chronic conditions account for a significant amount of health care spending.2
Accordingly, a key issue is whether the increased life expectancy is associated with an
increase or decrease in disability.3 Often these processes are measured in terms of healthy life
expectancy that combines mortality and morbidity rates into a summary measure of
population health that describes both the quantity and quality of life.4
Not all populations or population sub-groups uniformly enjoy the clear increase in longevity.
The difference in life expectancy between Western and Eastern European countries has still
been large in the past years. Also, socioeconomic differences in health and mortality have
been well documented, and those with, e.g., lower education, have higher mortality5 and
shorter life-expectancy.6 Most studies even suggest that these inequalities have increased.7,8
Socioeconomic differences in healthy life expectancy have also been studied to some extent.
Using Finnish data Valkonen and others9 found that health expectancy depends strongly on
the indicator – such as limiting long-standing illness, functional disability and poor selfperceived health – used to measure morbidity, but the patterns of differences between
socioeconomic categories were largely independent of the indicators. Crimmins and
Cambois10 have done a comprehensive review of articles published in the 1980s and 1990s
finding a consistent general conclusion that differences by socioeconomic indicators in
healthy life expectancy are larger than differences in total life expectancy irrespective of
socioeconomic or health outcome indicator used. However, to our knowledge, there are no
corresponding reviews with more recent data and the most recent developments in healthy
life expectancies remain unknown.
Cross-country comparisons of socioeconomic differences in healthy life expectancy are still
few and mostly include only few countries. According to Sihvonen, Kunst, Lahelma et al.11
the size of socioeconomic differences in healthy life expectancy was on the same level in
Finland and Norway. However, this similarity hides the fact that the size of inequalities was
larger in mortality in Finland and in morbidity in Norway. Majer, Nusselder, Mackenbach et
al.6 compared educational differences in healthy life expectancy between ages 50 and 65
years in ten Western-European countries in the latter part of the 1990s. Higher educated
persons lived longer in good health in all countries, but the longest disability-free life
expectancy was in Spain, and the largest absolute inequalities between educational groups
were in Portugal and France.
In the calculation of healthy life expectancy some studies have used activity limitation based
measures as the health indicator. Activity limitation can be seen to involve not only specific
diseases or basic physical functioning required for daily living like bathing, dressing and
walking but also activities at the level of the person in society.4 In other words, it generally
relates to the ability to care for one’s own needs independently.12 Studies on socioeconomic
differences in activity limitation are few, but they suggest that social inequalities exist in
several Western-European countries and in the United States according to this indicator as
well.6,13
8
At the moment it is a challenge to attempt to form a coherent picture of recent levels of
socioeconomic inequalities in healthy life expectancy because mostly the results from
previous studies are based on various data sets using different health outcomes and
socioeconomic indicators and they cover different age ranges. Especially, cross-country
comparisons with more recent data are lacking entirely. This study includes eight countries
from all parts of Europe, covers a broad age group of 30–79 years, uses a global and
evaluated health outcome, Global Activity Limitation Indicator (GALI), and utilises
comparable and harmonised mortality data from the 2000s to study educational inequalities in
healthy life expectancies.
METHODS
Data
Two kinds of data are needed to calculate the healthy life expectancies, one providing
mortality rates for sex- and age-groups and one supplying the health prevalences for
corresponding categories. Mortality data were collected and harmonised by the EURO-GBDSE project and covered at least the first part of the 2000s and the health indicator was
acquired from the European Union Statistics on Income and Living Conditions (EU-SILC)
cross sectional survey data for 2005.
As a whole the EURO-GBD-SE project includes twenty-one areas in eighteen European
countries, but to be included in this study several requirements were to be met. First of all, we
only included those countries where mortality data were linked to census data in order to
avoid numerator-denominator bias in the cross-sectional unlinked data. Secondly, we were
naturally only able to involve those countries that had also taken part in the EU-SILC survey
that provided the heath measure used in these analyses. Additionally, a few more limitations
were done. On the grounds of e.g. an evaluation report of the measurement of education in
EU-SILC,14 the measurement of education in the United Kingdom may be incorrect in the
EU-SILC survey data and thus be a poor match with the mortality data. The size of the
Swedish sample in EU-SILC survey was small and following the necessary exclusions based
on e.g., missing data the final sample was not, according to our judgement, representative
anymore. Furthermore, the Dutch data set was based on a labour force survey, so it mainly
included only people aged below 65, and was thus not suitable for the research questions this
study posed. These three countries were thus excluded from the final analyses.
