22. European Stroke Conference Table 1 Associations between socioeconomic status indicators and incident stroke, adjusted for groups of potential mediators Socioeconomic sta-tus indicator Model 1 OR (95% CI) Model 2 OR (95% CI) Model 3 OR (95% CI) Model 4 OR (95% CI) Model 5 OR (95% CI) Education* University degree or higher 1.00 1.00 1.00 1.00 1.00 Certificate / diplo-ma 1.54 (0.81 to 2.92) 1.44 (0.76 to 2.73) 1.41 (0.75 to 2.68) 1.52 (0.80 to 2.87) 1.35 (0.71 to 2.56) Trades and ap-prentice 2.72 (1.22 to 6.08) 2.45 (1.10 to 5.50) 2.49 (1.11 to 5.57) 2.68 (1.20 to 6.00) 2.33 (1.04 to 5.25) High school cer-tificate 1.43 (0.75 to 2.74) 1.29 (0.67 to 2.46) 1.34 (0.70 to 2.56) 1.40 (0.73 to 2.67) 1.22 (0.64 to 2.35) School certificate 1.58 (0.89 to 2.82) 1.39 (0.78 to 2.50) 1.43 (0.80 to 2.55) 1.53 (0.86 to 2.74) 1.30 (0.72 to 2.34) No formal qualifi-cations 2.57 (1.42 to 4.65) 2.03 (1.11 to 3.73) 2.20 (1.21 to 4.01) 2.34 (1.29 to 4.26) 1.80 (0.97 to 3.34) p-value 0.011 0.098 0.043 0.027 0.200 Homeowner*† Yes 1.00 1.00 1.00 1.00 1.00 No 1.99 (1.39 to 2.84) 1.78 (1.24 to 2.56) 1.85 (1.29 to 2.66) 1.78 (1.23 to 2.57) 1.63 (1.12 to 2.38) p-value <0.001 0.002 0.001 0.005 0.010 Model 1: Adjusted for age and home ownership or education Model 2: Model 2 + smoking, BMI, alcohol and physical activity Model 3: Model 2 + hypertension, diabetes mellitus, heart disease and hysterectomy/oophorec-tomy Model 4: Model2 + depression and marital status Model 5: Adjusted for all factors †Owns own home outright or has a mortgage 5 Etiology of stroke and risk factors A 15:10 - 15:20 Educational and homeownership inequalities in stroke incidence: a prospective longitudi-nal study of mid-aged women in Australia C.A. Jackson1, G.D. Mishra2 University of Queensland, Brisbane, AUSTRALIA1,University of Queensland, Brisbane, AUSTRALIA2 Background Socioeconomic status (SES) is associated with stroke risk, but age and gender dif-ferences may exist, underlying mechanisms are unclear and some measures such as homeown-ership have not been studied. We examined associations between SES and stroke and the con-tribution of lifestyle, physiological and psychosocial factors to these associations in mid-aged women. Methods We calculated odds ratios between individual measures of SES and incident stroke at 4 subsequent surveys using GEE models. Where associations were significant we de-termined the contribution of time-varying covariates, by calculating the percentage attenuation in the β coefficient, comparing baseline and adjusted models. Results Among 11,468 wom-en born 1946-51, aged 47-52 years at baseline, 177 incident strokes occurred during 12 year follow-up. Education (OR lowest vs highest 2.45, 95% CI 1.40 to 4.30) and homeownership (OR non-homeowner vs homeowner 2.10, 95% CI 1.47 to 2.99), but not occupation or manag-ing on income, were significantly associated with stroke. After full adjustment the association between education and stroke was non-significant. Lifestyle (smoking, exercise, alcohol and BMI), physiological (hypertension, diabetes, heart disease and hysterectomy/oophorectomy) and psychosocial (depression and marital status) factors explained 38% of the association in the lowest vs highest education groups. Lifestyle and physiological factors explained 25% and 17% of the association respectively, together accounting for 34%. Mediators accounted for 29% of the association between homeownership and stroke but a significant association remained in fully adjusted models (OR 1.63, 95% CI 1.12-2.38). Conclusion Lower education level is as-sociated with increased stroke risk in mid-age women, and is mainly explained by lifestyle and physiological factors. Non-homeownership in these post-war baby boomers is associated with increased stroke risk, but the underlying mechanism requires further research. 52 © 2013 S. Karger AG, Basel Scientific Programme
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