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Socio-economic differences in the health-related quality of life impact of cardiovascular conditions

Mai Stafford , Michael Soljak , Victoria Pledge , Jennifer Mindell
DOI: http://dx.doi.org/10.1093/eurpub/ckr007 301-305 First published online: 11 March 2011


Background: Those responsible for planning and commissioning health services require a method of assessing the benefits and costs of interventions. Quality-adjusted life years, based on health-related quality of life (HRQoL) estimates, can be used as part of this commissioning process. The purpose of this study was to generate nationally representative HRQoL estimates for cardiovascular disease (heart attack, angina and stroke) and predisposing conditions (diabetes, hypertension and obesity) and assess differential impacts by socio-economic position using data from the Health Survey for England. Methods: Regression modelling was used to estimate the relationship of EQ-5D index scores with each condition independently and differentially by socio-economic position. Results: Of the cardiovascular conditions/risk factors considered, having doctor-diagnosed stroke, heart attack or angina were each associated with the greatest decreases in EQ-5D. With the exception of heart attack, the reduction in EQ-5D associated with the condition/risk factor was greater for those occupying lower socio-economic positions, statistically significantly so for obesity, hypertension and diabetes. Conclusion: The estimates calculated provide nationally representative baseline data for England, which can be used for modelling the impact of interventions on HRQoL. They illustrate the importance of socio-economic circumstances for the association between a given condition/risk factor and HRQoL.


The UK’s National Health Service (NHS) has a responsibility to monitor the health of the population and to commission services and interventions that will improve health. The UK’s four national Departments of Health and other UK Government departments have a similar statutory responsibility to evaluate the impact of policy interventions on health and other outcomes.1 In the face of finite resources, it is useful to know which interventions yield the greatest benefit. One way of comparing across diverse interventions aimed at tackling different conditions and risk factors is to measure their impact on length of life and on health-related quality of life (HRQoL). Quality of life measures incorporate the perspective of the user. They can also be used as an input to calculate quality-adjusted life years (QALYs) for health economic evaluation.2 Cardiovascular disease is important in terms of disease burden in the UK and throughout Europe3,4 and quality of life5 and is the focus of the present study.

The EuroQoL EQ-5D has been used extensively in Europe, the USA and worldwide.5–14 The EQ-5D has been compared across a range of long-term conditions, including cardiovascular disease, for large samples representative of the USA, Canadian and European populations5,9,12–14 but not the UK population. In addition, several studies indicate a lower HRQoL among those occupying lower socio-economic positions and have demonstrated independent effects of socio-economic status and cardiovascular conditions.5,10,12–15 Few studies have investigated a possible differential impact of cardiovascular disease on HRQoL according to socio-economic position, some confirming statistically significant interactions between socio-economic indicators and HRQoL16,17 and others not finding evidence of clinically important interactions.18

The aims of the current study were (i) to generate HRQoL estimates for cardiovascular disease and predisposing conditions for a representative sample of adults in England, to provide baseline information for intervention studies to reduce cardiovascular disease and (ii) to investigate possible differential associations between EQ-5D index scores and cardiovascular disease/predisposing conditions by socio-economic position. We hypothesized that EQ-5D index scores would be lower for those with diagnosed cardiovascular disease or predisposing conditions compared with those with no disease or predisposing condition. Based on previous work,16–18 we further hypothesized that the differential between diseased and not diseased would be larger among those in greater socio-economic disadvantage.


Data source

The Health Survey for England (HSE) is an annual cross-sectional survey of a new random sample, representative of the non-institutionalized population in England. The HSE focuses on different conditions and/or population subgroups each year, in addition to including a core set of questions. In HSE 2003 and 2006, the EQ-5D was included along with measures of cardiovascular disease and cardiovascular risk factors.19 The HSE uses a multi-stage sampling procedure, stratified by proportion of households headed by someone in a non-manual occupation, and each address has an equal chance of selection. All adults within each household (to a maximum of 10, randomly selected if more) are eligible. Trained interviewers collected information face-to-face, including socio-demographical data, measured weight and height and provided the EQ-5D instrument for self-completion. Ethical approval was obtained from an appropriate Research Ethics Committee prior to each survey.


