Policy and research to reduce health inequalities. Jurate Klumbiene, Lithuanian University of Health Sciences.

1. Policy and research to reduce
health inequalities
Jurate KLUMBIENE
Lithuanian University of Health Sciences
The 10th Nordic Public Health Conference,
24-26 August 2011, Turku, Finland

2. HEALTH INEQUALITIES
can be defined as differences in health status or in the
distribution of health determinants between different
population groups.
World Health Organization

3. Socio-economic determinants
of health
• Level of education
• Place of residence
• Marital status
• Income
• Occupation

4. INEQUALITIES IN MORTALITY

5. Life expectancy of Lithuanian urban and rural
population in 2000 -2010
80
78
76
74
72
Urban women
70 Urban men
Rural women
68
Rural men
66
64
62
60
2000 2005 2006 2007 2008 2009 2010

6. Mortality from major causes of death among Lithuanian men
with university education and primary or no education
(mortality of the group with university education = 1)
5 *
*
4
3
*
* * *
2 *
1
0
All causes Cardiovascular Cancer External causes
diseases
1989 2001
* p<0.05 vs the group with university education Source - R.Kalediene, J.Petrauskiene,
Public Health (2005) 119

7. Mortality from major causes of death among Lithuanian
women with university education and primary or no education
(mortality of the group with university education = 1)
7 *
6
5
4
* *
3
* * *
2 *
1
0
All causes Cardiovascular Cancer External causes
diseases
1989 2001
* p<0.05 vs the group with university education Source - R.Kalediene, J.Petrauskiene,
Public Health (2005) 119

8. INEQUALITIES IN SUBJECTIVE
HEALTH
(Finbalt Health Monitor project)

9. Proportion of men, who assessed their own health
status to be good or reasonable good by level of
education in 1994-2010
66
70 65 58
59 64
56 56
60
49 51 52
50 42 41 51
42
37 37
40 31
%
30 25
20
Secondary University
10
0
1994 1996 1998 2000 2002 2004 2006 2008 2010

10. Proportion of women, who assessed their own health
status to be good or reasonable good by level of
education in 1994-2010
80
66
70 56 63 69
57 55
60 51 50
50 43
34 35 38 40
% 40 34 40
30 21 22
19
20
Secondary University
10
0
1994 1996 1998 2000 2002 2004 2006 2008 2010

11. Proportion of persons, who assessed their own health
status to be good or reasonable good in 1994 and
2010 by place of residence
60 56
53
50 46** 44* 46**
42
40 35*
31*
% 30 1994
2010
20
10
0
Rural Urban Rural Urban
MEN WOMEN
*p<0.05, compared to 2010
**p<0.05, compared to urban population in 2010

12. INEQUALITIES IN HEALTH
BEHAVIOUR
(Finbalt Health Monitor project)

13. Proportion of daily smoking men in 1994 – 2010
by level of education
65 Secondary University
56
53
55 50 49
47 48 48
45 40
%
41
35 35
35 32 31
25
25
25 21 21
21
15
1994 1996 1998 2000 2002 2004 2006 2008 2010

14. Proportion of daily smoking women in 1994 – 2010
by level of education
25 Secondary University
18 20
20 19 20
16
14 13
15 13
% 14
10 9 12
10 8 9 11
6 6
5
5
0
1994 1996 1998 2000 2002 2004 2006 2008 2010

15. Relative index of inequality (RII) in smoking
by education in 1994 and 2010
MEN WOMEN
Study year
RII 95 % CI RII 95 % CI
1994 2.4 1.3-4.1 0.7 0.2-1.9
2010 4.1 2.3-7.3 2.7 1.5-4.9

16. Prevalence of regular wine consumption and
odds ratios (OR)* by sex and level of education
Education MEN WOMEN
level
% OR 95 % CI % OR 95 % CI
University 14.4 1 18.6 1
Vocational 9.3 0.59 0.47-0.74 8.8 0.47 0.4-0.57
Secondary 7.4 0.47 0.37-0.59 4.5 0.25 0.2-0.31
* - age, level of education, place of residence and marital status were included into the
model of logistic regression

17. Prevalence of regular beer consumption and
odds ratios (OR)* by sex and place of residence
Place of MEN WOMEN
residence
% OR 95 % CI % OR 95 % CI
Cities 53.7 1 15.4 1
Towns 49.0 0.83 0.74-0.93 12.7 0.79 0.69-0.92
Villages 49.3 0.84 0.75-0.94 12.9 0.81 0.70-0.95
* - age, level of education, place of residence and marital status were included into the model
of logistic regression

18. Prevalence of daily consumption of fresh vegetables
during the last week among men by education
in 1996-2010
30 27
26
22 23
20
20 17
14 14
% 12 12 15
14
10 12
10
3 3 Secondary University
0
1996 1998 2000 2002 2004 2006 2008 2010

19. Prevalence of daily consumption of fresh vegetables
during the last week among women by education in
1996-2010
32 33
35
30 27 27 30
22 24
25 22
20 19 17
15 20
% 14 16
15
10 7
5 Secondary University
5
0
1996 1998 2000 2002 2004 2006 2008 2010

20. Prevalence of daily consumption of fresh vegetables
during the last week by sex and place of residence
30
25.3
25
20.9
17.9 18.3*
20
15.7
13.9*
Cities
% 15
Towns
Willages
10
5
0
MEN WOMEN
* p<0.05 compared to cities

21. International research projects on health
inequalities in Lithuania
• WHO project “Health Inequalities” (1997)
– Report “Equity in health and health care in Lithuania. A
situation analysis” (1998)
• Tackling health inequalities in Europe. An
integrated approach - EUROTHINE (2004-2007)

22. The use of Finbalt Health Monitor data on social
differences in health behaviour in Lithuania
• The assessment of the implementation of Lithuanian
Health programme
• The reports of National Health Board
• State Food and Nutrition Strategy and Action Plan for
2003-2010
• National alcohol and tobacco control programmes; the
laws on tobacco and alcohol control

23. Health policy formulation addressing health
inequalities in Lithuania
• Lithuanian Health Programme for 1998 – 2010
– general objective on equity in health and health care
• New Lithuanian Health Programme for 2011-2020
– great emphasis on reduction of inequalities in health

24. Policy addressing socio-economic determinants of
health in Lithuania
• National programme on tackling poverty and
diminishing social gap in Lithuania (2000).
• The strategies and the plans of specific measures
were developed in 2002, 2004, 2006, 2008.
– Reduction of unemployment
– Improvement of access to high quality social service
– Strengthening support for families and children
– More attention to education of socially disadvantaged
groups

25. Summing-up
Finbalt Health Monitor study was an important
vehicle for evaluation of social differences and
trends in health behaviour. The data are useful to
guide policy making and to support the evaluation
of strategies to reduce health inequalities.