Contributing to policy development onhealth inequalities

Information Systemexpectancies

Health&Life

European10 000

20 000

30 000

40 000

50 000

60 000

70 000

80 000

90 000

100 000

0 10 20 30 40 50 60 70 80 90 100 110

Sx LEx0 100 000 83,0

10 99 907 82,120 99 744 80,330 99 457 77,640 98 950 74,050 98 063 69,560 96 517 64,170 93 848 57,880 89 322 50,690 81 882 42,5

100 70 296 33,5110 53 858 23,6

Carol Jagger, Jean-Marie Robine and the EHLEIS team

12th European Health Forum, Gastein 2009

EHLEIS Team

• Main partner: – J-M Robine (INSERM, France)

• Associated partners: – C Jagger (University of Leicester, UK)– H Van Oyen (Scientific Institute of Public Health, Belgium)– E Cambois (National Institute of Demography, France)– W Nusselder (Erasmus Medical Center, The Netherlands)– G Doblhammer (Max Planck Institute, Germany)– J Rychtaříková (Charles University in Prague, Czech Republic)

Outline

• Historical background– Developing harmonised health indicators– Developing standardised calculation methods

• European Health Expectancy Monitoring Unit (EHEMU)– Aims– Achievements

• European Health and Life Expectancy Information System (EHLEIS)– Inequalities in health expectancies– Gender gaps in HLYs

Key messages

• To tackle health inequalities we need to be able to monitor multiple dimensions of health across all countries in an accurate and sustainable manner

• Health inequalities exist within European countries as well as between them so countries should use the same methods regionally as nationally

• The European Union provides a natural population laboratory for exploring solutions to health inequalities – if we can– sustain harmonised monitoring and measurement– maximally utilise the complementary data sources (EHIS, SHARE,

SILC)

Health Expectancies

Health expectancies• Add a quality dimension to life expectancy• Are useful to:

– monitor population health over time– compare different countries– compare regions within countries– compare different population subgroups

despite any differences in age structure

• Need data on :– the prevalence of health by age and sex from a survey and a

standard life tableor– health and survival from a longitudinal survey

HISTORICAL BACKGROUND

Timeline

1995 1999 2003 2007

Euro-REVES I 1995-7

Euro-REVES II 1997-2002

European Health Status Module

2002-3

EHEMU 2004-7

EHLEIS 2007-10

European Health Interview Survey

2008+

Health Monitoring Programme 1995-2002

Lisbon Strategy

Healthy Life Years Indicator

Historical background

• The sustained interest in health expectancy within countries led to a European research programme Euro-REVES I identifying reasons for the incomparability of European results (Biomed II, 1995-1997)– Collection of health expectancies made by European

members identified the problem– Detailed description of the concepts, questionnaires and

calculation methods used identified the reason for incomparability

– Recommendations for harmonised data collection (Euro-REVES II, Health Monitoring Programme 1997-2002) and calculation methods (EHEMU, Public Health Programme 2007-9)

Proposals for nine instruments dealing with:• Chronic morbidity

– Global– Detailed

• Functional limitation– Detailed (physical and sensory)

• Activity restriction – Global (GALI)– Detailed (personal care, household care, other activities)

• Perceived health– Global

• Mental health

Euro-REVES II: developing a coherent set of HE

MEHM*

*Minimum European Health Module (Module 2000) for inclusion in all the European health and social surveys and included in Health Surveys (Eurobarometer, SILC)

The European Union Statistics on Income and Living Conditions (EU-SILC) is an instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions.

See: http://epp.eurostat.ec.europa.eu/

Including the Mini European Health Module (MEHM)

[Limitation in activities people usually dobecause of health problemsfor at least the last 6 months].

Values:1 yes, strongly limited2 yes, limited3 no, not limited

Timeline

1995 1999 2003 2007

Euro-REVES I 1995-7

Euro-REVES II 1997-2002

European Health Status Module

2002-3

EHEMU 2004-7

EHLEIS 2007-10

European Health Interview Survey

2008+

Health Monitoring Programme 1995-2002

Lisbon Strategy

Healthy Life Years Indicator

Leading to: the wider vision

• European Health Survey System (EHSS)– Comprehensive and co-ordinated set of surveys – Allowing inter country comparisons– Aiming to monitor potential changes in health over

time requires a series of data collections not just a “one-off”

– Built around a European Health Interview Survey (EHIS) which should include

• European Health Status Module (EHSM) – standardized core covering main health dimensions including the Mini European Health Module (MEHM)

European Health Expectancy Monitoring Unit (EHEMU)

• Providing annual comparable values– HLY from SILC– Paper based country reports – Web (www.ehemu.eu)

• Analysing and interpreting the results– Health expectancies from other surveys– Variation between countries in HLY – Trends

• Educating about health expectancies– Sullivan Guide– Interpreting Guide

www.ehemu.eu

HEALTH INEQUALITIES

Example 1• Lancet 2008;

372: 2124–31

Healthy Life Years at age 50

Life years and HLY at age 50, 2005

32302826242220

LE at age 50 (years)

24

22

20

18

16

14

12

10

8

HLY

at a

ge 5

0 (y

ears

)

