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Population norms for the EQ-5D-3L: a cross-country analysis of population surveys for 20 countries

  • M. F. Janssen
  • A. Szende
  • J. Cabases
  • J. M. Ramos-Goñi
  • G. Vilagut
  • H. H. König
Open Access
Original Paper

Abstract

This study provides EQ-5D population norms for 20 countries (N = 163,838), which can be used to compare profiles for patients with specific conditions with data for the average person in the general population in a similar age and/or gender group. Descriptive EQ-5D data are provided for the total population, by gender and by seven age groups. Provided index values are based on European VAS for all countries, based on TTO for 11 countries and based on VAS for 10 countries. Important differences exist in EQ-5D reported health status across countries after standardizing for population structure. Self-reported health according to all five dimensions and EQ VAS generally decreased with increasing age and was lower for females. Mean self-rated EQ VAS scores varied from 70.4 to 83.3 in the total population by country. The prior living standards (GDP per capita) in the countries studied are correlated most with the EQ VAS scores (0.58), while unemployment appeared to be significantly correlated in people over the age of 45 only. A country’s expenditure on health care correlated moderately with higher ratings on the EQ VAS (0.55). EQ-5D norms can be used as reference data to assess the burden of disease of patients with specific conditions. Such information, in turn, can inform policy-making and assist in setting priorities in health care.

Keywords

Health state values EQ-5D Population norms Health-related quality of life 

JEL Classification

I10 I30 J11 H51 

Introduction

EQ-5D is a standardized health-related quality of life questionnaire developed by the EuroQol Group in order to provide a simple, generic measure of health for clinical and economic appraisal [1]. Applicable to a wide range of health conditions, it provides a simple descriptive profile, a self-report visual analogue scale (EQ VAS) and an index value (‘utility’) for health status that can be used in the clinical and economic evaluation of health care as well as in population health surveys.

Since EQ-5D was first developed, a substantial amount of research has been carried out worldwide using the instrument [2]. Among this research were surveys conducted in various countries that measured the health-related quality of life of the general population [3]. These EQ-5D surveys have been informative in providing new data on population health characteristics, complementing the traditionally collected morbidity and mortality data.

Although recently an expanded five-level version of the EQ-5D instrument (EQ-5D-5L) has become available and was translated for use across countries, the general population survey datasets available in the EuroQol archive that were analyzed in this study were still based on the original three-level version of the EQ-5D (EQ-5D-3L), here referred to as EQ-5D.

The purpose of the current study is to present EQ-5D population norms for 20 countries, including reported problems by the five EQ-5D dimensions, self-reported EQ VAS ratings (by country, age, and gender), and EQ-5D index values (by country, age, and gender). The index values, presented in country-specific value sets, are a major feature of the EQ-5D instrument. EQ-5D value sets are typically obtained using representative samples of the general public, thereby ensuring that they represent the societal perspective, traditionally based on visual analogue scale (VAS) and time trade-off (TTO) valuation techniques. Apart from VAS- and TTO-based value sets, we also included the European VAS-based value set as a common metric for all countries. We hypothesized that reported health problems will increase by age and will be higher for females. Cross-country analyses of population health based on EQ-5D are presented with the aim of exploring which macroeconomic factors are associated with the self-reported health of the population. Additionally, we performed exploratory analyses on comparing the different value sets.

Methods

Data

Datasets per country were generally made available through the central data archive of the EuroQol Research Foundation. Countries included in the analysis were: Argentina, Belgium, China, Denmark, England, Finland, France, Germany, Greece, Hungary, Italy, Korea, the Netherlands, New Zealand, Slovenia, Spain, Sweden, Thailand, United Kingdom, and the United States [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]. For two countries (Argentina and China), the dataset transfer to the central archive was not possible. For these countries, data were analyzed locally by two collaborating researchers (FA, SS, respectively). All of the surveys included the standardized three-level version of EQ-5D, using the appropriate language version in each country. The Dutch, Swedish, and Finnish versions were translated in 1987 according to a ‘simultaneous’ process while the remaining versions were translated according to the EuroQol Group’s translation protocol based on international guidelines.

