This study analyses the relationship between immigrants’ self-reported/rated health (SRH) and their perceived working conditions in Czechia materialized via discrimination, based on the example of Ukrainian immigrants analyzed by gender dimension. The role of age, education, and marital status is also analyzed. A sample of native-born Czechs serves as a reference frame. A cross-sectional design was applied. Using data from two surveys of Ukrainian immigrants in Czechia and a countrywide health interview survey for Czechs, we analyse inequalities in SRH and workplace discrimination loads. Four binary logistic regression models were computed separately for women and men from Ukraine and Czechia to identify the determinants of fair/poor SRH. We found that only Ukrainian immigrant females were heavily exposed to all four measured types of workplace discrimination, thereby modifying and worsening the quality of their SRH. Determinants which are behind respondents’ SRH differ between Ukrainian immigrants vis-à-vis Czechs with one exception. The “oldest age group” (41–62) contributes to poorer assessment of SRH among Ukrainian females, Czech females, and Czech males too. The lowest educational level (primary education) correlates with poor SRH within the sample of Czech males.
Health matters associated with international migration in general, and the health of migrants in particular, are crucial public health challenges faced by governments and societies [
The most common indicators reflecting a person’s health are based on self-rated health (SRH). Knowledge about SRH can help us understand the health status and needs of migrants. For example, in a recently published study by [
The globalization of migration flows has increased multicultural diversity, which also importantly contributes to posing “significant challenges for both policy and research” [
The nature of the association between health status and work discrimination has repeatedly been demonstrated for various populations [
International migration movements have increased in size and complexity across most European countries [
The main goal of this paper is therefore to analyse the inequalities relationship between SRH and perceived discrimination in working life based on the example of the immigrant population from Ukraine (see more below). Since migrants’ health is one of the most inadequately researched areas from a gender perspective, we will pay special attention to this aspect.
Since the Velvet Revolution, a total transformation of the society—from a socialist/communist regime to a democratic, parliamentary system based on a free market economy—has started in Czechia. This transformation process, along with ongoing globalization and the shift towards a postmodern society, has brought significant inflows of immigrants (438,000 foreigners at the end of 2012) [
Studies have begun to look at various aspects of immigrants’ integration in Czechia, albeit focusing predominantly on economic ones [
It seems that topics like differences in the health of migrant populations, in the health care which is being provided, or the working conditions in which migrants work are so far unknown, not well understood, underestimated, or often even ignored. Dealing with migration specifics is one of the challenges for policy and policymakers to balance disparities in labour and social rights. Indeed, all European countries with a substantial percentage of migrants in their population should consider adopting specific migrant health policies [
Generally, current research has looked at the effects of perceived discrimination and confirmed that it is a risk factor for multiple health outcomes [
Pascoe and Richman [
Agudelo-Suárez et al. [
The health of migrants is becoming very important in Czechia too—a country with very limited experience of dealing with immigrants, in general, and with immigrants’ health, in particular.
Our study is based on testing three basic hypotheses. First, we assume that immigrants who perceive that they are exposed to some type of workplace discrimination/loads would be more likely to report poor health than immigrants not exposed to workplace discrimination. The second hypothesis is that immigrants will differ from the Czech majority population in determinants which modify their poor health. The third hypothesis is based on the thesis that all the above realities will be significantly differentiated by gender dimension.
We applied a quantitative approach using a questionnaire survey as our main research tool. For the purpose of this study we combined two surveys that have recently targeted Ukrainian migrants in Czechia (both were supported by the Czech Science Foundation and we were involved in both (the project “Migration and Development-economic and social impacts of migration upon Czechia as a destination country and Ukraine as a country of origin (with special regard to analysis of remittances),” number P404/10/0581, was finished in 2012 whereas the project: “Migratory patterns of immigrants (and natives) in Czechia: concentration or diffusion processes?” number P404/12/1014, is still ongoing). Both of them, while having different main goals and partly diverse selection criteria, also included some common characteristics and questions related to ascertaining SRH and discrimination issues. Thus, by combining them we gained one robust sample, although at the expense of losing the possibility to include a wide variety of characteristics in our analysis. Furthermore, to be able to better interpret the results, we also work with a reference sample from the Czech population, which provided the information about the same relevant health/discrimination issues.
