Open Access

Health-related quality of life measured using the EQ-5D–5L: South Australian population norms

Health and Quality of Life Outcomes201614:133

https://doi.org/10.1186/s12955-016-0537-0

Received: 23 May 2016

Accepted: 9 September 2016

Published: 20 September 2016

Abstract

Background

Although a five level version of the widely-used EuroQol 5 dimensions (EQ-5D) instrument has been developed, population norms are not yet available for Australia to inform the future valuation of health in economic evaluations. The aim of this study was to estimate HrQOL normative values for the EQ-5D-5L preference-based measure in a large, randomly selected, community sample in South Australia.

Methods

The EQ-5D-5L instrument was included in the 2013 South Australian Health Omnibus Survey, an interviewer-administered, face-to-face, cross-sectional survey. Respondents rated their level of impairment across dimensions (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) and global health rating on a visual analogue scale (EQ-VAS). Utility scores were derived using the newly-developed UK general population-based algorithm and relationships between utility and EQ-VAS scores and socio-demographic factors were also explored using multivariate regression analyses.

Results

Ultimately, 2,908 adults participated in the survey (63.4 % participation rate). The mean utility and EQ-VAS scores were 0.91 (95 CI 0.90, 0.91) and 78.55 (95 % CI 77.95, 79.15), respectively. Almost half of respondents reported no problems across all dimensions (42.8 %), whereas only 7.2 % rated their health >90 on the EQ-VAS (100 = the best health you can imagine). Younger age, male gender, longer duration of education, higher annual household income, employment and marriage/de facto relationships were all independent, statistically significant predictors of better health status (p < 0.01) measured with the EQ-VAS. Only age and employment status were associated with higher utility scores, indicating fundamental differences between these measures of health status.

Conclusions

This is the first Australian study to apply the EQ-5D-5L in a large, community sample. Overall, findings are consistent with EQ-5D-5L utility and VAS scores reported for other countries and indicate that the majority of South Australian adults report themselves in full health. When valuing health in Australian economic evaluations, the utility population norms can be used to estimate HrQOL. More generally, the EQ-VAS score may be a better measure of population health given the smaller ceiling effect and broader coverage of HrQOL dimensions. Further research is recommended to update EQ-5D-5L population norms using the Australian general population specific scoring algorithm once this becomes publically available.

Keywords

EQ-5D Population norms Utility Quality of life

Background

Increasingly, economic evaluations are used to inform clinical, funding, public and health policy decisions [13]. Economic evaluations systematically compare the relative costs and benefits of competing courses of action, informing choices on how best to maximise benefits within budget-constrained funds [4]. One of the most commonly reported measures of benefit in economic evaluations is the quality adjusted life year (QALY) [5] which combines health-related quality of life (HrQOL) and length of life into a single index summary measure. Health-related quality of life is represented by quality weights (utilities) typically measured on a ‘0’ to ‘1’ scale where ‘0’ is defined as a health state equivalent to being dead and ‘1’ is full health. Generic, preference-based, multi-attribute utility instruments (MAUIs) such as the EuroQol 5 dimensions (EQ-5D) are the most popular mechanism for indirectly estimating these utilities [5]. Generic MAUIs have two main elements: a set of items with multiple response categories covering different dimensions of HrQOL (descriptive system); and an off-the-shelf scoring algorithm indicating the strength of preference for the health states defined by the instrument (quality weights). Typically, scoring algorithms are generated from large general population surveys to elicit values for a selection of health states (value set) defined by the descriptive system [5].

The EQ-5D was first developed in 1990 and is the world’s most widely applied generic MAUI [5]. The original descriptive system has five dimensions (mobility, self-care, usual activities, pain/discomfort, anxiety/depression), each with three response levels (EQ-5D-3L). A five response level version has been developed (EQ-5D-5L) in an effort to reduce the potential for ceiling effects and to address concerns about the sensitivity of the 3L version for detecting clinically important differences in HrQOL [6]. Emerging evidence suggests the newer 5-level version does have improved measurement properties including feasibility, ceiling effects, sensitivity and convergent validity and therefore may be more useful for measuring population-level health status [711]. A new off-the-shelf scoring algorithm estimated from a sample of the UK adult general population is now available for this version [12].

Health-related quality of life is also often used as a measure of population health status to inform public health and health care policy [13]. Population-wide studies of HrQOL facilitate surveillance of health status over time, identify groups at risk of poorer HrQOL, enable assessment of the burden of different diseases on HrQOL and capture the relationships between socio-demographic characteristics and health status [1315]. Consideration of the EQ-5D evidence to date suggests younger age, male gender and longer duration of education are associated with better health status when measured using this measure [7, 11, 1621]. Previous population-wide studies using the 3L version suggest household income, employment and marital status may also be associated with HrQOL [14, 2235].