All together the eight countries (Table 1) involved in this study included over 60 million
person-years and more than 508 000 deaths in the age group of 30–79 years. For Belgium,
Italy and Spain census-linked mortality data were not available for the whole country but
only a few areas which were Brussels in Belgium, Turin and Tuscany in Italy and Madrid and
Barcelona in Spain.
Education was categorised into three groups in this study: the first group includes those with
primary or lower secondary education (Isced level 2 or less), the second is comprised of those
with upper secondary education (Isced levels 3 and 4) and the third group involves those with
tertiary education (Isced level 5 or higher as well as level 4 in Belgium). The categorisation
of the education was mostly based on the harmonisation process in the EURO-GBD-SE –
project, but advice was also received through personal contacts with national representatives
9
from the participating countries. For the countries included, the educational distribution
seemed to match the OECD education statistics (Health at a Glance 200215) relatively well.
Table 1. Years of mortality follow-up, number of person-years and deaths and distribution of person-years by education in men and women
Men
Number of
Mortality
follow-up
Finland
personyears
deaths
Women
Education (%)
Primary &
lower
secondary
Upper
secondary
Number of
Tertiary
personyears
deaths
Education (%)
Primary &
lower
secondary
Upper
secondary
Tertiary
2001 - 2007
7 960 794
91 298
36.4
37.0
26.6
8 423 795
54 497
36.2
34.0
29.8
Norway
Lithuania
1
2001 - 2006
2001 - 2004
6 094 049
3 128 450
48 667
59 338
18.9
26.6
55.9
57.4
25.1
16.0
6 215 418
3 867 305
34 676
37 173
23.0
26.8
52.8
55.4
24.2
17.8
1
2001 - 2004
2001 - 2002
1999 - 2005
2001 - 2003
2000 - 2006
2001 - 2006
2001 - 2005
746 697
2 347 448
773 471
2 462 802
3 478 511
1 248 229
732 860
7 323
22 954
7 932
23 529
31 269
11 217
7 263
46.9
20.8
37.4
53.8
51.3
56.6
57.4
18.4
62.3
45.0
23.2
24.2
29.0
28.9
34.7
17.0
17.6
23.0
24.5
14.4
13.7
845 277
2 524 795
853 139
2 754 156
3 909 302
1 427 896
826 171
5 567
16 736
4 338
12 998
18 343
8 019
5 000
50.5
40.8
48.0
61.7
58.3
63.8
59.9
18.8
49.8
35.3
19.4
19.2
24.4
27.9
30.7
9.4
16.7
19.0
22.5
11.8
12.3
Brussels
Austria
France
Madrid
Barcelona
Turin
Tuscany
1
While all other countries reported age at death, Norway and Brussels reported age at baseline and attributed deaths and person-years to the baseline age. In the EURO-GBDSE -project an adjustment method was developed in order to ensure comparability between countries that reported differently. See Appendix.
The health indicator used in this study was acquired from the European Union Statistics on
Income and Living Conditions (EU-SILC) cross sectional survey data for 2005. The EUSILC has been established to provide data on structural social indicators, but it also includes a
few health measures. The survey is based on the idea of a common methodological
framework for all EU countries, which is defined by harmonised lists of variables, common
concepts and uniform classifications. The data are collected by national statistical offices or
by research institutes of the participating countries.
Eurostat along with the Euro-REVES and the EHEMU projects have done comprehensive
work to launch a coherent indicator that measures disability across Europe. The Global
Activity Limitation Indicator (GALI) which was used as the health outcome in this study, is
an evaluated16,17 indicator, though so far not used in many studies or to examine
socioeconomic differences. This single item instrument is based on health-related long-term
limitations on daily activities. It inquires: “For the past 6 months or more have you been
limited in activities people usually do because of a health problem?” There are three answer
categories: “Yes, severely”, “Yes, moderately” and “No”. In this study, the category
“severely limited” described disability. Sample weights were used in the analyses of the
survey data.
