Content area
Background
Since 2015, recurrent terrorist attacks in Burkina Faso have caused large-scale displacement, impacting the psychological health of affected populations. This study explores the effects of migration forced or voluntary on depression and anxiety among adolescents and young people aged 15–24, in line with SDG 3, which aims to “ensure healthy lives and promote well-being for all at all ages”.
Methods
The analysis is based on data from the baseline survey conducted by the Institut Supérieur des Sciences de la Population (ISSP) for the Sahel Resilience Building Program. A total of 1,911 adolescents and young people aged 15–24 living in four regions were interviewed. We measured mental health using two tools: the Patient Health Questionnaire-9 (PHQ-9) for depression and the Generalised Anxiety Disorder-7 (GAD-7) for anxiety. We used multinomial regressions to test the effects of migration status on depression and anxiety.
Findings
Forced migrants report higher symptoms of moderate or severe depression (11.1%) and anxiety (15.7%) compared to non-forced migrants (6.8% and 14.4%) and non-migrants (6.6% and 9.5%). Forced migrants were 2.16 times more likely (RRR = 2.16; p < 5%) than non-migrants to experience moderate or severe depression, and non-forced migrants were 2.12 times more likely (RRR = 2.12; p < 5%) than non-migrants to experience moderate or severe anxiety. Youth aged 20–24 and urban residents were also more likely to face these mental health issues.
Contributions
These findings call for more attention to the needs of both forced and non-forced migrants in terms of mental health. Psychological care mechanisms are needed in destination areas.
Introduction
Whether voluntary or forced, migration is a complex process that can exacerbate vulnerability to mental health problems. Voluntary migrants often faced acculturative stresses, disappointments linked to migratory expectations, economic difficulties and trauma before and during migration [1, 2–3]. In contexts of insecurity, the situation is even more critical. Indeed, armed conflicts and their consequences, including forced displacement, loss of social ties and extreme poverty, generate chronic stress and trauma that particularly affect the mental health of populations [4].
For a decade, Burkina Faso has been facing a security crisis of unprecedented magnitude, leading to the massive displacement of the population. These people are forced to leave their usual place of residence, sometimes to unknown places, without any social ties or psychosocial or economic support. The number of internally displaced people has increased from 12,345 in 2019 to reach 2,062,534 in 2023 [5]. One direct consequence of the conflict is the closure of public services, including health centers, in many affected areas, exacerbating an already limited healthcare supply. According to the Health Cluster’s estimates, the closure of health centres in 2023 could deprive more than 3.6 million people of care. Only 10% of displaced persons reported receiving health assistance [6]. In this context of conflict (insecurity, humanitarian crisis, closure of health facilities, etc.), what would be the psychological burden of the internally displaced populations? Are they more affected by mental health problems than non-forced migrants and non-migrants?
Mental health is one of the fundamental dimensions of human health, tightly linked to overall well-being and recognised as a universal right [7]. Absent on the global health agenda until recently, mental health is now recognised as an essential component of individual and collective well-being [7]. According to the Institute for Health Metrics and Evaluation (IHME), nearly one in eight people lives with a mental disorder, with anxiety and depressive disorders being the most common [8]. These conditions have a considerable impact on quality of life and are associated with a reduction in life expectancy of 10 to 20 years in severe cases [9]. A review of the empirical literature suggests that migrants are often affected by mental health problems due to multiple factors [10, 11]. Yet, the lack of data, particularly on forced migrants in conflict areas, limits further investigation on the interconnection between migration (voluntary, forced) and mental health. Assuming that forced migrants undergo a more restrictive migration process than non-forced migrants, we hypothesise that the risk of experiencing mental health problems is higher among migrants (forced and voluntary) than non-migrants.
Hence, the present paper aims to assess the effect of migration, whether forced or voluntary, on adolescents’ mental health across four regions of Burkina Faso. The paper begins with a review of migration and health, highlighting the effects of migration (whether forced or voluntary) on various dimensions of mental health. Secondly, data sources and methodological considerations are presented. Thirdly, we present findings and associated discussions in light of scientific facts and context.
Brief literature review
Migration and mental health
Migration is a complex process that can have a profound impact on mental health. In sub-Saharan Africa, particularly where migration dynamics are diverse and strongly influenced by social, economic, and political vulnerabilities, the psychological consequences of migration deserve special attention. This review considers migration conditions by distinguishing between voluntary and forced migration.
Research shows that women and young adults are more exposed to depressive and psychological disorders in contexts of forced migration and social precarity [3, 11, 12–13]. However, these effects depend on traumatic situations that have triggered the migration. While men are more exposed to torture, kidnapping and imprisonment, women are more vulnerable to sexual violence in conflict situations [14]. Similarly, older people, uncared children, single mothers and people with disabilities are at increased risk of mental disorders. Specific characteristics such as educational level, access to education in conflict zones, religion, ethnicity and marital status also influence mental health [10, 14, 15, 16–17]. Divorce, separation or loss of a spouse can lead to identity disorientation and depression [18]. These different challenges make women more vulnerable, which can harm their mental health.
In terms of living conditions, precariousness is a comprehensive factor affecting the mental health of migrants. Food insecurity, housing problems, unemployment, lack of financial resources, and integration difficulties contribute to the deterioration of psychological well-being, particularly among internally displaced persons and refugees [10, 14, 19, 20, 21–22]. Research shows that the poorest and richest people are more exposed to depression than those with an intermediate standard of living, which explains the U-shaped relationship between standard of living and depression [17]. A study on the psychological and social vulnerabilities of Central African populations heading to the North region shows that the scarcity of resources and the context of violence rooted in the region’s history reflect lasting structural imbalances in the distribution of wealth. Reported forms of violence include domestic violence, political persecution, discrimination based on sexual orientation, and dispossession of land or territory [23].
Other psychosocial, institutional, and cultural factors can also influence migrants’ mental health. The gap between migration expectations and reality, acculturative stress, loneliness, stigma, discrimination, and lack of integration can exacerbate mental health disorders, particularly among voluntary migrants [1, 24]. Social support plays an ambivalent role here: when it is inclusive, it protects; when it is absent or discriminatory, it aggravates mental health risks [10, 19]. Women migrants, in particular, face multiple forms of violence, isolation, lack of protection, concerns about the health and safety of their loved ones, and substance abuse problems among their relatives, which increase their psychological distress throughout the migration journey [16, 25, 26]. Exposure to stressful situations before migration, during the journey, and after arrival is generally linked to higher levels of post-traumatic stress disorder symptoms [12].