It is highly likely the 5L version will replace the 3L version as the instrument of choice in future health economic evaluations and population-wide studies. Although EQ-5D-5L population norms and relationships with socio-demographic characteristics have already been reported for Canada [16], Germany [17]; Italy [18], Japan [19], Poland [20], Spain [11], Uruguay [21], and the UK [7], none are yet available for Australia.

Objectives

The objectives of this study were to:
  1. 1.

    Estimate population norms for the EQ-5D-5L for South Australia using a large, randomly selected, community sample and the new UK scoring algorithm to inform economic evaluations; and

     
  2. 2.

    Examine the relationships between socio-demographic factors and HrQOL measured using EQ-5D-5L utility and VAS scores.

     

Methods

The STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines were followed during the preparation of this manuscript [36].

Study design

The South Australian Health Omnibus Survey (HOS) is an annual, interviewer-administered, face-to-face, cross-sectional observational study of a clustered area sample of households identified using a multistage, systematic, randomised approach [37].

Data collection

The EQ-5D-5L instrument was included in the 2013 South Australian Health Omnibus Survey (HOS). The HOS samples included 5,200 households randomly selected from Statistical Areas Level 1, from metropolitan Adelaide area and country towns with a population of 1,000 people or more. The HOS contains questions submitted by researchers on a user-pays basis. Typically, about 200 questions on health and social-related issues are included, with interviews conducted in the respondent’s home lasting 60–90 min [38]. One interview was conducted per household with verbally consented participants over the age of 15 years who most recently had a birthday. Interviews were conducted between 3rd September and 31st December 2013 and de-identified socio-demographic data and EQ-5D-5L responses were collected.

EQ-5D-5L

Each dimension in the EQ-5D-5L has five response levels: no problems (Level 1); slight; moderate; severe; and extreme problems (Level 5). There are 3,125 possible health states defined by combining one level from each dimension, ranging from 11111 (full health) to 55555 (worst health) [6]. EQ-5D-5L health states are converted into a single index ‘utility’ score using a scoring algorithm based on public preferences. In this study, the UK value set and scoring algorithm were used to calculate utility scores as an Australian scoring algorithm is not yet available for the 5L. The UK algorithm was estimated using a hybrid model of preference data collected using a time-trade off (TTO) and discrete choice experiment (DCE) [12, 39] and potential values from this algorithm ranged from -0.281 to 1, where values lower than 0 represent states considered to be worse than death [12]. The instrument also includes a visual analogue scale (EQ-VAS) which provides a single global rating of self-perceived health and is scored on a 0 to 100 mm scale representing “the worst…” and “the best health you can imagine”, respectively.

Socio-demographic characteristics

Socio-demographic characteristics collected in the HOS survey included age, area of residence, country of birth, educational attainment, employment status, gender, gross annual household income; and marital status.

Data analysis

Analyses were performed using SPSS for Windows version 22.0 (SPSS, Inc., Chicago, IL) and Stata version 13.1. [40]. The HOS data were weighted by the inverse of the respondent’s probability of selection for the survey, the response rates in metropolitan and country regions, and re-weighted to benchmarks from the 2011 Census to provide a demographic description of the South Australian population by age groups, gender and geographic profile [41].

Descriptive summary statistics were estimated for socio-demographic variables, the EQ-5D-5L dimensions, utility scores, EQ-VAS scores and the top 20 most frequently reported EQ-5D-5L health states [7, 42, 43]. A priori hypothesised correlations (presented in brackets), based on a review of the literature, between utility scores and EQ-VAS scores (moderate, positive) and age (strong, negative) [22, 4446] were evaluated with Spearman’s correlation coefficients. Correlations of <0.30 were considered weak, 0.30–0.50 moderate and >0.50 strong [47] and were judged statistically significant using a Bonferroni adjusted alpha level of 0.017 (0.05/3) [48]. Socio-demographic characteristics were categorised as shown in Table 1. As the EQ-5D-5L utility scores were non-normally distributed (Kolmogorov-Smirnov test, p < 0.05), differences between socio-demographic sub-groups were assessed using the non-parametric Mann Whitney U test (two groups) and Kruskal-Wallis one way analysis of variance (multiple groups) at the 0.00625 alpha level, following adjustment for multiple testing (0.05/8) [49, 50].
Table 1

Socio-demographic characteristics of the South Australian Health Omnibus Survey sample

Sample

Total

Males

Females

N = 2,908

n = 1,422

n = 1,486

Age, mean (SD)

46.3 (18.9)

45.7 (18.6)

47.0 (19.1)

Age category, years, %

 15–24

15.9

16.7

15.2

 25–34

15.4

15.8

15.1

 35–44

16.5

16.8

16.3

 45–54

17.2

17.4

17.0

 55–64

15.2

15.3

15.2

 65–74

11.9

10.8

13.0

 75+

7.8

7.3

8.2

Gender, % female

51.1

  