The EQ-5D has five domains capturing mobility, self-care, usual activities, pain/discomfort and anxiety/depression. Each domain has three possible levels indicating no problems, moderate problems or severe problems. This results in a series of 243 possible health states.20 Participant’s responses on these five domains are converted to the EQ-5D index score using a time trade-off (TTO) method. The tariffs used in the current study have been derived for the UK in a separate sample in a study undertaken in the 1990s. Details are given elsewhere21 but briefly, respondents were asked how long they were willing to spend in the optimal health state for it to be equivalent to 10 years in the particular health state in question. Shorter periods of time indicate a poorer health state. Responses were then transformed to create the EQ-5D scores, with higher scores corresponding to better health. The algorithm based on this external sample was applied to responses from HSE participants to create the EQ-5D index scores for each participant. The EQ Visual Analogue Scale was not included in the HSE.

Cardiovascular disease and risk factors

Measured height and weight were used to calculate body mass index and code participants to obese (BMI ≥ 30 kg m−2) or not obese (BMI < 30 kg m−2). Doctor-diagnosed heart attack, angina, stroke, hypertension and diabetes were reported by participants using a combination of two items: ‘Have you ever had a heart attack (including myocardial infarction or coronary thrombosis)?’ and ‘Were you told by a doctor that you had a heart attack (including myocardial infarction or coronary thrombosis)?’, and similarly for the other conditions.

Socio-demographic characteristics

Sex, age, ethnicity, occupation and educational attainment were reported by participants. For these analyses, ethnic group was coded as White, Mixed, Black/Black British, Asian/Asian British and other ethnic group according to the 2001 Census five category classification. EQ-5D scores for the mixed and other ethnic group categories are not presented due to small sample sizes but are included when ethnic group is used as an adjustment covariate. Socio-economic position based on the occupation was coded according to the National Statistics Socio-economic Classification (NSSEC) in five categories of occupation (managerial/professional occupations, intermediate occupations, small employers and own account workers, lower supervisory and technical occupations and semi-routine occupations) plus a separate category for those who could not be classified. Highest level qualification was coded as National Vocational Qualification (NVQ) NVQ4/NVQ5/degree level, Higher education below degree/NVQ3/A level equivalent, NVQ2/O level equivalent/NVQ1/CSE equivalent, No qualification.

Statistical analysis

Analyses accounted for the complex survey design and were weighted for non-response (using sampling weights based on known probability of sampling). Mean EQ-5D scores adjusted in 10-year age bands by socio-demographic group are presented initially. Linear regression was then used with EQ-5D as outcome to estimate EQ-5D scores for each cardiovascular or predisposing condition (each entered as a dummy variable) (i) in isolation (ii) simultaneously with other conditions to isolate the independent contribution of each and (iii) including interaction between condition and socio-economic position. In the latter model, social class was entered as a continuous variable and an interaction term representing the difference in social class gradient among those with and without the condition of interest was included. Age-adjusted mean EQ-5D scores by condition and social class are also presented. All regression models included sex, age in 10-year-age bands, ethnicity, educational attainment and socio-economic position.


Participants who were excluded because of missing EQ-5D index score were more likely to be older, in a lower socio-economic group (6% in managerial/professional occupations vs. 11% in semi-routine occupations), of lower educational attainment (15% of those with no qualifications vs. 5% of those with the highest qualifications) and non-White (20% vs. 7% of white participants). After excluding those with missing outcome and covariate data, a maximum of 26 104 were retained in the descriptive analyses but bases for each condition are given in the tables.


The unadjusted mean EQ-5D index score was lower among older participants (table 1). Age-standardized mean EQ-5D was lower for women compared with men and for those in lower socio-economic positions compared with those in the managerial and professional positions (table 1).

