Educational inequalities in risky health behaviours in 21 European countries: Findings from the European social survey (2014) special module on the social determinants of health

Abstract

Tim Huijts1, Anna Gkiouleka1, Nadine Reibling2, KatieH. Thomson3, Terje A. Eikemo4, Clare Bambra3 1 Department of Sociology, Wentworth College, University of York Heslington, York, UK 2 University of Siegen, Siegen, Germany 3 Institute of Health & Society, Faculty of Medical Sciences, Newcastle University, UK 4 Department of Sociology and Political Science, Norwegian University of Science and Technology, Dragvoll, Trondheim, Norway Correspondence: Tim Huijts, Department of Sociology, Wentworth College, University of York, Heslington, York YO10 5DD, UK, e-mail: tim.huijts@york.ac.uk Background: It has been suggested that cross-national variation in educational inequalities in health outcomes (e.g. NCDs) is due to cross-national variation in risky health behaviour. In this paper we aim to use highly recent data (2014) to examine educational inequalities in risky health behaviour in 21 European countries from all regions of the continent to map cross-national variation in the extent to which educational level is associated with risky health behaviour. We focus on four dimensions of risky health behaviour: smoking, alcohol use, lack of physical activity and lack of fruit and vegetable consumption. Methods: We make use of recent data from the 7th wave of the European Social Survey (2014), which contains a special rotating module on the social determinants of health. We performed logistic regression analyses to examine the associations between educational level and the risky health behaviour indicators. Educational level was measured through a three-category version of the harmonized International Standard Classification of Education (ISCED). Results: Our findings show substantial and mostly significant inequalities in risky health behaviour between educational groups in most of the 21 European countries examined in this paper. The risk of being a daily smoker is higher as respondents' level of education is lower (Low education (L): OR= 4.24 (95% CI: 3.83-4.68); Middle education (M): OR = 2.91 (95% CI: 2.65-3.19)). Respondents have a lower risk of consuming alcohol frequently if they have a low level of education (L: OR= 0.59 (95% CI: 0.54- 0.64); M: OR= 0.70 (95% CI: 0.65-0.76)), but a higher risk of binge drinking frequently (L: OR= 1.29 (95% CI: 1.16- 1.44); M: OR= 1.15 (95% CI: 1.04-1.27)). People are more likely to be physically active at least 3 days in the past week when they have a higher level of education (M: OR= 1.42 (95% CI: 1.34-1.50); H: OR= 1.67 (95% CI: 1.55- 1.80)). Finally, people are more likely to consume fruit and vegetables at least daily if they have a higher level of education (fruit: M: OR= 1.09 (95% CI: 1.03-1.16); H: OR= 1.77 (95% CI: 1.63-1.92); vegetables: M: OR = 1.34 (95% CI: 1.26-1.42); H: OR= 2.35 (95% CI: 2.16-2.55)). However, we also found considerable cross-national variation in the associations between education and risky health behaviour. Conclusions: Our results yield a complex picture: the lowest educational groups are more likely to smoke and less likely to engage in physical activity and to eat fruit and vegetables, but the highest educational groups are at greater risk of frequent alcohol consumption. Additionally, inequalities in risky health behaviour do not appear to be systematically weakest in the South or strongest in the North and West of Europe.