Education attainment, %

   

 Left school at ≤15 years of age

9.1

6.6

11.5

 Left school >15 years of age

21.5

19.0

24.0

 Trade, apprenticeship, certificate, diploma

36.5

42.5

30.7

 Degree or higher

22.4

21.1

23.6

 Still studying

10.2

10.2

10.1

 Not stated

0.3

0.5

0.1

Area of residence, %

 Metropolitan

74.4

75.5

73.6

 Regional

25.6

24.5

26.4

Country of birth, %

 Australia

73.5

74.1

72.9

 Europe

15.8

15.2

16.4

 Asia

7.1

7.7

6.4

 Other

3.5

2.7

4.2

 Not stated

0.1

0.1

0.1

Marital status, %

 Married/De Facto

61.5

63.4

59.9

 Never Married

24.7

27.7

21.8

 Separated/divorced

9.1

6.6

11.4

 Widowed

4.6

2.2

6.8

 Not stated

0.1

0.1

0.1

Employment status, %

 Full or part time

55.6

62.8

48.6

 Unemployed

3.3

3.2

3.4

 Not in the labour force

41.1

33.9

47.8

  Home Duties

5.5

0.4

10.4

  Retired

20.4

18.0

22.7

  Student

9.5

9.2

9.8

  Not working due to work-related injury or disability

4.1

4.7

3.4

  Other

1.6

1.6

1.5

  Not stated

0.1

0.1

0.1

Annual household income, %

 Up to $20,000

6.0

4.1

7.9

 $20,001–$40,000

12.8

11.2

14.4

 $40,001–$80,000

19.3

19.7

19.0

 $80,001–$120,000

15.1

16.2

14.0

 $120,001+

20.1

23.8

16.6

 Not stated

26.7

25.0

28.2

SD standard deviation

The relationships between socio-demographic variables and EQ-5D-5L utility scores were explored using a generalized linear model (GLM) with a Poisson distribution and a log link [51, 52]. This model controls for skewness and heteroscedasticity and approximates the distribution of the data based on the modified Park test [53, 54]. The EQ-5D-5L disutility score (1-utility score) was entered as the dependent variable as positive values are required for the specified regression model [52]. Explanatory variables were limited to those included in the HOS survey. Based on a literature review of previously reported EQ-5D-5L population norms, older respondents, females and those with a shorter duration of education were expected a priori to report higher disutility scores (poorer health status) and were entered first in the GLM (Model 1) [7, 11, 1621]. Subsequently, a series of exploratory multivariate regression analyses were conducted for individual variables which were statistically significant in the bivariate analysis, controlling for age, gender and education. Factors which were statistically significant in these exploratory analyses were then included in the final model to assess whether these variables independently predicted HrQOL (Model 2). Disutility scores were hypothesised to increase with lower gross annual household income; unemployed persons were expected to have higher disutility scores than employed persons; and widowed, separated and divorced people were anticipated to have higher disutility scores than married people [14, 2235]. The relationships between socio-demographic variables and EQ-VAS scores were similarly explored using a GLM with a Poisson distribution and a log link based on the modified Park test [53, 54].

Results

Participants

In total, 2,908 individuals participated in the 2013 Heath Omnibus Survey, achieving a participation rate of 63.4 % (Fig. 1). The socio-demographic characteristics of the respondents are summarised in Table 1.
Fig. 1

STROBE participant flow diagram

Main results

The EQ-5D-5L utility scores were heavily left-skewed with a clustering at 1.00, ‘full health’ (Fig. 2a). The EQ-VAS scores were also left-skewed, although to a lesser extent, and responses clustered predominantly around 80 and 90 on the 100 mm scale (Fig. 2b). The frequencies of item responses for each EQ-5D-5L dimension are presented in Table 2. As expected in a community-based general population sample, a substantial proportion of respondents reported no problems on any of the five dimensions (42.8 %). The most prevalent problems were reported for pain and discomfort (44.4 %).
Fig. 2

a Distribution of EQ-5D-5L scores (N = 2,908); b Distribution of EQ-VAS scores (N = 2,908)

Table 2

Frequencies of item responses in each EQ-5D-5L dimension by age and gender (%)

 