Table 2 shows the numbers of cases included in the analyses in this study by country and the
proportion of participants reporting severe disability. This varied from about 5% in Italy to
over 14% among Finnish women.
10
Table 2. Number of cases in the EU-SILC survey data and the proportion (%) reporting severe disability by sex
Finland
Norway
Lithuania
Belgium
Austria
France
Spain
Italy
Number
Men
Proportion (%) reporting
severe disabilty
Number
Women
Proportion (%) reporting
severe disabilty
4 183
2 240
3 208
3 369
3 839
6 711
10 119
16 325
12.6
9.0
10.3
7.3
10.0
6.1
7.7
4.7
4 194
2 252
4 013
3 526
4 148
7 352
10 933
16 917
14.4
10.3
12.0
9.3
10.1
6.3
9.6
5.3
Methods
Health expectancies were calculated by Sullivan’s18 method and the standard errors needed
for the calculation of the confidence intervals by the method proposed by Jagger et al. 19 using
five-year age intervals. We calculated partial life expectancies between ages 30 and 79.
Healthy life expectancy calculated this way demonstrates how many years men and women
with different educational attainment could expect to live without long-term activity
limitations between these ages. The theoretical maximum number is 50 years in a population
with no mortality and morbidity.
We settled on calculating partial healthy life expectancies for age-group 30–79 for two
reasons. First, among those aged 80 and over, the coverage of information on education
varies between countries. Typically, the proportion of observations with missing values is the
higher the older the age-group is also in these mortality data sets. Secondly, at older ages the
proportion of persons in institutional care increases notably and differently in different
countries and as the EU-SILC data only include the household population the comparative
results would be biased among the elderly.
RESULTS
The lowest disability-free life expectancy between ages 30 and 79 was among Lithuanian
men and women (33.0 and 39.0 years, respectively), and to a large extent this was a
consequence of short life expectancy (36.9 years among men and 44.5 among women)
(Tables 3 & 4). Disability-free life expectancy was rather low also among the Finns, but this
arose largely from the long life expectancy lived with disability (5.5 years among men and
6.6 among women). The highest figures were found among Italian men and women
(disability-free life expectancy was 42.6 and 44.4 years, respectively).
In all countries both life expectancy and disability-free life expectancy were longer the higher
the education was. However, educational differences were much smaller in life expectancy
than in disability-free life expectancy (Tables 3 & 4). The difference in life expectancy
between primary & lower secondary educated and tertiary educated was smallest in Italy (2.3
years among men and 0.6 among women) and largest in Lithuania (9.2 years among men and
4.7 among women). However, the difference in disability-free life expectancy between
11
primary & lower secondary educated and tertiary educated was as high as 10.2 years among
Lithuanian men and about 7 years for males in Austria, Finland and France. Among women
the figures were somewhat smaller, and they were, at the largest, 7.3 years in Lithuania, 6.4
in Norway and 5.3 in Austria. The smallest differences between the educational groups were
in Southern European countries. In Spain and Italy the difference in disability-free life
expectancy was 4.0 and 4.6 years among Italian and Spanish men and 2.0 and 2.8 among
women, respectively.
Results for the life spent disability-free were slightly different. The proportions were larger in
Lithuania (89.3 and 87.6% among men and women, respectively) than in Finland (87.2 and
85.8%) and among Austrian men (89.0%). The result for Lithuanian men arose as the
combination of the rather short life expectancy spent with disability and extremely short life
expectancy.
Among primary and lower secondary educated men the proportion of life spent disability-free
was the lowest in Austria (83%) and Finland (85%) and the highest in Italy (94%).
Corresponding figures were much higher among the tertiary educated: almost 93% in Finland
and Austria, and almost 98% in France and Italy. Among women the lowest figures (84–
85%) were found among primary and lower secondary educated in Finland, Lithuania and
Norway and the highest (97.5%) among tertiary educated French.