Furthermore, several cultural and structural barriers hinder access to healthcare for migrants: inadequacy of existing structures, linguistic and financial barriers and mistrust of the healthcare system [3, 27]. Forms of support already existing in communities, such as traditional healing practices or community mutual aid mechanisms, should be taken into account. This is particularly important for forcibly displaced people affected by war and facing protracted displacement [28]. Resilience also plays a crucial role in mitigating mental disorders related to forced migration through the reduction of psychological stress. This resilience is reinforced among displaced people through different social support networks, which mitigate the effects of conflict [29].
Environmental factors related to violence and displacement also amplify the risk of mental health problems. Armed conflicts, war, radicalisation, the recruitment of child soldiers, mass violence, suicide attacks, and a return to various forms of slavery can cause deep and lasting trauma [10, 15, 21, 30]. The place of residence and the length of stay in the place of destination can also modulate the psychological impact.
Measuring mental health
The review describes several tools used for measuring mental health [31]. propose eleven (11) self-assessment tools for mental health and well-being in children and adolescents, to inform clinical practice and public policies. These tools include: Achenbach System of Empirically Based Assessment, Beck Youth Inventories, Child Behavior Checklist, Child and Adolescent Needs and Strengths (CANS), Child Health Questionnaire, Child Outcome Rating Scale, Strengths and Difficulties Questionnaire (SDQ), Health of the Nation Outcome Scales for Children and Adolescents (HoNOSCA), Pediatric Symptom Checklist, Youth Outcome Questionnaire, and Behavior Assessment System for Children (BASC). However, the authors show that none of these tools offers a measure that captures the reliability of the severity of mental disorders and their evolution over time for all target groups.
Among the widely used instruments, the Patient Health Questionnaire-9 (PHQ-9), validated in several African countries, is sensitive and reliable for screening depression in adolescents and adults. In South Africa, the isiXhosa version of the PHQ-9 has a sensitivity of 91% and a specificity of 76% in adolescents. In Mozambique, it also showed good internal consistency and satisfactory test-retest reliability, with an optimal cut-off of 8 [32, 33].
The Generalised Anxiety Disorder-7 (GAD-7), used to assess generalised anxiety in adolescents and adults, provides moderate psychometric performance across contexts. This tool shows good validity and a stable unidimensional structure, with gender-differentiated results [32, 33, 34–35]. For example, a study in Rwanda reports a higher rate of anxiety symptoms in women (46.4%) than in men (29.8%), and a Cronbach’s alpha of 0.77 and Kaiser-Meyer-Olkin (KMO) and Bartlett’s Sphericity of 0.835 [35]. It has a robust unidimensional factor structure and gender invariance, allowing for reliable comparisons between girls and boys [36]. The Primary Care PTSD Screen for DSM-5 (PC-PTSD-5), used to screen for posttraumatic stress disorder (PTSD) in adolescents and adults, shows good internal consistency and validity, but requires threshold adjustment to optimise its sensitivity. When combined with the Columbia Suicide Severity Rating Scale (C-SSRS) to assess the severity of suicide risk at all ages, however, it shows lower than expected sensitivity, limiting its effectiveness for routine screening [33].
The Strengths and Difficulties Questionnaire (SDQ) assesses a range of emotional and behavioural disorders in children and adolescents. Validated in adolescents, it has good overall reliability, though the internalising and externalising subscales show lower test-retest reliability. Furthermore, the Italian version of the Mental Health Knowledge Schedule (MAKS-I), primarily used with adults, measures mental health knowledge. It has acceptable reliability, although it requires further validation to confirm its psychometric properties across different contexts [34, 37].
In summary, two main tools stand out due to their reliability in assessing mental health, particularly in adolescents. These are the PHQ-9, which screens for the degree of mental depression, and the GAD-7, which is more suited to screening for anxiety.
Study area
Burkina Faso is one of the Sahelian countries most affected by the rise of violent extremism in West Africa, particularly in the Sahelian subregion. Along with Mali and Niger, the country is experiencing a major security crisis marked by acts of extreme violence resulting in numerous deaths and forced displacement. The most affected regions are the Sahel (Liptako, Soum), Nord (Yadega), Centre-Nord (Kuilsé), Boucle du Mouhoun (Bankui, Sourou), and Est regions (Sirba, Tapoa, Goulmou). In response to this crisis, UNICEF, in collaboration with WFP and GIZ, is implementing an integrated, multi-sectoral resilience-building program in nine areas of Burkina Faso. The approach adopted is based on the child’s life cycle to meet the specific needs of vulnerable children, adolescents, and families. To assess the baseline situation of the program’s targets, a baseline survey was conducted, serving as the data source for the present research.
The populations considered in this analysis are located in the East, North-Central, Central Plateau, and East-Central regions. Among these regions, three are among the regions most affected by the security crisis. These are the Centre-Nord and Est regions, close to the Sahel belt, and the Centre-Est region, which borders the Est. The Plateau Central, however, is less affected but is home to internally displaced persons, just like the other regions. In total, 11 municipalities and 58 villages spread across these four regions were covered by the survey. These surveyed municipalities are shown on Fig. 1 below.
[See PDF for image]
Fig. 1
Geolocation of target locations
Data and methods
Data source
Data come from the baseline Survey of the Sahel Resilience Building Program. Data collection took place in February and March 2025. This is a sample survey that targeted two main strata. The first stratum concerns the Eastern region, where a single-stage random sample was conducted in 39 enumeration areas (clusters). This choice was made given the small number of clusters covered by this stratum. For a cluster effect of 1, a total of 907 households should be surveyed, with allocation proportional to each cluster’s size. Ultimately, 800 households were covered due to security issues in certain localities.
