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Socioeconomic gradients in coronary heart disease - the relative role of lifestyle. Linsay Gray 1 , Julie Armstrong 2 , Yvonne Brogan 2 , Andrea Sherriff 3 , Catherine Bromley 4 , Alastair H Leyland 1 1 MRC/CSO Social and Public Health Sciences Unit, Glasgow, UK
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Socioeconomic gradients in coronary heart disease - the relative role of lifestyle Linsay Gray1, Julie Armstrong2, Yvonne Brogan2, Andrea Sherriff3, Catherine Bromley4, Alastair H Leyland1 1 MRC/CSO Social and Public Health Sciences Unit, Glasgow, UK 2 School of Life Sciences, Glasgow Caledonian University, Glasgow, UK 3 Department of Dentistry and Medicine, University of Glasgow, Glasgow, UK 4 Scottish Centre for Social Research, Edinburgh, UK Royal Statistical Society Edinburgh local group talk, 15 November 2011
Background • CHD strongly correlated with SES • Lifestyle factors impact on health and are also linked with SES • Thus may drive the CHD-SES associations • Individual and combined lifestyle contributions to such inequalities not well quantified
Aims • To quantify contributions of lifestyle factors to social inequalities in CHD • Individual and combined
Data 6,060 participants >16 years in 2003 Scottish Health Survey (60% response) providing data on also consenting to linkage of mortality and hospital records ‘til 2008 • SES (social class) • Smoking status (current and previous) • Physical activity levels • Diet (quality index) next slide • BMI and • Alcohol consumption (weekly intake) • CHD death or admission
Diet quality index -DQI >20 foods Summary measure scores based on recommendations types and frequency Fish, red meat and products Starchy foods High fibre foods Sugary foods Fatty foods Fruit and vegetables (Alcohol) Armstrong, J. et al. (2009).
Statistical methods • Cox proportional hazards regression 1: Ascertain prediction of CHD event by SES adjusting for age only 2: Investigate degree of attenuation of the association of CHD with SES by lifestyle • Assess using Relative Index of Inequality (RII)
Relative Index of Inequality (RII) Commonly used measure of extent to which occurrence of an outcome - such as CHD - varies with risk factors such as SES Measures relative disparity by summarizing relative risk for extremes Calculation Rank values Scale ranks from 0 to 1 Analyse as a covariate in usual way Obtain estimates for a “one unit increase”
CHD and baseline social class data • 35,523 person-years of follow-up • 213/6,060 (4%) CHD events; including 59 (28%) deaths;
Lifestyle factors Mean SD Dietary quality index 52 17 BMI 27 kg/m2 5.3 kg/m2 Alcohol units/week 14 units 21 units
Lifestyle factor associations with SES and CHD SES CHD Smoking status all p<0.001 Never Ex-occ 0.287 Ex-reg <0.001 Current 0.003 Physical activity Low Medium <0.001 High <0.001 Dietary quality index 0.50 BMI 0.011 ln(Alcohol units/week) 0.004
Proportional hazards check p=0.539
CHD and social class Cox regression results Sex interaction p = 0.254
CHD and social class Cox regression results- attenuation by smoking
CHD and social class Cox regression results- attenuation by physical activity
CHD and social class Cox regression results- attenuation by diet
CHD and social class Cox regression results- attenuation by BMI
CHD and social class Cox regression results- attenuation by alcohol
CHD and social class Cox regression results- attenuation by all lifestyle factors
Strengths and limitations • Reasonable sample size • Covers the (home-dwelling) general population of Scotland • High linkage consent (>90%) • Range of covariates • Emigration – lost to follow-up but low levels • Excludes those living in communal establishments • e.g. • prisons • residential care • Bias from survey response (67% household; 60% individual)
Conclusions Adjusting for lifestyle factors attenuates association by over 50% Individually, greatest impacts made by smoking (31%), physical activity (15%) and diet (14%) Little impact of BMI and alcohol Valuable insight for tackling socio-economic health inequalities Importance of physical activity and healthy eating as well as smoking What else? Structural/environmental factors
Acknowledgements • Data were provided by • ISD • Funding was provided by the Chief Scientist Office for Scotland • Thanks to Joan Corbett of ScotCen for help with data queries
Thank you for your attention • Contact me on l.gray@sphsu.mrc.ac.uk for further information