Table 1 provides a detailed account of the data by country. All datasets were collected in representative samples of the general population for each country. The datasets were structured in a standardized format to facilitate comparative research, although each survey also has its own characteristics and variables specific to the individual research context in which they were conducted. The datasets captured for the current analyses include observations on 163,838 individuals. Sampling weights were applied for Belgium, England, France, Germany, Italy, the Netherlands, and Spain according to a stratified, multistage, cluster-area, probability-sample design [5]. For the United States, sampling weights were applied resulting from a sampling design including stratification, clustering, multiple stages of selection, and oversampling of minority populations [18].
Table 1

National representative EQ-5D population surveys

Country

Source

Sample size

Data collection

Survey method

Argentina

Second National Survey of Risk Factors, 2005 [4]

41,392

2005

Face-to-face interviews on the representative 2005 Risk Factors Survey on a random selection of households

Belgium

ESEMED, König et al. [5]

2411

2001–2003

Computer-assisted home interviews on a nationally representative sample of the noninstitutionalized general adult population as part of the European Study of the Epidemiology of Mental Disorders (ESEMeD), using a stratified probability sample design

China

Sun et al. [6]

8031

2010

Face-to-face interviews on the representative 2010 Household Health Survey (HHS), using a stratified, multi-stage, clustered, random sampling design

Denmark

Sørensen et al. [7]

16,861

2000–2001

Face-to-face interviews on three representative national surveys based on randomized samples, including a national health interview survey undertaken by the National Institute of Public Health (SUSY-2000), a health survey undertaken in Funen County (Funen data set) and a national health survey undertaken by the University of Southern Denmark (SDU data set) with a total of 22,486 individuals

England

Health Survey for England 2008 [8]

14,763

2008

Computer-assisted interviews on a randomly selected sample of households in England

Finland

Saarni et al. [9]

8028

2000

Face-to-face interviews on the Health 2000 survey sample, which is a representative survey of the Finnish population aged 30 and over, following a two-stage, stratified, clustered sampling design

France

ESEMED, König et al. [5]

2892

2001–2003

Computer-assisted home interviews on a nationally representative sample of the noninstitutionalized general adult population as part of the European Study of the Epidemiology of Mental Disorders (ESEMeD), using a stratified probability sample design

Germany

ESEMED, König et al. [5]

3552

2001–2003

Computer-assisted home interviews on a nationally representative sample of the noninstitutionalized general adult population as part of the European Study of the Epidemiology of Mental Disorders (ESEMeD), using a stratified probability sample design

Greece

Yfantopoulous [10]

464

1998

Face-to-face interviews on a sample of 500 individuals selected from the general population, using quota sampling to ensure representativeness

Hungary

Szende and Nemeth [11]

5503

2000

Self-administered questionnaire during a personal interview on a random sample of 7000 people from the electoral registry

Italy

ESEMED, König et al. [5]

4709

2001–2003

Computer-assisted home interviews on a nationally representative sample of the noninstitutionalized general adult population as part of the European Study of the Epidemiology of Mental Disorders (ESEMeD), using a stratified probability sample design

Korea

Lee et al. [12]

1307

2007

Face-to-face interviews on a random sample of the South Korean residential registry

Netherlands

ESEMED, König et al. [5]

2367

2001–2003

Computer-assisted home interviews on a nationally representative sample of the noninstitutionalized general adult population as part of the European Study of the Epidemiology of Mental Disorders (ESEMeD), using a stratified probability sample design

New Zealand

Devlin et al. [13]

1327

1999

Postal survey on a randomized sample of 3000 New Zealanders selected from the electoral roll

Slovenia

Prevolnik Rupel and Rebolj [14]

742

2000

Postal survey on a randomized sample of 3000 people selected from the general population

Spain

ESEMED, König et al. [5]

5473

2001–2003

Computer-assisted home interviews on a nationally representative sample of the noninstitutionalized general adult population as part of the European Study of the Epidemiology of Mental Disorders (ESEMeD), using a stratified probability sample design

Sweden

Bjork et al. [15]

534

1994

Postal survey on a randomized sample of 1000 Swedish citizens selected from the general population from an address register

Thailand

Tongsiri et al. [16]

1409

2007

Face-to-face interviews on a random national sample provided by the national statistical office

United Kingdom

Kind et al. [17]

3395

1993

Face-to-face interviews on a random sample of 5324 individuals selected from the general population (based on the Postcode Address file) from England, Scotland, and Wales

United States

MEPS, Sullivan et al. [18]

38,678

2000–2002

Paper-and-pencil questionnaire among the Medical Expenditure Panel Survey participants, a nationally representative survey of the US civilian noninstitutionalized population. The research pooled 2000, 2001, and 2002 MEPS data on 23,839, 32,122, and 37,418 individuals, using a stratified, multistage, clustered sampling design

Surveys differed in methods of data collection and sample sizes. Some of the surveys were postal, while others were performed as part of a face-to-face interview or administered by telephone. The Argentinean dataset had the largest sample with over 41,000 respondents, while the Greek and the Swedish national surveys had the smallest sample of around 500 respondents.