For the Ukrainian immigrant group, we used results of the following two surveys. In the first questionnaire survey, a total of 321 Ukrainian respondents were successfully contacted between May and October 2012. We excluded subjects with missing data, who were not working or were below 18 or older than 62 years and we analysed data for the remaining 228 respondents. A “snow ball” sampling method was applied. First, when searching for respondents we used our own contacts. Second, we also contacted potential respondents via the established Ukrainian community, namely, their firms, churches, schools, and ethnic associations. Some relevant intergovernmental and nongovernmental organizations were also asked for assistance. The survey was carried out in the capital city of Prague and the Central Bohemia region. Only citizens of Ukraine who had been in Czechia for more than 6 months at the moment of the survey (shorter visits to Ukraine were tolerated but for no longer than 1 month in total) and those who, at the same time remitted money, were qualified to take part in the survey. One family could be represented by only one adult member. In collecting data, there was an attempt to get a heterogeneous group of various types of respondents/migrants mainly in terms of characteristics like sex, age, educational level, and profession. Filling in the questionnaire with the given respondents was arranged and carried out on the spot (“face-to-face” contact; PAPI form—paper and pencil interview) by members of the Czech research team GEOMIGRACE (the team is affiliated with Charles University, Faculty of Science, Department of Social Geography and Regional Development (see: A questionnaire survey of two immigrant groups in Czechia—Ukrainians and Vietnamese—was carried out between March and May 2013 and, in this case, a total of 912 respondents were successfully contacted. In this study, however, we use only the subsample of working Ukrainians, which includes information about 342 Ukrainians aged between 18 and 62 years. For the sampling method, quota sampling was applied with quota characteristics (based on 2011 Census data) represented by sex, age, and region of migrant’s residence. Within the sample, immigrants who had already been in Czechia for more than 1 year could participate. Those who had moved at least once inside Czechia were intentionally overrepresented (the main research task of the project was to research internal mobility of immigrants). The data were collected via the interviewers’ network of The Centre of Independent Public Opinion Research (under the umbrella of the Institute of Sociology of the Academy of Sciences of the Czech Republic). The aim of the interviewers was to find appropriate respondents (they were supported by information about locations where immigrants may be possibly concentrated), to distribute questionnaires, and then to collect them. The respondents filled in the questionnaire (in self-administered mode) in their mother language and it took on average about 45 minutes. Though the main concern of this research task was primarily concentrated on immigrants’ internal mobility and related aspects of their lives, issues relevant to immigrants’ health were partly covered too (including SRH and discrimination issues). Data for Czech nationals were extracted from the 2008 European Health Interview Survey (EHIS) carried out by the Institute of Health Information and Statistics of the Czech Republic. From June to October 2008, 1,955 persons aged 15 and over were interviewed from a sample of 3,825 subjects originally selected from the Ministry of Interior Civil Registration Information System, through a two-stage stratified selection, with municipalities being the first stage and respondents the second stage. Data collection was conducted through personal interviews (PAPI form—paper and pencil interview). For the purpose of this study we used data for Czech-born working nationals aged between 18 and 62 (same minimum-maximum as the survey of Ukrainians), including a total of 928 persons.
To sum up, as already mentioned, this study is based on data from three questionnaire surveys. The studied population consisted of 570 immigrants from Ukraine who were living and working (employment and entrepreneurship) in Czechia and of 928 Czechs. The total number of respondents in our sample was 1,498.
The dependent variable, SRH, was measured on a 5-point scale ranging from very good to very poor (the question was “How is your health in general?”) and was categorized as “Good” (good/very good) or “Fair/poor” (fair/poor/very poor).
As for the independent variables, we used sociodemographic and workplace characteristics. Age was categorized into three age groups: 18–30 years, 31–40 years, and 41–62 years. Education was categorized as basic (or less), complete secondary education, and complete university education. In relation to marital status, people were classified as married/cohabitating and other possibilities (single, divorced, or widowed).
