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Prevalence and clinical severity of mood disorders among first-, second- and third-generation migrants
Journal of Affective Disorders 210 (2017) 174–180
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Research paper
Prevalence and clinical severity of mood disorders among first-, second- and third-generation migrants
MARK
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Baptiste Pignona, , Pierre Alexis Geoffroyc,d,e,f, Pierre Thomasa,h,i, Jean-Luc Roelandtg,i, Benjamin Rollanda,j, Craig Morganb, Guillaume Vaivaa,h, Ali Amada,b a
Univ. Lille, CNRS, CHU LILLE, UMR9193-PsychiC-SCALab, UMR9193-PsychiC-SCALab, Psychiatry Department, F-59000 Lille, France King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK c Inserm, U1144, Paris F-75006, France d Paris Descartes University, UMR-S 1144, Paris F-75006, France e Paris Diderot University, Sorbonne Paris Cité, UMR-S 1144, Paris F-75013, France f AP-HP, GH Saint-Louis – Lariboisière – F. Widal, Psychiatry and Addiction Medicine Department, 75475 Paris Cedex 10, France g World Health Organization Collaborative Centre (WHO-CC), EPSM Lille-Metropole, Lille, France h Federation of Mental Health Research, Lille, France i INSERM 1123, Equipe ECEVE, Paris, France j Univ. Lille, INSERM, CHU LILLE, U1171, Department of Addiction Medicine - Addiction Consultation Liaison Unit, Pôle de Psychiatrie, F-59000 Lille, France b
A R T I C L E I N F O
A BS T RAC T
Keywords: Migration Bipolar disorder Depressive disorder Dysthymia Comorbidities
Background: The role of migration as a risk factor remains unknown for mood disorders because of poor data. We sought to examine the prevalence and severity of mood disorders (bipolar disorder (BD), unipolar depressive disorder (UDD) and dysthymia) in first, second, and third generation migrants in France. Methods: The Mental Health in the General Population survey interviewed 38,694 individuals. The prevalence of lifetime mood disorders, comorbidities, and clinical features was compared between migrants and nonmigrants and by generation. All analyses were adjusted for age, sex and level of education. Results: The prevalence of any lifetime mood disorder was higher in migrants compared with non-migrants (OR=1.36, 95% CI [1.27–1.45]). This increased prevalence was significant for UDD (OR=1.44, 95% CI [1.34– 1.54]), but not for BD (OR=1.15, 95% CI [0.96–1.36]) or dysthymia (OR=1.09, 95% CI [0.94–1.27]), although the prevalence of BD was increased in the third generation (OR=1.27, 95% CI [1.01–1.60]). Migrants with BD or UDD were more likely to display a comorbid psychotic disorder compared to non-migrants with BD or UDD. Cannabis-use disorders were more common in migrant groups for the 3 mood disorders, whereas alcohol-use disorders were higher in migrants with UDD. Posttraumatic stress disorder was more frequent among migrants with UDD. Limitations: The study used cross-sectional prevalence data and could be biased by differences in the course of disease according to migrant status. Moreover, this design does not allow causality conclusion or generalization of the main findings. Conclusion: Mood disorders are more common among migrants, especially UDD. Moreover, migrants with mood disorders presented with a more severe profile, with increased rates of psychotic and substance-use disorders.
1. Introduction Mood disorders, including bipolar disorder (BD), unipolar depressive disorder (UDD) and dysthymia, are leading causes of morbidity around the world due to their high prevalence (approximately 1 to 2% for BD (Fagiolini et al., 2013), 16% for UDD (Kessler et al., 2003) and
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1% for dysthymia (Blanco et al., 2010)), their impact on functioning and quality of life, and their long disease course (Bruffaerts et al., 2012; Miret et al., 2013; Phillips and Kupfer, 2013). Subjects with mood disorders have, moreover, elevated mortality rates (Angst et al., 2002), particularly because of suicidal behaviour (Pompili et al., 2012; Schaffer et al., 2014) and cardiovascular diseases (Fagiolini et al.,
Correspondence to: Hôpital Fontan, Fontan, CHRU de Lille, F-59037 Lille Cedex, France. E-mail address: [email protected] (B. Pignon).
http://dx.doi.org/10.1016/j.jad.2016.12.039 Received 29 August 2016; Received in revised form 27 October 2016; Accepted 17 December 2016 Available online 27 December 2016 0165-0327/ Crown Copyright © 2016 Published by Elsevier B.V. All rights reserved.
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subjects with the same characteristics as the general population on predefined characteristics, such as age, sex, educational level, occupational category, and professional status (according to census figures from 1999 provided by the French National Institute for Statistics and Economic Studies). Subjects were included in the study if they met the following criteria: 1) provided informed consent to participate in the survey, 2) spoke French, 3) were aged 18 years and over, and 4) were neither institutionalized nor homeless. Legal authorisation was obtained by the “Commission Nationale Informatique et Liberté” (CNIL) and the “Comité consultatif sur le traitement de l'information en matière de recherche” (CCTIRS), with number 98.126. Additional methodological details can be found elsewhere (Amad et al., 2013; Caria et al., 2010; Leray et al., 2011).
2005; Mathur et al., 2016). Even if their pathophysiology remains mostly unknown, it is widely demonstrated that gene-environment interactions play an important role in the genesis of mood disorders (Craddock and Forty, 2006; Etain et al., 2008; Geoffroy et al., 2013). Foreign migration is associated with increased prevalence (i.e., cases in a given population at a specific time) of psychotic disorders and schizophrenia among some minority ethnic and/or migrant populations (Selten et al., 2012; Termorshuizen et al., 2014). Previous studies demonstrated increased incidences (i.e., new cases per given population per year) of psychotic disorders and schizophrenia in migrants in first and second generation, and thus confirmed migration as a risk factor (Bourque et al., 2011; Cantor-Graae and Selten, 2005), which has also been shown to occur in France (Amad et al., 2013; Tortelli et al., 2013). Nevertheless, migration remains a topic of debate concerning a potential influence on incidence and prevalence of mood disorders. For instance, a study using data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) of 43,093 individuals representative of the general population found that foreign-born Mexican Americans and foreign-born non-Hispanic whites had a lower prevalence of mood, anxiety and substance use disorders (SUD) compared with their US-born counterparts, which suggests a “healthy migrant effect” (Grant et al., 2004). More specifically, results from different studies of migration on either mania or BD (Lloyd et al., 2005; Selten et al., 2003), UDD (Bhugra, 2003; Kerkenaar et al., 2013; Selten et al., 2003) or dysthymia (Breslau et al., 2011) were contradictory, driving Swinnen and Selten to conduct a meta-analysis of the 14 incidence-based studies of migration and mood-disorders (BD, UDD, and mood disorders of unspecified polarity). They found that, adjusting for age and gender, the RR of developing any mood disorder was 1.38 (95% CI [1.17–1.62], p < 0.001) (Swinnen and Selten, 2007), which is less than the risk of developing schizophrenia. More recently, Cantor-Graae et al. studied the influence of migration on the incidence of a full spectrum of psychiatric disorders in a large Danish registry-based cohort study (n=1,859,419). After adjustment for sex, age, calendar year, and the interaction between age and sex, risk for at least one psychiatric disorder was increased in all migrant populations (except Danish expatriates who were born abroad). The incidence of the different psychiatric disorders varied according to generational status of migrants, in particular between the first and second generation. Interestingly, incidence rate ratios (IRR) of BD and affective disorders were only increased among second-generation migrants with one foreign-born parent (Cantor-Graae and Pedersen, 2013). Most of these migration studies are incidence-based and require long follow-up periods to be accurate. Prevalence studies, on the other hand, are appropriate to assess the severity of a disease and/or the comorbidities according to clinical or biological factors and can provide important insights on factors associated with different courses of the disease, i.e., modifiers of a disease (Stolk et al., 2007). Therefore, the present study aimed to examine the prevalence of mood disorders (including BD, UDD and dysthymia) in migrant groups, both overall and according to first (1GM), second (2GM) and third (3GM) generation, in a large cross-sectional survey. Finally, we compared psychiatric comorbidities and clinical features, including psychotic disorders, previous suicide attempts, anxiety disorders and SUD, according to migrant status.
