
==== Front
Eur J Psychotraumatol
Eur J Psychotraumatol
European Journal of Psychotraumatology
2000-8066
Taylor & Francis

39297220
2400835
10.1080/20008066.2024.2400835
Version of Record
Basic Research Article
Research Article
Links between ethnic discrimination, mental health, protective factors, and hair cortisol concentrations in asylum seekers living in Germany
Relación entre discriminación étnica, salud mental, factores protectores y concentraciones de cortisol en el cabello en solicitantes de asilo que viven en AlemaniaEUROPEAN JOURNAL OF PSYCHOTRAUMATOLOGY
J. GIESEBRECHT ET AL.
https://orcid.org/0009-0001-6201-8463
Giesebrecht Julia a
https://orcid.org/0000-0002-9577-1144
Reich Hanna b
https://orcid.org/0000-0001-5216-1031
Weise Cornelia a
https://orcid.org/0000-0002-2430-5090
Nater Urs M. c
https://orcid.org/0000-0002-4724-9597
Mewes Ricarda d
a Division of Clinical Psychology and Psychotherapy, Department of Psychology, University of Marburg, Marburg, Germany
b Depression Research Centre of the German Depression Foundation, Department of Psychiatry, Psychosomatics and Psychotherapy, Goethe University, Frankfurt am Main, Germany
c Department of Clinical and Health Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
d Outpatient Unit for Research, Teaching and Practice, Faculty of Psychology, University of Vienna, Vienna, Austria
CONTACT Ricarda Mewes ricarda.nater-mewes@univie.ac.at Outpatient Unit for Research, Teaching and Practice, Faculty of Psychology, University of Vienna, Liebiggasse 5, Vienna 1010, Austria
Supplemental data for this article can be accessed online at https://doi.org/10.1080/20008066.2024.2400835.

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https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

ABSTRACT

Objective: Asylum seekers often experience ethnic discrimination on the flight or in the host country, which may be associated with chronic stress and impaired mental health. Dysregulation of the hypothalamic–pituitary–adrenal axis, a known physiological correlate of chronic stress, can be assessed using hair cortisol concentrations (HCC). The present study aimed to investigate how different forms of perceived ethnic discrimination are associated with mental health outcomes, HCC, and protective factors in asylum seekers living in Germany.

Methods: Somatic symptoms (PHQ-15), symptoms of posttraumatic stress (PDS), depressive symptoms (PHQ-9), different forms of ethnic discrimination (active harm, passive harm, institutional discrimination), and protective factors (in-group identification, social support) were assessed cross-sectionally in 144 asylum seekers (average age 32 years, average duration of stay in Germany nine months; 67% men). HCC were obtained from 68 participants. Multiple regression analyses were conducted and social support and in-group identification were tested as potential moderators.

Results: Active ethnic discrimination was positively associated with all assessed mental health outcomes, and all forms of ethnic discrimination positively correlated with depressive symptoms. Ethnic discrimination was not associated with HCC. When controlling for other possible influences (e.g. age, gender, traumatic events), passive harm was negatively associated with depressive symptoms (β = −0.17, p = .033) and active harm was positively associated (β = 0.28, p = .022) with somatic symptoms. After the inclusion of the protective factors, the associations were no longer significant. Lower social support was associated with higher depressive symptoms (β = −0.35, p < .001), posttraumatic stress (β = −0.77, p < .001), and somatic symptoms (β = −0.32, p < .001), but did not moderate the associations between ethnic discrimination and the mental health outcomes.

Conclusions: Perceived ethnic discrimination may negatively influence asylum seekers’ mental health but does not seem to be associated with HCC. Social support was associated with psychological symptom severity, but did not buffer the effects of ethnic discrimination on mental health.

HIGHLIGHTS

The study examined the relationship between ethnic and institutional discrimination, protective factors (social support and in-group identification), hair cortisol concentrations (HCC) and mental health in asylum seekers.

Ethnic discrimination was associated with mental health outcomes and social support, but not with HCC.

Lower social support was associated with higher depressive and somatic symptoms, but did not moderate the relationship between ethnic discrimination and mental health.

Objetivos: Los solicitantes de asilo a menudo experimentan discriminación étnica en la huida o en el país de acogida, lo cual puede asociarse con el estrés crónico y deterioro mental. La desregulación del eje hipotálamo-hipofisario-adrenal, un conocido correlato fisiológico del estrés crónico, se puede evaluar utilizando las concentraciones de cortisol en el cabello (HCC por sus siglas em iglés). El presente estudio tuvo como objetivo investigar cómo las diferentes formas de discriminación étnica percibida se asocian con los resultados de salud mental, el HCC y los factores protectores en solicitantes de asilo que viven en Alemania.

Métodos: Los síntomas somáticos (PHQ-15), los síntomas de estrés postraumático (PDS), los síntomas depresivos (PHQ-9), las diferentes formas de discriminación étnica (daño activo, daño pasivo, discriminación institucional) y los factores protectores (identificación con el grupo, apoyo social) se evaluaron transversalmente en 144 solicitantes de asilo (edad promedio 32 años, duración promedio de la estadía en Alemania nueve meses; 67% hombres). Se obtuvieron datos de 68 participantes con HCC. Se realizaron análisis de regresión múltiple y se evaluaron el apoyo social y la identificación con el grupo como posibles moderadores.

Resultados: La discriminación étnica activa se asoció positivamente con todos los resultados de salud mental evaluados, y todas las formas de discriminación étnica se correlacionaron positivamente con los síntomas depresivos. La discriminación étnica no se asoció con HCC. Al controlar otras posibles influencias (p. ej., edad, género, eventos traumáticos), el daño pasivo se asoció negativamente con los síntomas depresivos (β = −0.17, p = .033) y el daño activo se asoció positivamente (β = 0.28, p = .022) con los síntomas somáticos. Después de la inclusión de los factores protectores, las asociaciones dejaron de ser significativas. Un menor apoyo social se asoció con mayores síntomas depresivos (β = −0.35, p < .001), estrés postraumático (β = −0.77, p < .001) y síntomas somáticos (β = −0.32, p < .001), pero no moderó las asociaciones entre la discriminación étnica y los resultados de salud mental.

KEYWORDS

Asylum seekers
ethnic discrimination
mental health
hair cortisol concentrations
protection
PALABRAS CLAVE

Solicitantes de asilo
discriminación étnica
salud mental
concentraciones de cortisol en el cabello
protección
This study was funded by the European Refugee Fund [EFF-12-775].
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pmc1. Introduction

Asylum seekers face a high risk of experiencing mental health problems, including posttraumatic stress disorder, anxiety, and depression, along with elevated rates of psychiatric comorbidity (Posselt et al., 2020). Besides traumatization and stressors experienced in their home countries and during flight (i.e. pre-settlement factors), post-migratory stressors in the host countries further contribute to their mental burden (Jannesari et al., 2020). For instance, experiences of discrimination (Hajak et al., 2021) have been shown to be associated with asylum seekers’ mental health (Jannesari et al., 2020).