Perceived workplace environment was determined by answers to the following four questions. “At your workplace, to what extent are you exposed to discrimination (as such)— violence or threat of violence— time pressure or overload of work— risk of accident—
Possible answers were not exposed, occasionally/slightly, repeatedly, regularly/strongly, do not know, or refusal and were categorized as “yes, exposed” (if he/she was exposed) or “no exposure.”
The data were organized into a database and further analyzed using SPSS 18 software. The basic descriptive statistics were obtained. We analysed sociodemographic and workplace variables in the four subgroups (Czech males and females and Ukrainian males and females). In the next steps of the analysis, binary logistic regression was applied. Multivariate odds ratios (OR) for fair/poor SRH (1—fair/poor health and 0—good health) were calculated and 4 logistic regression models fitted separately for women and men from Ukraine and Czechia to identify the determinants of fair/poor SRH. We added three sociodemographic characteristics (age, education level, and marital status) and one summarizing workplace environment coded 0–4. 0 indicates that the individual was not exposed to any workplace discrimination load (“partial discrimination”); the value 4 is loaded with all four selected workplace discrimination loads.
To sum up, to our knowledge, this is one of the first studies with sufficient data (a more robust sample, albeit not gained through a probability sampling method) to investigate the effects of work discrimination and other selected sociodemographic characteristics on the SRH of immigrants, specifically, immigrants from Ukraine. The disadvantage of this novelty is that there are not as yet any similar results in Czechia that we can refer to or we can compare with.
A total of 1,498 working individuals (age 18–62 years) were analyzed in the study. Out of this sample, 38.1% of respondents came from Ukraine and 61.9% from Czechia. Table
Sample characteristics, workplace exposition, and prevalence of fair/poor self-rated health.
Study sample | Prevalence of fair/poor health | |||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Males | Females | Total | Males | Females | Total | |||||||||||||||
Ukrainian | Czech | Ukrainian | Czech | Ukrainian | Czech | Ukrainian | Czech | |||||||||||||
|
% |
|
% |
|
% |
|
% |
|
% |
|
% |
|
% |
|
% |
|
% |
|
% | |
Sociodemographic characteristics | ||||||||||||||||||||
Age groups | ||||||||||||||||||||
18–30 | 72 | 19,6 | 125 | 24,0 | 59 | 29,2 | 88 | 21,6 | 344 | 23,0 | 12 | 16,9 | 15 | 12,0 | 8 | 13,8 | 12 | 13,6 | 47 | 13,7 |
