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Trends in cardiovascular disease biomarkers and their socioeconomic patterning among adults in the Scottish population 1995 to 2009: cross-sectional surveys Joel William Hotchkiss,1 Carolyn Anne Davies,1 Linsay Gray,1 Catherine Bromley,2 Simon Capewell,3 Alastair Leyland1
To cite: Hotchkiss JW, Davies CA, Gray L, et al. Trends in cardiovascular disease biomarkers and their socioeconomic patterning among adults in the Scottish population 1995 to 2009: cross-sectional surveys. BMJ Open 2012;2:e000771. doi:10.1136/ bmjopen-2011-000771 < Prepublication history and
additional materials for this paper are available online. To view these files please visit the journal online (http://dx. doi.org/10.1136/ bmjopen-2011-000771). Received 15 December 2011 Accepted 5 April 2012 This final article is available for use under the terms of the Creative Commons Attribution Non-Commercial 2.0 Licence; see http://bmjopen.bmj.com
1 Measuring Health, MRC/CSO Social and Public Health Sciences Unit, Glasgow, UK 2 Scottish Centre for Social Research, Edinburgh, UK 3 Division of Public Health, University of Liverpool, Liverpool, UK
Correspondence to Dr Joel William Hotchkiss;
[email protected]. ac.uk
ABSTRACT Objectives: To examine secular and socioeconomic changes in biological cardiovascular disease risk factor and biomarker prevalences in the Scottish population. This could contribute to an understanding of why the decline in coronary heart disease mortality in Scotland has recently stalled along with persistence of associated socioeconomic inequalities. Design: Cross-sectional surveys. Setting: Scotland. Participants: Scottish Health Surveys: 1995, 1998, 2003, 2008 and 2009 (6190, 6656, 5497, 4202 and 4964 respondents, respectively, aged 25e64 years). Primary outcome measures: Gender-stratified, agestandardised prevalences of obesity, hypertension, hypercholesterolaemia and low high-density lipoprotein cholesterol blood concentration as well as elevated fibrinogen and C reactive protein concentrations according to education and social class groupings. Inequalities were assessed using the slope index of inequality, and time trends were assessed using linear regression. Results: The prevalence of obesity, including central obesity, increased between 1995 and 2009 among men and women, irrespective of socioeconomic position. In 2009, the prevalence of obesity (defined by body mass index) was 29.8% (95% CI 27.9% to 31.7%) for men and 28.2% (26.3% to 30.2%) for women. The proportion of individuals with hypertension remained relatively unchanged between 1995 and 2008/2009, while the prevalence of hypercholesterolaemia declined in men from 79.6% (78.1% to 81.1%) to 63.8% (59.9% to 67.8%) and in women from 74.1% (72.6% to 75.7%) to 66.3% (62.6% to 70.0%). Socioeconomic inequalities persisted over time among men and women for most of the biomarkers and were particularly striking for the anthropometric measures when stratified by education. Conclusions: If there are to be further declines in coronary heart disease mortality and reduction in associated inequalities, then there needs to be a favourable step change in the prevalence of
ARTICLE SUMMARY Article focus -
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In Scotland, as in other developed countries, coronary heart disease mortality has substantially declined over time. This decline may have slowed among younger ages and there are still large socioeconomic inequalities in mortality. Examination of the secular and socioeconomic changes in biological cardiovascular disease risk factor and biomarker prevalences in the Scottish population.
Key messages -
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In Scotland, over a 14-year period since 1995, there has been a substantial increase in the prevalence of obesity with a persistence of large inequalities. At the same time, the prevalence of hypertension has changed little, while that of hypercholesterolaemia has declined, albeit from a very high level. Inequalities were generally smaller and, in the case of cholesterol in men, ill defined. Such trends can only serve to curb any further declines in coronary heart disease mortality and maintain associated inequalities.
Strengths and limitations of this study -
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This study utilised data from nationally representative surveys conducted over a 14-year period. Bias may have been introduced by declining survey response levels. Differential non-response by the socioeconomically disadvantaged may lead to an underestimation of the magnitude of inequalities.
cardiovascular disease risk factors. This may require radical population-wide interventions.
Hotchkiss JW, Davies CA, Gray L, et al. BMJ Open 2012;2:e000771. doi:10.1136/bmjopen-2011-000771
1
Cardiovascular biomarker patterns INTRODUCTION The rapid absolute decline in Scottish coronary heart disease (CHD) mortality since the 1970s1 has slowed from 1994 onwards for individuals 3.10
1998
*Negative lower limits have been set to zero. yAggregated data set. zA different analyser was used for fibrinogen in 2008/2009. M, manual; NM, non-manual; SII, slope index of inequality.