View this table:
Table 1

Mean EQ-5D index score by socio-demographic characteristics (based on the data from adult participants from the HSE 2003 and 2006)

All participantsParticipants (%) n = 26 104Mean EQ-5D index score95% CI
Agea (years)
    Black/Black British2.10.8340.801–0.867
    Asian/Asian British4.40.8430.816–0.871
Socioeconomic positionc
    Managerial and professional42.10.8940.890–0.897
    Intermediate occupations8.70.8590.850–0.868
    Small employers and own account workers11.10.8610.852–0.869
    Lower supervisory and technical occupations11.80.8510.842–0.859
    Semi-routine occupations26.30.8220.815–0.828
Educational attainmentc
    Higher education below degree/NVQ3/A level26.00.8870.881–0.892
    NVQ2/O level/NVQ1/CSE30.10.8630.857–0.868
    No qualification24.00.8170.809–0.825
  • CI = confidence interval

  • a: age adjusted

  • b: sex adjusted

  • c: age- and sex adjusted

The first set of adjusted analyses control for age, sex, ethnicity, educational attainment and socio-economic position, and consider each cardiovascular or predisposing condition in isolation (that is, ignoring comorbidity) (table 2). The prevalence of obesity (BMI ≥ 30 kg m−2) was 23%; being obese was associated with a 0.045 point lower mean EQ-5D score compared with non-obese participants. The cardiovascular condition associated with the greatest reduction in EQ-5D was stroke (0.160 point reduction). When all cardiovascular and associated conditions were included simultaneously, the independent contribution of stroke to EQ-5D index score remained the largest. Each of the conditions made a statistically significant contribution to mean EQ-5D score (P < 0.001 for each).

View this table:
Table 2

Association between EQ-5D index score and cardiovascular and predisposing conditionsa

ConditionParticipants with condition, % (base N)Mean (SE) difference in EQ-5D score (condition considered singly)Mean (SE) difference in EQ-5D score (all conditions simultaneously)
BMI ≥ 30 kg m−2 (ref. < 30 kg m−2)23.0 (23 405)−0.045 (0.003)*−0.033 (0.003)*
    Yes (ref. no.)26.1 (23 830)−0.051 (0.004)*−0.032 (0.004)*
    Yes (ref No)3.7 (23 915)−0.096 (0.010)*−0.046 (0.009)*
    Yes (ref. no.)3.0 (23 914)−0.141 (0.011)*−0.090 (0.012)*
Heart attack
    Yes (ref. no.)2.3 (23 914)−0.139 (0.013)*−0.060 (0.014)*
    Yes (ref. no.)1.8 (23 916)−0.160 (0.015)*−0.101 (0.015)*
  • SE = standard error, BMI = body mass index

  • a: adjusted for sex, age group, ethnicity, educational attainment and socioeconomic position

  • *P < 0.001

Age-adjusted mean EQ-5D index scores by cardiovascular or associated condition stratified by socio-economic position are shown in table 3. Occupying a lower socio-economic position was associated with lower EQ-5D index score (table 4), independently of age, sex, ethnicity, educational attainment and condition. With one exception, the reduction in EQ-5D index score associated with having a cardiovascular or associated condition was greater among those occupying lower socio-economic positions. This interaction was statistically significant for obesity, hypertension and diabetes (figure 1). In other words, the discrepancy in HRQoL associated with obesity, hypertension or diabetes was more marked among those in lower socio-economic positions. An exception to this general pattern was seen for heart attack status. The reduction in EQ-5D index score associated with having ever had a doctor diagnosed heart attack was smaller among those occupying lower compared with higher socio-economic positions. Those in lower socio-economic positions were more likely to have multiple cardiovascular and predisposing conditions but this did not explain the differential impact of each condition on EQ-5D score (table 4).