2.2. Assessment of psychiatric disorders and clinical features At each site, the Mini International Neuropsychiatric Interview (MINI, French version 5.0.0), a standardized psychiatric interview, was used to screen for psychiatric disorders. The MINI is a brief structured diagnostic interview developed by psychiatrists in the United States and Europe for screening of ICD-10 psychiatric disorders in the general population. The MINI has been previously validated in the general population and has good to very good validity, reliability (inter-rater and test-retest), sensitivity and specificity (Sheehan et al., 1997). All of the MHGP interviewers (nurses and psychologists) were trained to administer the MINI by using video recordings of interviews over a 3-d session by WHO-CC experts. Lifetime mood disorders, according to ICD-10 criteria, included the following: BD (F30 and F31), UDD (F32 and F33) and dysthymia during the last two years (F34.1). When compared with the Composite International Diagnostic Interview (CIDI), the MINI has “good” to “very good” kappa values. For BD, the kappa coefficients were 0.65– 0.74, the sensitivities were 0.74–0.89, and the specificities were 0.93– 0.97. For UDD, they were, 0.74, 0.93 and 0.80, respectively (Amorim et al., 1998). For dysthymia, when compared with the Structured Clinical Interview for DSM-III (SCID), they were 0.52, 0.67 and 0.99, respectively (Sheehan et al., 1998). Lifetime comorbidities and clinical features associated with mood disorders were also extracted from the MINI and analysed: previous suicide attempts, anxiety disorders (panic disorder with or without agoraphobia (F41.0 and F40.01)), social phobia (F40.1), generalized anxiety disorder (GAD) (F41.1), post-traumatic stress disorder (PTSD) (F43.1), and SUD (alcohol use disorders (AUD) and cannabis use disorders (CUD) (F10.1, F10.2, F12.1 and F12.2)). Lifetime psychotic disorders were also extracted. Indeed, the MINI includes a lifetime psychotic disorders section with nine items. The questions target the occurrence of paranoid delusions, delusions of persecution, thought broadcasting, delusions of control, delusions of reference, and visual and auditory hallucinations. The diagnoses of lifetime psychotic disorders were always confirmed by a senior psychiatrist familiar with transcultural psychiatry. For psychotic symptoms, when compared with the CIDI, kappa has been shown to have a good value, i.e., above 0.70. Sensitivity, specificity and positive predictive values have also been found to be above 0.85, 0.90 and 0.70, respectively (Amorim et al., 1998). 2.3. Assessment of migrant status
2. Materials and methods The designation of migrant status was based on the country of birth of the subject, the subject's parents, and the subject's grandparents. In light of the literature on migrant populations (Cantor-Graae and Pedersen, 2013; Selten et al., 2012, 2003; Sieberer et al., 2011; Swinnen and Selten, 2007; Tortelli et al., 2013), we defined a migrant as 1GM (a subject born outside of metropolitan France), 2GM (at least one parent born outside of metropolitan France), or 3GM (at least one grandparent born outside of metropolitan France). This information
2.1. Mental Health in General Population (MHGP) survey The French cross-sectional MHGP survey, conducted by the World Health Organization Collaborating Centre (WHO-CC), interviewed 38,694 subjects between 1999 and 2003. These subjects were selected in 47 study sites (900 subjects per site) by a quota sampling method (Lunsford and Lunsford, 1995). This method develops a sample of 175
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was obtained from answers to the following 3 questions: “Where were you born?” “Where were your parents born?” and “Where were your grandparents born?”. Migrant generations were exclusive from each other (e.g. a subject born outside of metropolitan France, even with parents or grand-parents also born outside, was considered as a firstgeneration migrant).
were migrants (25.7%), from 1GM (n=2052, 5.3%), 2GM (n=4151, 10.7%), or 3GM (n=3756, 9.7%). BD was diagnosed in 614 subjects (1.6% of the total population), of whom 194 were migrants (1.9% of the migrant sample) and 420 non-migrants (1.5% of the non-migrant sample). UDD was diagnosed in 4131 subjects (11.2% of the total population), consisting of 1325 migrants (13.3% of the migrant sample) and 2806 non-migrants (9.8% of the non-migrant sample). Dysthymia was diagnosed in 901 subjects (2.3% of the total population), comprised of 242 migrants (2.4% of the migrant sample) and 659 non-migrants (2.3% of the non-migrant sample). The most common source of migration was Europe (48%), Maghreb (28%) and subSaharan Africa (7%). The proportion of migrants from different regions of origin was not significantly different between the overall sample of migrants and migrants with BD, UDD or dysthymia, except 2GM with BD from Maghreb (48.3% in the sample with BD vs. 35.8% in the whole sample, p < 0.05) (see Supplementary Table 2).
2.4. Statistical analyses To assess if each of the mood disorders was associated with migration and/or different generations of migration (1GM, 2GM or 3GM), we performed logistic regression analyses adjusting for potential confounding factors identified from the literature: sex, age and level of education (Bhugra et al., 2014). We compared origins of migrants in the different samples by using chi-square tests or Fisher tests if sample sizes were not sufficient. Finally, we also assessed several comorbidities and clinical features according to migration-status using chi-square tests. All statistical analyses were performed using R software (R Core Team, 2013).
3.2. Risk of mood disorders according to migrant status After adjusting for age, sex and level of education, the logistic regression showed a higher prevalence of mood disorders in migrants (i.e., sum of prevalences for BD, UDD and dysthymia) compared to non-migrants (odds-ratio (OR)=1.36, 95% CI [1.27–1.45], p < 0.001). The prevalence of BD was not significantly different between the total sample of migrants and non-migrants (OR=1.15, 95% CI [0.96–1.36], p=0.126). The analysis by generation showed that the prevalence of BD was significantly higher in the 3GM (OR=1.27, 95% CI [1.01–1.60], p
3. Results 3.1. Population and sociodemographic characteristics Sociodemographic characteristics of individuals with mood disorders are summarized in Table 1 (for the whole sample, see Supplementary Table 1). Of the 38,694 individuals interviewed, 9959
Table 1 Sociodemographic characteristics of subjects with mood disorders in non-migrants and three generations of migrants. Non-migrants
All migrants
p-values*
First generation
Second generation
Third generation
n=3677 45.1 (18.6)
n=1665 37.8 (15.9)
< 0.001
n=352 44.0 (16.1)
n=702 37.0 (16.3)
n=611 35.1 (14.3)
35.5 64.5
45.9 54.1
< 0.001
49.4 50.6
46.4 53.6
43.2 56.8
28.9 51.4 19.6
20.3 56.6 23.1
< 0.001
29.5 46.3 24.1
19.8 58.8 21.4
15.5 60.1 24.4
n=420 39.4 (14.8)
n=194 33.4 (12.1)
n=34 43.2 (14.1)
n=76 32.8 (11.1)
n=84 30.1 (10.1)
54.2 45.8
68.0 32.0
0.001
50.0 50.0
75.0 25.0
69.0 31.0
20.5 54.8 24.8
11.3 63.4 25.3
0.018
26.5 50.0 23.6
11.8 68.3 19.7
4.8 64.3 30.9
Unipolar depressive disorder Mean age (SD) Sex (%) Men Women Education level (%) No education-primary level Secondary level University level
n=2950 45.8 (19.0)
n=1389 38.4 (16.6)
n=293 44.4 (16.6)
n=567 37.6 (17.2)
n=465 35.7 (14.8)
33.4 66.6
43.2 56.7
49.1 50.9
43.6 56.4
39.1 60.8
30.4 51.2 18.4
21.1 56.7 22.2
30.4 47.1 22.5
21.0 58.6 20.5
15.5 60.4 24.1
Dysthymia Mean age (SD) Sex (%) Men Women Education level (%) No education-primary level Secondary level University level
n=659 46.8 (18.41)
n=242 38.4 (15.2)
n=41 43.2 (14.5)
n=98 36.9 (14.5)
n=103 37.9 (15.9)
31.7 68.3
41.4 58.6
0.007
51.2 47.8
40.9 59.1
37.9 62.1
31.3 49.8 19.0
26.0 49.2 24.8
0.102
34.1 31.7 34.1
19.4 55.1 25.5
29.1 50.5 20.4
Any mood disorder Mean age (SD) Sex (%) Men Women Education level (%) No education-primary level Secondary level University level Bipolar disorder Mean age (SD) Sex (%) Men Women Education level (%) No education-primary level Secondary level University level
*
< 0.001
< 0.001 < 0.001
< 0.001
< 0.001
Comparisons between all generations of migrants and non-migrants using the t-test and chi-squared test.