The present study investigated the relationship between different forms of ethnic discrimination and the mental health of asylum seekers. Additionally, we examined the association of ethnic discrimination with hair cortisol concentrations (HCC), as a measure of long-term secretion of a central biological stress marker (cortisol), and factors that potentially buffer the negative influence of ethnic discrimination on mental health (social support and in-group identification). These variables are described below.

Ethnic discrimination can be defined as unfair treatment due to an individual’s (ascribed) ethnicity (Contrada et al., 2000), and can take different forms. Cuddy et al. (2007) distinguish between overt discrimination (active harm), which includes actions aimed at causing harm or creating obstacles for an individual (physical violence or bullying), and subtle discrimination (passive harm), which refers to behaviours demonstrating obstructive or paternalistic attitudes towards others, such as ignoring, patronizing, or neglecting group members. Multiple studies have demonstrated adverse effects of these forms of ethnic discrimination on both physical and mental health (Pascoe & Richman, 2009; Priest & Williams, 2017; Schmitt et al., 2014). However, discrimination experienced by asylum seekers is not limited to interpersonal interactions but can also be institutional in nature. Policies and practices that create barriers to accessing basic services such as healthcare or education can exacerbate feelings of marginalization and discrimination (Quinn, 2014). Research examining the effect of ethnic and religious discrimination on mental health in asylum seekers has yielded mixed findings. Whereas some studies found an association of discrimination with depression, posttraumatic stress, anxiety, and somatoform disorders (Borho et al., 2020; Dadras & Diaz, 2024; Viazminsky et al., 2022), one study found no association between ethnic discrimination and mental health, possibly because the asylum seekers studied reported experiencing only minimal ethnic discrimination (El Khoury, 2019). Despite the frequently reported link between ethnic discrimination and mental health, the precise mechanisms underlying this relationship are not fully understood.

One potential mechanism is the experience of stress and the resulting dysregulation of the body’s stress-related system. Ethnic discrimination is a type of stressor that, when experienced repeatedly, can lead to chronic stress (Pascoe & Richman, 2009). In turn, chronic stress can disrupt the functioning of the body's stress response system, i.e. the hypothalamic–pituitary–adrenal (HPA) axis, which regulates the release of cortisol (Juster et al., 2010). Cortisol secretion is often considered as the biological mechanism connecting stress with the development of illness (Chrousos, 2009). For instance, studies have shown that chronic stress due to discrimination can affect the HPA axis, leading to changes in cortisol levels and the diurnal cortisol secretion pattern (Busse et al., 2017). A systematic review has found associations between flatter daily cortisol curves and adverse health outcomes (Adam et al., 2017). HCC can reflect a long-term, chronic release of cortisol over several months. As such, they can be seen as a correlate of chronic stress (Russell et al., 2012). A more nuanced understanding of the factors influencing the strength and direction of the association between adversities and HCC was provided by Khoury et al. (2019) in their meta-analysis. The authors identified three key factors: the characteristics of the adversity (e.g. type of adversity, timing of adversity), the characteristics of the samples (e.g. clinical status, racial distribution), and the characteristics of the publication (e.g. type of publication, geographic region of study) that moderated the association (Khoury et al., 2019). Although studies have demonstrated a negative influence of discrimination on the mental health of asylum seekers and refugees (Hajak et al., 2021; Hynie, 2018; Jannesari et al., 2020; Ziersch et al., 2020), research on whether this influence is also reflected in biological stress markers such as HCC is lacking.

To gain a more balanced picture of the possible association of ethnic discrimination with mental health and its possible biological impact, it is necessary to consider protective factors that might influence these associations. The potential protective factor of social support may be particularly valuable in the face of ethnic discrimination, as it can help the individual to deal with the discrimination and subsequently reduce distress (Jetten et al., 2017). However, research findings on social support in refugee populations are inconsistent. One study reported that social support did not buffer the association between perceived discrimination and psychological distress (Alemi & Stempel, 2018), while in another study, social support mediated the effect of perceived discrimination on psychological well-being (Hashemi et al., 2019) . These inconclusive findings regarding the impact of social support on the effects of ethnic discrimination in refugee populations underline the need for further investigation, particularly among asylum seekers. Due to their legal status, this group faces significantly more challenges in accessing social support (e.g. no choice of housing, language barriers, family reunion is not permitted).

A further important protective factor buffering the negative consequences of ethnic discrimination might be an individual’s in-group identification (or ethnic identity), i.e. the sense of belonging and attachment to a particular social group (Cameron, 2007). Previous research suggests that the impact of perceived discrimination on health outcomes in immigrant and ethnic minority populations may be influenced by an individual's ethnic identity (Noh et al., 1999; Yip et al., 2008). However, the empirical evidence on this issue is inconclusive, with some studies demonstrating a buffering effect of ethnic identity on the relationship between perceived discrimination and health-related outcomes (Ikram et al., 2016; Mewes et al., 2015) and others (albeit fewer) reporting detrimental or mixed effects (Arbona & Jimenez, 2014; Yip et al., 2008). Therefore, further research is necessary to gain a better understanding of this relationship, particularly in asylum seekers, in whom no studies have investigated this issue.

The present study therefore aimed to investigate the association of perceived ethnic and institutional discrimination with mental health outcomes (i.e. somatic, depressive, and posttraumatic stress symptoms) and to examine the role of protective factors (social support and in-group identification) in asylum seekers. Moreover, to our knowledge, the study is the first to investigate the association between discrimination and HCC in asylum seekers.

We assumed that perceived ethnic and institutional discrimination would be positively associated with mental health outcomes and negatively associated with the protective factors (i). Additionally, we hypothesized that the protective factors would moderate the association of ethnic and institutional discrimination with mental health outcomes (ii). Finally, we posited that perceived ethnic and institutional discrimination, serving as chronic stressors, would be associated with HCC in asylum seekers (iii).

2. Methods

2.1. Study design

The data for the present study were collected between February 2014 and March 2015 through a cross-sectional survey within the larger project ‘Psychotherapeutic first aid for asylum seekers living in Hesse’ funded by the European Refugee Fund (EFF-12-775). Asylum seekers living in the German federal state of Hesse completed questionnaires to assess risk and protective factors for mental health. As eligibility criteria, participants had to be at least 18 years old, to have lived in Germany for a maximum of 12 months, to be in the process of applying for asylum without yet being recognized as a refugee, and to be able to communicate (reading and writing skills were not required) through their native language. To reach out to asylum seekers, the study team visited accommodation centres and common meeting points such as cafés. Since it was not possible to account for every possible language, we based our choices on the most frequent countries of origin and native languages of refugees arriving in Germany in 2014 and the preceding years (Federal Office for Migration and Refugees, 2014), leaving us with Farsi, Arabic, Kurdish, and English. Asylum seekers with a different language background could also participate but had to provide their own translator. The consent forms were translated into the study languages and proofread by another native speaker. Questionnaires (if not available in the language of interest) were translated using the forward–backward translation method suggested by van de Vijver et al. (1996) and administered digitally using the software ‘MultiCasi’ (Knaevelsrud & Müller, 2008) on a laptop with a touch screen. Throughout the assessment, interpreters and study personnel were available if participants required assistance.

