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OP84 Explaining Ethnic Inequalities in Health: Data from a National Cross-Sectional Survey
  1. J S Mindell1,
  2. C S Knott1,
  3. M Roth1,
  4. O Manor2,
  5. V Soskolne3,
  6. N Daoud4
  1. 1Department of Epidemiology and Public Health, University College London (UCL), London, UK
  2. 2School of Public Health and Community Medicine, Hebrew University, Jerusalem, Israel
  3. 3School of Social Work, Bar Ilan University, Tel Aviv, Israel
  4. 4Epidemiology and Health Services Evaluation, Ben Gurion University, Beer Sheva, Israel

Abstract

Background Although ethnic health inequalities remain a worldwide problem, underlying factors remain contested. Theories include genetic differences, culturally-patterned behavioural disparities, disadvantageous environmental exposures, and discrimination – as a psychosocial stressor and barrier to community and remunerative resources. A conceptual model was designed to explore the association between such factors and ethnic inequalities in self-rated health (SRH).

Methods Data: The Health Survey for England 2004, a nationally representative, random general population sample of 4445 men and 5682 women, including boost samples from major minority ethnic groups in England. SRH was dichotomised into very good/good health versus fair/bad/very bad health, the latter classified as poor SRH (pSRH). Inequalities in the odds of pSRH were compared across seven ethnic groups relative to the White British population.

Analyses: Potential correlates were grouped and tested separately using age-adjusted logistic regression models. These groups included demographic (religion, marital status, household size), socio-economic (education, equivalised family income, economic activity), psychosocial (anxiety/depression, social/emotional support), and health behaviour variables (fruit/vegetable intake, smoking status, frequency of alcohol consumption and physical activity), plus community characteristics (community participation, social capital, perceived neighbourhood quality). Analyses were stratified by sex, with final models created using backward selection.

Results Indian (OR 1.80 [95% CI 1.36, 2.37], Pakistani (1.81 [1.34, 2.43]) and Bangaldeshi (2.49 [1.92, 3.24]) men had raised age-adjusted odds of pSRH. These were attenuated by adjustment for psychosocial and community factors, and rendered non-significant following adjustment for demographic factors. Black African men showed lower odds of pSHR after adjustment for socio-economic (0.57 [0.39, 0.85]) and lifestyle (0.57 [0.37, 0.86]) factors. The final model adjusted for age, education, equivalised income, household size, economic activity, anxiety/depression, smoking, and physical activity. Black African men showed lower odds (0.64 [0.42, 0.98]) while Indian men had higher odds (1.78 [1.25, 2.53]) of pSRH relative to White British men. Ethnic health inequalities were greater among women. Irish women reported better age-adjusted SRH (0.70 [0.96, 1.51]) but black Caribbean (2.19 [1.72, 2.78]), Indian (1.47 [1.15, 1.87]), Pakistani (2.46 [1.87, 3.24]) and Bangladeshi (3.07 [2.35, 4.01]) women had worse SRH than White British women. The final model (adjusted as for men, plus marital status, social capital, and neighbourhood quality) attenuated risks among Pakistani (1.57 [1.06, 2.33]) and Bangladeshi (1.63 [1.09, 2.43]) women, but had little effect on pSRH in Irish, Black Caribbean or Indian women.

Conclusion Inequality in pSRH was greatest among ethnic minority women, while differences in demographic, socio-economic and health behaviour variables accounted for most ethnic health inequalities among Indian, Pakistani and Bangladeshi men.

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