Age category, years

Dimension

Total N = 2,908

15–24

n = 464

25–34

n = 449

35–44

n = 480

45–54

n = 499

55–64

n = 443

65–74

n = 346

75+

n = 226

Mobility

 No problems

74.3

93.1

89.3

82.9

73.4

63.9

54.6

40.3

 Slight

14.9

4.1

8.0

10.8

15.0

22.6

24.9

29.2

 Moderate

7.9

2.4

1.6

4.4

8.4

10.6

14.2

23.0

 Severe

2.6

0

0.9

1.7

3.0

2.9

6.1

5.8

 Extreme

0.3

0.4

0.2

0.2

0.2

0

0.3

1.8

Self-care

 No problems

95.4

98.5

99.3

96.9

95.4

94.4

90.8

87.1

 Slight

3.4

1.3

0.2

2.5

3.8

3.8

6.9

8.9

 Moderate

0.9

0

0.4

0.6

0.2

1.1

2.0

3.1

 Severe

0.3

0

0

0

0.6

0.5

0

0.9

 Extreme

0.1

0.2

0

0

0

0.2

0.3

0

Usual activities

 No problems

82.7

94.4

94.0

87.8

78.8

78.6

72.3

56.6

 Slight

10.2

5.0

4.2

8.3

12.2

12.8

13.6

22.1

 Moderate

5.0

0.4

0.9

2.9

6.0

6.1

9.8

15.5

 Severe

1.6

0

0.4

0.8

2.6

1.8

2.9

4.4

 Extreme

0.5

0.2

0.4

0.2

0.4

0.7

1.4

1.3

Pain/Discomfort

 No pain

55.6

80.8

70.8

62.7

46.9

41.1

40.2

29.6

 Slight

29.2

16.8

23.6

26.0

34.5

36.8

34.7

38.1

 Moderate

11.9

2.4

4.9

7.7

13.8

17.8

20.2

26.1

 Severe

2.7

0

0.4

2.7

4.0

3.4

4.3

5.8

 Extreme

0.6

0

0.2

0.8

0.8

0.9

0.6

0.4

Anxiety/Depression

 No problems

75.3

77.5

81.3

73.6

70.1

75.7

76.0

73.0

 Slight

16.3

14.7

11.4

16.6

19.4

16.7

16.2

20.8

 Moderate

6.2

5.8

5.6

7.3

6.4

5.9

6.9

4.9

 Severe

1.6

1.3

1.8

1.7

3.0

0.9

0.6

1.3

 Extreme

0.6

0.6

0

0.8

1.0

0.9

0.3

0

The mean utility score was 0.91 (SD 0.14; 95 % CI 0.90, 0.91) with values ranging from -0.06 to 1 (Table 3) and the mean EQ-VAS score was 78.55 (SD 16.57; 95 % CI 77.95, 79.15) (Table 4). Ten out of the 3,125 possible health states represented the majority of the sample (76.2 %) (Table 5). Individuals rating themselves as ‘11111’, assigned a mean score of 85.44 to their health state on the EQ-VAS. Overall, there was a moderate, positive statistically significant correlation between utility and EQ-VAS scores (rho = 0.46, p < 0.001) and a moderate, negative, statistically significant correlation between utility scores and age (rho = -0.31, p < 0.001).
Table 3

Mean EQ-5D-5L utility scores by socio-demographic characteristics (N = 2,908)

 

Total

Males

Females

Variable

n

mean (SD)

n

mean (SD)

n

mean (SD)

All

2908

0.91 (0.14)

1422

0.92 (0.13)

1486

0.90 (0.14)

  

Z = -3.42, p = 0.001

Age category, years

 15–24

464

0.96 (0.08)

238

0.96 (0.07)

226

0.95 (0.08)

 25–34

449

0.95 (0.10)

225

0.95 (0.10)

224

0.95 (0.11)

 35–44

480

0.92 (0.13)

238

0.93 (0.12)

241

0.91 (0.13)

 45–54

499

0.89 (0.16)

247

0.90 (0.16)

253

0.87 (0.16)

 55–64

443

0.89 (0.15)

217

0.90 (0.14)

226

0.88 (0.15)

 65–74

346

0.87 (0.16)

153

0.87 (0.16)

193

0.87 (0.16)

 75+

227

0.83 (0.16)

104

0.85 (0.16)

122

0.82 (0.15)

 Total

2908

X 2 = 282, p < 0.001

 

Educational attainment

 Up to secondary

1187

0.90 (0.15)

510

0.91 (0.16)

677

0.89 (0.15)

 Trade, apprenticeship, certificate, diploma

1061

0.90 (0.14)

605

0.91 (0.12)

456

0.89 (0.15)

 Degree or higher

651

0.93 (0.12)

301

0.93 (0.12)

351

0.93 (0.12)

 Total

2900

X 2 = 15.0, p = 0.001

 

Area of residence

 Metropolitan

2163

0.91 (0.14)

1047

0.92 (0.13)

1114

0.90 (0.14)

 Regional

745

0.90 (0.15)

373

0.91 (0.14)

372

0.90 (0.15)

 Total

2908

Z = -0.21, p = 0.83

 

Country of birth

 Australia

2138

0.91 (0.14)

