Data are described in the Data section. The presentation of the results and discussion will follow. Finally, in the last section we briefly conclude. The theoretical framework is based on socio-ecological models assuming that self-assessed health is affected by a large set of determinants at multiple levels. Being a non-European citizen or non-born in Europe, as a proxy for migrant status, is considered one of the socio-economic determinants of health acting at the individual or family level [ 19 ]. At the group level, socio-economic factors contribute to unequal social and physical environmental exposures, which increase health inequalities [ 20 ].
In this context, the aim is to test if migrant policies affect the socio-economic environment in which both migrants and non-migrants live. If individuals live in a country where there are problems in terms of granting rights to migrants, this could reasonably negatively affect the way they live and, ultimately, their health. This hypothesis is tested in the present analysis by considering country policies towards migration as a component of the social environment in which both migrants and non-migrants live. Therefore, migrant policies are introduced at the country level using a migrant integration policy variable in order to explain the observed socio-economic inequalities in health.
Migrant integration policies at country level may influence health through several pathways. They are part of the social context of the country where individuals live, and as such they can affect the health of all people living in the country. Furthermore, their specific interaction with the status of non-EU citizenship, can affect migrants health status at the individual level, such as other individual socio-economic determinants e.
The conceptual model. Source: adapted from Franzini and Giannoni [ 20 ]. We use multilevel models with a dataset of individual observations made available by Eurostat through the release of the wave of EU-SILC cross-sectional data [ 21 ]. Using multilevel models allows to estimate the proportion of the variation in health that can be explained by the social status, controlling for other determinants of health at both individual and country level, as well as country level unobserved factors [ 16 ]. Moreover, by using multilevel models it is possible to introduce simultaneously individual level variables and country level factors, such as country specific policies and attitudes towards migration.
The use of cross-sectional data has its own limitations, partially overcome by multilevel techniques. In this case, we decided not to use the longitudinal survey. The main reason is that information on the citizenship status or country of birth is limited compared to cross sectional waves, and it is not always representative at country level. Moreover, cross-sectional data are overall richer in terms of information recorded, i. For each response variable, we carried out two analyses: a global analysis and a two-step analysis.
The global analysis involves the entire study sample, whereas the two-step analysis is conducted by running separate regressions for each country using only individual level variables. Both analyses treat self-reported measures of health status as dependent variables. In the global analysis, due to the multistage sampling design used to collect the data and considering the nature of the response variables, we use two-level models with individuals nested within countries.
In the first step of the analysis, multilevel ordered mixed effects logit models are estimated for the dependent variables: self-assessed poor health and self-reported limiting severe or very severe long standing illnesses. These models allow for the estimation of the direct effect of individual-level and group-level explanatory variables, as well as interactions between levels [ 23 ]. We consider the following two-level mixed effects ordered logistic model for the dependent variable, y ij for individual i , country j.
The probability of observing outcome k for response y ij is:.
X ij are the demographic and socio-economic explanatory variables at individual level level 1 , and Z j are the explanatory variables at country level level 2. X ij does not contain a constant term because its effect is absorbed into the cutpoints. Moreover, we estimate multilevel mixed-effects logistic regression models for self-reported chronic illness. In order to analyze the differential influence of individual characteristics over health, further models are estimated adding the interactions between the ecological variables and the individual characteristics.
The first part of the analysis is based on cross-sectional micro-data from the Eurostat, EU-SILC, reference year: cross sectional [ 21 ]. Participants are adults regularly residents in European countries. We select countries for which citizenship status and country of birth is recorded and the sample is representative of the population.
Migration, Health And Inequality
The three dependent variables modeled are: self-assessed poor health, self-reported limiting long-standing illnesses and self-reported chronic illness. Respondents choose from a scale of five options: very good, good, fair, bad and very bad. SAH is one of the most widely used indicators of health in survey research, and recommended by both the World Health Organization and the European Union Commission.
Evidence shows that SAH is a strong and independent predictor of morbidity and mortality, as there is an association between SAH and mortality even after adjusting for prevalent diseases and health behavioral factors [ 24 ]. Therefore, the analysis looks at the risk factors of SAH taking into account the ordered nature of the variable. Estimates are reported for ordered logit models. To complement the analysis, we also considered other measures of health: limiting long-standing illness and chronic diseases. Respondents choose their answer among the following three options: severely limited, limited but not severely, not limited at all.
For the purpose of this study, we consider the ordered nature of the variable and estimate ordered logit models. In this case, the estimates are reported for logit models. Moreover, in order to perform the two-step analysis, responses for each of the three measures of health are condensed into a dichotomous variable. This aims to measure the extent of any limitation, for at least six months, because of health problems that may have affected respondents regarding activities they usually do the so-called GALI - Global Activity Limitation Instrument foreseen in the annual EU-SILC survey.
The indicator is therefore also called disability-free life expectancy DFLE. HLY is a composite indicator that combines mortality data with health status data. There is a noticeable variation across countries in all the three health measures. Overall, we do not observe a clear geographical gradient North—south or East—west. The individual independent variables correspond to socio-demographic age, sex, marital status and nationality and socio-economic educational level, personal income and employment status dimensions.