Methods of describing population norms

Population norm data were calculated for the five dimensions, self-rated EQ VAS, and EQ-5D index values for the total population, by gender, and the following age groups: 18–24, 25–34, 35–44, 45–54, 55–64, 65–74, and 75 + years. Aggregate EQ-5D dimension results were dichotomized, reporting the proportion of respondents scoring any problem on each dimension (the sum of the proportion of reported level-2 and level-3 problems). EQ-5D index value were calculated using the following value sets: European VAS value set for all countries, country-specific time trade-off (TTO) value set if available (11 countries), and country-specific VAS value set if available (10 countries).

The TTO method has played an important role in generating value sets for the EQ-5D as one of the most widely accepted preference elicitation methods in economic evaluation [19] and the method of choice in the first [20] and several subsequent large-scale EQ-5D valuation studies [21]. The VAS has become the other widely used valuation method to elicit preferences for the EQ-5D, including 9 countries. Note that the VAS valuation method needs to be distinguished from the EQ VAS, which is a self-reported rating of the respondents’ own health. The European VAS value set was constructed using data from 11 valuation studies in 6 countries: Finland (1), Germany (3), The Netherlands (1), Spain (3), Sweden (1), and the UK (2). This survey included sufficient data from different European regions to make the European VAS dataset moderately representative for Europe [22, 23]. Relevant information on the TTO- and VAS-based value sets, including the scoring algorithms, can be found in Szende et al. [21], Xie et al. [24], and Scalone et al. [25].

Results were tabulated in alphabetic order.

Cross-country analysis

It is important to note that while results in each age group may be compared across countries, the total population scores cannot be compared directly, as they reflect the unique age structure within each country. Cross-country summary data for reported problems by the five dimensions and EQ VAS were estimated using a standardized population structure for all countries with national EQ-5D surveys. Standardization for age was performed to avoid bias due to the fact that some populations have a relatively higher proportion of elderly people. Age standardization of reported problems by dimension and EQ VAS were based on the European population structure using Eurostat data from 2010 [26], using the following proportions for each age group: 11% (18–24), 17% (25–34), 18% (35–44), 18% (45–54), 15% (55–64), 11% (65–74), and 10% (75 +).

To explore reasons for cross-country differences in EQ-5D data, correlations between country-specific EQ-5D data (five dimensions and self-rated EQ VAS) and country-specific macroeconomic indicators were calculated, including indicators of living standards and health system performance. Living standards were estimated by means of gross domestic product (GDP) per capita and unemployment rate. Indicators for health care system performance were health expenditure per capita and health expenditure as a percentage of GDP, number of hospital beds per 1000 people, and number of physicians per 1000 people. The indicators were selected on the basis of a presumed or possible relationship with self-reported health. Data were obtained from the World Health Organization Statistical Information System and the World Bank [27, 28]. The data were from 2010 or the closest year with available data (Table 2). An alternative set of macro data was also used to see how results might change when using macro data from the same year as the EQ-5D data collection, including variables on gross national income on purchasing power parity, unemployment rate, and health expenditure data.
Table 2

Country-specific macroeconomic indicators

 

GDP per capita ($) 2010

Unemployment rate (%) 2010*

Health expenditure (% of GDP) 2010*

Health expenditure per capita ($) 2010*

Physicians per 1000 people 2004-2009

Argentina

9124

8.6

8.1

742

3.2

Belgium

43,006

8.3

10.7

4618

3.0

China

4433

4.3

5.1

221

1.4

Denmark

56,486

7.4

11.4

6422

3.4

France

39,170

9.3

11.9

4691

3.5

Germany

40,164

7.1

11.6

4668

3.5

Greece

25,832

12.5

10.2

2729

6.0

Hungary

12,863

11.2

7.3

942

3.1

Italy

33,787

8.4

9.5

3248

4.2

Korea

20,540

3.7

6.9

1439

2.0

Netherlands

46,623

4.5

11.9

5593

3.9

New Zealand

32,407

6.5

10.1

3279

2.4

Slovenia

22,898

7.2

9.4

2154

2.5

Spain

29,956

20.1

9.5

2883

3.7

Sweden

49,360

8.4

9.6

4710

3.8

Thailand

4614

1.2

3.9

179

0.3

United Kingdom

36,256

7.8

9.6

3503

2.7

United States

46,612

9.6

17.9

8362

2.4

*Data availability for last year varies in some countries

A non-parametric measure (Spearman rank correlation) was used to assess the association between self-reported health using EQ-5D and the above-mentioned indicators of living standards and health system performance. We expected that poorer populations will show more reported health problems than richer populations, and countries with a shorter life expectancy will also display more reported health problems. Generally, the positive association of good health with higher health expenditures probably rests on a common explanatory factor, i.e., wealth on the country level. As additional exploratory analysis, we performed linear regression analyses on macroeconomic indicators and mean VAS rating.