1054

0.91 (0.14)

1082

0.90 (0.14)

 European

461

0.88 (0.16)

217

0.90 (0.14)

244

0.86 (0.17)

 Asian

206

0.97 (0.07)

110

0.97 (0.06)

96

0.96 (0.08)

 Other

101

0.93 (0.12)

39

0.93 (0.15)

62

0.94 (0.10)

 Total

2906

X 2 = 75.6, p < 0.001

 

Marital status

 Married/De Facto

1791

0.91 (0.14)

900

0.92 (0.13)

890

0.90 (0.14)

 Never Married

718

0.93 (0.13)

394

0.93 (0.14)

325

0.93 (0.12)

 Separated/Divorced

264

0.87 (0.17)

94

0.88 (0.17)

169

0.86 (0.16)

 Widowed

133

0.84 (0.16)

31

0.87 (0.15)

100

0.83 (0.16)

 Total

2906

X 2 = 92.8, p < 0.001

 

Employment status

 Full time or part-time

1616

0.94 (0.09)

891

0.95 (0.08)

723

0.93 (0.10)

 Home Duties

161

0.90 (0.14)

6

0.96 (0.04)

155

0.90 (0.14)

 Unemployed

96

0.88 (0.15)

45

0.90 (0.15)

51

0.87 (0.15)

 Retired

593

0.86 (0.15)

256

0.87 (0.15)

337

0.86 (0.15)

 Student

275

0.95 (0.07)

130

0.96 (0.08)

145

0.95 (0.07)

 Not working due to work-related injury or disability

118

0.61 (0.26)

67

0.62 (0.26)

51

0.60 (0.26)

 Other

45

0.88 (0.16)

23

0.96 (0.06)

23

0.81 (0.19)

 Total

2904

X 2 = 407, p < 0.001

 

Annual household income

 Up to $20,000

176

0.83 (0.19)

59

0.83 (0.20)

117

0.82 (0.19)

 $20,001-$40,000

372

0.84 (0.17)

159

0.84 (0.18)

214

0.85 (0.17)

 $40,001-$80,000

562

0.91 (0.14)

280

0.92 (0.13)

282

0.90 (0.15)

 $80,001-$120,000

438

0.93 (0.10)

230

0.94 (0.10)

208

0.93 (0.11)

 $120,001+

584

0.94 (0.10)

338

0.95 (0.08)

246

0.93 (0.11)

 Not Stated

776

0.91 (0.14)

354

0.92 (0.15)

417

0.91 (0.14)

 Total

2908

X 2 = 156, p < 0.001

 

SD standard deviation; comparisons of the EQ-5D-5L sum score distributions by gender and area of residence were analysed using the Mann-Whitney U test. All other differences amongst groups were analysed with the Kruskal-Wallis test

Table 4

Mean EQ-VAS scores by age and gender (N = 2,908)

 

Total

Males

Females

Variable

n

mean (SD)

n

mean (SD)

n

mean (SD)

All

2905

78.55 (16.57)

1422

78.89 (15.71)

1483

78.23 (17.35)

  

Z = -1.29, p = 0.20

Age category, years

 15–24

464

82.00 (14.06)

238

81.75 (13.02)

226

82.26 (15.11)

 25–34

449

80.84 (14.58)

225

81.51 (14.56)

224

80.16 (14.60)

 35–44

480

78.50 (16.65)

238

79.05 (15.03)

241

77.96 (18.13)

 45–54

499

75.64 (18.00)

247

76.08 (16.94)

253

75.20 (18.96)

 55–64

443

78.92 (16.84)

217

79.01 (16.02)

225

78.83 (17.63)

 65–74

346

78.56 (17.19)

153

78.21 (17.04)

193

78.83 (17.34)

 75+

225

72.68 (17.66)

104

73.66 (17.38)

121

71.83 (17.93)

 Total

2906

X 2 = 58.8, p < 0.001

 

SD standard deviation, comparisons of the EQ-VAS score distributions by gender was analysed using the Mann-Whitney U test. Differences amongst age groups were analysed with the Kruskal-Wallis test

Table 5

Most frequently reported EQ-5D-5L health states with mean utility scores and EQ-VAS values (N = 2,908)

Health statea

n

%

Cumulative %

Mean utility

Mean EQ-VAS (SD)

11111

1245

42.8

42.8

1.00

85.44 (11.75)

11121

356

12.2

55.0

0.94

81.32 (13.02)

11112

162

5.6

60.6

0.92

79.20 (13.33)

21121

116

4.0

64.6

0.89

80.71 (12.75)

11122

93

3.2

67.8

0.87

77.72 (14.08)

11131

69

2.4

70.2

0.93

74.67 (14.67)

21111

53

1.8

72.0

0.95

83.56 (8.70)