The second stratum covers three regions, including the Centre-Nord, Centre-Est, and Plateau Central. In this stratum, 1,754 households were targeted. The strategy used for sample selection is a two-stage random sampling. The primary sampling unit was the cluster. The first-stage sample consists of 88 clusters. Before selecting the primary units, the sampling frame was sorted by region, province, municipality, and, finally, by enumeration area. The primary unit selection procedure was systematic, with probability proportional to size; the size of an EA was the number of households it contained. In the second stage, a sample of 20 households was selected systematically with equal probability.
A total of 10 questionnaires were used in this study, including household, women aged 15–49, and men aged 15–49 questionnaires. In both questionnaires, a 45-question mental health module was administered to adolescents and young people aged 15–24. The construction of mental health-related variables is based on this module, which addresses dimensions such as anxiety and depression.
Study population
We were interested in the population of 15–24-year-olds living in ordinary households in the areas covered by the survey. This population comprises 1488 women (77.86%) and 423 men (22.14%), totalling 1911 individuals. Table 1 describes the study population.
Table 1. Description of the study population
Variables | Modalities | Percentage(%) |
|---|---|---|
Migration status | Non-migrants | 85.82 |
Non-forced migrant | 6.17 | |
Forced migration | 8.01 | |
Place of residence | Urban | 33.23 |
Rural | 66.77 | |
Region of residence | Centre-Est | 6.91 |
Centre-Nord | 26.37 | |
Est | 35.90 | |
Plateau-Central | 30.82 | |
Sex | Men | 22.14 |
Women | 77.86 | |
Age group | 15–19 | 57.82 |
20–24 years | 42.18 | |
Education level | No | 30.72 |
Primary | 23.50 | |
Secondary & + | 45.79 | |
Marital status | In union | 37.41 |
Not in union | 62.59 | |
Household size | Small (1–4) | 24.39 |
Medium (5–7) | 32.50 | |
Large (8+) | 43.12 | |
Religion of the head of household | Muslim | 62.27 |
Christian | 34.22 | |
Other religions | 3.51 | |
Standard of living | Poor | 24.12 |
Medium | 34.43 | |
Riche | 41.44 | |
Relationship to the head of household | Head of household | 7.43 |
Spouse | 15.02 | |
Sons/daughters | 52.43 | |
Other parents | 25.12 | |
Total | 1911 |
Ethical considerations
This survey received approval from the Burkina Faso Health Research Ethics Committee (Decision n°2024-10−327). The design and tools were deemed acceptable in accordance with ethical requirements regarding interaction with human subjects. The various questionnaires developed for the study included a request for consent to respond before any interview. Thus, all survey participants, including young people aged 15–24, provided their consent and authorised the release of their data for scientific research.
Analysis variables
dependent variables
We used two independent variables. These are the severity of depression and anxiety constructed from the PHQ-9 and GAD-7 approaches, respectively. In general, PHQ-9 scores are categorised into none (score = 0), minimal depression (score 1–4), mild depression (score 5–9), moderate depression (score 10–14), moderately severe depression (score 15–19), and severe depression (score 20–27). Regarding GAD-7 cutoffs, in general, scores of 5, 10, and 15 correspond to the categories normal (0–4), mild anxiety (5–9), moderate anxiety (10–14), and severe anxiety (15–21), respectively. However, given the small sample sizes, we classified each score into three categories: none (score = 0), minimal (score = 1–4), and moderate or severe (score > = 5).
Independent variables
The independent variables considered in this study are recognised in the literature as influencing the severity of anxiety and/or depression. They can be grouped into three categories: sociodemographic, cultural factors and socio-economic. Sociodemographic factors consist of migratory status (non-migrant/non-forced migrant/forced migrant), residential area (urban/rural), marital status (not in union/in union), age (15–19,/20–24 years), sex (male/female), household size (small/medium/large), education level (none/primary/secondary or higher) and family relationship. For cultural variables, only the woman’s religion (Muslim/Christian/other) is used. As for socio-economic factors, we use the household’s standard of living, computed from household assets and equipment through a principal component analysis.
Analytical strategy
Two types of analyses were implemented: descriptive and explanatory. Descriptive analyses primarily measure the statistical association between dependent and independent variables. In particular, the chi-square test was used to assess the strength of associations, and 95% confidence intervals for the estimates are also reported. To determine the effects of migration status on depression and anxiety, we used the generalised multinomial logistic regression [38, 39]. The raw effect of migration status on depression and anxiety is tested before controlling for demographic, cultural and socio-economic factors. The general model is written as :
where j represents the form of depression or anxiety, i the individual, and the probability of observing the form of depression or anxiety j for individual i, given the independent variables . The parameters are estimated using the maximum likelihood method. is the odds ratio between the different modalities with respect to the reference modality if is the estimated coefficient for j with respect to a reference level of 0 (by convention) for . For each model, we test overall significance using the likelihood ratio test (LR test) and the independence of irrelevant alternatives (IIA) assumption, which states that the ratio of probabilities associated with choices between two spacing categories is independent of the other choices.
Results
We begin with descriptive results on the percentage of adolescents and young people exhibiting depression and anxiety symptoms. Then, we examine the effect of migration status and other factors on the two independent variables.
Descriptive results
Table 1 presents the percentage of adolescents who present symptoms of depression according to specific socio-economic and socio-demographic characteristics of adolescents and young people. More than half of them are affected by depression, 44.1% with a minimal form and 7% with a moderate or severe form. According to migration status, the results highlight a higher percentage of moderate or severe depression symptoms among forced migrants (11.1%) compared to non-forced migrants (6.8%) and non-migrants (6.6%). On the other hand, minimal depression symptoms are more common among non-forced migrants (51.7%) than forced migrants (45.8%) and non-migrants (43.4%). In general, depression symptoms appear to be less common among non-migrants. However, these differences are only significant at the 10% threshold (p < 10%). Furthermore, significant associations at the 5% threshold are highlighted between depression symptoms and residential environment, sex, age and marital status. In urban areas, we found that 49.3% of adolescents and young people experience minimal depression symptoms and 7.6% moderate or severe depression, compared to 41.5% and 6.7% respectively, in rural areas. Women seem to be more affected, with 45.9% of them experiencing minimal depression symptoms and 7.5% moderate or severe depression symptoms, compared to 37.6% and 5.2% respectively, among men. In addition, half of adolescents and young people engaged in union (50.5%) experience minimal depression symptoms and 7.8% moderate or severe depression symptoms, compared to 40.2% and 6.5% respectively among those who are not in a union. Finally, young people aged 20–24 have a higher percentage (49% for the minimal form and 9.9% for the moderate or severe form) than adolescents (40.5% and 4.9% respectively).