UKSE

ES

SI

SK

PT

PL

NL

MT

LU

LTLV

IT

IE

HU

GR

DE

FR

FI

EE

DK

CZ

CY

BE

AT

Men

What explain inequalities in HLY?EU25 EU15 EU10

Macro indicator Quality grade M F M F M FGDP per capita A + +

Expenditure on elderly care Not available + + + +

Poverty risk for >65yrs (%) C

Inequality of income distribution C

Employment rate of older workers A -

Long term unemployment rate A -

Mean exit age from labour force Not available +

Life-long learning (%) Not available +

Low education attainment (%) Not available + + +

HLY age 15: EU-10 vs. EU-15

• EU-10: 15-year old girl can expect to live 3.1 more HLYsthan boy

• EU-15: no gender differences

0 10 20 30 40 50 60

dif

men

women

dif

men

women

EU

-15

EU

-10

Dif (women - men)HLY

Are HLY gaps due to mortality or disability?

• EU-10: gender gap mostly due male disadvantage in mortality• EU-15: no gender gap in HLYs but male disadvantage in mortality

offset by male advantage in disability• EU-10 v EU15: male disadvantage in mortality larger and male

advantage in disability smaller than EU15

EU-15 HLY- Men 48.4- Women 48.4

Difffence-Women-Men 0.02

Decomposition- Mortality effect 2.6- Disability effect -2.6

EU-10 HLY- Men 42.7- Women 45.8

Difffence-Women-Men 3.1

Decomposition- Mortality effect 3.9- Disability effect -0.8

Mortality disadvantage by age

• EU-10: mortality disadvantage at young age

LE gender gap: decomposition by age

00.5

11.5

15-19

25-29

35-39

45-49

55-59

65-69

75-79 85

+EU-10EU-15

Mortality effect

Disability advantage by age

• EU-15: men less disability at all ages than women• EU-10: men less disability at older ages than women but not at

younger ages

ULY decomposition by age: F-M

-0.2

0

0.2

0.4

15-19

25-29

35-39

45-49

55-59

65-69

75-79 85

+EU-10EU-15

Disability effect

Inequalities in other HE: SHARE 2006

1 2 3 4 5 ALL LE at age 50 (yrs) M

F 29.7 34.3

29.8 33.2

30.9 35.2

26.1 31.3

24.8 31.5

29.1 33.8

Proportion of LE free of morbidity M F

10.0 9.3

10.3 8.5

13.1 14.0

6.0 6.7

6.7 5.7

9.7 9.1

Proportion of LE free of PFL M F

18.2 14.5

15.7 10.1

22.3 19.9

15.7 11.5

10.5 7.1

17.5 13.8

Proportion of LE free of activity restriction M F

17.3 17.3

21.7 21.5

21.2 22.2

12.3 11.7

10.5 11.1

17.1 17.1

Proportion of LE free of IADL restriction M F

25.6 25.8

26.2 23.7

28.3 30.3

23.0 24.4

18.5 20.3

25.1 25.5

Proportion of LE free of ADL restriction M F

26.7 29.2

28.1 29.4

28.5 32.6

24.0 28.2

19.4 23.7

26.2 29.0

Proportion of LE in good self-rated health M F

19.4 20.4

22.4 22.4

25.4 28.2

14.9 16.7

10.3 10.9

19.0 20.2

1= Austria, Belgium, Denmark, France, Germany, Italy, Netherlands, Spain, Sweden2= Greece3=Switzerland4=Czech Republic5=Poland

Conclusions• Considerable inequalities exist within the EU-25 at age 65

– in LE– and more so in HLY

• Harmonisation of measures not optimal as yet• Current project EHLEIS is extending research to:

– Contribution of macro-level factors to HLY variation– Gender gaps– Decomposition

• Task Force on Health Expectancies constituted with three year strategic plan to:

– further improve comparability within EU– support National Statistics Office– improve comparability outside EU

• Observers from US Healthy People 2010, Japan, OECD, WHO, Washington Group, Budapest Initiative

Key messages

• To tackle health inequalities we need to be able to monitor multiple dimensions of health across all countries in an accurate and sustainable manner

• Health inequalities exist within European countries as well as between them so countries should use the same methods regionally as nationally

• The European Union provides a natural population laboratory for exploring solutions to health inequalities – if we can– sustain harmonised monitoring and measurement– maximally utilise the complementary data sources (EHIS, SHARE,

SILC)

Information Systemexpectancies

Health&Life

European10 000

20 000

30 000

40 000

50 000

60 000

70 000

80 000

90 000

100 000

0 10 20 30 40 50 60 70 80 90 100 110

Sx LEx0 100 000 83,0

10 99 907 82,120 99 744 80,330 99 457 77,640 98 950 74,050 98 063 69,560 96 517 64,170 93 848 57,880 89 322 50,690 81 882 42,5

100 70 296 33,5110 53 858 23,6

www.ehemu.eu

12th European Health Forum Gastein 2009

This paper was produced for a meeting organized by Health & Consumers DG and represents the views of its author on thesubject. These views have not been adopted or in any way approved by the Commission and should not be relied upon as a statement of the Commission's or Health & Consumers DG's views. The European Commission does not guarantee the accuracy of the dataincluded in this paper, nor does it accept responsibility for any use made thereof.