31–40 | 130 | 35,3 | 160 | 30,7 | 63 | 31,2 | 120 | 29,5 | 473 | 31,7 | 24 | 18,5 | 26 | 16,3 | 13 | 20,6 | 24 | 20,0 | 87 | 18,4 |
41–62 | 162 | 44,0 | 236 | 45,3 | 79 | 39,1 | 199 | 48,9 | 676 | 45,3 | 40 | 24,7 | 72 | 30,5 | 36 | 46,2 | 70 | 35,2 | 218 | 32,3 |
Education level | ||||||||||||||||||||
Primary | 174 | 47,3 | 263 | 50,5 | 60 | 29,7 | 160 | 39,3 | 657 | 44,9 | 44 | 25,4 | 70 | 26,6 | 17 | 28,3 | 50 | 31,3 | 181 | 27,6 |
Secondary | 136 | 37,0 | 187 | 35,9 | 81 | 40,1 | 194 | 47,7 | 598 | 40,8 | 22 | 16,2 | 31 | 16,6 | 19 | 23,5 | 47 | 24,2 | 119 | 19,9 |
University | 54 | 14,7 | 58 | 11,1 | 57 | 28,2 | 40 | 9,8 | 209 | 14,3 | 9 | 16,7 | 9 | 15,5 | 20 | 36,4 | 7 | 17,5 | 45 | 21,7 |
Marital status | ||||||||||||||||||||
Married | 221 | 60,1 | 308 | 59,1 | 94 | 46,5 | 252 | 61,9 | 875 | 58,5 | 49 | 22,3 | 76 | 24,7 | 31 | 33,0 | 70 | 27,8 | 226 | 25,9 |
Other | 147 | 39,9 | 213 | 40,9 | 108 | 53,5 | 154 | 37,8 | 622 | 41,5 | 27 | 18,4 | 37 | 17,4 | 26 | 24,5 | 36 | 23,4 | 126 | 20,3 |
|
||||||||||||||||||||
Exposure at the workplace | ||||||||||||||||||||
Discriminations | ||||||||||||||||||||
Not exp. | 242 | 65,8 | 487 | 93,5 | 118 | 58,4 | 364 | 89,4 | 1 211 | 83,9 | 46 | 19,1 | 100 | 20,5 | 25 | 21,6 | 92 | 25,3 | 263 | 21,8 |
Exposed | 106 | 28,8 | 21 | 4,0 | 77 | 38,1 | 29 | 7,1 | 233 | 16,1 | 26 | 24,5 | 10 | 47,6 | 29 | 37,7 | 11 | 37,9 | 76 | 32,6 |
Violence or threat of violence | ||||||||||||||||||||
Not exp. | 304 | 82,6 | 491 | 94,2 | 166 | 82,2 | 385 | 94,6 | 1 346 | 93,7 | 60 | 19,8 | 105 | 21,4 | 41 | 25,0 | 100 | 29,4 | 306 | 22,8 |
Exposed | 44 | 12,0 | 17 | 3,3 | 21 | 10,4 | 8 | 2,0 | 90 | 6,3 | 11 | 25,0 | 5 | 26,0 | 8 | 38,1 | 4 | 50,0 | 28 | 31,1 |
Time pressure or overload of work | ||||||||||||||||||||
Not exp. | 137 | 37,2 | 167 | 32,1 | 79 | 39,1 | 173 | 42,5 | 556 | 38,5 | 22 | 16,2 | 39 | 23,4 | 10 | 13,0 | 38 | 22,0 | 109 | 19,7 |
Exposed | 211 | 57,3 | 343 | 65,8 | 116 | 57,4 | 219 | 53,8 | 889 | 61,5 | 50 | 23,7 | 70 | 20,4 | 43 | 37,1 | 64 | 29,2 | 227 | 25,5 |
Risk of accident | ||||||||||||||||||||
Not exp. | 191 | 51,9 | 172 | 33,0 | 142 | 70,3 | 243 | 59,7 | 748 | 52,3 | 34 | 17,9 | 42 | 24,4 | 31 | 22,1 | 60 | 24,7 | 167 | 22,4 |
Exposed | 150 | 40,8 | 336 | 64,5 | 47 | 23,3 | 149 | 36,6 | 682 | 47,7 | 35 | 23,3 | 67 | 19,9 | 19 | 40,4 | 44 | 29,5 | 165 | 24,2 |
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||||||||||||||||||||
Total* | 368 | 100 | 521 | 100 | 202 | 100 | 407 | 100 | 1 498 | 100 | 76 | 20,7 | 113 | 21,7 | 57 | 28,5 | 106 | 26,0 | 352 | 23,6 |
The second part of Table
Table
Structure of the four types of workplace exposures: Ukrainian and Czech males and females.