The study was approved by the Institutional Review Board of the Department of Psychology, University of Marburg, Germany, and was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent before completing the questionnaires.

2.2. Measures

Below, we provide brief descriptions of the scales assessed in the study. For item examples and additional theoretical background, please refer to the Appendix.

2.2.1. Sociodemographic data

Sociodemographic data included gender (male/female), age, country of origin, native language, religion, educational level, and length of stay in Germany.

2.2.2. Ethnic discrimination

2.2.2.1. Active and passive ethnic discrimination

To assess active (overt) and passive (subtle) ethnic discrimination, the short version of the BIAS-TS (Behaviours from Intergroup Affect and Stereotypes-Treatment Scale; Sibley, 2011) was used. Participants rated the frequency of experiences of active and passive ethnic discrimination, with three items each, on a 7-point Likert scale from ‘I have never experienced that’ to ‘I often experience that’. Cronbach's alpha for the present sample was .73 for active harm and .65 for passive harm.

2.2.2.2. Institutional discrimination

We developed a 10-item questionnaire to assess perceived discrimination within institutional contexts such as accommodation (i.e. conditions, size of housing, shared rooms, eating arrangements, location, attendance regulations), access to education, financial support, applying for asylum, or permission to work in the host countr. The questionnaire was developed following expert consultation because at the time of the survey, there was no questionnaire that adequately captured institutional discrimination specific to the situation of asylum seekers. The wording of the items relates to the self-assessed fairness of the treatment. Items were rated on a 5-point scale from ‘I feel treated fairly’ to ‘I feel treated unfairly’. Cronbach’s alpha was .89. The items on institutional discrimination were constructed through expert consultation.

2.2.3. Protective factors

2.2.3.1. In-group identification

Participants rated four items measuring their degree of identification with their country of origin. The items were adapted from a study by Mewes et al. (2015) on Turkish immigrants, and modified to include people of different origins for this study, based on evidence supporting their validity in research on Russian (Goreis et al., 2020; Simon & Grabow, 2010) and Turkish (Mewes et al., 2015) populations. An example item is ‘Belonging to my people is an important part of who I am’, with response options ranging from 1 (don't agree at all) to 7 (totally agree). Cronbach's alpha for the current sample was .81.

2.2.3.2. Social support

To assess social support, we used the ENRICHD Social Support Inventory (ESSI; Mitchell et al., 2003), with participants rating how often a range of different types of social support is available on a 5-point Likert scale ranging from 1 = never to 5 = usually. Cronbach’s alpha for the current sample was .92.

2.2.4. Mental health outcomes

2.2.4.1. Somatic symptoms

The Somatic Symptom Scale of the Patient Health Questionnaire (PHQ-15) includes the 15 most prevalent somatic symptoms in primary care (Kroenke et al., 2002). Participants indicate how much they have been bothered by certain symptoms over the past four weeks on a 3-point Likert scale ranging from ‘0’ (not at all) to ‘2’ (bothered a lot). Cronbach’s alpha for the present sample was .86.

2.2.4.2. Posttraumatic stress symptoms

The Posttraumatic Diagnostic Scale (PDS) (Foa, 1995) is a self-report instrument to assess posttraumatic stress symptoms and traumatic events. The cardinal symptoms of posttraumatic stress such as intrusions, avoidance, and hyperarousal were measured by 17 items rated on a 4-point Likert scale ranging from 0 (not at all or only once) to 3 (five or more times per week/almost always). A higher score indicates more severe symptoms of posttraumatic stress. In order to gain a more comprehensive understanding of the adverse events that may have occurred, an extended list of traumatic events was used. The format of responses to the list of traumatic events has been modified to align with DSM-5 recommendations for traumatic events (American Psychiatric Association, 2013). Cronbach’s alpha for the present sample was .96.

2.2.4.3. Depressive symptoms

Each item in the Patient Health Questionnaire-9 (PHQ-9) (Kroenke et al., 2001) represents one of the nine DSM-IV depression criteria (American Psychiatric Association, 1994). Items are rated on a 4-point scale ranging from ‘0’ (not at all) to ‘3’ (nearly every day) and the total score indicates the intensity of depressive symptoms. Cronbach’s alpha for the present sample was .89.

2.2.5. HCC

HCC accumulated over the preceding two-month period was measured in 68 asylum seekers. For this purpose, a small number of hair strands were cut from the posterior vertex of the head, as close as possible to the scalp. The first 2 cm segment closest to the scalp is thought to reflect the cumulative secretion of cortisol over the past two months (Wennig, 2000). Consecutive sampling was employed, enabling us to include participants in the study who met the inclusion criteria (additional consent to provide a hair sample, the posterior vertex hair is longer than 2 cm, the hair is of sufficient weight, and no evidence that glucocorticoids are being taken). The laboratory protocol developed by Stalder et al. (2012) served as the basis for the hair-washing and cortisol extraction procedures, with a few minor adjustments. The determination of HCC was based on the cortisol luminescence immunoassay (LIA) approach as described in Mewes et al. (2017). Namely, three millilitres of isopropanol were used to shake the hair samples twice for three minutes. 10 ± 0.5 mg of each sample were finely cut and incubated for 18 h at room temperature in 1.8 ml methanol to extract the cortisol. After that, samples were completely dried by evaporating 1.6 ml of the supernatant at 50°C. After being resuspended with 150 μl HPLC gradient grade water (Fisher Scientific, Schwerte, Germany), the samples were vortexed for 20 s before being stored at – 20°C until they were tested. By using 50 μl of a commercially available luminescence immunoassay (IBL, Hamburg, Germany) the cortisol was determined. The local Clinical Biopsychology laboratory of the University of Marburg performed the analyses. Furthermore, the following potentially confounding variables were collected via a hair questionnaire, which assessed relevant personal characteristics (height and weight to calculate BMI), general hair characteristics (natural hair colour and curl), cosmetic treatment of the hair (bleaching, colouring, and frequency of shampooing), and the use of hair products.

2.3. Data analysis

All statistical procedures were performed using IBM SPSS (IBM Corp., 2019), and the moderation analyses were conducted using the PROCESS macro by Hayes (SPSS Version 4.1; Hayes, 2013). All hypotheses were tested at an alpha level of .05.