SII (p value)
e
I, professional
SII (p value) C reactive protein Threshold for top quintile (mg/l) All e
e
I, professional
Fibrinogen Threshold for top quintile (g/l)z All e
1995
18.9 (15.8 to 22.1) 4.1 (0 to 10.1) 19.5 (13.6 to 25.4) 25.1 (17.8 to 32.4) 17.4 (12.7 to 22.1) 15.9 (9.3 to 22.5) 34.6 (26.8 to 42.4) 8.9 (0.43)
>3.71
19.1 (15.6 to 22.6) 14.1 (3.8 to 24.4) 13.2 (7.3 to 19.0) 16.8 (8.0 to 25.7) 20.5 (14.6 to 26.4) 16.5 (8.4 to 24.6) 39.6 (24.8 to 54.4) 13.6 (0.15)
>3.40
2008/2009y
Proportion in highest quintile (%) (95% CI)*
Men
e e
0.3 (0.31) 0.7 (0.51)
0.5 (0.56)
e
e
0.2 (0.92)
0.9 (0.72)
e
e
0.2 (0.90) 0.4 (0.24)
e
e
0.1 (0.08)
0.0 (0.95)
1.9 (0.15)
e
0.7 (0.58)
e
0.6 (0.74) e
e
0.1 (0.84)
0.0 (0.97)
e
e
1995
0.5 (0.39)
0.0 (0.75)
Trend (p value)
Women
20.0 (18.3 to 21.7) 7.5 (1.6 to 13.4) 18.8 (15.6 to 22.1) 19.5 (16.6 to 22.5) 23.5 (17.5 to 29.5) 23.1 (19.0 to 27.3) 19.9 (13.5 to 26.2) 6.7 (0.14)
>4.30
18.0 (16.3 to 19.7) 23.3 (15.8 to 30.7) 14.1 (11.2 to 17) 19.5 (16.5 to 22.6) 19.8 (14.3 to 25.3) 19.9 (15.9 to 23.9) 19.8 (13.6 to 26.1) 6.4 (0.15)
>3.30
1998
19.4 (17.3 to 21.5) 16.8 (9.2 to 24.4) 16.4 (12.9 to 20.0) 19.4 (15.6 to 23.2) 16.9 (10.0 to 23.8) 22.6 (17.8 to 27.4) 19.1 (11.8 to 26.3) 6.4 (0.06)
>4.80
17.9 (15.8 to 20.1) 10.9 (3.3 to 18.5) 17.4 (13.5 to 21.3) 16.3 (12.6 to 20.1) 20.9 (12.1 to 29.8) 20.2 (15.0 to 25.3) 23.9 (15.2 to 32.7) 7.3 (0.08)
>3.60
2003
19.5 (16.1 to 22.8) 12.2 (2.2 to 22.2) 17.1 (12.4 to 21.9) 22.2 (15.5 to 28.8) 23.9 (10.1 to 37.7) 19.1 (11.0 to 27.3) 20.7 (4.8 to 36.6) 7.2 (0.11)
>4.80
19.4 (15.9 to 23.0) 18.4 (5.0 to 31.7) 15.9 (11.1 to 20.8) 27.0 (19.3 to 34.6) 13.8 (3.2 to 24.4) 21.0 (11.3 to 30.7) 13.0 (1.0 to 25.1) 7.1 (0.45)
>3.60
2008/2009y
Proportion in highest quintile (%) (95% CI)*
0.1 (0.56)
0.1 (0.64)
0.4 (0.23)
0.1 (0.95)
0.3 (0.35)
0.2 (0.53)
0.4 (0.68)
0.1 (0.41)
0.1 (0.51)
0.7 (0.56)
0.1 (0.15)
0.6 (0.41)
0.7 (0.51)
0.2 (0.64)
0.4 (0.76)
0.1 (0.35)
Trend (p value)
Table 9 Gender-specific, age-standardised prevalences for elevated plasma fibrinogen and serum C reactive protein concentrations in five Scottish Health Surveys 1995, 1998, 2003 and 2008/2009 stratified by occupational social class
Cardiovascular biomarker patterns
15
Cardiovascular biomarker patterns contributed to the decline as interviewers, after enrolling one individual, may have been disincentivised to pursue additional householders. Responders to health examination surveys tend to be older, women, of higher socioeconomic class and live healthier lifestyles.49 The 2003 SHeS response was modestly skewed towards those in less deprived areas.50 A differential response could lead to underestimates for most risk factors in the lower socioeconomic groups, which suggests that true inequalities are even greater than shown here, particularly so for the most recent surveys. Measurement error will have contributed to bias in these analyses, as will the need to use predicted Omron readings in the first two surveys, the change in laboratories between 1995 and 1998 and the change in fibrinogen analyser between 2003 and 2008/2009. However, the laboratories did use the same methods and reference standards16 and a high correlation has been demonstrated between the different analysers.51 These limitations mean caution should be exercised when interpreting prevalence trends but should not greatly distort comparison of socioeconomic gradients. The cross-sectional nature of these surveys does not capture the lifelong burden of these risk markers within individuals, something that might have a considerable effect on subsequent CVD. The increase over time in the proportion of individuals with intermediate educational qualification may in part be a consequence of the subtle changes in how educational achievement was determined across the surveys. These changes would have had no impact on those with no qualifications or those with the highest qualifications. A substantial component of this increase is likely to be due to the rise in the school staying on rates beyond compulsory education seen since the 1970s in Scotland, where intermediate qualifications would be achieved.52 Inconsistency between the socioeconomic measures highlights the benefits of using both education and social class and why they should not be used interchangeably.53 54 There were sometimes apparent differences in inequalities depending on the measure used. This may reflect the imperfection of these proxies as measures of deprivation or it may demonstrate the underlying heterogeneity of inequalities. Further research would be valuable. Education reflects the longterm influence of early life circumstance and not only strongly determines an individual’s occupation, implying some interdependency with social class, but also provides the knowledge and life skills required to adopt healthier lifestyles and maximise access to health resources.55 Alternative indices such as area of deprivation, income and NS-SEC were not consistently available across these surveys. The use of NS-SEC-derived social class in the last three surveys may have led to the misclassification of individuals. This, combined with the shift over time in the composition of social classes and education, may have led to some of the observed alterations in prevalence patterning. However, the SII accounts for the size 16