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dysthymia) in migrant groups in France. Several important findings resulted from this investigation: i) a higher lifetime prevalence of any mood disorder in migrants among all three generations; ii) a higher lifetime prevalence of BD in the third generation; iii) a higher lifetime prevalence of UDD in migrants among all three generations; and iv) no significant differences in the prevalence of dysthymia according to migrant status. This design, based on a general population survey, can help to overcome biases met by most of the previous prevalence-based migration studies (Kerkenaar et al., 2013; Selten et al., 2012), which were based on psychiatric registers and census figures, and thus might underestimate the migrant populations in these data (e.g., undocumented or recently moved), and therefore overestimate the risk of psychiatric disorders (Bourque et al., 2011). We were also able to compare the psychiatric comorbidities and the clinical features according to migrant status. Among migrants, mood disorders were globally more severe, with: i) a higher prevalence of psychotic disorders for migrants with BD and UDD; ii) a higher prevalence of CUD for migrants group with each of the mood disorders, iii) a higher prevalence of AUD for migrants with UDD, and iv) a higher prevalence of PTSD for migrants with UDD. Furthermore, this study found a significantly increased prevalence of BD in the 3GM, without statistically significant differences when the whole migrant group was analysed. These results are consistent with the Swinnen and Selten meta-analysis that showed that the incidence of BD was not increased in the 1GM and the 2GM in comparison with non-migrants, after excluding a single migrant group of AfricanCaribbean origin living in the UK (RR=1.75, 95% CI [0.94–3.28], p=0.26) (Swinnen and Selten, 2007). In a more recent registry-based study, the risk of receiving treatment for BD (i.e., prevalent cases) was compared between migrants and Dutch nationals in an ethnically mixed catchment area. The risk of being treated for BD was not significantly increased for any group, except for the Turkish-Dutch of the 2GM (Selten et al., 2012). Thus, migrants' risk for BD appears to be far less significant than their risk of schizophrenia and psychotic disorders, both in prevalence (Selten et al., 2012; Termorshuizen et al., 2014) and in incidence (Bourque et al., 2011; Cantor-Graae and Selten, 2005). These results suggest that environmental risk factors could be different or could play a different role in schizophrenia and BD. For example, several factors that impair neurodevelopment (e.g., copy number variants and obstetric complications) have been associated with schizophrenia but not with BD (Demjaha et al., 2012), as seems to be specifically the case for migration. Indeed, even if few studies have focused on pre-migration factors (Chou, 2009), exposition to trauma cannot seem to explain the difference between schizophrenia and BD, as trauma is highly prevalent in both disorders (Demjaha et al., 2012). Alternatively, we can also hypothesize that these results reflect an increased prevalence of BD in migrants that only reached significance in the 3GM. Indeed, in Denmark, Cantor Graae et al. interestingly found an increasing incidence of BD among migrants only in the 2GM (Cantor-Graae and Pedersen, 2013). This generation effect suggests that the increased prevalence of BD in the 3GM may be caused by post-migration factors (social defeat, achievement-expectation mismatch, etc.), rather than selective migration or migration per-se (Bourque et al., 2011). Conversely, the lifetime prevalence rate of UDD was higher in migrants of all three generations and ORs tended to decrease across the generations (for 1GM: OR=1.52; for 2GM: OR=1.37; for 3GM: OR=1.19). These results require cautious interpretation, because the literature concerning UDD in migrants is scarce and heterogeneous. This may be due to different ways that depression is expressed in patients across cultures (Bhugra et al., 2014). For example, in their meta-analysis, Swinnen and Selten did not find sufficient data to be able to study the influence of migration on the incidence of UDD (Swinnen and Selten, 2007). More recently, Sieberer et al. showed that women from 1GM and 2GM were more likely to suffer from depressive symptoms compared with non-migrant women (Sieberer et al., 2011).
Table 2 Prevalence and odds ratio (OR) of mood disorders comparing non-migrants to different generations of migrants adjusted for age, sex and level of education. n (%)
OR [95%CI]
p-values*
Non-migrants All generation migrants First generation Second generation Third generation
Any mood disorder 3677 (12.80) 1665 (16.72) 352 (17.15) 702 (16.91) 611 (16.27)
(n=5342) – 1.36 [1.27; 1.39 [1.23; 1.28 [1.17; 1.20 [1.07;
1.45] 1.56] 1.40] 1.32]
– < 0.001 < 0.001 < 0.001 < 0.001
Non-migrants All generation migrants First generation Second generation Third generation
Bipolar disorder (n=614) 420 (1.46) – 194 (1.95) 1.15 [0.96; 34 (1.66) 1.07 [0.74; 76 (1.83) 0.99 [0.78; 84 (2.24) 1.27 [1.01;
1.36] 1.49] 1.27] 1.60]
– 0.126 0.724 0.960 0.047
Non-migrants All generation migrants First generation Second generation Third generation
Unipolar depressive disorder (n=4131) 2806 (9.77) – 1325 (13.30) 1.44 [1.34; 1.54] 293 (14.28) 1.52 [1.33; 1.73] 567 (13.66) 1.37 [1.24; 1.50] 465 (12.38) 1.19 [1.07; 1.32]
– < 0.001 < 0.001 < 0.001 0.001
Non-migrants All generation migrants First generation Second generation Third generation
Dysthymia (n=901) 659 (2.29) – 242 (2.43) 1.09 41 (2.00) 0.89 98 (2.36) 1.03 103 (2.74) 1.22
– 0.253 0.480 0.761 0.059
*
[0.94; [0.64; [0.83; [0.99;
1.27] 1.21] 1.27] 1.50]
Results from logistic regression, with non-migrants as reference category.
< 0.05) and was not significantly changed in 1GM and 2GM. The prevalence of UDD was significantly higher in the whole sample of migrants and in each generation of migrants (OR=1.44, 95% CI [1.34– 1.54], p≤0.001 for all generations, OR=1.52, 95% CI [1.33–1.73] for 1GM, OR=1.37, 95% CI [1.24–1.50] for 2GM, OR=1.19, 95% CI [1.07– 1.32] for 3GM). Finally, we did not observe any significant differences in the prevalence of dysthymia according to migrant status (Table 2). 3.3. Comorbidities and clinical features of mood disorders associated with migrant status The analyses of comorbidities and clinical features associated with the pooled mood disorders showed that migrants presented significantly more psychotic disorders (10.2% vs. 7.5%, p=0.001), as well as more CUD (3.2% vs. 1.3%, p < 0.001), AUD (11.6% vs. 8.5%, p < 0.001) and PTSD (2.9% vs. 1.7%, p < 0.005) in comparison with non-migrants with a mood disorder. There were no statistically significant differences concerning proportions of subjects with previous suicide attempts. More specifically, migrant individuals with BD (28.2% vs. 20.2%, p < 0.05) and with UDD (16.2% vs. 15.5%, p < 0.05) had significantly more psychotic disorders. Concerning SUD, CUD was significantly more frequent in the migrant groups for each of the mood disorders (8.8% vs. 3.4% for BD, 2.4% vs. 1% for UDD, 4.5% vs. 1.4% for dysthymia, p < 0.005). AUD was also more frequent in migrants with UDD (10.6% vs. 7.8%, p < 0.005), but not with BD and dysthymia. PTSD was significantly more frequent among migrants with UDD (2.6% vs. 1.5%, p < 0.05), but did not reach significance with BD (5.7% vs. 2.6%, p=0.06) or dysthymia (3.3% vs. 1.5%, p=0.09). Concerning other comorbid anxiety disorders (i.e., panic disorder, social phobia, and GAD), no statistically significant differences were found between migrants and non-migrants (Table 3). 4. Discussion Using a large sample of the general population, this is the first study to investigate the prevalence of mood disorders (BD, UDD and 177
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Table 3 Comorbidities of subjects with mood disorders in non-migrants and three generations of migrants.