The inclusion of both the European VAS value set as well as country-specific VAS value sets allowed for exploring the impact of the preferences of a specific country, using the European VAS value set as a reference. The inclusion of the country-specific TTO value sets also allowed for exploring the effect of valuation method (VAS versus TTO). All data analyses were performed using SPSS version 19 and Stata version 12 statistical software packages.

Results

EQ-5D population norms

Results for reported problems along the five dimensions by gender for each country are presented in Table 3. As hypothesized, reported health problems were generally higher for females, with the exception of Slovenia. Problems with pain/discomfort were generally the most prevalent in each country, while problems with self-care were the least prevalent across countries. Thailand and Slovenia appeared to have generally high reported problems in all dimensions compared to other countries, while China and Korea showed the lowest reported problems. The pattern of reported problems across the five dimensions was rather similar across countries, although the absolute number of reported problems varies.
Table 3

Reported problems by five dimensions (proportions (%) of respondents scoring any problem, not standardized)

 

Mobility

Self-care

Usual activities

Pain/discomfort

Anxiety/depression

Females

Males

Females

Males

Females

Males

Females

Males

Females

Males

Argentina

13

9

3

2

10

6

36

25

26

19

Belgium

15

10

5

3

15

10

31

26

8

5

China

6

4

3

3

6

4

13

8

10

7

Denmark

12

10

3

2

20

15

40

33

19

12

Finland

29

24

12

9

24

18

52

43

15

12

France

16

11

4

4

11

9

38

33

16

13

Germany

17

15

3

2

11

9

30

25

5

4

Greece

14

13

9

3

12

9

20

14

12

10

Hungary

23

16

7

6

17

12

45

32

42

27

Italy

12

9

5

2

12

7

31

22

11

6

Korea

9

3

1

0

6

2

27

16

23

12

Netherlands

13

9

4

2

16

10

38

30

4

2

New Zealand

20

20

4

5

22

21

41

40

24

18

Slovenia

28

32

14

14

33

33

48

47

38

34

Spain

16

11

6

3

14

8

27

17

10

5

Sweden

10

7

2

1

8

8

42

40

31

21

Thailand

28

24

8

9

22

23

68

62

51

43

UK

19

18

4

4

16

17

34

32

23

18

UK—England

21

18

6

5

18

15

37

33

22

16

United States

22

17

5

5

23

17

49

41

32

23

Table 4 shows results for self-rated EQ VAS scores for each country by age and gender and for the total population. EQ VAS ratings decreased with increasing age and were generally lower for females in all countries, which confirmed our hypotheses. Country-specific differences can be observed in the overall level of health (mean EQ VAS ratings), and to a lesser extent in the level of health decrease (age-slope). Korea displayed a very small age slope. The age slope was considerably higher in Southeastern Europe compared to Northwestern Europe. Gender differences were generally more pronounced with increasing age, and stronger for some countries while almost absent in others (New Zealand, Slovenia, and Thailand). For illustrative purposes, Fig. 1 shows the detailed age and gender pattern for the pooled dataset.
Table 4

Self-reported EQ VAS ratings by age group and total population (mean values, not standardized)

 

