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01.12.2016 | Research article | Ausgabe 1/2016 Open Access

BMC Public Health 1/2016

Migrant integration policies and health inequalities in Europe

Zeitschrift:
BMC Public Health > Ausgabe 1/2016
Autoren:
Margherita Giannoni, Luisa Franzini, Giuliano Masiero
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12889-016-3095-9) contains supplementary material, which is available to authorized users.

Abstract

Background

Research on socio-economic determinants of migrant health inequalities has produced a large body of evidence. There is lack of evidence on the influence of structural factors on lives of fragile groups, frequently exposed to health inequalities. The role of poor socio-economic status and country level structural factors, such as migrant integration policies, in explaining migrant health inequalities is unclear. The objective of this paper is to examine the role of migrant socio-economic status and the impact of migrant integration policies on health inequalities during the recent economic crisis in Europe.

Methods

Using the 2012 wave of Eurostat EU-SILC data for a set of 23 European countries, we estimate multilevel mixed-effects ordered logit models for self-assessed poor health (SAH) and self-reported limiting long-standing illnesses (LLS), and multilevel mixed-effects logit models for self-reported chronic illness (SC). We estimate two-level models with individuals nested within countries, allowing for both individual socio-economic determinants of health and country-level characteristics (healthy life years expectancy, proportion of health care expenditure over the GDP, and problems in migrant integration policies, derived from the Migrant Integration Policy Index (MIPEX).

Results

Being a non-European citizen or born outside Europe does not increase the odds of reporting poor health conditions, in accordance with the “healthy migrant effect”. However, the country context in terms of problems in migrant integration policies influences negatively all of the three measures of health (self-reported health status, limiting long-standing illnesses, and self-reported chronic illness) in foreign people living in European countries, and partially offsets the “healthy migrant effect”.

Conclusions

Policies for migrant integration can reduce migrant health disparities.
Zusatzmaterial
Additional file 1: Figure S1. Two-stage logit estimation results – Estimated probability of reporting limitations in daily life for non-EU migrants vs. number of problematic areas of migrant integration policies by country– year: 2012. Legend: AT = Austria, BG = Bulgaria, CH = Switzerland, DE = Germany, DK = Denmark, EE = Estonia, EL = Greece, ES = Spain, FI = Finland, FR = France, HR = Croatia, HU = Hungary, IT = Italy, LT = Lithuania, LU = Luxembourg, LV = Latvia, MT = Malta, NL = The Netherlands, PT = Portugal, RO = Romania, SE = Sweden, SK = Slovak Republic, UK = United Kingdom. Source: Graphical output obtained from Stata v.13 command mlt2scatter, using Eurostat [21, 27], OECD [26] data for 2012 and MIPEX [25] data. Results were obtained by running two-stage logit models using Stata v.13 command: mlt2stage. In the first step, separate country estimates were obtained by running logit models for the probability of reporting limitations in daily life using only individual level variables and controlling for age, gender, log(income), employment status, marital status and migrant status. In the second step the estimated slopes of the dependent variable for the non-EU citizen status from the first step were plotted against the country-level variable for problems in migrant integration policies. (PDF 71 kb)
Additional file 2: Figure S2. Two-stage logit estimation results – Estimated probability of reporting chronic conditions for non-EU migrants vs. number of problematic areas of migrant integration policies by country– year: 2012. Legend: AT = Austria, BG = Bulgaria, CH = Switzerland, DE = Germany, DK = Denmark, EE = Estonia, EL = Greece, ES = Spain, FI = Finland, FR = France, HR = Croatia, HU = Hungary, IT = Italy, LT = Lithuania, LU = Luxembourg, LV = Latvia, MT = Malta, NL = The Netherlands, PT = Portugal, RO = Romania, SE = Sweden, SK = Slovak Republic, UK = United Kingdom. Source: Graphical output obtained from Stata v.13 command mlt2scatter, using Eurostat [21, 27], OECD [26] data for 2012 and MIPEX [25] data. Results were obtained by running two-stage logit models using Stata v.13 command: mlt2stage. In the first step, separate country estimates were obtained by running logit models for the probability of reporting chronic conditions using only individual level variables and controlling for age, gender, log(income), employment status, marital status and migrant status. In the second step the estimated slopes of the dependent variable for the non-EU citizen status from the first step were plotted against the country-level variable for problems in migrant integration policies. (PDF 69 kb)
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