Missing values ranged between 3.5% (ESSI social support) and 24.3% (institutional discrimination) and were missing completely at random according to Little’s MCAR test (Little, 1988). Therefore, multiple imputation using the fully conditional specification method was applied (m = 20, number of iterations = 10). Parametric and nonparametric tests were employed for group comparisons due to the violation of the assumption of normal distribution in some instances. Group differences were evaluated using Chi-square tests for categorical variables. T-tests and Mann–Whitney U tests were conducted for interval-scaled variables.

To test for correlations between different forms of ethnic discrimination and the mental health outcomes, bivariate correlation analyses (Spearman rank correlations) were performed. To test the first hypothesis, multiple regression models were run for each mental health outcome (depressive, somatic, posttraumatic stress symptoms) to assess which forms of discrimination were related to which mental health outcomes. The number of traumatic events, age, and gender were included as covariates in all multiple regression models and entered in the first block. The second block contained all forms of ethnic discrimination (active, passive, and institutional). Finally, in the last block, protective factors, i.e. in-group identification and social support, were added. All variables were entered blockwise. Rubin’s rules (Rubin, 1987) and combination rules suggested by Van Ginkel (van Ginkel, 2019) were applied for pooling estimates (such as F and p values, R2) across all 20 datasets, since SPSS does not provide these statistics in regression analysis when using multiple imputation.

Moderation analyses were conducted to determine whether the association between mental health outcomes and ethnic discrimination was moderated by either social support or in-group identification (hypothesis 2). For each dependent variable (depressive, somatic, and posttraumatic stress symptoms), a moderation model was performed. Gender, number of traumatic events, and age were included as covariates. Social support or in-group identification were incorporated as moderators, while each type of ethnic discrimination was examined separately as the independent variable. The analyses focused exclusively on the interaction term, so no mean centring was performed (Hayes, 2022). The PROCESS macro by Hayes (2022) was employed, using ordinary least squares regression to yield unstandardized coefficients for all effects. Macro procedures were employed to estimate interaction terms for each multiply imputed sample separately, and the results from all 20 samples were pooled using Rubin’s rules (Rubin, 1987). Bootstrapping with 5,000 samples was applied to obtain 95% confidence intervals, in addition to heteroscedasticity-consistent standard errors (HC3; Davidson & Mackinnon, 1993). In the case of significant interaction terms, Johnson-Neyman intervals were used for slope analysis.

The HCC data were positively skewed and therefore underwent log-transformation to obtain a normally distributed variable. We conducted a hierarchical multiple regression analysis to test whether HCC was associated with any facet of ethnic discrimination (hypothesis 3). In step 1 of the model, we controlled for covariates such as age, gender, body mass index (BMI), frequency of hair-washing per week, hair-colouring, and curled hair. Forms of ethnic discrimination were added in step 2.

3. Results

3.1. Sample characteristics

In total, 144 participants were included in the study, of whom 68 contributed samples to assess HCC (see Table 1). The most frequent country of origin was Iran (47.9%), followed by Afghanistan (16%) and Syria (12.5%). The distribution of nationalities was similar in the 68 participants with HCC, though with a higher percentage from Iran (55.9%). Depressive, posttraumatic stress, and somatic symptoms appeared to be evenly distributed across the two groups. The group with HCC had been in Germany for a shorter period (Cohen’s d r = –.22) and included a greater number of female participants than the group without hair samples (Cohen’s d φ = –.25). In terms of ethnic discrimination, the HCC group scored lower on institutional discrimination with a medium effect size (MnoHCC = 3.4 vs MHCC = 2.8, SDboth = 1.0; Cohen’s d r = –.41). Table 1. Sociodemographic characteristics of the overall sample and participants contributing HCC samples, mean scores on the outcome measures.

 	Total sample
(n = 144)
M (SD/ %)/
n (%)	Participants contributing sample to determine HCC
(n = 68)
M (SD/ %)/
n (%)	Participants NOT contributing sample to determine HCC
(n = 76)
M (SD/ %)/
n (%)	Group differences	
t, X2 or U	p	
Age
Years	31.9 (7.8)	33.1 (7.4)	30.7 (8.0)	1.729	.076	
Gender
Men	96 (66.7)	37 (54.4)	59 (77.6)	8.71	.003	
Highest education	 	 	 	8.30	.081	
 No completion of formal schooling	18 (12.5)	9 (13.2)	9 (11.8)	 	 	
 Primary school	12 (8.3)	4 (5.9)	8 (10.5)	 	 	
 Secondary school	21 (14.6)	16 (23.5)	5 (6.6)	 	 	
 School-leaving examination or higher	74 (51.4)	36 (52.9)	38 (50)	 	 	
 Missing information	19 (13.2)	3 (4.4)	16 (21.1)	 	 	
Years in school	9.4 (4.0)	9.7 (3.9)	9.2 (4.1)	2341.00	.772	
Nationality	 	 	 	9.569	.386	
 Iran	69 (47.9)	38 (55.9)	31 (40.8)	 	 	
 Afghanistan	23 (16.0)	12 (17.6)	11 (14.5)	 	 	
 Syria	18 (12.5)	10 (14.7)	8 (10.5)	 	 	
 Somalia	10 (6.9)	3 (4.4)	7 (9.2)	 	 	
 Eritrea	8 (5.6)	4 (5.9)	4 (5.3)	 	 	
 Algeria	3 (2.1)	–	3 (3.9)	 	 	
 Other countries/missing information	13 (9.1)	1 (1.5)	12 (15.7)	 	 	
Length of stay in Germany in months	8.5 (7.5)	6.6 (4.7)	10.5 (9.2)	1690.00	.009	
PHQ-15 total score	13.5 (6.4)	11.2 (6.9)	13.4 (6.6)	2409.50	.900	
 Missing values, n (%)	6 (4.2)	2 (2.9)	6 (7.9)	 	 	
PHQ-9 total score	16.4 (7.3)	16.6 (7.4)	15.8 (7.6)	1.010	.314	
 Missing values, n (%)	7 (4.9)	6 (8.8)	7 (9.2)	 	 	
PDS total score	26.9 (16)	27.6 (16.2)	26.4 (15.9)	.526	.600	
 Missing values, n (%)	11 (7.6)	13 (19.1)	7 (9.2)	 	 	
Number of traumatic events	12.5 (6.2)	12.7 (6.5)	12.3 (5.9)	.156	.767	
 Missing values, n (%)	53 (36.8)	25 (36.8)	28 (36.8)	 	 	
BIAS-TS active harm	0.4 (0.6)	0.4 (0.6)	0.3 (0.6)	2186.50	.523	
 Missing values, n (%)	8 (5.6)	2 (2.9)	6 (7.9)	 	 	
BIAS-TS passive harm	0.9 (0.8)	0.8 (0.8)	0.9 (0.9)	2164.00	.511	
 Missing values, n (%)	8 (5.6)	1 (1.5)	7 (9.2)	 	 	
Institutional discrimination	3.2 (1.0)	2.8 (1.0)	3.4 (1.0)	.728	.032	
 Missing values, n (%)	35 (24.3)	14 (20.6)	21 (27.6)	 	 	
ESSI total score	15.8 (7.2)	16.3 (6.9)	15.4 (7.5)	.479	.564	
 Missing values, n (%)	5 (3.5)	0	5 (6.6)	 	 	
In-group identification total score	3.5 (1.2)	2.3 (1.1)	3.4 (1.3)	.156	.364	
 Missing values, n (%)	4 (2.8)	0	4 (5.3)	 	 	
Two-month hair cortisol (pg/mg):	 	 	 	 	 	
Raw	 	10.3 (21.4)	 	 	 	
Log transformed	 	0.8 (0.3)	 	 	 	
 Missing values, n (%)	 	9 (13.2)*	 	 	 	
Curled hair/ waves, n (%)	 	41 (60.3)	 	 	 	
 Missing values, n (%)	 	1 (1.5)	 	 	 	
Hair washes per week	 	4.2 (2.9)	 	 	 	
 Missing values, n (%)	 	1 (1.5)	 	 	 	
Hair colouring	 	34 (50)	 	 	 	
 Missing values, n (%)	 	0	 	 	 	
BMI	 	25.0 (5.4)	 	 	 	
 Missing values, n (%)	 	0	 	 	 	
Notes: n = number of participants. M = mean. SD = standard deviation. PHQ-15 = Patient Health Questionnaire-15 assessing somatic symptoms; PHQ-9 = Patient Health Questionnaire-9 assessing depressive symptoms; PDS = Posttraumatic Diagnostic Scale; BIAS-TS active harm = Behaviours from Intergroup Affect and Stereotypes – Treatment Scale; ESSI = ENRICHD Social Support Inventory; BMI = Body Mass Index.