18–24

25–34

35–44

45–54

55–64

65–74

75 +

Total

Females

Males

Females

Males

Females

Males

Females

Males

Females

Males

Females

Males

Females

Males

Females

Males

Argentina

80

84

78

81

76

79

73

76

69

70

66

70

61

64

74

77

Belgium

84

84

83

82

79

82

75

79

72

76

72

71

70

69

77

79

China

89

89

85

86

82

83

78

81

75

78

71

73

67

72

80

81

Denmark

86

86

88

88

86

86

83

82

81

82

77

80

77

76

84

84

France

84

83

84

82

78

79

78

78

75

73

67

70

60

64

76

77

Germany

86

85

84

84

83

82

79

78

73

73

66

72

60

61

76

78

Greece

82

85

87

85

83

86

77

79

59

77

70

65

47

61

78

80

Hungary

83

84

80

82

75

76

68

70

62

66

57

63

53

55

69

73

Italy

86

89

83

84

81

82

76

78

73

76

65

71

60

61

75

79

Korea

79

79

79

82

81

81

81

80

74

81

74

79

79

80

Netherlands

82

89

84

85

83

85

80

82

81

80

79

77

70

79

81

83

New Zealand

83

81

82

82

84

81

82

82

82

81

80

79

68

74

81

80

Slovenia

86

84

83

82

82

79

76

75

69

67

64

67

55

56

77

76

Spain

81

83

78

82

77

77

74

73

71

73

66

77

59

67

73

77

Sweden

83

86

87

86

85

88

84

83

78

80

78

84

66

84

82

84

Thailand

82

85

82

80

80

81

79

78

81

77

77

75

83

65

80

79

UK

86

87

87

87

86

87

82

82

80

84

77

78

74

73

82

83

United States

84

88

83

85

81

83

79

80

76

78

75

75

68

69

79

81

Fig. 1

Self-rated mean EQ VAS by age and gender (pooled dataset*). * Including data for all countries except Argentina and China, which were not added to the central data archive

EQ-5D index norm values based on the European value set generally decreased with age, with values ranging from 0.814–0.990 in the youngest group, to 0.621–0.840 in the 75 + group. Corresponding EQ-5D index values in countries where TTO-based value sets were available ranged from 0.924 to 0.984 in the youngest group to 0.703–0.839 in the 75 + group. Finally, EQ-5D index values in countries where VAS-based value sets were available ranged from 0.869 to 0.962 in the youngest group to 0.498–0.817 in the 75 + group. Population norms based on the European VAS value set were generally higher than or similar to country-specific VAS value sets (except for Germany), while population norms based on country-specific TTO value sets tended to be higher compared to the same countries using country-specific VAS-based value sets (see Tables 5, 6).
Table 5

EQ-5D index value population norms by age group and total population (European VAS value set)

 

18–24

25–34

35–44

45–54

55–64

65–74

75 +

Total

Argentina

0.907

0.889

0.869

0.849

0.829

0.796

0.724

0.856

Belgium

0.953

0.921

0.920

0.889

0.881

0.848

0.761

0.891

China

0.990

0.980

0.970

0.960

0.930

0.900

0.840

0.951

Denmark

0.914

0.914

0.881

0.861

0.845

0.818

0.753

0.866

Finland

N/A

0.919

0.891

0.853

0.805

0.762

0.573

0.815

France

0.924

0.921

0.883

0.893

0.836

0.804

0.756

0.872

Germany

0.950

0.949

0.943

0.908

0.881

0.838

0.771

0.902

Greece

0.979

0.972

0.957

0.916

0.817

0.793

0.739

0.913

Hungary

0.934

0.911

0.873

0.802

0.755

0.716

0.639

0.823

Italy

0.969

0.956

0.943

0.910

0.877

0.823

0.724

0.899

Korea

0.957

0.958

0.949

0.915

0.828

0.787

N/A

0.915

Netherlands

0.938

0.910

0.922

0.874

0.869

0.863

0.798

0.892

New Zealand

0.913

0.906

0.893

0.858

0.817

0.800

0.712

0.848

Slovenia

0.879

0.859

0.831

0.772

0.697

0.663

0.621

0.788

Spain

0.968

0.963

0.939

0.911

0.884

0.870

0.773

0.915

Sweden

0.888

0.893

0.868

0.835

0.813

0.836

0.701

0.851

Thailand

0.814

0.785

0.771

0.717

0.694

0.670

0.657

0.742

UK

0.934

0.922

0.905

0.849

0.804

0.785

0.734

0.856

UK—England

0.922

0.915

0.891

0.857

0.819

0.785

0.720

0.857

US

0.899

0.883

0.853

0.809

0.776

0.756

0.677

0.825

N/A: not available

Table 6

EQ-5D index value population norms by age group and total population (country-specific TTO and VAS value sets)

 