All mood disorders Previous suicide attempt (n, %) Psychotic disorder (n, %) Cannabis use disorder (n, %) Alcohol use disorder (n, %) Panic disorder (n, %) Social phobia (n, %) Generalized anxiety disorder (n, %) Post-traumatic stress disorder (n, %) Bipolar disorder Previous suicide attempt (n, %) Psychotic disorders (n, %) Cannabis use disorder (n, %) Alcohol use disorder (n, %) Panic disorder (n, %) Social phobia (n, %) Generalized anxiety disorder (n, %) Post-traumatic stress disorder (n, %) Unipolar depressive disorder Previous suicide attempt (n, %) Psychotic disorders (n, %) Cannabis use disorder (n, %) Alcohol use disorder (n, %) Panic disorder (n, %) Social phobia (n, %) Generalized anxiety disorder (n, %) Post-traumatic stress disorder (n, %) Dysthymia Previous suicide attempt (n, %) Psychotic disorders (n, %) Cannabis use disorder (n, %) Alcohol use disorder (n, %) Panic disorder (n, %) Social phobia (n, %) Generalized anxiety disorder (n, %) Post-traumatic stress disorder (n, %) *
Non-migrants (n)
All-generation of migrants (n)
p-values*
First generation (n)
Second generation (n)
Third generation (n)
n=3677 675 (18.4) 276 (7.5) 46 (1.3) 311 (8.5) 686 (18.7) 427 (11.6) 1018 (27.7)
n=1665 301 (18.1) 169 (10.2) 53 (3.2) 193 (11.6) 341 (20.5) 198 (11.9) 480 (28.8)
0.790 0.001 < 0.001 < 0.001 0.117 0.768 0.388
n=352 51 (14.5) 38 (10.8) 2 (0.6) 29 (8.2) 79 (22.4) 44 (12.5) 95 (27.0)
n=702 122 (17.4) 57 (8.1) 14 (2.0) 74 (10.5) 142 (20.2) 88 (12.5) 201 (28.6)
n=611 128 (20.9) 74 (12.1) 37 (6.1) 90 (14.7) 120 (19.6) 66 (10.8) 184 (30.1)
62 (1.7)
48 (2.9)
0.004
20 (4.8)
20 (2.8)
11 (1.8)
n=420 95 (22.6) 85 (20.2) 14 (3.4) 70 (16.6) 128 (30.5) 66 (15.7) 84 (20.0)
n=194 68 (35.1) 55 (28.2) 17 (8.8) 43 (22.2) 62 (32) 36 (18.6) 47 (24.2)
0.856 0.026 0.004 0.102 0.711 0.379 0.234
n=34 34 (32.4) 4 (11.8) 0 (0.0) 6 (17.6) 12 (35.3) 6 (17.6) 8 (23.5)
n=76 24 (31.6) 21 (27.6) 5 (6.6) 14 (18.4) 25 (32.9) 18 (23.7) 18 (23.7)
n=84 33 (39.2) 30 (35.7) 12 (14.3) 23 (27.4) 25 (29.8) 12 (14.3) 21 (25.0)
11 (2.6)
11 (5.7)
0.058
3 (8.8)
6 (7.9)
2 (2.4)
n=2806
n=1325
n=293
n=567
n=465
455 (16.2) 161 (5.7) 29 (1.0) 220 (7.8) 501 (17.9) 312 (11.1) 829 (29.5)
205 (15.5) 101 (7.6) 32 (2.4) 141 (10.6) 256 (19.3) 154 (11.6) 395 (29.8)
0.555 0.020 < 0.001 0.003 0.256 0.633 0.861
36 (12.3) 30 (10.2) 2 (0.7) 22 (7.5) 61 (21.2) 35 (11.9) 81 (27.6)
85 (15.0) 33 (5.8) 8 (1.4) 58 (10.2) 109 (19.2) 67 (11.8) 172 (30.3)
38 (8.2) 30 (35.7) 22 (4.7) 61 (13.1) 85 (18.3) 52 (11.2) 142 (30.5)
43 (1.5)
35 (2.6)
0.015
13 (4.4)
14 (2.5)
8 (1.7)
n=659 144 (21.9) 85 (20.2) 9 (1.4) 45 (6.8) 117 (17.8) 80 (12.1) 163 (24.7)
n=242 50 (20.7) 30 (12.4) 11 (4.5) 10 (8.3) 55 (22.7) 31 (12.8) 63 (26.0)
0.828 0.065 0.004 0.460 0.092 0.786 0.690
n 4 5 0 2 8 7 8
n=98 25 (25.5) 9 (9.2) 4 (4.1) 5 (5.1) 21 (21.4) 12 (12.2) 21 (21.4)
n=103 21 (20.4) 16 (15.5) 7 (6.8) 13 (12.6) 25 (25.2) 12 (11.7) 34 (33.3)
10 (1.5)
8 (3.3)
0.089
3 (7.3)
3 (3.1)
2 (1.9)
=41 (9.8) (12.2) (0.0) (4.9) (19.5) (17.1) (19.5)
Comparisons between all generations of migrants and non-migrants using the t-test and chi-squared test.
(increasing) and UDD (decreasing), which suggests different time effects of migration on the expression of mood disorders. These results, taken as a whole, suggest that the 1GM appears more vulnerable to UDD, whereas the 3GM is more vulnerable to BD, while still at risk for UDD. Concerning dysthymia, which is also known to be a persistent depressive disorder, there were not any statistically significant differences between migrants and non-migrants. The literature concerning the effects of migration on dysthymia is particularly scarce and it would be difficult to draw definitive conclusions. In a cross-national study, Breslau et al. reported that, after arrival in the United States, Mexican migrants had a significantly higher incidence of any depressive disorder (including dysthymia) than non-migrant family members in Mexico, but there were not any analyses on dysthymia alone (Breslau et al., 2011). Other studies have reported an increased prevalence of dysthymia among elderly subjects who experienced migration (Carta et al., 2005; Robison et al., 2003). Regarding comorbidities and clinical features, migrants with mood
Nevertheless, our results are consistent with the implication of risk factors from migration itself, as trauma and other major life events, leaving a native country and family or resettlement-related stress (Laban et al., 2008; Schweitzer et al., 2006). Pre-migration factors might also be involved, such as trauma or life events in the country of origin, which could lead to the migration decision (and thus to a selective migration phenomenon). The increased prevalence of PTSD among migrants with UDD in the study sample is consistent with this assumption. Other hypotheses address migrants’ socio-economic conditions (Selten et al., 2012), as UDD has been previously found to be associated with psychosocial and economic adversities (Lorant et al., 2003). Similarly, the achievement-expectation mismatch in migrant populations could also increase the risk of UDD, as well as the potential role of discrimination (Bhugra et al., 2014; Tortelli et al., 2013). The fact that ORs for UDD approach 1 in the 3GM could be an evidence of successful coping across generations – or the result of shorter evolutions of disease for 3GM with UDD. Furthermore, migration results were in opposite trends in prevalence rates through generations for BD
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Importantly, we highlighted that migration appears to be a severity factor of mood disorders, i.e., migrant status was associated with more psychiatric comorbidities. These findings suggest that migration, as an environmental factor, may have a different effect or a gradient of effects on schizophrenia versus mood disorders, and possibly between UDD and BD, and future research on environmental risk factors is needed to improve our understanding of the aetiology of these disorders.
disorders suffered from more comorbidities than non-migrants with mood disorders. They displayed lifetime comorbid psychotic disorders more frequently than non-migrants, which is consistent with previous findings from the MHGP regarding the effect of migration on the prevalence of psychotic disorders (Amad et al., 2013). Interestingly, the higher frequency of affective symptoms has also been found in migrants with schizophrenia and could be the result of an interaction between life events, family history and developmental factors (Hutchinson et al., 1999). Concerning increased SUD prevalences, different explanatory models could explain this complex socio-cultural phenomenon (Bhugra et al., 2014). Findings from previous studies of migrants in the general population were conflicting (Agic et al., 2015; Borges et al., 2011; Grant et al., 2004). Moreover, the increased prevalences of SUD among migrants could imply that mood disorders are more severe clinically (Aas et al., 2014). However, as CUD predicts UDD and BD (Van Laar et al., 2007), it could also reflect the involvement of SUD as causal factors of mood disorders. Finally, there were no differences between migrants and non-migrants concerning the prevalence of previous suicide attempts or anxiety disorders, except for PTSD. The higher prevalence of PTSD for migrants with UDD confirms their higher exposure to trauma and their vulnerability to PTSD (Donath et al., 2011; Schweitzer et al., 2006). Interaction between trauma with SUD might also be involved (Aas et al., 2014). Nonetheless, considering the prevalence of comorbid PTSD among migrants with mood disorders (between 2.6% and 5.7%), trauma exposure does not seem to be the sole explanation of higher prevalence of mood disorders among migrants. Some limitations of this study should be acknowledged. First, one of the inclusion criteria was the ability to speak French, which could lead to a selection bias for studies on migration, particularly for the 1GM. Second, it has been suggested that misdiagnosis of mood disorders can appear across cultures and could be related to misinterpretations of the migrants’ cultural backgrounds, although there has been no clear data to support this (Bhugra et al., 2014; Mukherjee et al., 1983). Third, as the sampling was done by quotas within regions, and thus non probabilistic, we can’t assume that our sample was representative of the general population. However, quota sampling method warrant same socio-demographic characteristics of the general population. Moreover, we can’t exclude selective refusal to inclusion according to migrant or mood disorder status. In fact, there was no data concerning characteristic of subjects that refused. Fourth, the cross-sectional nature of the study does not allow causality conclusion or generalization of the main findings (Bradford-Hill, 1965). Moreover, as mentioned before, this study is prevalence-based, and differences in the course of disease can bias the analyses. For instance, increased incidence and unchanged prevalence may indicate that migrants have a shorter duration of disease, as was the case for African-Caribbeans with schizophrenia in England (McKenzie et al., 2001). However, the potential biases stemming from differences in disease course are minimized by the fact that our analyses concerned lifetime prevalences of mood disorders. Furthermore, migrants with mood disorders experiencing chronic social defeat and poor quality of life could have returned to their country of origin. This could have biased analyses based on prevalence, which could be influenced by a “selective return” to natal country, a mirror image of the Ødegaard's “selective migration” hypothesis (Ødegaard, 1932). However, the “selective return” bias applies particularly to the 1GM and tends to decrease across subsequent generations. Furthermore, it would tend to decrease the observed risk and avoid overestimations. A fifth limitation is that other factors could have been factored into our analyses, such as urbanicity, but these data were not available in the MHPG survey, because its main objective was not the study of psychiatric disorders among migrants. In conclusion, the prevalence of UDD and, to a lesser extent, BD (reaching significance only in the 3GM) was increased among migrants. However, these prevalences were not increased as much among migrants as the prevalences of psychotic disorders and schizophrenia.