12
Table 3. Educational differences in partial life expectancy, partial disability-free life expectancy
and proportion of life spent disability-free between the age of 30 and 79 years among men
Partial life expectancy between the age of 30 and 79 years
Finland
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
Norway
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
Lithuania
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
Belgium (Brussels)
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
Austria
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
France
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
Spain (Madrid & Barcelona)
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
Italy (Turin & Tuscany)
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
Life
expectancy
Life expectancy
with disability
Disability-free
life expectancy
(95% CI for
disability-free
life expectancy)
Proportion (%) of
life spent disabilityfree
42.8
5.5
37.3
(36.9-37.8)
87.2
40.8
43.0
45.4
5.9
7.6
3.4
34.9
35.4
42.0
(34.0-35.7)
(33.3-37.5)
(41.1-42.8)
85.4
82.4
92.4
4.6
2.5
7.1
44.5
42.3
44.7
46.5
4.3
5.1
5.0
2.8
40.2
37.2
39.7
43.7
(39.6-40.8)
(35.4-39.0)
(38.8-40.5)
(42.6-44.7)
90.3
87.8
88.8
93.9
4.2
2.3
6.5
36.9
32.9
37.4
42.1
3.9
4.1
4.2
3.0
33.0
28.9
33.2
39.1
(32.6-33.4)
(27.6-30.1)
(32.6-33.9)
(38.1-40.0)
89.3
87.6
88.7
92.8
9.2
1.1
10.2
43.5
42.3
43.6
45.7
3.3
4.8
3.2
2.1
40.2
37.6
40.4
43.6
(39.7-40.6)
(36.7-38.4)
(39.6-41.2)
(42.9-44.3)
92.3
88.7
92.7
95.4
3.4
2.7
6.0
43.3
41.7
43.3
45.5
4.8
7.3
4.6
3.2
38.5
34.5
38.7
42.3
(38.1-39.0)
(33.2-35.7)
(38.1-39.3)
(41.4-43.2)
89.0
82.5
89.3
92.9
3.8
4.1
7.8
43.4
41.9
43.9
46.2
2.8
3.6
2.7
1.0
40.6
38.3
41.3
45.1
(40.3-40.9)
(37.7-38.8)
(40.8-41.8)
(44.6-45.7)
93.5
91.4
94.1
97.6
4.3
2.6
6.8
44.0
43.1
44.7
45.6
3.7
4.4
3.0
2.3
40.3
38.7
41.7
43.3
(40.1-40.6)
(38.4-39.0)
(41.0-42.4)
(42.8-43.9)
91.5
89.8
93.4
95.0
2.5
2.1
4.6
44.9
2.3
42.6
(42.4-42.8)
94.8
44.2
45.8
46.5
2.8
1.6
1.1
41.4
44.2
45.4
(41.1-41.6)
(43.9-44.5)
(44.5-45.9)
93.7
96.5
97.6
2.3
1.7
4.0
13
Table 4. Educational differences in partial life expectancy, partial disability-free life expectancy
and proportion of life spent disability-free between the age of 30 and 79 years among women
Partial life expectancy between the age of 30 and 79 years
Proportion (%) of
(95% CI for
life spent disabilitydisability-free
Life expectancy
Disability-free
free
life expectancy)
with disability
life expectancy
Life
expectancy
Finland
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
Norway
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
Lithuania
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
Belgium (Brussels)
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
Austria
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
France
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
Spain (Madrid & Barcelona)
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
Italy (Turin & Tuscany)
Primary & lower secondary education
Upper secondary education
Tertiary education
Difference between lowest and highest education
46.4
45.2
46.7
47.5
6.6
7.2
6.8
6.1
39.8
38.0
39.8
41.4
2.3
1.1
3.4
46.5
45.3
46.8
47.7
5.0
6.9
5.0
2.8
41.6
38.4
41.7
44.8
2.4
4.1
6.4
44.5
41.7
44.8
46.4
5.5
6.4
6.0
3.7
39.0
35.3
38.8
42.6
4.7
2.7
7.3
46.1
45.6
46.3
47.2
4.5
6.3
3.7
3.0
41.6
39.2
42.5
44.2
1.6
3.3
5.0
46.3
45.8
46.6
47.2
5.1
6.4
4.7
2.6
41.2
39.4
41.9
44.7
1.4
3.8
5.3
46.9
46.4
47.2
47.9
3.1
4.1
2.2
1.2
43.8
42.3
45.0
46.7
1.5
2.9
4.4
47.2
47.0
47.4
47.8
5.2
5.6
3.6
3.6
42.1
41.4
43.8
44.2
0.8
2.0
2.8
47.1
46.9
47.3
47.5
2.7
3.0
2.3
1.7
44.4
43.8
44.9
45.8
0.6
1.3
2.0
14
(39.3-40.3)
(36.9-39.1)
(38.8-40.9)
(40.2-42.6)
85.8
84.0
85.3
87.2
(40.9-42.2)
(36.6-40.2)
(40.9-42.6)
(43.7-46.0)
89.3
84.8
89.2
94.1
(38.5-39.4)
(33.9-36.7)
(38.0-39.6)
(41.5-43.8)
87.6
84.6
86.6
92.0
(41.1-42.1)
(38.3-40.2)
(41.7-43.4)
(43.1-45.3)
90.2
86.1
91.9
93.6
(40.7-41.7)
(38.5-40.2)
(41.2-42.6)
(43.3-46.1)
89.0
86.0
89.8
94.6
(43.5-44.1)
(41.8-42.8)
(44.5-45.5)
(46.0-47.3)
93.3
91.2
95.3
97.5
(41.8-42.4)
(41.0-41.7)
(42.9-44.7)
(43.3-45.1)
89.1
88.0
92.4
92.6
(44.2-44.6)
(43.6-44.1)
(44.5-45.4)
(45.0-46.6)
94.3
93.5
95.1
96.4
DISCUSSION
This study compared socioeconomic differences in partial disability-free life expectancy