18–24

25–34

35–44

45–54

55–64

65–74

75 +

Total

TTO value sets

 Argentina

0.951

0.936

0.919

0.898

0.874

0.835

0.756

0.902

 Denmark

0.928

0.927

0.901

0.882

0.870

0.847

0.794

0.887

 France

0.948

0.946

0.913

0.922

0.853

0.810

0.735

0.892

 Germany

0.972

0.973

0.966

0.945

0.922

0.891

0.839

0.938

 Italy

0.984

0.978

0.973

0.955

0.936

0.904

0.839

0.947

 Korea

0.981

0.982

0.976

0.960

0.909

0.888

N/A

0.958

 Netherlands

0.950

0.927

0.935

0.890

0.890

0.886

0.830

0.910

 Spain

0.982

0.975

0.949

0.923

0.901

0.891

0.781

0.929

 UK

0.940

0.927

0.911

0.847

0.799

0.779

0.726

0.856

 UK—England

0.929

0.919

0.893

0.855

0.810

0.773

0.703

0.855

 US

0.924

0.912

0.889

0.855

0.830

0.817

0.755

0.867

VAS value sets

 Argentina

0.928

0.911

0.888

0.867

0.837

0.793

0.712

0.871

 Belgium

0.948

0.915

0.912

0.881

0.871

0.836

0.748

0.883

 Denmark

0.885

0.884

0.845

0.822

0.799

0.766

0.691

0.826

 Finland

N/A

0.909

0.878

0.835

0.781

0.738

0.583

0.800

 Germany

0.962

0.966

0.962

0.937

0.915

0.882

0.817

0.930

 New Zealand

0.890

0.883

0.869

0.827

0.782

0.763

0.672

0.818

 Slovenia

0.869

0.841

0.794

0.712

0.619

0.554

0.498

0.738

 Spain

0.969

0.963

0.939

0.912

0.883

0.866

0.761

0.914

 UK

0.931

0.920

0.902

0.846

0.799

0.778

0.726

0.852

 UK—England

0.922

0.914

0.888

0.854

0.814

0.775

0.706

0.853

N/A: not available

Cross-country comparison

Table 7 shows the impact of age standardization of population norms, which were usually within a few percentage points of difference. Mean EQ VAS score varied from 70.4 to 83.3 in the total population. The largest differences between any two countries in reporting problems were 28.6, 12.7, 31.9, 53.7, and 43.8% in absolute terms along the five dimensions, respectively. Hungary reported the lowest EQ VAS ratings (70.4), followed by Korea (71.3), while Denmark (83.3) and the United Kingdom (82.8) reported the highest EQ VAS ratings. The highest proportion of problems on the five EQ-5D dimensions was reported by Slovenia and Thailand. It needs to be noted that while Hungary and Korea reported a lower mean EQ VAS than Slovenia and Thailand, generally more problems were reported in Slovenia and Thailand across the five dimensions. At the other end of the spectrum, China reported the lowest proportion of problems but reported average EQ VAS ratings, while Denmark and the UK reported the highest EQ VAS ratings and average proportions of problems. These results indicate that countries also differed in the overall level of health resulting from the more general EQ VAS question relative to the more specific questions on the EQ-5D dimensions.
Table 7

Self-reported EQ-5D results after age standardization (mean EQ VAS and proportions (%) of respondents scoring any problem)

 

EQ-VAS

Mobility

Self-care

Usual activity

Pain/discomfort

Anxiety/depression

Argentina

73.9

13.3

3.7

9.8

33.9

23.8

Belgium

77.4

13.9

4.8

12.9

29.4

6.1

China

79.9

6.1

3.4

6.1

11.5

9.2

Denmark

83.3

11.5

2.8

18.6

37.0

16.2

France

76.3

14.4

4.6

10.7

35.8

14.5

Germany

77.2

17.2

3.1

10.5

27.8

4.5

Greece

76.5

17.2

8.3

13.7

20.4

11.2

Hungary

70.4

20.9

7.2

15.8

40.4

36.2

Italy

76.9

12.3

4.4

11.1

27.7

9.2

Korea

71.3

6.5

1.0

4.6

29.6

22.9

Netherlands

81.4

11.8

3.5

12.5

32.6

3.2

New Zealand

80.8

19.2

4.3

20.8

39.3

21.2

Slovenia

74.5

34.7

16.7

36.5

51.0

38.0

Spain

74.3

12.7

4.0

11.0

21.3

7.3

Sweden

82.5

11.3

2.5

9.6

42.5

26.4

Thailand

78.9

29.8

9.2

25.9

65.2

47.0

United Kingdom

82.8

18.2

4.3

16.2

33.1

20.9

US

79.3

19.3

3.7

18.3

48.0

22.4

Table 8 shows the association on the country level of the macroeconomic indicators and the EQ VAS rating and reported health problems. As hypothesized, the prior living standards (GDP per capita) and health expenditure per capita in the countries studied were correlated with the mean EQ VAS scores (0.58 and 0.55, respectively). Unemployment significantly correlated in people over the age of 45 only. The number of physicians did not correlate with better EQ VAS data (0.03). Contrary to our expectations, life expectancy did not result in any significant association.
Table 8