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|
Males | Females | Total | |||||||
|
Ukrainian | Czech | Ukrainian | Czech | Ukrainian | Czech | Ukrainian | Czech | ||
|
|
|
% | % |
|
|
% | % |
|
% |
Without exp. | 103 | 76 | 31,3 | 15,1 | 59 | 121 | 32,4 | 31,3 | 359 | 25,7 |
Exp. |
10 | 3,0 | 12 | 2 | 6,6 | ,5 | 24 | 1,7 | ||
Exp. |
1 | ,3 | 3 | 1,6 | 4 | 0,3 | ||||
Exp. |
13 | 2 | 4,0 | ,4 | 21 | 13 | 11,5 | 3,4 | 49 | 3,5 |
Exp. |
4 | 3 | 1,2 | ,6 | 7 | 0,5 | ||||
Exp. |
4 | 1 | 1,2 | ,2 | 2 | 1 | 1,1 | ,3 | 8 | 0,6 |
Exp. |
3 | ,9 | 1 | ,5 | 4 | 0,3 | ||||
Exp. |
37 | 10 | 11,2 | 2,0 | 20 | 13 | 11,0 | 3,4 | 80 | 5,7 |
Exp. |
24 | 5 | 7,3 | 1,0 | 10 | 5,5 | 39 | 2,8 | ||
Exp. |
1 | 1 | ,5 | ,3 | 2 | 0,1 | ||||
Exp. |
3 | ,9 | 2 | 1 | 1,1 | ,3 | 6 | 0,4 | ||
Exp. |
1 | ,2 | 1 | 0,1 | ||||||
Exp. |
2 | 10 | ,6 | 2,0 | 2 | 4 | 1,1 | 1,0 | 18 | 1,3 |
Exp. |
52 | 90 | 15,8 | 17,9 | 37 | 100 | 20,3 | 25,9 | 279 | 19,9 |
Exp. |
12 | 87 | 3,6 | 17,3 | 47 | 12,2 | 146 | 10,4 | ||
Exp. |
61 | 217 | 18,5 | 43,2 | 12 | 83 | 6,6 | 21,5 | 373 | 26,7 |
|
||||||||||
Without exp. | 103 | 76 | 31,3 | 15,1 | 59 | 121 | 32,4 | 31,3 | 359 | 25,7 |
Exposed by 1 factor | 74 | 177 | 22,5 | 35,3 | 50 | 150 | 27,5 | 38,9 | 451 | 32,2 |
Exposed by 2 factors | 82 | 223 | 24,9 | 44,4 | 38 | 97 | 20,9 | 25,1 | 440 | 31,5 |
Exposed by 3 factors | 46 | 21 | 14,0 | 4,2 | 25 | 18 | 13,7 | 4,7 | 110 | 7,9 |
Exposed by 4 factors | 24 | 5 | 7,3 | 1,0 | 10 | 5,5 | 39 | 2,8 | ||
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Total valid | 329 | 502 | 100,0 | 100,0 | 182 | 386 | 100,0 | 100,0 | 1 399 | 100,0 |
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Missing | 39 | 19 | 20 | 21 | 99 | |||||
Total |
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|
|
|
|
|
|
|
|
As for the males, the most frequent exposition/load is time pressure in combination with the risk of accident (Exp.
Table
The association of fair/poor self-rated health with self-reported workplace loads and demographic variables: Ukrainian and Czech males and females (Adj. odds ratio (OR) and 95% CI, 4 log. models).
Adj. OR | 95% CI | Adj. OR | 95% CI | Adj. OR | 95% CI | Adj. OR | 95% CI | |||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Ukrainian males | Ukrainian females | Czech males | Czech females | |||||||||
Age groups | ||||||||||||
18–30 (ref.) | 1 | 1 | 1 | 1 | ||||||||
31–40 | ,985 | ,408 | 2,379 | ,902 | ,274 | 2,967 | 1,578 | ,717 | 3,473 | 1,224 | ,524 | 2,859 |
41–62 | 1,779 | ,769 | 4,116 |
|
|
|
|
|
|
|
|
|
Education level | ||||||||||||
Univ. (ref.) | 1 | 1 | 1 | 1 | ||||||||
Primary | 2,065 | ,800 | 5,327 | ,514 | ,182 | 1,451 |
|
|
|
1,562 | ,573 | 4,256 |
Secondary | 1,156 | ,423 | 3,164 | ,425 | ,159 | 1,133 | 1,264 | ,527 | 3,030 | 1,297 | ,482 | 3,485 |
Marital status | ||||||||||||
Other (ref.) | 1 | 1 | 1 | 1 | ||||||||
Married | 1,071 | ,573 | 2,004 | 1,457 | ,650 | 3,265 | 1,144 | ,678 | 1,931 | ,957 | ,559 | 1,639 |
|
||||||||||||
Workplace discrimination by factors | ||||||||||||
Not exposed (ref.) | 1 | 1 | 1 | 1 | ||||||||
Exposed by 1 factor | 1,313 | ,582 | 2,958 |
|
|
|
,665 | ,341 | 1,295 | |||
Exposed by 2 factors | 1,780 | ,830 | 3,816 |
|
|
|
,578 | ,302 | 1,108 | 1,073 | ,588 | 1,958 |
Exposed by 3 factors | 2,140 | ,904 | 5,068 |
|
|
|
,818 | ,266 | 2,517 | 1,879 | ,999 | 3,534 |
Exposed by 4 factors | 1,277 | ,370 | 4,416 |
|
|
|
3,041 | ,439 | 21,074 | 2,687 | ,890 | 8,118 |
Educational level was confirmed as a significant determinant of poor health (SRH) but only in the case of Czech males (OR = 2.32, 95% CI = [1.01–5.32]—the lower the education the worse the parameters of SRH (taking university education as the reference frame)).