Table 3 reveals that in 2025, nearly six out of 10 adolescents and young people (57.4%) have symptoms of anxiety, of which 47.1% have a minimal form and 10.3% a moderate or severe form. As with depression, moderate or severe anxiety symptoms are more common among forced migrants (15.7%) compared to non-forced migrants (14.4%) and non-migrants (9.5%), while minimal anxiety symptoms are more common among non-forced migrants (52.5%) than forced migrants (42.5%) and non-migrants (47.1%). These differences are significant at the 5% level (p < 5%). Anxiety symptoms are also more common in urban areas (51% have a minimal form and 11.5% a moderate or severe form) than in rural areas (41.7% and 9.7% respectively; p < 1%). Regarding gender, men are more likely to have minimal anxiety symptoms (51.1%) than women (46%), while the latter are more likely to have moderate or severe anxiety symptoms (11.7% versus 5.4% in men; p < 1%). Marital status plays a determining role: adolescents and young people in a union display relatively higher levels of anxiety symptoms (48% in minimal form and 13.1% in moderate or severe form) than those who are not (46.6% and 8.6% respectively). Age also seems to influence the level of anxiety, with older people (20–24 years) displaying higher levels of symptoms (49.4% for minimal form and 13.3% for moderate or severe form) than adolescents (45.4% and 8.1% respectively). The household size also affects anxiety, with a relatively more frequent occurrence of anxiety among adolescents and young people living in small households (53% in minimal form and 11.2% in moderate or severe form) (Table 2).
Table 2. Percentage of depression symptoms by selected socio-economic characteristics
Severity of depression | No | Minimale | Moderate or severe | N | Pvalue |
|---|---|---|---|---|---|
% [95% IC] | % [95% IC] | % [95% IC] | |||
Migration status | |||||
Non-migrant | 50.0[47.6–52.4] | 43.4[41.0–45.8.0.8] | 6.6[5.5–8.0.5.0] | 1640 | 0.072 |
Non-forced migrant | 41.5[33.0–50.6.0.6] | 51.7[42.7–60.6] | 6.8[3.4–13.0] | 118 | |
Forced-migrant | 43.1[35.5–51.1] | 45.8[38.0–53.7.0.7] | 11.1[7.0–17.2.0.2] | 153 | |
Place of residence | |||||
Urban | 43.1[39.3–47.0] | 49.3[45.4–53.2] | 7.6[5.7–9.9] | 635 | 0.002 |
Rural | 51.8[49.1–54.5] | 41.5[38.8–44.2] | 6.7[5.5–8.3] | 1276 | |
Sex | |||||
Men | 57.2[52.4–61.9] | 37.6[33.1–42.3] | 5.2[3.4–7.8] | 423 | 0.000 |
Women | 46.6[44.0–49.1.0.1] | 45.9[43.4–48.4] | 7.5[6.3–9.0.3.0] | 1488 | |
Age group | |||||
Under 20 | 54.7[51.7–57.6] | 40.5[37.6–43.4] | 4.9[3.8–6.3] | 1105 | 0.000 |
20–24 years | 41.1[37.7–44.5] | 49.0[45.6–52.5] | 9.9[8.0–12.2.0.2] | 806 | |
Education level | |||||
No | 50.9[46.9–55.0] | 43.1[39.1–47.1] | 6.0[4.3–8.2] | 587 | 0.288 |
Primary | 47.4[42.9–52.1] | 46.5[42.0–51.2.0.2] | 6.0[4.2–8.6] | 449 | |
Secondary or higher | 48.3[45.0–51.7.0.7] | 43.4[40.2–46.7] | 8.2[6.6–10.2] | 875 | |
Marital status | |||||
In union | 41.7[38.1–45.3] | 50.5[46.8–54.1] | 7.8[6.1–10.0] | 715 | 0.000 |
Not in union | 53.3[50.4–56.1] | 40.2[37.5–43.0] | 6.5[5.3–8.1] | 1196 | |
Household size | |||||
Small (1–4) | 45.9[41.4–50.5] | 46.1[41.7–50.7] | 7.9[5.8–10.8] | 466 | 0.439 |
Medium (5–7) | 49.8[45.8–53.7] | 42.7[38.8–46.6] | 7.6[5.7–9.9] | 621 | |
Large (8+) | 50.0[46.6–53.4] | 43.9[40.6–47.3] | 6.1[4.6–7.9] | 824 | |
Religion of the head of household | |||||
Muslim | 50.3[47.4–53.1] | 43.4[40.7–46.3] | 6.3[5.1–7.8] | 1190 | 0.441 |
Christian | 46.8[43.0–50.6.0.6] | 45.0[41.2–48.8] | 8.3[6.4–10.6] | 654 | |
Other religions | 46.3[34.7–58.2] | 46.3[34.7–58.2] | 7.5[3.1–16.7] | 67 | |
Household standard of living | |||||
Poor | 51.6[46.5–56.7] | 41.5[36.6–46.6] | 6.9[4.6–10.0] | 461 | 0.972 |
Medium | 49.2[45.0–53.5.0.5] | 42.9[38.7–47.1] | 7.9[5.9–10.6] | 658 | |
Riche | 47.7[43.9–51.6] | 45.8[42.0–49.6.0.6] | 6.5[4.9–8.6] | 792 | |
Relationship to head of household | |||||
Head of household | 44.4[36.4–52.6] | 46.5[38.4–54.7] | 9.2[5.4–15.1] | 142 | 0.000 |
Spouse | 42.2[36.6–48.0] | 51.2[45.4–57.0] | 6.6[4.3–10.1] | 287 | |
Sons/daughters | 54.3[51.2–57.4] | 39.3[36.3–42.4] | 6.4[5.0–8.1.0.1] | 1,002 | |
Other parents | 43.1[38.8–47.6] | 49.0[44.5–53.4] | 7.9[5.8–10.7] | 480 | |
Total | 48.9[46.7–51.2] | 44.1[41.8–46.3] | 7.0[5.9–8.2] | 1911 | |