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Research paper
Prevalence and clinical severity of mood disorders among first-, second- and third-generation migrants
MARK
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Baptiste Pignona, , Pierre Alexis Geoffroyc,d,e,f, Pierre Thomasa,h,i, Jean-Luc Roelandtg,i, Benjamin Rollanda,j, Craig Morganb, Guillaume Vaivaa,h, Ali Amada,b a
Univ. Lille, CNRS, CHU LILLE, UMR9193-PsychiC-SCALab, UMR9193-PsychiC-SCALab, Psychiatry Department, F-59000 Lille, France King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK c Inserm, U1144, Paris F-75006, France d Paris Descartes University, UMR-S 1144, Paris F-75006, France e Paris Diderot University, Sorbonne Paris Cité, UMR-S 1144, Paris F-75013, France f AP-HP, GH Saint-Louis – Lariboisière – F. Widal, Psychiatry and Addiction Medicine Department, 75475 Paris Cedex 10, France g World Health Organization Collaborative Centre (WHO-CC), EPSM Lille-Metropole, Lille, France h Federation of Mental Health Research, Lille, France i INSERM 1123, Equipe ECEVE, Paris, France j Univ. Lille, INSERM, CHU LILLE, U1171, Department of Addiction Medicine - Addiction Consultation Liaison Unit, Pôle de Psychiatrie, F-59000 Lille, France b
A R T I C L E I N F O
A BS T RAC T
Keywords: Migration Bipolar disorder Depressive disorder Dysthymia Comorbidities
Background: The role of migration as a risk factor remains unknown for mood disorders because of poor data. We sought to examine the prevalence and severity of mood disorders (bipolar disorder (BD), unipolar depressive disorder (UDD) and dysthymia) in first, second, and third generation migrants in France. Methods: The Mental Health in the General Population survey interviewed 38,694 individuals. The prevalence of lifetime mood disorders, comorbidities, and clinical features was compared between migrants and nonmigrants and by generation. All analyses were adjusted for age, sex and level of education. Results: The prevalence of any lifetime mood disorder was higher in migrants compared with non-migrants (OR=1.36, 95% CI [1.27–1.45]). This increased prevalence was significant for UDD (OR=1.44, 95% CI [1.34– 1.54]), but not for BD (OR=1.15, 95% CI [0.96–1.36]) or dysthymia (OR=1.09, 95% CI [0.94–1.27]), although the prevalence of BD was increased in the third generation (OR=1.27, 95% CI [1.01–1.60]). Migrants with BD or UDD were more likely to display a comorbid psychotic disorder compared to non-migrants with BD or UDD. Cannabis-use disorders were more common in migrant groups for the 3 mood disorders, whereas alcohol-use disorders were higher in migrants with UDD. Posttraumatic stress disorder was more frequent among migrants with UDD. Limitations: The study used cross-sectional prevalence data and could be biased by differences in the course of disease according to migrant status. Moreover, this design does not allow causality conclusion or generalization of the main findings. Conclusion: Mood disorders are more common among migrants, especially UDD. Moreover, migrants with mood disorders presented with a more severe profile, with increased rates of psychotic and substance-use disorders.
1. Introduction Mood disorders, including bipolar disorder (BD), unipolar depressive disorder (UDD) and dysthymia, are leading causes of morbidity around the world due to their high prevalence (approximately 1 to 2% for BD (Fagiolini et al., 2013), 16% for UDD (Kessler et al., 2003) and
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1% for dysthymia (Blanco et al., 2010)), their impact on functioning and quality of life, and their long disease course (Bruffaerts et al., 2012; Miret et al., 2013; Phillips and Kupfer, 2013). Subjects with mood disorders have, moreover, elevated mortality rates (Angst et al., 2002), particularly because of suicidal behaviour (Pompili et al., 2012; Schaffer et al., 2014) and cardiovascular diseases (Fagiolini et al.,
Correspondence to: Hôpital Fontan, Fontan, CHRU de Lille, F-59037 Lille Cedex, France. E-mail address: [email protected] (B. Pignon).
http://dx.doi.org/10.1016/j.jad.2016.12.039 Received 29 August 2016; Received in revised form 27 October 2016; Accepted 17 December 2016 Available online 27 December 2016 0165-0327/ Crown Copyright © 2016 Published by Elsevier B.V. All rights reserved.
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subjects with the same characteristics as the general population on predefined characteristics, such as age, sex, educational level, occupational category, and professional status (according to census figures from 1999 provided by the French National Institute for Statistics and Economic Studies). Subjects were included in the study if they met the following criteria: 1) provided informed consent to participate in the survey, 2) spoke French, 3) were aged 18 years and over, and 4) were neither institutionalized nor homeless. Legal authorisation was obtained by the “Commission Nationale Informatique et Liberté” (CNIL) and the “Comité consultatif sur le traitement de l'information en matière de recherche” (CCTIRS), with number 98.126. Additional methodological details can be found elsewhere (Amad et al., 2013; Caria et al., 2010; Leray et al., 2011).
2005; Mathur et al., 2016). Even if their pathophysiology remains mostly unknown, it is widely demonstrated that gene-environment interactions play an important role in the genesis of mood disorders (Craddock and Forty, 2006; Etain et al., 2008; Geoffroy et al., 2013). Foreign migration is associated with increased prevalence (i.e., cases in a given population at a specific time) of psychotic disorders and schizophrenia among some minority ethnic and/or migrant populations (Selten et al., 2012; Termorshuizen et al., 2014). Previous studies demonstrated increased incidences (i.e., new cases per given population per year) of psychotic disorders and schizophrenia in migrants in first and second generation, and thus confirmed migration as a risk factor (Bourque et al., 2011; Cantor-Graae and Selten, 2005), which has also been shown to occur in France (Amad et al., 2013; Tortelli et al., 2013). Nevertheless, migration remains a topic of debate concerning a potential influence on incidence and prevalence of mood disorders. For instance, a study using data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) of 43,093 individuals representative of the general population found that foreign-born Mexican Americans and foreign-born non-Hispanic whites had a lower prevalence of mood, anxiety and substance use disorders (SUD) compared with their US-born counterparts, which suggests a “healthy migrant effect” (Grant et al., 2004). More specifically, results from different studies of migration on either mania or BD (Lloyd et al., 2005; Selten et al., 2003), UDD (Bhugra, 2003; Kerkenaar et al., 2013; Selten et al., 2003) or dysthymia (Breslau et al., 2011) were contradictory, driving Swinnen and Selten to conduct a meta-analysis of the 14 incidence-based studies of migration and mood-disorders (BD, UDD, and mood disorders of unspecified polarity). They found that, adjusting for age and gender, the RR of developing any mood disorder was 1.38 (95% CI [1.17–1.62], p < 0.001) (Swinnen and Selten, 2007), which is less than the risk of developing schizophrenia. More recently, Cantor-Graae et al. studied the influence of migration on the incidence of a full spectrum of psychiatric disorders in a large Danish registry-based cohort study (n=1,859,419). After adjustment for sex, age, calendar year, and the interaction between age and sex, risk for at least one psychiatric disorder was increased in all migrant populations (except Danish expatriates who were born abroad). The incidence of the different psychiatric disorders varied according to generational status of migrants, in particular between the first and second generation. Interestingly, incidence rate ratios (IRR) of BD and affective disorders were only increased among second-generation migrants with one foreign-born parent (Cantor-Graae and Pedersen, 2013). Most of these migration studies are incidence-based and require long follow-up periods to be accurate. Prevalence studies, on the other hand, are appropriate to assess the severity of a disease and/or the comorbidities according to clinical or biological factors and can provide important insights on factors associated with different courses of the disease, i.e., modifiers of a disease (Stolk et al., 2007). Therefore, the present study aimed to examine the prevalence of mood disorders (including BD, UDD and dysthymia) in migrant groups, both overall and according to first (1GM), second (2GM) and third (3GM) generation, in a large cross-sectional survey. Finally, we compared psychiatric comorbidities and clinical features, including psychotic disorders, previous suicide attempts, anxiety disorders and SUD, according to migrant status.