Marital status was not significantly associated with SRH in any analysed samples.
This study has some limitations that are worth mentioning. The two surveys this analysis is based on originated in different time periods (May–October 2012 and March–May 2013) and regions. Whereas the first investigation was carried out in the capital city of Prague and the Central Bohemia region, the second was done throughout whole Czechia. The data samples are slightly different also in regard to employment and educational characteristics (due to regionally specific work opportunities). The different structures of the respondents have logically had some impact on differences in the crude values of the dependent variable, SRH. There are differences between immigrant and Czech data samples, too. Males dominated in the group of the immigrants (again logical in the context of labour migration). On the other hand, in the Czech population sample, gender characteristics are more or less balanced. We are aware of the differences in the structures described above and therefore used methods of logistic regression which makes it possible to overcome problems with different structures entering the analyses. Obviously, the immigrant data are unique and variation between them is not significant in terms of our goals. All the data were self-reported. Hence, despite care taken with methodology, self-reporting may partly be influenced by memory and/or social desirability factors.
Several aspects are worth discussing. It does not seem that the “healthy immigrant effect” is recognised in many developed immigration countries [
We tested three hypotheses. All were, albeit not fully, confirmed.
First, among the studied samples (Ukrainian males and females and Czech males and females), only Ukrainian immigrant females were heavily exposed to all four measured types of workplace discrimination loads within the given model, while also being more likely to report rather poor health. Within other analysed samples respondents’ possible workplace discrimination did not modify the quality of their SRH. This confirms how difficult the situation of immigrant females in the labour market of a host country often is (they are predisposed to various forms of discrimination; they are more prone to various threats); Czechia as the immigration/host country is no exception to this trend.
The second tested hypothesis holds true in the sense that determinants (which were used within the analysis through binary logistic regression) which are behind respondents’ quality of health are different among Ukrainian immigrants vis-à-vis Czechs with one exception. The “oldest age” (41–62) contributes jointly to poorer assessment of SRH among Ukrainian females and Czech females, and indeed Czech males too. Workplace discrimination in its four different forms is clearly tied to poorer SRH but only in the case of Ukrainian females. On the other hand, it seems that the lowest educational level (primary education) correlates with poor SRH within the sample of Czech males.
On one hand, the results show us how complicated relationships between SRH and workplace discrimination and also the given sociodemographic characteristics are. On the other hand, the results clearly call for policy interventions to more effectively prevent workplace discrimination (as a whole and in its various forms) in the Czech labour market targeted especially towards Ukrainian immigrants, in general, and Ukrainian immigrant females, in particular (as the results from testing the third hypothesis tell us).
Further studies with a larger sample size are needed to further explore and test our findings. Moreover, in addition to the quantitative approach, a qualitative one is also necessary in order to understand the issue better and to gain in-depth knowledge and nuances. Czech migration and health policy should focus not only on supporting foreigners’ integration into society but also especially on the prevention of possible problems within immigration communities. The primary goal should be to ensure the same conditions of life as the majority society.
The health of immigrants is a topical issue and it calls for further studies tackling various aspects in this field. The current situation resembles “the precarious safety of an ice float.”
The authors declare that there is no conflict of interests regarding the publication of this paper.
This study was supported by two Czech Science Foundation projects: (1) “Migration and Development—economic and social impacts of migration upon Czechia as a destination country and Ukraine as a country of origin (with special regard to analysis of remittances),” no. P404/10/0581 and (2) “Migratory patterns of immigrants (and natives) in Czechia: concentration or diffusion processes?” no. P404/12/1014 and forms part of the SOPHIE project “Evaluating the impact of structural policies on health inequalities and their social determinants and fostering change” which has received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under Grant agreement no. 278173.