Figure 1

Age-adjusted mean EQ-5D score by socio-economic position and (a) obesity and (b) hypertension

View this table:
Table 3

Age-adjusted mean (95% CI) EQ-5D index score, by cardiovascular and predisposing conditions and socio-economic position

AllBy socio-economic position
Managerial and professionalIntermediate, small employers, lower supervisorySemi-routine
Obesity (kg m−2)
    BMI < 300.887 (0.884–0.890)0.9110.8780.855
    BMI ≥ 300.836 (0.830–0.842)0.8680.8400.790
    No0.880 (0.876–0.883)0.9060.8710.847
    Yes0.825 (0.819–0.831)0.8630.8220.774
    No0.868 (0.865–0.871)0.8970.8610.830
    Yes0.771 (0.745–0.796)0.8400.7730.680
    No0.870 (0.867–0.872)0.8990.8620.832
    Yes0.711 (0.659–0.763)0.7650.6720.649
Heart attack
    No0.868 (0.865–0.871)0.8980.8610.828
    Yes0.636 (0.602–0.669)0.6670.6690.682
    No0.867 (0.864–0.870)0.8970.8610.828
    Yes0.680 (0.619–0.741)0.8130.6360.652
  • CI, confidence interval; BMI, body mass index

View this table:
Table 4

Association between EQ-5D index score and cardiovascular and predisposing conditions by socio-economic position

Model 1a Regression coefficient (SE)Model 2b Regression coefficient (SE)
    BMI ≥ 30 kg m−2−0.028 (0.006)***−0.018 (0.006)**
    SES−0.008 (0.001)***−0.007 (0.001)***
    BMI ≥ 30 kg m−2 × SES−0.006 (0.002)**−0.005 (0.002)**
    Hypertension−0.024 (0.006)***−0.011 (0.006)
    SES−0.007 (0.001)***−0.007 (0.001)***
    Hypertension × SES−0.010 (0.002)***−0.007 (0.002)***
    Diabetes−0.055 (0.017)**−0.017 (0.015)
    SES−0.009 (0.001)***−0.008 (0.001)***
    Diabetes × SES−0.013 (0.005)*−0.010 (0.005)*
    Angina−0.122 (0.021)***
    SES−0.009 (0.001)***
    Angina × SES−0.006 (0.006)
Heart attack
    Heart attack−0.158 (0.026)***
    SES−0.010 (0.001)***
    Heart attack × SES0.006 (0.007)
    Stroke−0.137 (0.028)***
    SES−0.010 (0.001)***
    Stroke × SES−0.007 (0.008)
  • SES = Socio-economic status

  • a: Model 1 adjusted for sex, age group, ethnicity and educational attainment

  • b: Model 2 adjusted for sex, age group, ethnicity, educational attainment and all other cardiovascular and predisposing conditions where statistically significant interaction between condition and SES was found in Model 1

  • *P < 0.05; **P < 0.01; ***P < 0.001


This study considered reductions in HRQoL associated with a set of cardiovascular and predisposing conditions. Among those considered, stroke was associated with the greatest reduction in HRQoL. The conditions considered are correlated and several participants had two or more of these. Nevertheless, each made an independent contribution to lower HRQoL.

Previous studies point to the importance of stroke as a major determinant of EQ-5D index scores.22–24 Mean EQ-5D index scores by stroke and angina status were very similar to estimates from the Medical Expenditure Panel Survey (MEPS).5 Mean EQ-5D index scores for those who had diagnosed hypertension were a little higher in the current study and for those who had a diagnosed heart attack a little lower in the current study compared with counterparts in the MEPS (though the definitions for heart attack differed in the two studies). However, differences were small in magnitude and the estimates demonstrate considerable consistency across countries and studies.

One important finding of the study is the differential association between conditions and HRQoL according to socio-economic position. While the presence of obesity, hypertension or diabetes was associated with some reduction in HRQoL among those in the highest socio-economic groups, it was associated with a greater reduction in HRQoL for those in lower socio-economic groups. Explanations for this are beyond the scope of the current study. However, we did identify that the co-existence of multiple cardiovascular and associated conditions did not explain this finding and further analysis (available from the author) indicated that smoking behaviour did not explain it either. A complex set of pathways could underlie the interaction, including biomedical, psychosocial and behavioural factors, such that those in lower socio-economic groups with these conditions may have poorer physiological functioning, poorer adherence to medication, greater difficulty in adopting a healthy lifestyle and/or greater psychosocial stress which promotes disease progression25 alongside less agency in amending their work and other social roles to cope with symptoms and disease. Narrowing of the health gap between those in more and less disadvantaged positions has been a key focus of previous UK governments in recent years and has led to targets for reductions in social inequalities in life expectancy and premature deaths from coronary heart disease and stroke and from cancer (as well as for smoking prevalence, infant mortality and teenage pregnancy).26,27 A further step, and one supported by the current study, would be to reduce socio-economic inequalities in HRQoL.