2.2. Assessment of psychiatric disorders and clinical features At each site, the Mini International Neuropsychiatric Interview (MINI, French version 5.0.0), a standardized psychiatric interview, was used to screen for psychiatric disorders. The MINI is a brief structured diagnostic interview developed by psychiatrists in the United States and Europe for screening of ICD-10 psychiatric disorders in the general population. The MINI has been previously validated in the general population and has good to very good validity, reliability (inter-rater and test-retest), sensitivity and specificity (Sheehan et al., 1997). All of the MHGP interviewers (nurses and psychologists) were trained to administer the MINI by using video recordings of interviews over a 3-d session by WHO-CC experts. Lifetime mood disorders, according to ICD-10 criteria, included the following: BD (F30 and F31), UDD (F32 and F33) and dysthymia during the last two years (F34.1). When compared with the Composite International Diagnostic Interview (CIDI), the MINI has “good” to “very good” kappa values. For BD, the kappa coefficients were 0.65– 0.74, the sensitivities were 0.74–0.89, and the specificities were 0.93– 0.97. For UDD, they were, 0.74, 0.93 and 0.80, respectively (Amorim et al., 1998). For dysthymia, when compared with the Structured Clinical Interview for DSM-III (SCID), they were 0.52, 0.67 and 0.99, respectively (Sheehan et al., 1998). Lifetime comorbidities and clinical features associated with mood disorders were also extracted from the MINI and analysed: previous suicide attempts, anxiety disorders (panic disorder with or without agoraphobia (F41.0 and F40.01)), social phobia (F40.1), generalized anxiety disorder (GAD) (F41.1), post-traumatic stress disorder (PTSD) (F43.1), and SUD (alcohol use disorders (AUD) and cannabis use disorders (CUD) (F10.1, F10.2, F12.1 and F12.2)). Lifetime psychotic disorders were also extracted. Indeed, the MINI includes a lifetime psychotic disorders section with nine items. The questions target the occurrence of paranoid delusions, delusions of persecution, thought broadcasting, delusions of control, delusions of reference, and visual and auditory hallucinations. The diagnoses of lifetime psychotic disorders were always confirmed by a senior psychiatrist familiar with transcultural psychiatry. For psychotic symptoms, when compared with the CIDI, kappa has been shown to have a good value, i.e., above 0.70. Sensitivity, specificity and positive predictive values have also been found to be above 0.85, 0.90 and 0.70, respectively (Amorim et al., 1998). 2.3. Assessment of migrant status
2. Materials and methods The designation of migrant status was based on the country of birth of the subject, the subject's parents, and the subject's grandparents. In light of the literature on migrant populations (Cantor-Graae and Pedersen, 2013; Selten et al., 2012, 2003; Sieberer et al., 2011; Swinnen and Selten, 2007; Tortelli et al., 2013), we defined a migrant as 1GM (a subject born outside of metropolitan France), 2GM (at least one parent born outside of metropolitan France), or 3GM (at least one grandparent born outside of metropolitan France). This information
2.1. Mental Health in General Population (MHGP) survey The French cross-sectional MHGP survey, conducted by the World Health Organization Collaborating Centre (WHO-CC), interviewed 38,694 subjects between 1999 and 2003. These subjects were selected in 47 study sites (900 subjects per site) by a quota sampling method (Lunsford and Lunsford, 1995). This method develops a sample of 175
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was obtained from answers to the following 3 questions: “Where were you born?” “Where were your parents born?” and “Where were your grandparents born?”. Migrant generations were exclusive from each other (e.g. a subject born outside of metropolitan France, even with parents or grand-parents also born outside, was considered as a firstgeneration migrant).
were migrants (25.7%), from 1GM (n=2052, 5.3%), 2GM (n=4151, 10.7%), or 3GM (n=3756, 9.7%). BD was diagnosed in 614 subjects (1.6% of the total population), of whom 194 were migrants (1.9% of the migrant sample) and 420 non-migrants (1.5% of the non-migrant sample). UDD was diagnosed in 4131 subjects (11.2% of the total population), consisting of 1325 migrants (13.3% of the migrant sample) and 2806 non-migrants (9.8% of the non-migrant sample). Dysthymia was diagnosed in 901 subjects (2.3% of the total population), comprised of 242 migrants (2.4% of the migrant sample) and 659 non-migrants (2.3% of the non-migrant sample). The most common source of migration was Europe (48%), Maghreb (28%) and subSaharan Africa (7%). The proportion of migrants from different regions of origin was not significantly different between the overall sample of migrants and migrants with BD, UDD or dysthymia, except 2GM with BD from Maghreb (48.3% in the sample with BD vs. 35.8% in the whole sample, p < 0.05) (see Supplementary Table 2).
2.4. Statistical analyses To assess if each of the mood disorders was associated with migration and/or different generations of migration (1GM, 2GM or 3GM), we performed logistic regression analyses adjusting for potential confounding factors identified from the literature: sex, age and level of education (Bhugra et al., 2014). We compared origins of migrants in the different samples by using chi-square tests or Fisher tests if sample sizes were not sufficient. Finally, we also assessed several comorbidities and clinical features according to migration-status using chi-square tests. All statistical analyses were performed using R software (R Core Team, 2013).
3.2. Risk of mood disorders according to migrant status After adjusting for age, sex and level of education, the logistic regression showed a higher prevalence of mood disorders in migrants (i.e., sum of prevalences for BD, UDD and dysthymia) compared to non-migrants (odds-ratio (OR)=1.36, 95% CI [1.27–1.45], p < 0.001). The prevalence of BD was not significantly different between the total sample of migrants and non-migrants (OR=1.15, 95% CI [0.96–1.36], p=0.126). The analysis by generation showed that the prevalence of BD was significantly higher in the 3GM (OR=1.27, 95% CI [1.01–1.60], p
3. Results 3.1. Population and sociodemographic characteristics Sociodemographic characteristics of individuals with mood disorders are summarized in Table 1 (for the whole sample, see Supplementary Table 1). Of the 38,694 individuals interviewed, 9959
Table 1 Sociodemographic characteristics of subjects with mood disorders in non-migrants and three generations of migrants. Non-migrants
All migrants
p-values*
First generation
Second generation
Third generation
n=3677 45.1 (18.6)
n=1665 37.8 (15.9)
< 0.001
n=352 44.0 (16.1)
n=702 37.0 (16.3)
n=611 35.1 (14.3)
35.5 64.5
45.9 54.1
< 0.001
49.4 50.6
46.4 53.6
43.2 56.8
28.9 51.4 19.6
20.3 56.6 23.1
< 0.001
29.5 46.3 24.1
19.8 58.8 21.4
15.5 60.1 24.4
n=420 39.4 (14.8)
n=194 33.4 (12.1)
n=34 43.2 (14.1)
n=76 32.8 (11.1)
n=84 30.1 (10.1)
54.2 45.8
68.0 32.0
0.001
50.0 50.0
75.0 25.0
69.0 31.0
20.5 54.8 24.8
11.3 63.4 25.3
0.018
26.5 50.0 23.6
11.8 68.3 19.7
4.8 64.3 30.9
Unipolar depressive disorder Mean age (SD) Sex (%) Men Women Education level (%) No education-primary level Secondary level University level
n=2950 45.8 (19.0)
n=1389 38.4 (16.6)
n=293 44.4 (16.6)
n=567 37.6 (17.2)
n=465 35.7 (14.8)
33.4 66.6
43.2 56.7
49.1 50.9
43.6 56.4
39.1 60.8
30.4 51.2 18.4
21.1 56.7 22.2
30.4 47.1 22.5
21.0 58.6 20.5
15.5 60.4 24.1
Dysthymia Mean age (SD) Sex (%) Men Women Education level (%) No education-primary level Secondary level University level
n=659 46.8 (18.41)
n=242 38.4 (15.2)
n=41 43.2 (14.5)
n=98 36.9 (14.5)
n=103 37.9 (15.9)
31.7 68.3
41.4 58.6
0.007
51.2 47.8
40.9 59.1
37.9 62.1
31.3 49.8 19.0
26.0 49.2 24.8
0.102
34.1 31.7 34.1
19.4 55.1 25.5
29.1 50.5 20.4
Any mood disorder Mean age (SD) Sex (%) Men Women Education level (%) No education-primary level Secondary level University level Bipolar disorder Mean age (SD) Sex (%) Men Women Education level (%) No education-primary level Secondary level University level
*
< 0.001
< 0.001 < 0.001
< 0.001
< 0.001
Comparisons between all generations of migrants and non-migrants using the t-test and chi-squared test.