between eight European countries in the early 2000s. Both life expectancy and disability-free
life expectancy varied considerably between countries with the lowest figures in Lithuania
and the highest in Italy. While the short disability-free life expectancy stemmed mostly from
short life expectancy in Lithuania, it was caused by high prevalence of health-based activity
limitation in some areas, notably in Finland, Austria and Norway.
Still at the beginning of the 21st century highly educated Europeans can expect to live longer,
and also to spend these years in better health than those with lower education. The largest
educational differences in disability-free life expectancy were in Lithuania, Norway and
Austria. There was, as in life-expectancy, a clear distinction between whether this was caused
by socioeconomic difference in mortality or in disability: again in Lithuania differences in
mortality were much more important while in Austria differences in disability were more
essential. Furthermore, among women educational difference in life expectancy with
disability was in most cases more important than difference in life expectancy while the
opposite holds true for men.
An important finding in this study was that the proportion of life spent disability-free was
similar among men and women indicating that even though women live longer, they spend a
larger absolute amount of time in disability. The use of a summary measure of population
health instead of only one indicator – either morbidity or mortality – provides a more
versatile understanding of the sex differences in health.
Methodological issues
We used survey data which were based on a common framework and study design and
harmonised mortality data sets. By this we aimed to avoid two common, yet severe,
limitations that cross-country comparisons often face, these are inter-survey differences in
indicators included and differences in variable classifications.20
Furthermore, all mortality data used in this study were census-linked meaning that the death
certificates were linked to census records, and, thus, information comes from one source.
The reliability of the mortality data especially from the post-communist countries in the
Eastern Europe has been discussed, and the overall conclusion is that the unlinked cross
sectional mortality data overestimate the educational inequality in mortality as they tend to
overstate mortality in disadvantaged groups and understate mortality in advantaged groups
through the numerator – denumerator bias.21 The use of individual linkage of deaths to the
census in the case of Lithuania in the current study removes the risk of such a bias.
Life expectancies for Belgium, Italy and Spain were based on mortality data on only a few
areas in these countries which were Brussels in Belgium, Turin and Tuscany in Italy and
Madrid and Barcelona in Spain. The high life expectancy found especially in Italy and Spain
may to some extent occur from this. It is possible that the mortality data are not
representative of the whole of these countries, but represents areas with a somewhat lower
than average mortality.1 However, educational differences in mortality in Turin and Tuscany
are of the same magnitude as differences in Italy altogether.22 We thus believe that our results
on educational differences in disability-free life expectancy are not crucially affected by the
use of regional mortality data instead of national data.
15
The highest educational level attained was used as a socioeconomic status indicator in this
study. As education is unlikely to change after early adulthood, morbidity in later life has less
of an influence on education than it has on occupation or income. There were, however, large
differences in the distribution of educational level between countries. It is possible that a
selection effect is shown in the results concerning those countries where the proportion of
primary educated is small, e.g. among Austrian men where not only is the per cent of primary
and lower secondary education low (21%), but also life expectancy with limitations high in
this group. The same health problems that cause activity limitation later in life may have
prevented the accomplishment of further education earlier.