11113

52

1.8

73.8

0.90

71.99 (15.4)

21221

41

1.4

75.2

0.84

76.89 (14.90)

21122

30

1.0

76.2

0.82

74.41 (12.56)

11221

24

0.8

77.0

0.89

78.70 (11.68)

21131

24

0.8

77.8

0.88

76.69 (13.63)

11123

23

0.8

78.6

0.84

70.91 (15.28)

11132

19

0.6

79.2

0.85

71.58 (18.01)

21222

18

0.6

79.8

0.77

64.75 (16.82)

31231

17

0.6

80.4

0.82

66.86 (15.00)

31221

16

0.6

81.0

0.83

70.92 (17.31)

21231

16

0.6

81.6

0.83

63.27 (14.33)

11211

15

0.5

82.1

0.95

73.14 (17.48)

31121

15

0.5

82.6

0.88

77.07 (15.60)

adigits represent response levels (1–5) for the five dimensions (mobility, self-care, usual activities, pain/discomfort, anxiety/depression); EQ-VAS visual analogue scale

The mean EQ-5D-5L utility scores by socio-demographic variables and mean EQ-VAS scores by age and gender are summarised in Tables 3 and 4 respectively. Overall, men had statistically significantly higher utility scores than women (mean 0.92 (SD 0.13) versus 0.90 (SD 0.13); Z = -3.42, p < 0.01), although EQ-VAS scores were similar. Lower utility scores were reported with advancing age categories (chi-squared = 282, p < 0.01) and this relationship was somewhat U-shaped when EQ-VAS scores were considered. In the bivariate analyses, there were statistically significant differences in utility scores for the whole sample in terms of different marital statuses, educational attainment, employment statuses, country of birth and annual household income categories but not for areas of residence. After controlling for age, gender and duration of education, individual variables which remained significant in the exploratory multivariate regression analyses included employment, household income and marital status (data not shown). In the final model, advancing age was significantly associated with a negative impact on health status and disutility was higher for those unemployed or not working due to work-related injury or disability (Table 6). In addition, male gender, longer duration of education, higher annual household income and marriage or de facto relationships were also independent, statistically significant predictors of better health in the EQ-VAS multivariate regression analysis (Table 6).
Table 6

Multivariate Poisson regression analyses of EQ-5D-5L disutility scores, EQ-VAS scores and socio-demographic variables

Variablea

Disutility scores

EQ-VAS scores

Model 1

Model 2

Model 1

Model 2

B b

95 % CI

p-value

B b

95 % CI

p-value

B b

95 % CI

p-value

B b

95 % CI

p-value

Control variables

 Gender (positive)

  Malec

  Female

0.18

−0.06, 0.43

0.14

0.19

−0.05, 0.44

0.13

0.01

0.01, 0.02

<0.01

0.02

0.01, 0.02

<0.01

 Age category, years (positive)

  15–29c

  30–49

0.68

0.25, 1.11

<0.01

0.62

0.13, 1.10

0.01

−0.06

−0.07,−0.05

<0.01

−0.06

−0.07,−0.05

<0.01

  50–69

0.98

0.58, 1.40

<0.01

0.85

0.34, 1.36

<0.01

−0.05

−0.06, −0.03

<0.01

−0.05

−0.06, −0.03

<0.01

  70+

1.28

0.84, 1.72

<0.01

1.25

0.58, 1.92

<0.01

−0.63

−0.08, −0.05

<0.01

−0.10

−0.02, −0.07

<0.01

 Education attainment (negative)

  Up to secondaryc

  Trade, apprenticeship, certificate, diploma

−0.05

−0.32, 0.22

0.71

0.07

−0.21, 0.34

0.63

0.02

0.01, 0.03

<0.01

0.01

0.00, 0.02

0.04

  Degree or higher

−0.32

−0.67, 0.03

0.07

−0.07

−0.43, 0.30

0.72

0.07

0.06, 0.08

<0.01

0.05

0.03, 0.06

<0.01

Exploratory variables

 Annual household income (negative)

  Up to $20,000c

  $20,001–$60,000

   

−0.01

−0.44, 0.43

0.98

   

0.02

0.00, 0.05

0.02

  $60,001–$100,000

   

−0.25

−0.80, 0.29

0.37

   

0.07

0.05, 0.09

<0.01

  $100,001+

   

−0.33

−0.88, 0.22

0.24

   

0.04

0.01, 0.06

<0.01

  Not stated

   

−0.17

−0.62, 0.29

0.48

   

0.02

0.00, 0.04

0.04

 Employment status (positive)

  Full or part time, student, home dutiesc

  Unemployed

   

0.67

0.04, 1.29

0.04

   

−0.09

−0.11, −0.07

<0.01

  Retired

   

0.14

−0.30, 0.58

0.54

   