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dysthymia) in migrant groups in France. Several important findings resulted from this investigation: i) a higher lifetime prevalence of any mood disorder in migrants among all three generations; ii) a higher lifetime prevalence of BD in the third generation; iii) a higher lifetime prevalence of UDD in migrants among all three generations; and iv) no significant differences in the prevalence of dysthymia according to migrant status. This design, based on a general population survey, can help to overcome biases met by most of the previous prevalence-based migration studies (Kerkenaar et al., 2013; Selten et al., 2012), which were based on psychiatric registers and census figures, and thus might underestimate the migrant populations in these data (e.g., undocumented or recently moved), and therefore overestimate the risk of psychiatric disorders (Bourque et al., 2011). We were also able to compare the psychiatric comorbidities and the clinical features according to migrant status. Among migrants, mood disorders were globally more severe, with: i) a higher prevalence of psychotic disorders for migrants with BD and UDD; ii) a higher prevalence of CUD for migrants group with each of the mood disorders, iii) a higher prevalence of AUD for migrants with UDD, and iv) a higher prevalence of PTSD for migrants with UDD. Furthermore, this study found a significantly increased prevalence of BD in the 3GM, without statistically significant differences when the whole migrant group was analysed. These results are consistent with the Swinnen and Selten meta-analysis that showed that the incidence of BD was not increased in the 1GM and the 2GM in comparison with non-migrants, after excluding a single migrant group of AfricanCaribbean origin living in the UK (RR=1.75, 95% CI [0.94–3.28], p=0.26) (Swinnen and Selten, 2007). In a more recent registry-based study, the risk of receiving treatment for BD (i.e., prevalent cases) was compared between migrants and Dutch nationals in an ethnically mixed catchment area. The risk of being treated for BD was not significantly increased for any group, except for the Turkish-Dutch of the 2GM (Selten et al., 2012). Thus, migrants' risk for BD appears to be far less significant than their risk of schizophrenia and psychotic disorders, both in prevalence (Selten et al., 2012; Termorshuizen et al., 2014) and in incidence (Bourque et al., 2011; Cantor-Graae and Selten, 2005). These results suggest that environmental risk factors could be different or could play a different role in schizophrenia and BD. For example, several factors that impair neurodevelopment (e.g., copy number variants and obstetric complications) have been associated with schizophrenia but not with BD (Demjaha et al., 2012), as seems to be specifically the case for migration. Indeed, even if few studies have focused on pre-migration factors (Chou, 2009), exposition to trauma cannot seem to explain the difference between schizophrenia and BD, as trauma is highly prevalent in both disorders (Demjaha et al., 2012). Alternatively, we can also hypothesize that these results reflect an increased prevalence of BD in migrants that only reached significance in the 3GM. Indeed, in Denmark, Cantor Graae et al. interestingly found an increasing incidence of BD among migrants only in the 2GM (Cantor-Graae and Pedersen, 2013). This generation effect suggests that the increased prevalence of BD in the 3GM may be caused by post-migration factors (social defeat, achievement-expectation mismatch, etc.), rather than selective migration or migration per-se (Bourque et al., 2011). Conversely, the lifetime prevalence rate of UDD was higher in migrants of all three generations and ORs tended to decrease across the generations (for 1GM: OR=1.52; for 2GM: OR=1.37; for 3GM: OR=1.19). These results require cautious interpretation, because the literature concerning UDD in migrants is scarce and heterogeneous. This may be due to different ways that depression is expressed in patients across cultures (Bhugra et al., 2014). For example, in their meta-analysis, Swinnen and Selten did not find sufficient data to be able to study the influence of migration on the incidence of UDD (Swinnen and Selten, 2007). More recently, Sieberer et al. showed that women from 1GM and 2GM were more likely to suffer from depressive symptoms compared with non-migrant women (Sieberer et al., 2011).
Table 2 Prevalence and odds ratio (OR) of mood disorders comparing non-migrants to different generations of migrants adjusted for age, sex and level of education. n (%)
OR [95%CI]
p-values*
Non-migrants All generation migrants First generation Second generation Third generation
Any mood disorder 3677 (12.80) 1665 (16.72) 352 (17.15) 702 (16.91) 611 (16.27)
(n=5342) – 1.36 [1.27; 1.39 [1.23; 1.28 [1.17; 1.20 [1.07;
1.45] 1.56] 1.40] 1.32]
– < 0.001 < 0.001 < 0.001 < 0.001
Non-migrants All generation migrants First generation Second generation Third generation
Bipolar disorder (n=614) 420 (1.46) – 194 (1.95) 1.15 [0.96; 34 (1.66) 1.07 [0.74; 76 (1.83) 0.99 [0.78; 84 (2.24) 1.27 [1.01;
1.36] 1.49] 1.27] 1.60]
– 0.126 0.724 0.960 0.047
Non-migrants All generation migrants First generation Second generation Third generation
Unipolar depressive disorder (n=4131) 2806 (9.77) – 1325 (13.30) 1.44 [1.34; 1.54] 293 (14.28) 1.52 [1.33; 1.73] 567 (13.66) 1.37 [1.24; 1.50] 465 (12.38) 1.19 [1.07; 1.32]
– < 0.001 < 0.001 < 0.001 0.001
Non-migrants All generation migrants First generation Second generation Third generation
Dysthymia (n=901) 659 (2.29) – 242 (2.43) 1.09 41 (2.00) 0.89 98 (2.36) 1.03 103 (2.74) 1.22
– 0.253 0.480 0.761 0.059
*
[0.94; [0.64; [0.83; [0.99;
1.27] 1.21] 1.27] 1.50]
Results from logistic regression, with non-migrants as reference category.
< 0.05) and was not significantly changed in 1GM and 2GM. The prevalence of UDD was significantly higher in the whole sample of migrants and in each generation of migrants (OR=1.44, 95% CI [1.34– 1.54], p≤0.001 for all generations, OR=1.52, 95% CI [1.33–1.73] for 1GM, OR=1.37, 95% CI [1.24–1.50] for 2GM, OR=1.19, 95% CI [1.07– 1.32] for 3GM). Finally, we did not observe any significant differences in the prevalence of dysthymia according to migrant status (Table 2). 3.3. Comorbidities and clinical features of mood disorders associated with migrant status The analyses of comorbidities and clinical features associated with the pooled mood disorders showed that migrants presented significantly more psychotic disorders (10.2% vs. 7.5%, p=0.001), as well as more CUD (3.2% vs. 1.3%, p < 0.001), AUD (11.6% vs. 8.5%, p < 0.001) and PTSD (2.9% vs. 1.7%, p < 0.005) in comparison with non-migrants with a mood disorder. There were no statistically significant differences concerning proportions of subjects with previous suicide attempts. More specifically, migrant individuals with BD (28.2% vs. 20.2%, p < 0.05) and with UDD (16.2% vs. 15.5%, p < 0.05) had significantly more psychotic disorders. Concerning SUD, CUD was significantly more frequent in the migrant groups for each of the mood disorders (8.8% vs. 3.4% for BD, 2.4% vs. 1% for UDD, 4.5% vs. 1.4% for dysthymia, p < 0.005). AUD was also more frequent in migrants with UDD (10.6% vs. 7.8%, p < 0.005), but not with BD and dysthymia. PTSD was significantly more frequent among migrants with UDD (2.6% vs. 1.5%, p < 0.05), but did not reach significance with BD (5.7% vs. 2.6%, p=0.06) or dysthymia (3.3% vs. 1.5%, p=0.09). Concerning other comorbid anxiety disorders (i.e., panic disorder, social phobia, and GAD), no statistically significant differences were found between migrants and non-migrants (Table 3). 4. Discussion Using a large sample of the general population, this is the first study to investigate the prevalence of mood disorders (BD, UDD and 177
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Table 3 Comorbidities of subjects with mood disorders in non-migrants and three generations of migrants.