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The variable for low education is a dummy variable that takes the value of one if the individual attained up to a lower secondary level of education, and zero otherwise. The reference individual is a local citizen or EU born living in a country without problems in migrant integration policies. In order to measure migrant integration policies in European countries we use MIPEX data for , the latest available year of the survey [ 25 ]. Research activities are coordinated by the Migration Policy Group, in cooperation with the research partners.
MSc ( 1 year Full-time / 2 years Part-time )
Our MIPEX data cover the following six policy areas: labor market mobility, family reunion for third countries nationals, political rights, long-term residence, access to nationality, anti-discrimination policies. The index measures the number of problematic policy areas in , i. The problematic migrant policy scale can take values from 0 to 5. For example, in countries scoring the maximum value of the index, such as Latvia, political participation and anti-discrimination policies are limited, while access to citizenship is difficult, labor mobility and access policies are limited. Moreover, procedures for family reunion and long-term residence acquisition are complicated, as well as rights of access to health care.
We observe high variation across countries for all 6 areas as well as for the overall score. There is a remarkable correlation between the scores of different dimensions. The overall score more than doubles when moving from countries with problematic integration policies minimum of 33 in Latvia to countries with good levels of migrant integration maximum of 84 in Sweden.
The sub-dimensions were aggregated using a factor analysis. We also considered all sub-dimensions separately as independent variables in the model. However, the best and most parsimonious specification was obtained by using the number of problematic dimensions. This approach is also particularly useful for the interpretation of the results.
In the estimation, we included country-level variables controlling for both the health care system and the overall economy. The following country-level variables were obtained from the OECD Health Data and the Eurostat statistics [ 26 , 27 ]: the Gini index for income inequality, poverty, pollution and homicide rates, the number of hospital beds per inhabitants, the proportion of immigrants amongst residents, the Gross Domestic Product GDP per capita, total healthcare expenditure as a share of GDP, the healthy life years expectancy, and the level of corruption.
Health of Migrants in the UK: What Do We Know?
Out of these variables, only two were significant in some models, namely: the healthy years life expectancy and the healthcare expenditure as a share of GDP. Therefore, the results reported were obtained by controlling for these variables. For each dependent variable we estimated 6 models. Model 1 includes individual demographic and socio-economic determinants. Model 2 adds the country level characteristics, healthy life years expectancy and the proportion of health care expenditure over the GDP. Model 3 adds the country level variable measuring problems in migrant integration policies.
Conversely, Model 4 adds an interaction term between the non-EU citizenship or born status and the policy variable measuring the country-level number of problematic migrant policy areas. Model 5 adds both the policy variable and the interaction term with the variable measuring non-EU migrant status.
Migration and health
Estimates obtained by controlling for individuals age, gender, education, individual income, occupational status, marital status. Multilevel logit estimates for the probability of reporting chronic diseases —Year: a. For all the three measures of the health status, the probability of reporting poor health is affected by socio-economic determinants, as it is suggested by the empirical literature.
The odds of reporting poor health increase with age, and decrease with education, income, employment status, and widow, separated, divorced or single status. Working individuals, either as employee or self-employed, report better health as compared to non-working individuals.
In order to focus on the main variables of interest, the coefficients of individual demographic and SES characteristics are not reported in the tables. Model 2 adds the country level characteristics: healthy years life expectancy and the proportion of total health care expenditure over the GDP. E-mail after purchase.
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Of particular concern is the lack of nationally and regionally co-ordinated strategies to ensure treatment continuity for chronic conditions. Co-ordinated, evidence-informed responses to migration, mobility and health are urgently needed. These will have developmental and public health benefits for all. S Afr Med J ; 10 Healthy migration is good for development, but migration is rarely managed in a healthy way. A large body of evidence acknowledges that the relationship between migration, mobility and health in the region is complex: while the migrant labour system was clearly implicated in the early transmission and spread of syphilis, tuberculosis TB and HIV especially in relation to the ongoing systems of labour migration associated with the mines in South Africa SA , these dynamics have changed over time, and the association between movement and the spread of communicable diseases is less clear cut today.
Key concerns in relate to the lack of effective management of chronic conditions for those who move. This has negative implications not only on the morbidity and mortality of a highly mobile population, but also on the healthcare systems and family structures that are forced to manage the costs associated with delayed healthcare seeking.
The daily stressors that may be experienced in these spaces are increasingly acknowledged to affect emotional wellbeing and mental health. Within the SADC region as is often the case globally , health-seeking is an assumed reason for movement — yet evidence suggests otherwise; the majority move in search of improved livelihood opportunities, and to do so they need to be in good health. This is particularly the case in cities, and is mostly associated with the inability of migrants — both internal and cross-border — to access positive determinants of health in the city, a phenomenon known as the urban health penalty.
When considering the development of appropriate responses to population movements and health in the SADC, it is essential that discussions do not get twisted as they often do into debates that focus solely on cross-border migrants. As indicated above, the majority who move are internal migrants i.
When health is added to the equation, with unsupported assumptions relating to the health burden presumed to be presented by cross-border migrants and the communicable diseases they are assumed to spread, questions relating to whether non-citizens deserve public healthcare, and the rationing of healthcare services, prevail. As a result, population movement remains excluded from the development of improved health system responses to communicable and non-communicable diseases in the SADC, with negative public health consequences. This calls for the development of a co-ordinated regional response to migration and health, including cross-border referral systems and financing mechanisms.