*Hair samples that did not meet the criteria for proper analysis, such as being too short, too thin, or insufficient in quantity.

3.2. Associations between different forms of discrimination, protective factors, and mental health outcomes in asylum seekers

Active harm was positively correlated to institutional discrimination, depressive symptoms, somatic symptoms, posttraumatic stress symptoms, and the number of traumatic events (rs = .18–.26), and negatively correlated to social support, in-group identification, and age (rs = –.17–.23; see Table 2). By contrast, passive harm was negatively correlated to institutional discrimination (rs = –.23) and positively correlated to depressive symptoms (rs = .21). Higher scores for institutional discrimination were associated with stronger depressive symptoms (rs = .21). Social support was positively correlated to in-group identification (rs = –.29) but negatively correlated to all mental health outcomes (number of traumatic events, posttraumatic stress, depressive, and somatic symptoms; rs = –.39 to –.44). In-group identification was negatively correlated to the number of traumatic events (rs = –.23). Women had higher scores on somatic symptoms (rs = –.21). More active harm correlated with length of stay (rs = .20). Participants who were older had spent more years in education (rs = .31). No correlation was found between HCC and forms of discrimination, mental health outcomes, or protective factors (rs = –.14 to .14, n.s.). Table 2. Bivariate correlations between model variables.

Associations between different facets of discrimination, protective factors, and mental health outcomes in asylum seekers	
 	M	SD	1	2	3	4	5	6	7	8	9	10	11	12	13	
1. Active harm	.35	.61	 	 	 	 	 	 	 	 	 	 	 	 	 	
2. Passive harm	.85	.83	–.01	 	 	 	 	 	 	 	 	 	 	 	 	
3. Institutional	3.14	1.05	.26**	–.23**	 	 	 	 	 	 	 	 	 	 	 	
4. Social support	15.77	7.21	–.23**	.11	–.06	 	 	 	 	 	 	 	 	 	 	
5. Identification	3.49	1.20	–.17*	–.05	–.15	.29**	 	 	 	 	 	 	 	 	 	
6. Posttraumatic stress symptoms	26.87	15.90	.18*	–.15	.17	–.42**	–.12	 	 	 	 	 	 	 	 	
7. Number of traumatic events	12.65	6.18	.28**	–.03	.20	–.25*	–.23*	.48**	 	 	 	 	 	 	 	
8. Depressive symptoms	16.38	7.32	.25**	.21*	.21*	–.44**	–.16	.78**	.49**	 	 	 	 	 	 	
9. Somatic symptoms	13.48	6.43	.26**	–.11	.18	–.39**	–.09	.69**	.34**	.77**	 	 	 	 	 	
10. Gender	.67	.47	–.05	–.09	.03	–.10	–.01	–.08	–.01	–.12	–.21*	 	 	 	 	
11. Age	32.02	7.75	–.18*	.07	–.17	–.02	.12	.10	–.13	.06	.07	–.13	 	 	 	
12. Length of stay	8.53	7.55	.20*	–.02	.13	.05	–.01	–.15	.12	–.13	–.07	–.03	–.12	 	 	
13.Years in school	9.43	3.98	–.01	.09	.07	.16	.12	–.07	–.03	.00	.03	–.08	.31**	.04	 	
14. HCCa	10.31	21.39	.07	–.10	.06	–.05	–.14	.10	.07	–.01	–.05	.04	.14	–.11	–.01	
Notes: M represents means and SD represents standard deviations calculated from the pooled datasets. * indicates p < .05 and ** indicates p < .01. n = 144. aParticipants contributing sample to determine HCC, n = 68. Gender (female = 0, male = 1).

3.2.1. Results for hypothesis 1: hierarchical multiple regression models with mental health outcomes

3.2.1.1. Depressive symptoms

The variance explained increased by 7% when ethnic and institutional discrimination were added in the second block (see Model 2 in Table 3). Overall, the model explained 32% of the variance and number of traumatic events, age, and passive harm were associated with depressive symptoms (see Table 3; R2 = .32, F(6,137) = 12.382, p < .001). The addition of protective factors resulted in the explanation of 41% of the variance in depressive symptoms (R2 = .41, F(8,135) = 13.327, p < .001; see Model 3 in Table 3). Higher depressive symptoms were associated with lower social support and a higher number of traumatic events. Table 3. Hierarchical multiple linear regression model analysing the associations between depressive symptoms, forms of ethnic discrimination, and protective factors.