The outcome measure – long-term activity limitation caused by health problems – used in this
study is self-reported. This may complicate the interpretation of results in cross-country
comparison studies because of cross-cultural differences in response styles. These differences
have been studied in self-rated health which is probably quite a sensitive measure for these
kinds of issues.
According to J rges23 the differences in self-reported health between countries may thus
reflect – in addition to true health differences – different reference levels of health among
respondents from different cultures, different connotations response alternatives on selfreported health have or differences in the tendency to choose extreme points of the response
scale. The outcome measure used in the current study, however, differs from self-rated health
in some essential features. First, as a measure of morbidity and disability GALI is a more
concrete measure as compared to self-rated health and is probably easier for respondents to
assess similarly in various countries and contexts. Furthermore, GALI specifies disability to
be long-term and detrimental enough to limit usual activities, and the response alternatives
only include three categories which decreases the possibility of bias relating to national
differences in extreme reporting. To further assess the reliability of the GALI, we compared
results from the EU-SILC data to those from the SHARE in equivalent age groups, and found
similar country-specific prevalences for severe limitation in these two data sets (results not
shown). These findings suggest that the GALI is a reliable measure in cross-country
comparisons.
We also assessed the validity of severe disability in GALI in different educational groups by
using the objective measures available in SHARE data. We compared educational differences
in the probability that a person with functional disability according to an objective measure
(maximum grip strength) rates him/herself severely hampered, and we found no differences
between the three educational categories studied (results not shown). This seems to indicate
that educational differences in self-reports of severe disability reflect true differences in
functional health and that severe disability in GALI is also a valid indicator for comparative
analyses of socioeconomic differentials.
Such large differences in severe disability between countries that we found in this study are
hardly explained only by the issues mentioned above. It is likely that also different living
conditions and distinctions in contextual factors between areas affect on how limiting
disability is experienced and most probably mediate it. Variety in support in everyday life
that those with severe disability receive is an obvious example of such mediators. Several
studies have shown that frequent contacts between elderly parents and their adult children are
much more common in Southern than in Central or Northern European countries. 24 While the
proportion of parents aged 65 years or over with at least one weekly face-to-face contacts
16
with a child was over 90% in Italy, it was only 64 and 76% among Finnish men and women,
respectively.25 Frequent contacts are likely to indicate also assistance in everyday life which
may result in experiencing limitation as less severe.
Previous studies
Comparing these results to previous studies is challenging because healthy life expectancies
can be and have been calculated for many different age ranges. Also, many studies with no
upper age limit have not paid attention to the fact that most survey data only include
household population and in the older age groups the proportion of persons with disabilities
in institutional care can be notable. We avoided this problem by calculating partial life
expectancies for ages 30–79 when institutional care is still uncommon.26 Furthermore, several
different indicators have been used to measure health. Even those studies that have used the
same health indicator that was in this study (global activity limitation indicator) may differ on
the ground of disability categorisation. Jagger, Gillies, Moscone et al.27 defined disability to
be any activity limitation. In the 25 EU countries in 2005 an average 50-year-old man could
expect to live until 67.3 years free of activity limitation, and a woman to 68.1 years. Years
lived with activity limitation were 11.4 and 15.4, respectively. We, on the other hand,
considered disabled only those who were severely hampered, and disability-free life
expectancy was thus longer.
Majer, Nusselder, Mackenbach et al.6 studied disability-free life expectancy among WesternEuropeans in the 1990s using European Community Household Panel survey data. Disability
was defined as any activity limitation, and on average, disability-free life expectancy between
the ages 50 and 65 was 10.1 years among highly educated men, 8.9 among those in the
middle level of education and 8.0 among men with low education and 10.8, 10.0 and 8.9
years among women, respectively. To some extent these results are similar to ours. Both
studies show that partial disability-free life expectancy is the highest in Spain and Italy, and
that the largest differences between educational categories are found in France and Austria
and among Finnish men. However, the difference in the definition of disability (any activity
limitation vs. severe limitation) had an impact: the prevalence of disability was larger and the
differences between both countries and educational groups were notably greater than in the
current study.