Table 3. Descriptive results of anxiety symptoms
Severity of anxiety | No | Minimale | Moderate or severe | Effectif | Pvalue |
|---|---|---|---|---|---|
% [95% IC] | % [95% IC] | % [95% IC] | |||
Migration status | |||||
Non-migrant | 43.4[41.0–45.8.0.8] | 47.1[44.7–49.6] | 9.5[8.2–11.0] | 1640 | 0.022 |
Non-forced migrant | 33.1[25.2–42.0] | 52.5[43.5–61.4] | 14.4[9.1–22.0] | 118 | |
Forced migration | 41.8[34.3–49.8] | 42.5[34.9–50.4] | 15.7[10.7–22.3] | 153 | |
Place of residence | |||||
Urban | 37.5[33.8–41.3] | 51.0[47.1–54.9] | 11.5[9.2–14.2] | 635 | 0.006 |
Rural | 45.1[42.4–47.9] | 45.1[42.4–47.9] | 9.7[8.2–11.5] | 1276 | |
Sex | |||||
Men | 43.5[38.8–48.3] | 51.1[46.3–55.8] | 5.4[3.6–8.1] | 423 | 0.001 |
Women | 42.3[39.8–44.9] | 46.0[43.4–48.5] | 11.7[10.2–13.4] | 1488 | |
Age group | |||||
Under 20 | 46.4[43.5–49.4] | 45.4[42.5–48.4] | 8.1[6.7–9.9] | 1105 | 0.000 |
20–24 years | 37.3[34.1–40.7] | 49.4[45.9–52.8] | 13.3[11.1–15.8] | 806 | |
Education level | |||||
No | 45.3[41.3–49.4] | 42.8[38.8–46.8] | 11.9[9.5–14.8] | 587 | 0.112 |
Primary | 40.1[35.6–44.7] | 49.9[45.3–54.5] | 10.0[7.6–13.2] | 449 | |
Secondary or higher | 42.1[38.8–45.4] | 48.6[45.3–51.9] | 9.4[7.6–11.5] | 875 | |
Marital status | |||||
In union | 38.9[35.4–42.5] | 48.0[44.3–51.6] | 13.1[10.9–15.8] | 715 | 0.002 |
Not in union | 44.8[42.0–47.7.0.7] | 46.6[43.8–49.4] | 8.6[7.1–10.3] | 1196 | |
Household size | |||||
Small (1–4) | 35.8[31.6–40.3] | 53.0[48.5–57.5] | 11.2[8.6–14.4] | 466 | 0.002 |
Medium (5–7) | 41.9[38.0–45.8.0.8] | 48.6[44.7–52.6] | 9.5[7.4–12.1] | 621 | |
Large (8+) | 47.0[43.6–50.4] | 42.6[39.3–46.0] | 10.4[8.5–12.7] | 824 | |
Religion of head of household | |||||
Muslim | 43.8[41.0–46.6.0.6] | 45.8[43.0–48.6.0.6] | 10.4[8.8–12.3] | 1190 | 0.020 |
Christianity | 39.1[35.5–42.9] | 51.1[47.2–54.9] | 9.8[7.7–12.3] | 654 | |
Other religions | 55.2[43.2–66.6] | 31.3[21.4–43.4] | 13.4[7.1–23.9] | 67 | |
Household standard of living | |||||
Poor | 44.9[39.8–50.0] | 44.0[39.0–49.1.0.1] | 11.1[8.3–14.8] | 461 | 0.900 |
Medium | 42.6[38.4–46.8] | 47.2[43.0–51.5.0.5] | 10.2[7.9–13.1] | 658 | |
Riche | 40.6[36.9–44.4] | 49.1[45.3–53.0] | 10.3[8.2–12.8] | 792 | |
Relationship to head of household | |||||
Head of household | 38.7[31.1–47.0] | 47.2[39.1–55.4] | 14.1[9.3–20.8] | 142 | 0.006 |
Spouse | 35.2[29.9–40.9] | 54.4[48.6–60.0] | 10.5[7.4–14.6] | 287 | |
Sons/daughters | 45.8[42.7–48.9] | 45.6[42.5–48.7] | 8.6[7.0–10.5.0.5] | 1,002 | |
Other parents | 41.5[37.1–45.9] | 45.8[41.4–50.3] | 12.7[10.0–16.0] | 480 | |
Total | 42.6[40.4–44.8] | 47.1[44.9–49.3] | 10.3[9.0–11.8.0.8] | 1911 | |
Results of explanatory analyses
Effects of migratory status
Figure 2 presents the relative risk ratios obtained from the multinomial logistic regressions implemented. There was a strong relationship between forced migration and moderate or severe depression symptoms (RRR = 1.94; p < 5%), a weak relationship between non-forced migration and minimal depression (RRR = 1.4; p < 10%), a weak relationship between minimal anxiety symptoms and non-forced migration (RRR = 1.46; p < 10%), and a strong relationship between non-forced migration (RRR = 1.99; p < 5%), forced migration (RRR = 1.71; p < 5%) and moderate or severe anxiety symptoms. Most of these effects are still significant after controlling for other variables. It appears that forced migrants have a 2.16 times greater risk (RRR = 2.16; p < 5%) than non-migrants of presenting moderate or severe forms of depression symptoms rather than no depression. Non-forced migrants have a 47% greater risk (RRR = 1.47; p < 10%) than non-migrants of presenting the minimal form of depression symptoms rather than no depression. The effect of non-forced migration on moderate or severe depression and that of forced migration on minimal depression are not significant at the 10% level. Regarding anxiety, non-forced migrants have a 2.12 times greater risk (RRR = 2.12; p < 5%) than non-migrants of presenting a moderate or severe rather than no anxiety symptoms. Similarly, these non-forced migrants have a 51% higher risk (RRR = 1.51; p < 10%) than non-migrants of experiencing minimal anxiety rather than no anxiety symptoms. The net effects of forced migration on anxiety are not significant at the 10% level.