 	Model 2	Model 3	
Predictors	b[95% CI]	SEb	Beta	t	P	b[95% CI]	SEb	Beta	t	P	
(Constant)	5.287	3.590	 	1.473	.141	10.928	3.997	 	2.734	.006	
 	[−1.758, 12.332]	 	 	 	 	[3.084, 18.772]	 	 	 	 	
Number of traumatic events	.554	.103	.386	5.355	<.001*	.471	.105	.277	4.474	<.001*	
 	[.351, .757]	 	 	 	 	[.263, .678]	 	 	 	 	
Gender	−1.438	1.217	–.141	−1.182	.238	−2.079	1.127	–.274	−1.844	.065	
 	[−3.829, .952]	 	 	 	 	[−4.291, .133]	 	 	 	 	
Age	.149	.071	.130	2.098	.036*	.126	.067	.103	1.895	.058	
 	[.042, .299]	 	 	 	 	[–.004, .257]	 	 	 	 	
Active harm	1.693	.971	.237	1.743	.082	1.134	.908	.149	1.248	.212	
 	[–.213, 3.598]	 	 	 	 	[–.647, 2.915]	 	 	 	 	
Passive harm	−1.493	.700	–.170	−2.135	.033*	−1.102	.660	–.186	−1.670	.095	
 	[−2.867, –.120]	 	 	 	 	[−2.397, .193]	 	 	 	 	
Institutional discrimination	.319	.639	.040	.499	.618	.551	.618	.033	.893	.373	
 	[–.939, 1.576]	 	 	 	 	[–.666, 1.769]	 	 	 	 	
Social support	 	 	 	 	 	–.346	.078	–.430	−4.439	<.001*	
 	 	 	 	 	 	[–.499, –.193]	 	 	 	 	
In-group identification	 	 	 	 	 	.333	.455	–.032	.731	.465	
 	 	 	 	 	 	[–.560, 1.126]	 	 	 	 	
R2	 	 	.32	 	 	 	 	.41	 	 	
Adjusted R2	 	 	.26	 	 	 	 	.38	 	 	
Change in R2	 	 	.06	 	.022	 	 	.11	 	<.001	
p	 	 	<.001	 	 	 	 	<.001	 	 	
Notes: Beta = standardized beta from original data, * p < .05, b = regression coefficient and 95% confidence interval for b, SEb= standardized error for b, n = 144.

3.2.1.2. Somatic symptoms

When added to the basic model, ethnic and institutional discrimination explained an additional 6% of model variance (see Model 2 in Table 4). Somatic symptoms were associated with active harm, gender, and the number of traumatic events in Model 2, with Model 2 explaining 23% of the variance (R2 = .23, F(6,137) = 8.179, p < .001). The addition of protective factors in Model 3 resulted in an explanation of 33% of the variance in somatic symptoms (R2= .33, F(8,135) = 10.042, p < .001). Being female, experiencing a higher number of traumatic events, and having lower social support were associated with more somatic symptoms. Table 4. Hierarchical multiple linear regression model analysing the associations between somatic symptoms, forms of ethnic discrimination, and protective factors.

 	Model 2	Model 3	
Predictors	b[95% CI]	SEb	Beta	t	P	b[95% CI]	SEb	Beta	t	P	
(Constant)	5.498	3.350	 	1.641	.002	9.745	3.755	 	2.595	<.010*	
 	[−1.076, 12.072]	 	 	 	 	[2.374, 17.116]	 	 	 	 	
Number of traumatic events	.324	.103	.229	3.162	.002*	.256	.105	.126	2.441	.016*	
 	[.122, .527]	 	 	 	 	[.049, .462]	 	 	 	 	
Gender	−2.421	1.073	–.247	−2.256	.024*	−2.979	1.007	–.374	−2.957	.003*	
 	[−4.525,–.317]	 	 	 	 	[−4.954, −1.004]	 	 	 	 	
Age	.115	.066	.075	1.752	.080	.091	.062	.039	1.481	.139	
 	[–.014, .244]	 	 	 	 	[–.030, .212]	 	 	 	 	
Active harm	2.060	.899	.284	2.291	.022*	1.585	.846	.197	1.874	.061	
 	[.296, 3.823]	 	 	 	 	[–.074, 3.243]	 	 	 	 	
Passive harm	–.582	.619	–.072	–.939	.348	–.193	.589	–.078	–.328	.743	
 	[–.1.796, .633]	 	 	 	 	[−1.349, .962]	 	 	 	 	
Institutional discrimination	.515	.596	.082	.864	.388	.755	.584	.075	1.293	.198	
 	[–.658, 1.688]	 	 	 	 	[–.397, 1.907]	 	 	 	 	
In-group identification	 	 	 	 	 	.529	.411	–.444	1.287	.198	
 	 	 	 	 	 	[–.277, 1.336]	 	 	 	 	
Social support	 	 	 	 	 	–.318	.071	.014	−4.463	<.001*	
 	 	 	 	 	 	 	 	 	 	 	
R2	 	 	.23	 	 	 	 	.33	 	 	
Adjusted R2	 	 	.19	 	 	 	 	.30	 	 	
Change in R2	 	 	.06	 	.017	 	 	.10	 	<.001	
p	 	 	<.001	 	 	 	 	<.001	 	 	
Notes: Beta = standardized beta from original data, *p < .05, b = regression coefficient and 95% confidence interval for b, SEb= standardized error for b, n = 144.

3.2.1.3. Posttraumatic stress symptoms

In Model 2, when ethnic and institutional discrimination were added to the analysis, posttraumatic stress symptoms were associated with the number of traumatic events and age. This model accounted for 29% of the variance in posttraumatic stress symptoms (see Table 5; R2 = .29, F(6,137) = 9.509, p < .001). The contribution of forms of discrimination to the Model 2 was not significant (R2change = .04, p = .080) The addition of the protective factors in Model 3 contributed an additional 10% to the explanation of the total variance in posttraumatic stress symptoms, resulting in an overall explanation of 39% (Model 3; R2 = .39, F(8,135) = 10.982, p < .001). Increased posttraumatic stress symptoms were associated with a higher number of traumatic events, older age of participants, and lower social support. Table 5. Hierarchical multiple linear regression model analysing the associations between posttraumatic stress symptoms, forms of ethnic discrimination, and protective factors.