0.03

0.02, 0.05

<0.01

  Otherd

   

1.41

1.06, 1.77

<0.01

   

−0.31

−0.33, −0.29

<0.01

 Marital status (positive)

  Married/De Factoc

  Never Married

   

0.09

−0.32, 0.50

0.68

   

−0.22

−0.04, −0.01

<0.01

  Separated/Divorced

   

0.57

−0.33, 0.44

0.77

   

−0.06

−0.07, −0.04

<0.01

  Widowed

   

0.51

−0.45, 0.55

0.84

   

−0.05

−0.08, −0.03

<0.01

  Constant

−3.17

−3.57, −2.78

<0.01

−3.29

−3.99, −2.59

<0.01

4.38

4.37, 4.39

<0.01

4.39

4.37, 4.42

<0.01

  AIC

0.55

  

0.53

  

12.97

  

12.53

  

  BIC

−22714

  

−22703

  

−3367

  

−4589

  

aThe direction of a priori hypothesied associations relative to the reference group are presented in brackets; bcoefficient; creference group; dother, not working due to work-related injury or disability; AIC Akaike information criteria, BIC Bayesian information criteria

Significant results at the p < 0.05 level are shown in bold

Discussion

This is the first study to report EQ-5D-5L data for a large, randomly selected, community-based general population sample in Australia calculated using a scoring algorithm specifically developed for the five level version. These results provide important insights into the HrQOL of the South Australian population. Overall, the mean utility score (0.91) was similar to those recently reported using the EQ-5D-5L instrument for populations in Italy (0.92) [18], Germany (0.92) [17] and Poland (0.89) [20] and higher than the previously reported Australian value (0.87) measured using the 3L version in a representative adult sample in Queensland, Australia [22]. The difference in the Australian values could be due to variation in the populations sampled, the value sets employed [55] or versions of the EQ-5D administered.

The prevalence of the most frequently reported EQ-5D-5L health states were similar to those reported recently in a large survey in the UK (N = 996) [7] except for the health state representing slight problems in mobility and pain/discomfort and no problems in self-care, usual activities and anxiety/depression (health state 21121), which was more prevalent in the South Australian sample (4.0 % versus 2.1 %). This South Australian community-based population reported greater problems with pain and discomfort compared with other dimensions, similar to previously reported EQ-5D-5L population norms from other countries [7, 11, 17, 18, 20].

Primarily, the findings indicate that, in general, South Australians report high HrQOL according to the EQ-5D-5L classification with 42.8 % of respondents reporting no problems (11111) consistent with other EQ-5D-5L data generated from large general population samples in developed countries such as Germany (47.5 %) and the UK (47.6 %) [7, 17]. Although these data suggest a reduced ceiling effect for the 5L as a lower proportion of respondents reported no problems for each individual dimension compared with 3L general population sample data in Queensland, Australia [22] there remains a considerable proportion of respondents who report full health. Only 7.2 of respondents reported their health above 90 on the EQ-VAS and 4.7 % “the best health you can imagine”, suggesting ceiling effects persist with the utility scores. Consequently, the EQ-VAS scores may be a more appropriate measure of population global health rating [7, 56].

Consistent with findings from other countries [7, 25, 26, 3234], collectively, the multivariate regression analyses suggested respondents who were younger, male, had higher levels of education or household income, were employed or married/de facto, were more likely to have a better health status. However, only age and employment status were independently associated with health status when utility, rather than the EQ-VAS scores, were considered, perhaps indicating fundamental differences between these measures. Further, despite mean utility and EQ-VAS scores declining with age, as anticipated a priori [7, 17, 23, 32, 35, 57], this association was more U-shaped for the latter scores, with a nadir during 45–54 years. This pattern is similar to the relationship between wellbeing and age [57] and indicates the EQ-VAS may cover broader dimensions of HrQOL than those included in the EQ-5D-5L descriptive system [56].

The EQ-VAS provides a comprehensive rating of overall health at the individual and population-level [56] and has been used to routinely monitor patients’ self-reported health in the National Health Service in England, monitor population health over time and estimate the burden of different diseases [55]. Purported advantages of the EQ-VAS include robust psychometric properties and simplicity and ease of use, although recent evidence suggests 55 % of respondents do not strictly adhere to the scoring instructions, presenting challenges for data analysis [5, 56]. Further, respondents’ interpretation of the ends of the scale may be different, potentially limiting comparability of EQ-VAS scores. Despite these limitations, the EQ-VAS scores are not subject to artificial differences introduced by the choice of different value sets [55, 58] and perhaps more accurately capture a global health rating from the individual’s perspective [56]. Ultimately, when considering the valuation of health in economic evaluations, regulatory reimbursement guidelines typically recommend using preference elicitation techniques such as standard gamble or time-trade off [5961], whereas the EQ-VAS is choice-less [5].