[See PDF for image]
Fig. 2
Gross and net effects of migration status
Effects of other variables
The results show that in addition to migration status, other variables such as residential area, sex, age and educational level are significantly associated with the share of adolescents presenting depression symptoms (Table 4). Residents of rural areas have a 33% lower risk (RRR = 0.67; p < 1%) than those of urban areas of presenting minimal depression rather than no depression symptoms. Women have a 34% (RRR = 1.34; p < 5%) and 86% (RRR = 1.86; p < 5%) higher risk than men of presenting forms of minimal depression and moderate or severe depression symptoms, respectively, rather than no depression. Young people aged 20–24 years were 46% (RR = 1.46; p < 1%) more likely to experience minimal depression and 3.13 times more likely (RR = 3.13; p < 1%) to experience moderate or severe depression rather than no depression symptoms compared to adolescents. Adolescents and young people with primary education were 27% more likely (RR = 1.27; p < 10%) to experience symptoms of minimal depression rather than no depression symptoms compared to those with no education.
Regarding anxiety, the results highlighted significant associations with residential area, gender, age, household size, education level, kinship, and migration status (Table 4). Indeed, rural residents have a 21% (RRR = 0.69; p < 1%) and 36% (RRR = 0.637; p < 1%) lower risk of experiencing symptoms of minimal or moderate/severe anxiety, respectively, compared to those in urban areas. Compared to men, women have a 2.2 times greater risk (RRR = 2.2; p < 1%) of experiencing moderate or severe anxiety rather than no anxiety symptoms. The effect of gender on minimal anxiety is not significant. In addition, young people have a 27% greater risk (RRR = 1.27; p < 5%) and a 2.1 times greater risk (RRR = 2.06; p < 1%) of experiencing minimal anxiety symptoms and moderate or severe anxiety, respectively, rather than no anxiety, compared to adolescents. Furthermore, Christians have a 22% higher risk (RRR = 1.22; p < 10%) while those of other religions have a 50% lower risk (RRR = 0.5; p < 5%) of suffering from minimal anxiety rather than no anxiety symptoms compared to Muslims. Living in a large household reduces by 33% (RRR = 0.67; p < 1%) the risk of manifesting symptoms of minimal anxiety rather than no anxiety. In addition, primary education is associated with a 1.3 times higher risk (RRR = 1.29; p < 10%) compared to uneducated people, of presenting symptoms of minimal anxiety rather than no anxiety. Finally, spouses of heads of household have a 64% higher risk (RRR = 1.64; p < 10%) of suffering from minimal anxiety rather than no anxiety, compared to heads of household.
Table 4. Effects of other variables on depression and anxiety
Depression | Anxiety | ||||
|---|---|---|---|---|---|
Minimale | Moderate or severe | Minimale | Moderate or severe | ||
Place of residence | Urban | 1 | 1 | 1 | 1 |
Rural | 0.669*** (0.0839) | 0.897 (0.222) | 0.692*** (0.0886) | 0.637** (0.134) | |
Sex | Men | 1 | 1 | 1 | 1 |
Women | 1.344** (0.170) | 1.861** (0.493) | 0.835 (0.105) | 2.193*** (0.554) | |
Age | Under 20 | 1 | 1 | 1 | 1 |
20–24 years | 1.460*** (0.172) | 3.133*** (0.711) | 1.272 ** (0.152) | 2.056*** (0.403) | |
Education level | No | 1 | 1 | 1 | 1 |
Primary | 1.266* (0.174) | 1.260 (0.361) | 1.286* (0.180) | 1.030 (0.234) | |
Secondary or higher | 1.068 (0.132) | 1.428 (0.352) | 1.106 (0.139) | 0.807 (0.165) | |
Marital status | In union | 1 | 1 | 1 | 1 |
Not in union | 0.797 (0.141) | 1.046 (0.348) | 0.992 (0.179) | 0.842 (0.243) | |
Household size | Small | 1 | 1 | 1 | 1 |
Medium | 0.962 (0.140) | 0.895 (0.243) | 0.807 (0.120) | 0.762 (0.185) | |
Large size | 0.984 (0.145) | 0.660 (0.189) | 0.674*** (0.102) | 0.722 (0.178) | |
Religion of head of household | Muslim | 1 | 1 | 1 | 1 |
Christianity | 1.072 (0.115) | 1.197 (0.241) | 1.215* (0.132) | 0.984 (0.177) | |
Other religions | 0.972 (0.266) | 1.194 (0.620) | 0.495** (0.144) | 0.833 (0.339) | |
Standard of living | Poor | 1 | 1 | 1 | 1 |
Medium | 1.095 (0.144) | 1.252 (0.324) | 1.059 (0.141) | 0.922 (0.202) | |
Riche | 1.021 (0.146) | 1.133 (0.327) | 0.959 (0.140) | 0.933 (0.223) | |
Relationship | Head of household | 1 | 1 | 1 | 1 |
Spouse | 1.091 (0.248) | 0.779 (0.324) | 1.641** (0.387) | 0.769 (0.271) | |
Sons/daughters | 1.058 (0.269) | 1.285 (0.589) | 1.226 (0.319) | 1.206 (0.489) | |
Other parents | 1.237 (0.287) | 1.445 (0.595) | 1.373 (0.328) | 1.189 (0.423) | |
*p < 0.1, **p < 0.05, ***p < 0.01
Discussion
This article aimed to explore the effects of forced and unforced migration on mental health, particularly depression and anxiety in a context of security crisis. Overall, the results show a preponderance of depression and anxiety among migrants. Forced migrants are more prone to severe or moderate depression, and non-forced migrants to minimal depression. Non-forced migrants are more prone to minimal, moderate, or severe anxiety. Thus, a change of residence through migration, whether forced or not, exposes young people and adolescents to a greater risk of depression and anxiety. Forced migrants in particular generally experience war-related trauma either because they have been victims or indirect witnesses of abuses (murder, rape, mistreatment, etc.). Another pathway is the perverse effects of conflicts linked to restrictions on movement, which can generally lead to difficulties in supplying localities with food and other supplies. This situation exposes forced migrants to different crises such as famine, malnutrition and certain diseases, especially in a context of a breakdown in the provision of healthcare.