of the total population and changes in the distribution of groupings making comparison over time possible. One disadvantage of the SII is that it assumes a linear relationship across the socioeconomic groups, something that may not necessarily be true. The low numbers in the highest and lowest categories may have led to potentially volatile individual estimates, especially in the last two surveys. This was particularly a problem in the 2008/2009 surveys among men with no qualifications in the younger age groups and this may have led to imprecise age-standardised estimates for the lowest educational category and care should be taken in their interpretation. Small changes in risk factor prevalence at the population level could elicit substantial reductions in CHD mortality.56 57 Interventions to achieve such reductions can centre on targeting high-risk individuals,21 such as the Keep Well programme58 that is aimed at increasing the rate of health improvement among high-risk individuals in the most deprived communities in Scotland. It is too early to assess the impact of this programme on biomarkers for CVD risk but its proposed expansion may increase its effectiveness. Altering the health behaviours of the whole population may have greater impact.59 60 In Scotland, there are a number of population-based initiatives aimed at improving health behaviours and consequently the biomarkers for CVD. These include a tobacco control action plan,61 a national physical strategy,62 healthy eating advice63 and an action plan relating to obesity.64 If best practice interventions to reduce classic risk factors were successful, most of the absolute socioeconomic differences in CHD mortality could be eliminated.12 Elimination of relative inequalities may require interventions to be targeted at the socioeconomically disadvantaged (eg, the Keep Well programme) or possibly by improving the circumstances that create social inequality.65 66 The increase in obesity prevalence is probably the driver behind the rise in prevalence of diabetes reported in Scotland over the same period.13 In contrast, there has not been a comparable increase in hypertension and cholesterol prevalence, as might be expected. This disassociation between obesity and other risk factors could be due to interventions, such as medication, ameliorating the adverse effects of obesity. It has been shown that obese people in the current century, although having higher risk factor levels than the nonobese, have a lower prevalence of high cholesterol, high blood pressure and smoking than obese individuals 40 years ago.67 68 All the above could contribute to explanations as to why overall CHD death rates have continued to fall, despite increasing rates of obesity. If obesity had not increased, there might have been a decline in hypertension prevalence and a greater reduction in hypercholesterolaemia. It is sobering to consider that any gains in CHD mortality by reduction in some risk factors may be offset by additional deaths from obesity and diabetes. These trends could certainly
Hotchkiss JW, Davies CA, Gray L, et al. BMJ Open 2012;2:e000771. doi:10.1136/bmjopen-2011-000771
Cardiovascular biomarker patterns contribute to the observed levelling or slowing of the decline among younger groups. The lack of progress in reducing risk factor socioeconomic inequalities can only ensure such differentials in CVD mortality will remain in the Scottish population. More effective population-wide policies are now urgently required. Contributors JWH drafted the manuscript, with contributions from all other co-authors. AL and JWH conceived the study design with contributions from the other authors. JWH did all the analyses. JWH is guarantor. All authors had full access to all the data in the study and can take full responsibility for their integrity and the accuracy of their analysis. Funding The Social and Public Health Sciences Unit is jointly funded by the Medical Research Council and the Chief Scientist Office of the Scottish Government Health Directorate. This research was funded by the Chief Scientist Office as part of the ‘Measuring health, variations in health and determinants of health’ programme, MC_US_A540_0001. The sponsor had no role in study design or in the collection, analysis and interpretation of data and the writing of the article and the decision to submit it for publication.
14.
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16. 17. 18.
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20.
Competing interests None. Ethical approval No ethical approval was required for this study, which was based on publicly available, anonymised Scottish Health Survey data sets.
21.
Provenance and peer review Not commissioned; externally peer reviewed. Data sharing statement The Scottish Health Survey data sets used in these analyses are available from the Economic and Social Data Service Data Catalogue http://www.esds.ac.uk/findingData/shlsTitles.asp.
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Hotchkiss JW, Davies CA, Gray L, et al. BMJ Open 2012;2:e000771. doi:10.1136/bmjopen-2011-000771