Similar results showing large educational differences in healthy life expectancies have also
been found outside Europe. Molla and others13 used a National Health Interview Survey in
the U.S. in 1998 and found that expected years with any activity limitations were 16% of the
total life expectancy among 25 year old males with at least 13 years of education, but 29%
among those with less than 9 years. Corresponding figures for females were 20 and 32%,
respectively. As the proportion of remaining life with activity limitation increased with age
educational differences diminished to some extent but were still clear among those aged 80
years.
Furthermore, using Longitudinal Studies of Aging from the NCHS Hagedorn12 shows that for
American men and women with less than 12 years of education there was no change in the
percent of remaining life that is active across the ten year period from 1987 to 1997 while
among those with longer education an increase in the proportion of expected life active
existed. This implies an increase in the difference in health expectancy between the
socioeconomic groups. On the other hand, this result also supports the viewpoint that increase
in life expectancy is not necessarily accompanied by increase in disability or functional
limitations.
17
CONCLUSION
Highly educated Europeans can not only expect to live longer, but also to spend these years
in better health than those with lower education. However, the size of the educational
difference in disability-free life expectancy varies significantly between countries. The
smallest differences are found in Southern and the largest in Eastern and Northern Europe.
ACKNOWLEDGEMENTS
The authors would like to thank Dalia Ambrozaitiene (Statistics Lithuania), Vlada
Stankuniene (Institute for Demographic Research, Lithuania), Dmitri A. Jdanov (Max Planck
Institute for Demographic Research, Germany), and Vladimir M. Shkolnikov (Max Planck
Institute for Demographic Research, Germany) for their contributions to the census-linked
dataset for Lithuania.
APPENDIX
For most countries persons were allowed to age during the mortality follow-up so that each
death and person-year was attributed to the age of the individual at the time. However,
Norway and Brussels reported only age at baseline and attributed all deaths and person-years
to the baseline age. In the EURO-GBD-SE -project an adjustment method was developed in
order to ensure comparability between countries that reported differently. The adjustment
method can be described as the following:
where
is the estimated death risk in the ith age interval, Mxi is the observed death risk, 2
is a coefficient describing the relationship between ln(Mxi ) and xi , s is a constant estimating
the size of the bias, based on the length of follow-up and xi is age interval. The adjustment
method is based on the roughly linear relationship between age and ln death risk in adult
populations. For more details, see EURO-GBD-SE working document.28
18
REFERENCES
1. Eurostat. Health statistics – Atlas on mortality in the European Union. Luxembourg:
Eurostat; 2009.
2. Alecxih L, Shen S, Chan I, Taylor D, The Lewin Group. Individuals Living in the
Community with Chronic Conditions and Functional Limitations: A Closer Look.
Washington: U.S. Department of Health and Human Services; 2010.
3. Fries JF. The Compression of Mortality. Milbank Meml Fund Q. 1983;61(3) :397–
419.
4. Robine J-M, Jagger C, Mathers CD, Crimmins EM, Suzman RM, editors.
Determining Health Expectancies. Chichester: Wiley; 2003.
5. Mackenbach JP, Stirbu I, Roskam A-JR, Schaap MM Menvielle G, Leinsalu M, et al.
Socioeconomic Inequalities in Health in 22 European Countries. N Engl J Med. 2008;
358 :2468–2481.
6. Majer IM, Nusselder WJ, Mackenbach JP, Kunst AE. Socioeconomic inequalities in
life and health expectancies around official retirement age in 10 Western-European
countries. J Epidemiol Commun H. 2011; 65: 972–979.
7. Mackenbach JP, Bos V, Andersen O, Cardano M, Costa G, Harding S, et al. Widening
socioeconomic inequalities in mortality in six Western European countries. Int J
Epidemiol. 2003; 32 :830–837.
8. Shkolnikov VM, EM Andreev, Jdanov DA, Jasilionis D, Kravdal, Ø, Vågerö D &
Valkonen T. Increasing absolute mortality disparities by education in Finland, Norway
and Sweden, 1971–2000. J Epidemiol Commun H. 2012; 66: 372–378
9. Valkonen T, Sihvonen A-P, Lahelma E. Health expectancy by level of education in
Finland. Soc Sci Med. 1997;44 :801–808.
