Evidence on health inequality and multiple discrimination Alessio D’Angelo, Lecturer in Social Sciences, Middlesex University

Major EU-level datasets A large number of EU datasets focusing on health, but: ▫ Little focus on access to health and treatment ▫ Not fit for intersectional analysis EU-SILC the most useful source for our study (special datasets have been commissioned) Other major Eurostat datasets include: ▫ Health Interview Survey (EHIS); ▫ SHARE (Survey on Health, Ageing and Retirement in Europe) ▫ EU Labour Force Survey (LFS) ▫ EU-MIDIS (FRA)

National datasets and Surveys Single countries have a diverse range of data sources, e.g.: ▫ Austria: Health Survey is rich in data on health status and care, but no data on disability due to legal restrictions ▫ Czech Republic: IHIS collects data on residents, but not on ethnicity/migrants (but there have been surveys on e.g. Roma) ▫ Italy: Multiscopo includes data on migrants, but the sample size is limited. Data on disability not available for migrants. ▫ Sweden: Living Conditions Survey publishes data on broad groups of immigrants as well as second generation ▫ UK: Health Survey 1999 and 2004 focused on ethnicity. National LFS includes data on health and ethnicity.

Equality ‘strands’ in EU datasets Most EU datasets include data on: Age and Gender Disability: proxy variables are collected (e.g. LLTI), but no survey adopts WHO definition. Mostly self-assessed. Ethnicity: Not available at EU-level (collected systematically only in the UK). Data on ‘citizenship’ and ‘country of birth’ available but mostly limited. Even when variables are collected, samples are usually not fit for disaggregation and thus not disseminated.

Inequalities: Health condition Data largely based on self-assessment ‘Healthy migrant effect’ vs ‘unhealthy’ ethnic minorities EU-SILC: no health gap? (need to go beyond the averages) Table a4 - Share of foreign born population perceiving their health status as good, fair or poor (Source: SILC 2009)

Inequalities: Disability Different definitions lead to different results (SILC vs LFS) Strong correlation with age (differences between countries) Women have slightly higher rates of ‘limitation in activities’ SILC: higher ‘limitation in activities’ among migrants (though no significant intersection with gender) Table a8 - Limitation in activities because of health problems(Source: SILC 2009)

Life expectancy and mortality Life-expectancy gender-gap: F 82.4 ; M 76.4 (EU 2009) This is due to mortality gaps in relation to specific causes Healthy life-expectancy at age 65 varies across the EU No EU-data on migration. Some evidence from individual countries indicate a complex scenario. Recent EU data identify socio-economic status as key factor Most EU data also indicate higher infant mortality rates for some migrant groups (and especially refugees)

Morbidity and mental health Gender differences in most major diseases are due to both genetic and behavioural factors, as well as treatment. Older people suffer disproportionately from the effects of infectious diseases, cancer, musculoskeletal disorders etc. Differences among migrants are due to: country of origin, socio-economic deprivation, discrimination in treatment. Migrants disproportionately face mental health issues due to: the migratory experience, social exclusion, different cultural attitudes towards mental health

Inequality in access to healthcare Access to health care is uneven across social groups, place of residence, ethnic group and gender (EGGSI 2009) Evidence from the review highlight inequalities in terms of: ▫ Gender: stereotypes on gendered-behaviour, financial barriers ▫ Age: entitlement to free treatment, staff attitudes and prejudice ▫ Age&Ethnicity: lack of intercultural competence and language EU-SILC collects data on ‘unmet health needs’ and ‘main reason’ (n.b. our analysis included both total migrant population and specific nationalities, but sample size is too small to be reliable)

Unmet needs – disability and gender Evidence on people with disability is very limited, but our SILC analysis indicates higher rates of ‘unmet needs’. Slightly lower rates among men than women Also more likely to give ‘couldn’t afford’ as reason for unmet need Table b1 - EU27: Unmet health needs by ‘disability’ and gender (Source: SILC 2009)

Unmet needs - migrants Foreign-born overall more likely to report unmet needs (but major differences between EU countries) Inability to pay and fear of health services more often a cause In some cases migrant women have highest unmet needs rates Table b3 - % Unmet need for medical examination or treatment (Source: SILC 2009)

Reproductive health and Mental health In both cases, no EU-wide dataset, but ‘patchy’ evidence Reproductive health for migrant women: ▫ Austria: lower uptake of gynaecological health care ▫ Italy: lower access to private gynaecologists ▫ UK more likely to access services later in pregnancy Mental health for ethnic minorities (very limited data) ▫ Language often reported as major barrier (e.g. Austria) ▫ But also cultural differences and biases among practitioners (e.g. high rates of coercive admission for black men in UK)

Respect, discrimination and user satisfaction Limited data and of difficult interpretation FRA MIDIS: only EU dataset on perceived discrimination Significant differences between countries and groups Table b4 - Discrimination in the past 12 months (Source: EU-MIDIS)

Conclusions EU datasets are not fit for intersectional analysis Data on ethnicity (or proxy) and disability is very limited At this stage, analysis of large survey data must be integrated with review of case study research Generalisation is often neither possible nor desirable E.g. significant results would emerge only when disaggregating in terms of individual nationalities