All mood disorders Previous suicide attempt (n, %) Psychotic disorder (n, %) Cannabis use disorder (n, %) Alcohol use disorder (n, %) Panic disorder (n, %) Social phobia (n, %) Generalized anxiety disorder (n, %) Post-traumatic stress disorder (n, %) Bipolar disorder Previous suicide attempt (n, %) Psychotic disorders (n, %) Cannabis use disorder (n, %) Alcohol use disorder (n, %) Panic disorder (n, %) Social phobia (n, %) Generalized anxiety disorder (n, %) Post-traumatic stress disorder (n, %) Unipolar depressive disorder Previous suicide attempt (n, %) Psychotic disorders (n, %) Cannabis use disorder (n, %) Alcohol use disorder (n, %) Panic disorder (n, %) Social phobia (n, %) Generalized anxiety disorder (n, %) Post-traumatic stress disorder (n, %) Dysthymia Previous suicide attempt (n, %) Psychotic disorders (n, %) Cannabis use disorder (n, %) Alcohol use disorder (n, %) Panic disorder (n, %) Social phobia (n, %) Generalized anxiety disorder (n, %) Post-traumatic stress disorder (n, %) *
Non-migrants (n)
All-generation of migrants (n)
p-values*
First generation (n)
Second generation (n)
Third generation (n)
n=3677 675 (18.4) 276 (7.5) 46 (1.3) 311 (8.5) 686 (18.7) 427 (11.6) 1018 (27.7)
n=1665 301 (18.1) 169 (10.2) 53 (3.2) 193 (11.6) 341 (20.5) 198 (11.9) 480 (28.8)
0.790 0.001 < 0.001 < 0.001 0.117 0.768 0.388
n=352 51 (14.5) 38 (10.8) 2 (0.6) 29 (8.2) 79 (22.4) 44 (12.5) 95 (27.0)
n=702 122 (17.4) 57 (8.1) 14 (2.0) 74 (10.5) 142 (20.2) 88 (12.5) 201 (28.6)
n=611 128 (20.9) 74 (12.1) 37 (6.1) 90 (14.7) 120 (19.6) 66 (10.8) 184 (30.1)
62 (1.7)
48 (2.9)
0.004
20 (4.8)
20 (2.8)
11 (1.8)
n=420 95 (22.6) 85 (20.2) 14 (3.4) 70 (16.6) 128 (30.5) 66 (15.7) 84 (20.0)
n=194 68 (35.1) 55 (28.2) 17 (8.8) 43 (22.2) 62 (32) 36 (18.6) 47 (24.2)
0.856 0.026 0.004 0.102 0.711 0.379 0.234
n=34 34 (32.4) 4 (11.8) 0 (0.0) 6 (17.6) 12 (35.3) 6 (17.6) 8 (23.5)
n=76 24 (31.6) 21 (27.6) 5 (6.6) 14 (18.4) 25 (32.9) 18 (23.7) 18 (23.7)
n=84 33 (39.2) 30 (35.7) 12 (14.3) 23 (27.4) 25 (29.8) 12 (14.3) 21 (25.0)
11 (2.6)
11 (5.7)
0.058
3 (8.8)
6 (7.9)
2 (2.4)
n=2806
n=1325
n=293
n=567
n=465
455 (16.2) 161 (5.7) 29 (1.0) 220 (7.8) 501 (17.9) 312 (11.1) 829 (29.5)
205 (15.5) 101 (7.6) 32 (2.4) 141 (10.6) 256 (19.3) 154 (11.6) 395 (29.8)
0.555 0.020 < 0.001 0.003 0.256 0.633 0.861
36 (12.3) 30 (10.2) 2 (0.7) 22 (7.5) 61 (21.2) 35 (11.9) 81 (27.6)
85 (15.0) 33 (5.8) 8 (1.4) 58 (10.2) 109 (19.2) 67 (11.8) 172 (30.3)
38 (8.2) 30 (35.7) 22 (4.7) 61 (13.1) 85 (18.3) 52 (11.2) 142 (30.5)
43 (1.5)
35 (2.6)
0.015
13 (4.4)
14 (2.5)
8 (1.7)
n=659 144 (21.9) 85 (20.2) 9 (1.4) 45 (6.8) 117 (17.8) 80 (12.1) 163 (24.7)
n=242 50 (20.7) 30 (12.4) 11 (4.5) 10 (8.3) 55 (22.7) 31 (12.8) 63 (26.0)
0.828 0.065 0.004 0.460 0.092 0.786 0.690
n 4 5 0 2 8 7 8
n=98 25 (25.5) 9 (9.2) 4 (4.1) 5 (5.1) 21 (21.4) 12 (12.2) 21 (21.4)
n=103 21 (20.4) 16 (15.5) 7 (6.8) 13 (12.6) 25 (25.2) 12 (11.7) 34 (33.3)
10 (1.5)
8 (3.3)
0.089
3 (7.3)
3 (3.1)
2 (1.9)
=41 (9.8) (12.2) (0.0) (4.9) (19.5) (17.1) (19.5)
Comparisons between all generations of migrants and non-migrants using the t-test and chi-squared test.
(increasing) and UDD (decreasing), which suggests different time effects of migration on the expression of mood disorders. These results, taken as a whole, suggest that the 1GM appears more vulnerable to UDD, whereas the 3GM is more vulnerable to BD, while still at risk for UDD. Concerning dysthymia, which is also known to be a persistent depressive disorder, there were not any statistically significant differences between migrants and non-migrants. The literature concerning the effects of migration on dysthymia is particularly scarce and it would be difficult to draw definitive conclusions. In a cross-national study, Breslau et al. reported that, after arrival in the United States, Mexican migrants had a significantly higher incidence of any depressive disorder (including dysthymia) than non-migrant family members in Mexico, but there were not any analyses on dysthymia alone (Breslau et al., 2011). Other studies have reported an increased prevalence of dysthymia among elderly subjects who experienced migration (Carta et al., 2005; Robison et al., 2003). Regarding comorbidities and clinical features, migrants with mood
Nevertheless, our results are consistent with the implication of risk factors from migration itself, as trauma and other major life events, leaving a native country and family or resettlement-related stress (Laban et al., 2008; Schweitzer et al., 2006). Pre-migration factors might also be involved, such as trauma or life events in the country of origin, which could lead to the migration decision (and thus to a selective migration phenomenon). The increased prevalence of PTSD among migrants with UDD in the study sample is consistent with this assumption. Other hypotheses address migrants’ socio-economic conditions (Selten et al., 2012), as UDD has been previously found to be associated with psychosocial and economic adversities (Lorant et al., 2003). Similarly, the achievement-expectation mismatch in migrant populations could also increase the risk of UDD, as well as the potential role of discrimination (Bhugra et al., 2014; Tortelli et al., 2013). The fact that ORs for UDD approach 1 in the 3GM could be an evidence of successful coping across generations – or the result of shorter evolutions of disease for 3GM with UDD. Furthermore, migration results were in opposite trends in prevalence rates through generations for BD
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Importantly, we highlighted that migration appears to be a severity factor of mood disorders, i.e., migrant status was associated with more psychiatric comorbidities. These findings suggest that migration, as an environmental factor, may have a different effect or a gradient of effects on schizophrenia versus mood disorders, and possibly between UDD and BD, and future research on environmental risk factors is needed to improve our understanding of the aetiology of these disorders.
disorders suffered from more comorbidities than non-migrants with mood disorders. They displayed lifetime comorbid psychotic disorders more frequently than non-migrants, which is consistent with previous findings from the MHGP regarding the effect of migration on the prevalence of psychotic disorders (Amad et al., 2013). Interestingly, the higher frequency of affective symptoms has also been found in migrants with schizophrenia and could be the result of an interaction between life events, family history and developmental factors (Hutchinson et al., 1999). Concerning increased SUD prevalences, different explanatory models could explain this complex socio-cultural phenomenon (Bhugra et al., 2014). Findings from previous studies of migrants in the general population were conflicting (Agic et al., 2015; Borges et al., 2011; Grant et al., 2004). Moreover, the increased prevalences of SUD among migrants could imply that mood disorders are more severe clinically (Aas et al., 2014). However, as CUD predicts UDD and BD (Van Laar et al., 2007), it could also reflect the involvement of SUD as causal factors of mood disorders. Finally, there were no differences between migrants and non-migrants concerning the prevalence of previous suicide attempts or anxiety disorders, except for PTSD. The higher prevalence of PTSD for migrants with UDD confirms their higher exposure to trauma and their vulnerability to PTSD (Donath et al., 2011; Schweitzer et al., 2006). Interaction between trauma with SUD might also be involved (Aas et al., 2014). Nonetheless, considering the prevalence of comorbid PTSD among migrants with mood disorders (between 2.6% and 5.7%), trauma exposure does not seem to be the sole explanation of higher prevalence of mood disorders among migrants. Some limitations of this study should be acknowledged. First, one of the inclusion criteria was the ability to speak French, which could lead to a selection bias for studies on migration, particularly for the 1GM. Second, it has been suggested that misdiagnosis of mood disorders can appear across cultures and could be related to misinterpretations of the migrants’ cultural backgrounds, although there has been no clear data to support this (Bhugra et al., 2014; Mukherjee et al., 1983). Third, as the sampling was done by quotas within regions, and thus non probabilistic, we can’t assume that our sample was representative of the general population. However, quota sampling method warrant same socio-demographic characteristics of the general population. Moreover, we can’t exclude selective refusal to inclusion according to migrant or mood disorder status. In fact, there was no data concerning characteristic of subjects that refused. Fourth, the cross-sectional nature of the study does not allow causality conclusion or generalization of the main findings (Bradford-Hill, 1965). Moreover, as mentioned before, this study is prevalence-based, and differences in the course of disease can bias the analyses. For instance, increased incidence and unchanged prevalence may indicate that migrants have a shorter duration of disease, as was the case for African-Caribbeans with schizophrenia in England (McKenzie et al., 2001). However, the potential biases stemming from differences in disease course are minimized by the fact that our analyses concerned lifetime prevalences of mood disorders. Furthermore, migrants with mood disorders experiencing chronic social defeat and poor quality of life could have returned to their country of origin. This could have biased analyses based on prevalence, which could be influenced by a “selective return” to natal country, a mirror image of the Ødegaard's “selective migration” hypothesis (Ødegaard, 1932). However, the “selective return” bias applies particularly to the 1GM and tends to decrease across subsequent generations. Furthermore, it would tend to decrease the observed risk and avoid overestimations. A fifth limitation is that other factors could have been factored into our analyses, such as urbanicity, but these data were not available in the MHPG survey, because its main objective was not the study of psychiatric disorders among migrants. In conclusion, the prevalence of UDD and, to a lesser extent, BD (reaching significance only in the 3GM) was increased among migrants. However, these prevalences were not increased as much among migrants as the prevalences of psychotic disorders and schizophrenia.
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