 	Model 2	Model 3	
Predictors	b[95% CI]	SEb	Beta	t	P	b[95% CI]	SEb	Beta	t	P	
(Constant)	−1.479	8.194	 	–.180	.857	9.927	9.050	 	1.097	.273	
 	[−17.577, 14.619]	 	 	 	 	[−7.850, 27.704]	 	 	 	 	
Number of traumatic events	1.255	.239	.464	5.256	<.001*	1.080	.242	.369	4.267	<.001*	
 	[.785, 1.725]	 	 	 	 	[.603, 1.557]	 	 	 	 	
Gender	−1.460	2.676	–.102	–.546	.586	−2.845	2.485	–.227	−1.145	.252	
 	[−6.715, 3.794]	 	 	 	 	[−7.722, 2.031]	 	 	 	 	
Age	.409	.163	.144	2.514	.012*	.354	.153	.112	2.232	.020*	
 	[.090, .728]	 	 	 	 	[.055, .654]	 	 	 	 	
Active harm	1.658	2.170	.104	.764	.445	.458	2.018	.031	.227	.820	
 	[−2.600, 5.916]	 	 	 	 	[−3.500, 4.417]	 	 	 	 	
Passive harm	−2.870	1.511	–.097	−1.899	.058	−1.963	1.436	–.092	−1.367	.172	
 	[−5.834, .095]	 	 	 	 	[−4.780, .855]	 	 	 	 	
Institutional discrimination	.719	1.415	.097	.508	.612	1.267	1.382	.071	.917	.361	
 	[−2.067, 3.505]	 	 	 	 	[−1.461, 3.995]	 	 	 	 	
In-group indentification	 	 	 	 	 	1.006	1.003	.032	1.003	.316	
 	 	 	 	 	 	[–.962, 2.974]	 	 	 	 	
Social support	 	 	 	 	 	–.769	.172	–.424	−4.476	<.001*	
 	 	 	 	 	 	[−1.106, –.432]	 	 	 	 	
R2	 	 	.29	 	 	 	 	.39	 	 	
Adjusted R2	 	 	.26	 	 	 	 	.36	 	 	
Change in R2	 	 	.04	 	.080	 	 	.10	 	<.001	
p	 	 	<.001	 	 	 	 	<.001	 	 	
Notes: Beta = standardized beta from original data, *p < .05, b = regression coefficient and 95% confidence interval for b, SEb= standardized error for b, n = 144.

3.3. Results for hypothesis 2: moderation of association between ethnic discrimination and mental health outcomes by social support and in-group identification

Moderation analyses were performed to determine whether the relations between the three forms of ethnic discrimination, i.e. active harm, passive harm, and institutional discrimination, and the mental health outcomes (depression, somatic symptoms, posttraumatic stress symptoms) were moderated by the potential protective factors social support and in-group identification. The results of all moderation analyses are presented in tables in the Appendix.

3.3.1. Depressive symptoms

The three interaction terms were not significant (active harm: b = −0.04, p = .70; passive harm b = −0.01, p = .86; institutional discrimination: b = 0.02, p = .73), indicating that social support did not moderate the associations between the three forms of ethnic discrimination and depressive symptoms. The same applies to in-group identification (active harm: b = −0.06, p = .95; passive harm b = −0.03, p = .96; institutional discrimination: b = 0.06, p = .89).

3.3.2. Somatic symptoms

Social support did not significantly moderate the association between ethnic discrimination and somatic symptoms (active harm: b = −0.03, p = .80; passive harm b = 0.03, p = .70; institutional discrimination: b = −0.03, p = .60). No significant interaction terms emerged regarding in-group identification (active harm: b = 0.20, p = .82; passive harm b = 0.38, p = .41; institutional discrimination b = −0.68, p = .09).

3.3.3. Posttraumatic stress symptoms

The association between discrimination and posttraumatic stress symptoms was not moderated by social support (active harm: b = 0.05, p = .83; passive harm b = 0.04, p = .78; institutional discrimination: b = −0.02, p = .89) or by in-group identification (active harm: b = −.99, p = .62; passive harm b = −0.26, p = .80; institutional discrimination: b = −1.0, p = .25).

3.4. Results for hypothesis 3: multiple regression model with HCC

No significant correlation was observed between any of the confounding variables (age, gender, BMI, hair-washing per week, hair-colouring and curled hair) and HCC (rs = –.04 to .18). Due to the limited sample size of participants contributing HCC, the confounding variables were not included in the regression analysis. The model was not significant (R2= .06, F(3,64) = 1.365, p = .262; see Table 6). HCC in asylum seekers was not associated with ethnic or institutional discrimination. Table 6. Multiple linear regression model analysing the associations between HCC and ethnic and institutional discrimination.

 	Model 1	
Predictors	b [95% CI]	SEb	Beta	t	P	
(Constant)	.746	.146	 	5.089	<.001	
 	[.458, 1.033]	 	 	 	 	
Active harm	–.039	.069	–.131	–.591	.575	
 	[–.174, .097]	 	 	 	 	
Passive harm	–.027	.049	–.157	–.541	.588	
 	[–.123, .070]	 	 	 	 	
Institutional discrimination	.049	.048	.182	1.010	.313	
 	[–.046, .144]	 	 	 	 	
R2	 	 	.06	 	 	
Adjusted R2	 	 	–.00	 	 	
p	 	 	.262	 	 	
Notes: Beta = standardized beta (reported from original data), b = regression coefficient and 95% confidence interval for b, SEb= standardized error for b, n = 68.

4. Discussion

This study investigated the associations between perceived ethnic and institutional discrimination, mental health outcomes, and protective factors in asylum seekers, and further examined whether HCC was related to experiences of discrimination.

In line with hypothesis 1, active harm was positively correlated with all mental health outcomes, while all forms of ethnic and institutional discrimination were linked to higher depressive symptoms. Although lower social support was associated with higher psychological symptom severity, our findings did not support hypothesis 2, i.e. social support and in-group identification did not mitigate or buffer the associations between ethnic discrimination and mental health. Furthermore, and contrary to hypothesis 3, there was no significant association between discrimination and HCC in our sample.

In the present sample, passive forms of ethnic discrimination were reported more than twice as often as active forms. This corresponds to findings from a study examining discrimination against immigrant populations in Finland (Rask et al., 2018).

Regarding the first hypothesis, the correlation of depressive symptoms with all forms of discrimination is in accordance with previous studies in refugee and immigrant populations (Alemi et al., 2017; Ellis et al., 2008; Um et al., 2015). The finding that increased posttraumatic stress and somatic symptoms were only correlated with active harm corresponds to a previous study in a Syrian refugee population in Germany, which reported an association between posttraumatic stress symptoms and perceived discrimination (Viazminsky et al., 2022). Participants with posttraumatic stress symptoms often suffer from hyperarousal and hypervigilance, which might sensitize them to the consequences of discriminatory actions. In line with our findings on somatic symptoms, a study by Jang et al. (Jang et al., 2023) reported a positive association between both subtle and overt discrimination and somatic symptom disorder among young adults with immigrant backgrounds in South Korea. It is also worth noting that there was an inverse relationship between passive harm and institutional discrimination. This may reflect two distinct poles of experience, one involving a greater degree of neglect and the other a greater degree of interference.

After accounting for other forms of discrimination and potential confounding factors in the regression analyses, the associations between active harm and depressive, somatic or posttraumatic stress symptoms were no longer significant, as was the association between institutional discrimination and depressive symptoms. In light of the remaining associations, it is reasonable to conclude that trauma has a particularly pronounced effect on depressive, somatic, and posttraumatic stress symptoms, with active harm and institutional discrimination playing a less important role. The correlation analyses also indicated a positive correlation between passive harm and depression. However, in the multivariate analysis, passive harm was found to be negatively associated with depression. The effect was no longer evident when social support was incorporated into the model. It may be the case that asylum seekers who have recently arrived in the host country perceive passive harm as a form of social support and friendly interaction with the wider society, particularly when considering the traumatic experiences they have faced. Instances of unwanted help and assistance may then have a protective effect on mood, when considering the impact of traumatic events and other forms of discrimination. In other words, the act of reaching out in a well-intentioned yet misguided manner may prove beneficial in decreasing depressive symptoms in the context of overt discriminatory actions.