Strengths and limitations

Some limitations must be acknowledged. Although the assumption, supported by longitudinal studies, is that disease precedes declines in HRQoL, this cannot be tested in the current study. A range of cardiovascular conditions and risk factors was considered but the HSE lacks details on the severity and management of these. Only one measure of HRQoL, namely the EQ-5D, was considered here. This measure has been extensively used in international studies, nevertheless, it is possible that alternative measures of HRQoL may accentuate different health conditions as being important.28 The EQ-5D has also been criticized as a measure of HRQoL for community samples due to the ceiling effect. Indeed, over 50% of the respondents in this study scored the maximum of 1 and the scale additionally has a substantial jump between the maximum and next possible score (0.883). Alternative specifications of the regression model have been used in other studies though there is no agreement on the most suitable approach.5,13 Given this ceiling effect, the estimated differences by condition/risk factor and socio-economic position may be underestimated in this study.


These analyses are based on cross-sectional data and ignore the sequential development of conditions predisposing to cardiovascular disease to manifest disease. Nevertheless, the finding that the various conditions relate independently to HRQoL suggests that interventions at each stage of the disease process could benefit patients. The cost-effectiveness of many of those interventions remains to be determined and health policy-makers are required to produce impact assessments of costly new policies.29 Increasingly, these use QALY valuations, as do NICE technology assessments.30 The estimates produced in this study (also see Supplementary Table A1) could be used to compare the outcomes and utilities of various treatment strategy options. Commissioners and service providers may also wish to compare the outcomes of treatment.31 While this may involve adjustment for socio-demographic characteristics, there is increasing interest in addressing equity and distributional issues in such comparisons. This study demonstrates that, after adjustment for other socio-demographic factors, HRQoL is strongly associated with socio-economic position and that socio-economic position modifies the relationship between some conditions predisposing to cardiovascular disease and HRQoL. The estimates produced in this study could be used to refine modelling and forecasting which supports health inequalities reduction,32 by extending them to include HRQoL. In combination with prevalence or incidence data, the estimates could be used to help prioritize health and social care interventions.

Supplementary data

Supplementary data are available at EURPUB online.


M.St. received funding from the UK National Institute of Health Research. M.So. is funded by the Economic and Social Research Council PhD studentship. J.M. is funded by the NHS Information Centre and others to conduct the HSE and other surveys. The HSE was funded by the Department of Health until 2004 and by the NHS Information Centre since 2005. These analyses were conducted independently of the funders, who have not been involved in any way with the work or the decision to publish it. The views expressed are those of the authors, not the survey funders.

Conflicts of interest: None declared.

Key points

  • A larger deficit in HRQoL among those with some cardiovascular conditions and other conditions (obesity, hypertension and diabetes) is seen among those in lower socio-economic positions.

  • The estimates produced in this study can provide nationally representative data for England useful for modelling the impact of interventions on HRQoL.

  • They can also be used to model the impact of interventions to reduce socio-economic inequalities in health-related quality of life.


M.St. is with the MRC Unit for Lifelong Health and Ageing. J.M. is with the Department of Epidemiology and Public Health, UCL. V.P. was with the Department of Epidemiology and Public Health, UCL in 2008–09. M.St. designed the study, advised on and undertook analysis and drafted the article. M.So. conceived and designed the study, advised on analyses and helped to draft the article. V.P. carried out analysis and contributed to drafting the article. J.M. designed the study, advised on analysis and data issues and helped draft the article. All authors read and approved the final article.


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