This study presents findings from a large representative survey sample from a single state and likely reflects Australian norms but may not be generalizable to other countries. Compared with the rest of Australia, South Australians are slightly older (16.1 % ≥65 years of age versus 14.0 %), less educated (people aged ≥15 years who have completed Year 12 44.8 % versus 49.2 %; 15–64 year olds participating in vocational education 10.9 % versus 12.0 %; 25–64 years old with a Bachelor degree or above 22.9 % versus 27.9 %), have a lower mean weekly household income ($798 versus $848), and a higher rate of unemployment (5.6 % versus 5.2 %) [62]. Consequently, the population norms reported for the EQ-5D-5L for South Australia may be lower than those for the wider Australian general population. Further, individuals who live in remote areas and culturally and linguistically diverse populations were under-represented in the survey.

A UK, rather than Australian, value set was used to calculate utility scores as the latter is not yet available. Previous evidence comparing EQ-5D-3L population norms in Queensland, Australia [22] estimated using value sets from Australia, the UK and USA, suggests the UK value set provides relatively comparable valuations, although higher health states have been reported elsewhere when using the Australian algorithm [63]. Generally, guidelines recommend using preference weights specific to the jurisdiction of interest [59, 64] as empirical evidence suggests population values may differ for health states across countries, possibly due to cultural differences [31, 63, 65]. Previously, Norman et al [66] recently published a pilot scoring algorithm to generate Australian general population-specific utilities and an Australian general population-specific scoring algorithm for the EQ-5D-5L is currently in development1.

The utility and EQ-VAS results provide clinicians, funders and policy-makers with an alternative set of population norms to monitor policy changes and inform future public health and health care investment decisions. In the Australian setting, when the EQ-5D is the instrument of choice for valuing health in economic evaluations, population norms for South Australia or Queensland [22] can be used to estimate health-related quality of life. Whilst the former values were generated using a UK- rather than Australian-based algorithm and may underestimate the HrQOL of Australians [63], the latter values were estimated using the 3L version which may be less sensitive and responsive to problems than the 5L version [710].

Conclusions

The findings from this study provide the first population norms for South Australia based on a large, community-based sample by incorporating the newly developed UK general population scoring algorithm specifically developed for the five level version of the EQ-5D instrument [12]. The population-based values will facilitate empirical comparisons of the HrQOL of the general population with more specific patient groups and will be useful for estimating QALYs in economic evaluations in the Australian context. Overall, findings are consistent with EQ-5D-5L utility and VAS scores reported for other countries and indicate that the majority of South Australian adults report themselves in full health, with higher health status associated independently with younger age, male gender, longer duration of education, higher annual household income, employment and marriage or de facto relationships. Further research is recommended to update these population norms by applying the Australian general population specific scoring algorithm, currently in development, once this become publically available.

Abbreviations

CI: 

Confidence intervals

DCE: 

Discrete choice experiment

EQ-5D: 

the EuroQol 5 dimensions

GLM: 

Generalized linear model

HOS: 

Health Omnibus Survey

HrQOL: 

Health-related quality of life

IQR: 

Interquartile range

IRSAD: 

Index of relative social advantage and disadvantage

MAUI: 

Multi-attribute utility instrument

QALY: 

Quality-adjusted life year

SD: 

Standard deviation

STROBE: 

Strengthening the Reporting of Observational Studies in Epidemiology

TTO: 

Time trade off

EQ-VAS: 

Visual analogue scale

Declarations

Acknowledgements

The authors wish to warmly thank the survey respondents for their participation in the study and the academic departments who provided funding for this study.

Funding

This work was supported by discretionary research funds held by the respective academic departments at Flinders University who had no control over study design, analysis, reporting or dissemination.

Availability of data and materials

The dataset supporting the conclusions of this article may be available upon request from the lead author to ensure any changes to the project do not invalidate the project’s ethical approval in accordance with the University of Adelaide Human Research Ethics Committee approval letter.

Authors’ contributions

All co-authors developed the study design. The data were analysed by NM with assistance from BK. All co-authors contributed to data interpretation and drafting of the manuscript. NM is the guarantor for the overall content. All authors read and approved the final manuscript.

Authors’ information

Not applicable.

Competing interests

Dr McCaffrey, Dr Kaambwa, Professor Currow and Professor Ratcliffe declare that they have no conflict of interest relevant to the content of this article.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Ethical approval for the survey was granted by the Social and Behavioural Research Ethics Committees of Flinders University (6241) and the University of Adelaide (H–097–2010) and informed consent was obtained from all individual participants included in the study.

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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Flinders Health Economics Group, Flinders University, Repatriation General Hospital
(2)
Palliative and Supportive Care, Bedford Park, Flinders University

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