Spearman rank correlations between macroeconomic indicators and self-reported health (mean self-rated EQ VAS and proportion of any reported problem)

EQ-VAS

Age group

GDP per capita

Unemployment

Health expenditure (% of GDP)

Health expenditure per capita

Physicians per 1000 people

Life expectancy

 18–24

0.38

− 0.12

0.29

0.40

0.09

− 0.15

 25–34

0.55*

− 0.06

0.44

0.53*

0.32

0.02

 35–44

0.50*

− 0.26

0.35

0.47

0.18

0.09

 45–54

0.49*

− 0.50*

0.29

0.48*

− 0.13

0.13

 55–64

0.45

− 0.50*

0.26

0.45

− 0.25

0.13

 65–74

0.47

− 0.48*

0.20

0.44

− 0.21

0.21

 75 +

0.42

− 0.51*

0.17

0.37

− 0.24

0.02

 Total

0.58*

− 0.35

0.39

0.55*

− 0.03

0.00

EQ-5D dimension

 

GDP per capita

Unemployment rate

Health expenditure (% of GDP)

Health expenditure per capita

Physicians per 1000 people

Life expectancy

 Mobility

− 0.19

0.14

0.04

− 0.13

− 0.27

− 0.34

 Self-care

− 0.35

0.26

− 0.14

− 0.35

− 0.05

− 0.19

 Usual activities

0.08

− 0.03

0.13

0.09

− 0.24

− 0.27

 Pain/discomfort

0.10

− 0.11

− 0.01

0.12

− 0.38

− 0.31

 Anxiety/depression

− 0.38

− 0.04

− 0.51*

− 0.38

− 0.46

− 0.41

*p < 0.05

The positive relationship between living standards and self-reported EQ VAS was further examined and is graphically presented in Fig. 2. As shown, EQ VAS correlated well with a country’s GDP, although China and Thailand were outliers with an exceptionally low GDP (combined with relatively high EQ VAS scores). The European value set showed a more moderate correlation with GDP with only China as outlier and a smaller slope.
Fig. 2

Self-rated EQ VAS and index values (European value set) according to GDP* per capita in 18 countries (mean values after age standardization). *GDP is the total value of all goods and services produced by an economy in 1 year

Linear regression analyses showed that GDP level explained 29% of EQ VAS at the country level (p = 0.02), but explained 67% of the EQ VAS when excluding ‘outliers’ China and Thailand. Health expenditure per capita was the only other statistically significant explanatory factor that explained 26% of the country mean VAS (p = 0.03). Another set of regression analyses, which used macro data from the year of EQ-5D data collection in each country on gross national income expressed in purchasing power parity in 2010 values, did not yield statistically significant results. However, health care expenditure remained a statistically significant factor (p = 0.03), explaining 27% of variation in the country mean VAS scores.

Discussion

The current study generated population norms for self-rated EQ VAS and EQ-5D index values, and for self-reported problems on each of the five dimensions of the EQ-5D descriptive system for 20 countries, all classified by age. These EQ-5D norms are highly relevant for future research initiatives, as they can be used to compare EQ-5D data from patients to the average person in the general population of a certain country in a similar age (or gender) group, which also helps to identity the burden of the disease of patients or patient groups. This multi-country analysis is unique in terms of reporting EQ-5D data based on a standard methodology and allowing for comparisons across countries and explaining differences using macroeconomic indicators.

Our hypothesis on age and gender was confirmed by results for both the EQ VAS and reported problems on the five dimensions (where the age effect was visible through the index values, providing a summary score for the five dimensions). Cross-country differences occurred in EQ-5D outcomes in terms of the overall level of health but also in terms of the age slope, which was considerably higher in Southeastern Europe compared to Northwestern Europe. The overall patterns in each country regarding reported problems were spectacularly similar in terms of pain/discomfort being the most prevalent and self-care being the least prevalent problem. However, the actual rates of reporting problems differed widely across countries after accounting for demographic differences, and no consistent trend was observed on how countries score in terms of EQ VAS relative to morbidity reported along the five dimensions, which seems to indicate that the EQ VAS is measuring a different (or at least wider) health concept than the five dimensions of EQ-5D, or that countries differ in responses to the various dimensions. An obvious implication of these findings for multi-country studies with the EQ-5D is the need to factor in the country of origin of patients when analyzing and interpreting results.