10. Crimmins EM, Cambois E. Social Inequalities in Health Expectancy. In Robine J-M,
Jagger C, Mathers CD, Crimmins EM, Suzman RM, editors. Determining Health
Expectancies. Chichester: Wiley; 2003. p. 111–126.
11. Sihvonen AP, Kunst AE, Lahelma E, Valkonen T, Mackenbach JP. Socioeconomic
inequalities in health expectancy in Finland and Norway in the late 1980s. Soc Sci
Med. 1998; 47 :303–315.
12. Hagedorn AT. Longitudinal change in active life expectancy: the longitudinal studies
of aging 1984–2000. Los Angeles: University of Southern California; 2008.
13. Molla MT, Madans JH & Wagener DK. Differentials in Adult Mortality and Activity
Limitation by Years of Education in the United States at the end of the 1990s. Popul
Dev Rev. 2004;30(4) :625–646.
14. Schneider S, M ller W. Measurement of Education in EU-SILC – Preliminary
Evaluation of Measurement Quality. Equasolc Working Paper 2009/5.
http://www.equalsoc.org/publication/283. (21th November 2011).
15. OECD. Health at a Glance 2002: OECD Indicators. Paris: OECD Publishing; 2002.
16. Jagger C, Gillies C, Cambois E, Van Oyen H, Nusselder W, Robine J-M. The Global
Activity Limitation Indicator (GALI) Measured Function and Disability Similarly
across European Countries. J Clinical Epidemiol. 2010; 63 :892–899.
17. van Oyen H, Van der Heyden J, Perenboom R, Jagger C. Monitoring population
disability: evaluation of a new Global Activity Limitation Indicator (GALI). Soz
Praventiv Med. 2006;51(3) :153–161.
18. Sullivan DF. A Single Index of Mortality and Morbidity. HSMHA Health Reports.
1971; 86(4) :347–354.
19. Jagger C, Cox B, Le Rou S, EHEMU. Health Expectancy Calculations by the Sullivan
Method. Third Edition. EHEMU Technical report 2006.
19
20. Van Oyen H, Bossuyt N, Deboosere P, Gadeyne S, Abatih E, Demarest SS.
Differential inequality in health expectancy by region in Belgium. Soz Praventiv Med.
2005;50 :301–310.
21. Shkolnikov VM, Jasilionis D, Andreev EM, Jdanov DA, Stankuniene V.
Ambrozaitiene D. Linked versus unlinked estimates of mortality and length of life by
education and marital status: Evidence from the first record linkage study in
Lithuania. Soc Sci Med. 2007;64 :1392–1406.
22. Marinacci C, Sebastiani G, Ferracin E, Grippo F, Pappagallo M, Landriscina T et al.
La distanza tra gli Italiani attraverso le disuguaglianze geografiche e socioeconomiche
[Distance among Italians through geographic and socio-economic inequalities].
Epidemiologia&Prevenzione. 2011; 35 (5 Suppl.1) :2–9.
23. rges H. True Health vs. Response Styles: Exploring Cross-Country Differences in
Self-Reported Health. Health Econ. 2007;16 :163–178.
24. Fokkema T, Bekke S, Dykstra PA. Solidarity between parents and their adult children
in Europe. Amsterdam: Netherlands interdisciplinary demographic institute; 2008.
25. Tomassini C, Kalogirou S, Grundy E, Fokkema, T, Martikainen P, Broese van
Groenou, M, Karisto A. Contacts between elderly parents and their children in four
European countries: current patterns and future prospects. Eur J Ageing. 2004;1(1)
:54–63.
26. Bebbington A, Shapiro J. Incidence of Poor Health and Long-Term Care. Health
Transition in Europe – Results from the European Community Household Panel
Survey and Institutional Data. ENEPRI Research Report No. 34/December 2006.
27. Jagger C, Gillies C, Moscone F, Cambois E, Van Oyen H, Nusselder W et al.
Inequalities in healthy life years in the 25 countries of the European Union in 2005: a
cross-national meta-regression analysis. Lancet. 2008;372 :2124–2131.
28. Östergren O, Menvielle G & Lundberg O. Adjustment method to ensure
comparability between populations reporting mortality data in different formats in the
EURO-GBD-SE project. Working document. Downloaded from http://www.eurogbd-se.eu/index.php?id=815 (27th March 2012).
20