In destination areas, the conditions of social and economic integration (housing, employment, food, etc.) are more difficult for forced migrants than non-forced migrants. The latter has certainly had more time to prepare for the migration journey. It therefore follows that the previous trauma associated with the current situation of economic and social precariousness has a greater impact on the mental health of forced migrants. This impact would be much more focused on the past and the negative present, hence the greater risk of experiencing depression instead of anxiety, which is much more associated with feelings of uncertainty about the future. Non-forced migrants are nonetheless affected by changes in residential arrangements that can lead to isolation or difficulties in terms of socio-economic integration in destination areas. This situation can be the backbone of excessive worries about the future, thus increasing the risk of experiencing minimal, moderate or severe anxiety.
Similar results have already been highlighted in other contexts. Symptoms of post-traumatic stress are said to be particularly high among forced migrants [21, 40]. These symptoms would increase depending on the number and type of traumatic life events experienced before, during and after migration [40]. Also, the precarious conditions in which migrants sometimes live reinforce their vulnerability to depression and anxiety [14, 16].
Furthermore, it appears that young people, compared to adolescents, are at greater risk of experiencing mental depression or anxiety. The preponderant subjection of young people to depression and anxiety could be due to more economic responsibility, social and family pressure and major life decisions, the latter being in transition to adulthood, they would thus assume more psychosocial pressures. It also appears that women are more vulnerable to depression and anxiety than men. Indeed, in conflict areas, women face gender-specific risks as potential victims of rape, sexual abuse, widowhood and pregnancy-related complications due to insufficient prenatal and postnatal care [41]. Furthermore, despite the complexity of the sex difference in depression, recent evidence suggests that biological factors, particularly the decline in estrogen during menstruation, breastfeeding, and menopause, may contribute to the increased percentage of depression symptoms in women [42]. On the contrary, in men, the conversion of testosterone in the brain by aromatase, the presence of androgen receptors in hippocampal neurons, the non-recyclable nature of testosterone, and the presence of sexually dimorphic brain nuclei confer particular protection against depression [43].
These findings corroborate other research that has shown that women and young adults have a higher percentage of depressive symptoms in the context of forced migration and social precarity [3, 11, 12, 13–14]. For example, Roberts et al. [14], analysing factors associated with post-traumatic stress disorder and depression among displaced people in northern Uganda showed that women are twice as likely as men to experience post-traumatic stress disorder and more than 4 times more likely than men to experience depressive symptoms. However, these effects depend on the traumatic experiences of both men and women [14]. This is also in line with findings in Guatemala, where high symptoms of depression were observed in refugee women with a risk ratio of 3.64 compared to refugee men [44].
Similarly, characteristics such as level of education, religion and family ties also influence mental health, particularly through minimal anxiety. Educated adults and adolescents, those of the Christian faith and those engaged in marital union would be at greater risk of experiencing anxiety disorders, even if these associations are established only for minimal anxiety. However, certain variables such as standard of living and marital status have not shown any significant association with either depression or with severe anxiety. Yet in other contexts, they have proven decisive. Elsewhere, poverty has been recognised as a key factor affecting the mental health of migrants. It is expressed through food insecurity, housing problems, unemployment, lack of financial resources and integration difficulties contributing to the deterioration of psychological well-being, particularly among internally displaced persons and refugees [10, 14, 17, 19, 20, 21–22].
Study limits
This research has highlighted the risks associated with the mental health of adolescents and young people in Burkina Faso, which has been plagued by insecurity for the past ten years. The results sound the alarm about the need to pay greater attention to the mental health needs of young forced and unforced migrants. Although some convincing results were highlighted, certain limitations need to be highlighted.
This research relied on cross-sectional data without accounting for the timing of exposure to migration, the real causes of each migration, or the effects induced by migration itself. Knowing, for example, the timing of exposure to migration would have enabled control of the immediate effects associated with displacement and their persistence over time. Also, collecting experiences of trauma experienced (rape, kidnapping or sudden death of a loved one, distance travelled, etc.), especially on forced migrants, would have reinforced and allowed us better to understand the effects of forced migration on mental health.
Furthermore, the data are quite limited in scope, covering relatively specific localities. The sample allocation does not take into account regional-specificities, whose contexts of exposure to the risk of post-migration violence remain distinct. For example, migrants settled in the Centre-Nord and Est regions do not live in the same psychosocial environment as those living in the Central Plateau region, the first two regions being among the regions most affected by the security crisis. However, the Central Plateau region has not been directly affected by the crisis so far. The populations residing there are more likely to be secure than those living in the most affected regions.
Finally, the analysis focused on a relatively small population size. So it was not possible to use the recommended breakdown of depression and anxiety scores. This finer categorisation would undoubtedly have refined the analysis.
Conclusion
This research aimed to explore the effects of migration on mental health in the context of a security crisis, building on the case of Burkina Faso, which has experienced recurrent terrorist attacks since 2015. The results indicate that depression and anxiety are common among young and adolescent migrants. Forced migrants are more prone to a severe or moderate form of depression, and non-forced migrants to the minimal form of depression. Also, non-forced migrants are more subject to minimal, moderate or severe forms of anxiety compared to non-migrants. These findings particularly call for more actions to support people in migration situations, including internally displaced persons (IDPs). This assistance can be based on community integration through programs to strengthen social cohesion in localities that host the most IDPs, and must also combine better access to health care with psychosocial assistance programs. The health system should therefore evolve towards greater consideration of mental health care, which has so far remained less accessible, especially in rural areas.
Author contributions
Conceptualization: B.H., T.T.A., L.Y.B., D.N.; Methodology: B.H., T.T.A., L.Y.B, N.D.; Validation: B.H., L.Y.B., T.T.A., M.R.M., C.Y., O.S., Formal analysis: B.H., D.N.; Data curation: B.H., D.N.; Writing—original draft: B.H., N.D., S.M.; Writing—review & editing: L.Y.B., T.T.A., O. S. M.D.; Funding acquisition: This research received no external funding; Project administration: B.H.; Supervision: B.H. All authors read and approved the final manuscript.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Institutional review board statement
The data was collected as part of a program involving research institutions, with a view to making advanced use of the data and highlighting scientific evidence.
Informed consent
Informed consent was obtained from all subjects involved in the study.
Competing interests
The authors declare no competing interests.