In addition, when examining population norms for EQ-5D index values, results highlighted the importance of also taking into account the value set used to calculate the EQ-5D index when interpreting results or making comparisons across studies. Country-specific value sets are generally recommended for use in the corresponding country, while for comparative purposes, the European value set seems to be the most optimal choice. Country-specific value sets showed differences between valuation methods, which is consistent with previous evidence indicating that TTO methodology leads to higher values than VAS-based techniques [29].

The fact itself that self-reported health differs across countries is not unexpected. Previous studies, such as those based on categorical assessment of self-assessed health [30], or those based on generic quality of life questionnaires [31], found results that self-reported health differed across countries. These cross-country differences in the general level of health (EQ VAS) were at least partially explained by looking at macro data on the living standards and health system characteristics of each country. The analysis highlighted that it is the prior living standards of a country that mostly explain cross-country differences in self-reported health. Indeed, the result that GDP level explained 67% of EQ VAS at the country level when excluding two ‘outlier’ countries underlined the high importance of viewing self-reported health within a broader macroeconomic context. At the same time, health expenditure per capita was also quantified to be an important factor, one that policy-makers at a national level have more control over than determining annual GDP. In addition, while GDP showed a stronger correlation with VAS than health expenditure, a dollar unit of health expenditure had eight times the impact of a dollar unit of GDP on the country mean VAS scores (with coefficients of 0.0001 for GDP and 0.0008 for health expenditure). However, expenditure might be confused with GDP, since a high GDP might lead to higher health care expenditures, which in turn might influence the number and quality of interventions per capita, and consequently lead to better health in a population.

The most important limitation of this analysis relates to differences in samples across countries. While all samples were representative samples of the general population of each country, differences exist across study methodologies, such as sample size, administration method, purpose of data collection, and time of the data collection. While adjustments were made for sample structure, some of these factors may have influenced the comparability of the results. In particular, some surveys in the dataset archive were older, and limited evidence suggests that population norms may or may not change over time, depending on the country [3]. Non-response may have introduced a potential bias towards underestimation of self-reported health problems. Some countries applied a sampling design, whereas other countries did not, which might lead to a more accurate reflection of representativeness for the former. Although mode of administration might contribute to observed differences, a recent study showed equivalence between various modes of administration using the EQ-5D [32]. Further variability between countries might be caused by translations of the different versions of the EQ-5D. Another limitation is the use of the European population structure for age standardization, which might not be fully justified for the non-European countries, especially for China, where the population structure is quite different. Finally, influences due to reporting behavior heterogeneity, such as education, might also impact variability between self-reported health problems [33].

While results from these analyses can be used to compare profiles for patients with specific conditions or to assess the burden of disease in question, understanding inequalities in self-assessed health among the population is also important, but fell beyond the aims of this paper. However, more in-depth analyses on contributors to levels of population health could be important.

Finally, this manuscript focused on existing data from the three-level version of the EQ-5D instrument; however, a more refined version of the EQ-5D (EQ-5D-5L), which extends the three response levels in each dimension to five levels, has been introduced [34]. The extra levels are expected to lead to a much more accurate reflection of population health, especially in relation to mild health problems. Further important research in the field would be the reporting of population norms using the EQ-5D-5L version of the questionnaire.

Notes

Acknowledgements

This research was funded  by the EuroQol Research Foundation under Grant no. 201108. All authors disclose that they are members of the EuroQol Group, a not-for-profit group that develops and distributes instruments to assess and value health. The views of the authors expressed in the paper do not necessarily reflect the views of the EuroQol Group. The authors wish to acknowledge all researchers and organizations who contributed EQ-5D country data described in this article. The authors would like to thank two anonymous reviewers for valuable comments and suggestions.

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Copyright information

© The Author(s) 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • M. F. Janssen
    • 1
    • 2
  • A. Szende
    • 3
  • J. Cabases
    • 4
  • J. M. Ramos-Goñi
    • 2
  • G. Vilagut
    • 5
    • 6
  • H. H. König
    • 7
  1. 1.Section Medical Psychology and Psychotherapy, Department of PsychiatryErasmus MCRotterdamThe Netherlands
  2. 2.EuroQol GroupRotterdamThe Netherlands
  3. 3.Global Health Economics and Outcomes ResearchCovanceLeedsUK
  4. 4.Department of EconomicsPublic University of NavarraPamplonaSpain
  5. 5.IMIM (Institut Hospital del Mar d′Investigacions Mèdiques)BarcelonaSpain
  6. 6.CIBER Epidemiología y Salud Pública (CIBERESP)MadridSpain
  7. 7.Department of Health Economics and Health Services ResearchUniversity Medical Center Hamburg-EppendorfHamburgGermany

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