Regarding the second hypothesis, our finding that social support and in-group identification did not buffer the association between discrimination and mental health outcomes was surprising. However, a study by Alemi et al. (2017) reported similar results regarding social support and ethnic identity in first- and second-generation Afghan-Americans: Neither factor buffered the relationship between depressive symptoms and perceived discrimination. In-group identification showed a negative correlation with active harm and a positive correlation with social support in the bivariate correlation analyses. This may indicate that perceived support may have originated from the in-group, as individuals who had a stronger social identity towards a specific group reported higher levels of social support from that group (Guan & So, 2016). However, when an individual receives social support from their in-group, the potential buffering effects of that support may be diminished because the higher in-group identification may simultaneously increase their vulnerability to discrimination, as discriminatory acts directly affect core aspects of their social identity, i.e. belonging to the discriminated in-group (Alemi et al., 2017; Goreis et al., 2020). The finding that in-group identification has no mitigating effect on perceived discrimination is also consistent with the majority of studies included in the meta-analytic review by Pascoe and Richman (2009), but not with the studies by Mewes et al. (2015) and Ikram et al. (2016). However, the present sample differs from other study samples as it includes participants from multiple countries and numerous ethnic groups. For example, Afghan society is composed of multiple different ethnic groups with a history of intergroup conflicts (Mazhar et al., 2012). Consequently, Afghan ethnic minorities may additionally face discrimination from Afghan ethnic majorities both in their home country and in host countries (Groen et al., 2018). This can lead to a decrease in in-group identification. Moreover, in-group identification was negatively correlated to the number of traumatic events. It is possible that traumatic events were perpetrated by one's own in-group, nation, or representatives of the home country. This could explain why in-group identification did not serve as a protective factor in the present study, as one's own in-group was not perceived positively. If this group is then subjected to discrimination, in-group identification may diminish further (Bobowik et al., 2017). Future research should address these issues by examining larger groups of specific ethnic heritages to gain a more comprehensive understanding of in-group identification in the context of asylum seekers.

Contrary to our third hypothesis, no association was found between HCC and perceived ethnic or institutional discrimination. Due to the lack of previous studies investigating this relationship, it is difficult to interpret this finding. However, a study by Dajani et al. (2018) likewise found no association between perceived stress and HCC in forcibly displaced Syrian adolescents living in Jordan. The ongoing stress within forcibly displaced people may moderate the impact of chronic stress on HCC (Stalder et al., 2017). Immediately after the occurrence of psychologically traumatic experiences, cortisol levels often rise (Etwel et al., 2014; Mewes et al., 2017), while in the long term, when traumatic experiences are more distant or recurrent, cortisol secretion tends to decrease (Steudte et al., 2013). As the participants in the present study were newly arrived in Germany, and since the impact of ethnic discrimination may become more apparent after chronic exposure (Goreis et al., 2022), it is possible that its impact on HCC will only become evident after a certain period of time. The present data is in line with this assumption, as there was a positive correlation between active harm and length of stay. It is possible that governmental regulations and the initial housing situation after arriving in the host country contribute to a limited range of motion of asylum seekers, which in turn results in less contact with individuals from the host society and a reduction in the risk of being attacked by perpetrators. Due to the high genetic variability in the sample, it is possible that this study was unable to detect an association between adversity and HCC. Previous research suggests that different racial groups may respond to stressors in different ways, potentially through a multitude of mechanisms, including genetic, lifestyle, and social factors (Brunst et al., 2014).

From an intersectional perspective, asylum seekers may experience discrimination that is influenced by multiple categories, including ethnicity, migratory background, residence status, religion, and gender (Ziersch et al., 2020). Intersectionality is a framework that analyses how various social identities intersect and interact to shape individuals’ experiences of privilege, oppression, and disadvantage. It acknowledges that different dimensions of identity, such as race, gender, and class, intersect within systems of power, resulting in distinct and interconnected forms of discrimination and inequality. As a result, asylum seekers may experience a multiplicative impact of discrimination on their health, as argued in a study focusing on immigrants and was found in a study with asylum seekers conducted in Australia (Viruell-Fuentes et al., 2012; Ziersch et al., 2020). The present study was unable to entangle the interplay between the various factors associated with ethnic discrimination. However, it is intended to provide a preliminary basis for further studies that will investigate the relationships more precisely, for example, within specific groups.

4.1. Limitations

The present findings should be interpreted in view of several limitations. First, due to the cross-sectional design, causal relationships cannot be derived. Second, self-report questionnaires may yield less reliable results compared to clinical interviews, and the scale assessing passive harm had questionable internal consistency, limiting the informative value and generalizability of the results on passive harm. In the context of assessing unique experiences, such as trauma exposure or discrimination, Cronbach's alpha may not be as useful or as high as for assessing psychological constructs. For instance, having experienced one type of trauma or discrimination does not necessarily indicate exposure to others. While a lower Cronbach's alpha may be a concern, it is of greater importance to ensure the validity of the measures used, which has been done in previous research (Sibley, 2011). Third, the PDS-5 was not used to measure posttraumatic stress symptoms, but its predecessor. This means that recent changes to the criteria for posttraumatic stress disorder in the DSM-5 have not yet been taken into account in this study. Fourth, one major shortcoming is the use of a modified, unvalidated measure in the context of institutional discrimination. Fifth, the differences in gender, length of stay in Germany and institutional discrimination indicate variations between the sample with and without HCC. It is important to acknowledge that the participants in the HCC sample may represent a specific subgroup within the total sample. Lastly, the generalizability of the study may be limited by the high educational level of the participants. However, the gender and age distribution align with asylum seekers living in Germany (Federal Office for Migration and Refugees, 2014).

5. Conclusion

This study highlights the potentially detrimental relation between ethnic and institutional discrimination and the mental health of asylum seekers. Psychological treatment programmes aimed at addressing the mental health of asylum seekers should incorporate discussions on ethnic and institutional discrimination and provide strategies for effectively coping with such experiences. Consistent with previous research, subtle forms of ethnic discrimination were more prevalent than overt forms. As members of the host society may not realize that these subtle forms constitute discrimination, awareness of such passive harm needs to be fostered (Duveau et al., 2023). In general, social support appears to be crucial in promoting mental health, and further research should explore how to provide effective social support in the face of ethnic and institutional discrimination.

Supplementary Material

Appendix_revised_R2_anonymous.docx

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, [RM], upon reasonable request. The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is therefore only available with further anonymization.
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