Disclaimer
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of Population Health Metrics and/or the editor(s). Population Health Metrics and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
These tools include: Achenbach System of Empirically Based Assessment, Beck Youth Inventories, Child Behavior Checklist, Child and Adolescent Needs and Strengths (CANS), Child Health Questionnaire, Child Outcome Rating Scale, Strengths and Difficulties Questionnaire (SDQ), Health of the Nation Outcome Scales for Children and Adolescents (HoNOSCA), Pediatric Symptom Checklist, Youth Outcome Questionnaire, and Behavior Assessment System for Children (BASC). However, the authors show that none of these tools offers a measure that captures the reliability of the severity of mental disorders and their evolution over time for all target groups.
Among the widely used instruments, the Patient Health Questionnaire-9 (PHQ-9).
Abbreviations
Behavior ssessment system for children
Confidence interval
Generalized anxiety disorder-7
Child and adolescent needs and strengths
internally displaced persons
Institute for health metrics and evaluation
Institut supérieur des sciences de la population
Health of the nation outcome scales for children and adolescents
Patient health questionnaire-9
Relative risk ratio
Sustainable development goals
Strengths and difficulties questionnaire
Sub-Saharan Africa
World health organization
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
1. Wittig, U; Lindert, J; Merbach, M et al. Mental health of patients from different cultures in Germany. Eur Psychiatry J Assoc Eur Psychiatr; 2008; 23,
2. Feseha G, G/mariam A, Gerbaba M. Intimate partner physical violence among women in Shimelba refugee camp, northern Ethiopia. BMC Public Health [Internet]. 2012 [cited 2023 Sep 21];12(1):125.
3. Markkula, N; Lehti, V; Gissler, M et al. Incidence and prevalence of mental disorders among immigrants and native Finns: a register-based study. Soc Psychiatry Psychiatr Epidemiol; 2017; 52,
4. UNHCR. Strengthening Mental Health and Psychosocial Support in UNHCR Annual Report 2023 [Internet]. 2023. p. 21. Available from: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/ https://www.unhcr.org/sites/default/files/2024-04/annual-report-mhpss-2023.pdf
5. CONASUR. Situation des personnes Déplacées internes Au 31 Janvier 2023. Ministère En charge de l’action humanitaire. Burkina Faso; 2023.
6. World Bank Group. UNHCR. Global Trends: Forced Displacement in 2023|JDC [Internet]. [cited 2025 Nov 7]. Available from: https://www.jointdatacenter.org/literature_review/global-trends-forced-displacement-in-2023/
7. Organisation Mondiale de la Santé. Rapport mondial Sur La santé mentale transformer La santé mentale pour tous. VUE D’ENSEMBLE [World mental health report: transforming mental health for all. Executive summary]. Genève, Suisse; 2022.
8. Institute for Health Metrics and Evaluation(IHME). Global Health Data Exchange (GHDx) [Internet]. Inst. Health Metr. Eval. [cited 2025 Jun 28]. Available from: https://vizhub.healthdata.org/gbd-results
9. Laursen, TM; Nordentoft, M; Mortensen, PB. Excess early mortality in schizophrenia. Annu Rev Clin Psychol; 2014; 10, pp. 425-48. [DOI: https://dx.doi.org/10.1146/annurev-clinpsy-032813-153657] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24313570]
10. Bogic M, Njoku A, Priebe S. Long-term mental health of war-refugees: a systematic literature review. BMC Int Health Hum Rights [Internet]. 2015 [cited 2025 May 9];15(1):29.
11. Porter, M; Haslam, N. Predisplacement and postdisplacement factors associated with mental health of refugees and internally displaced personsa Meta-analysis. JAMA [Internet]; 2005; 294,
12. Perera, S; Gavian, M; Frazier, P et al. A longitudinal study of demographic factors associated with stressors and symptoms in African refugees. Am J Orthopsychiatry; 2013; 83,
13. Rask, S; Suvisaari, J; Koskinen, S et al. The ethnic gap in mental health: a population-based study of Russian, Somali and Kurdish origin migrants in Finland. Scand J Public Health; 2016; 44,
14. Roberts, B; Ocaka, KF; Browne, J et al. Factors associated with post-traumatic stress disorder and depression amongst internally displaced persons in Northern Uganda. BMC Psychiatry [Internet]; 2008; 8,
15. Musisi S, Kinyanda E. Long-Term Impact of War, Civil War, and Persecution in Civilian Populations—Conflict and Post-Traumatic Stress in African Communities. Front Psychiatry [Internet]. 2020 [cited 2025 May 9];11.
16. Akurang-Parry, KO; Nkumbaan, SN; Appiah, AL. Small-Scale wars in the Northern parts of ghana: A case study of the forced migration during 1994 Nanumba-Konkomba wars and its effects on women. Afr J Soc Sci Educ [Internet]; 2025; 3,
17. Duthé, G; Rossier, C; Bonnet, D et al. Mental health and urban living in sub-Saharan africa: major depressive episodes among the urban poor in Ouagadougou, Burkina Faso. Popul Health Metr; 2016; 14,
18. Tinghög, P; Al-Saffar, S; Carstensen, J et al. The association of immigrant- and non-immigrant-specific factors with mental ill health among immigrants in Sweden. Int J Soc Psychiatry; 2010; 56,
19. Hou, WK; Liu, H; Liang, L et al. Everyday life experiences and mental health among conflict-affected forced migrants: a meta-analysis. J Affect Disord; 2020; 264, pp. 50-68. [DOI: https://dx.doi.org/10.1016/j.jad.2019.11.165] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31846902]
20. Fennig, M; Denov, DM. The impact of trauma, flight and protracted displacement on the mental health of Eritrean refugees living in israel: an exploratory study of coping strategies. SSM; 2022; 2, 100102.
21. Carpiniello, B. The mental health costs of armed conflicts—a review of systematic reviews conducted on refugees, asylum-seekers and people living in war zones. Int J Environ Res Public Health; 2023; 20,
22. Yohannes, HT. Refugee trafficking in a carceral age: a case study of the Sinai trafficking. J Hum Trafficking; 2023; 9,
23. Faret, L. Migrations de La violence, violence En migration. Les vulnérabilités des populations centraméricaines En mobilité vers Le Nord. Revue européenne des migrations internationales; 2020; 36,
24. Chambon N, Cochet P, Goff GL. Soigner des migrants précaires En psychiatrie publique. Ecarts Identité [Internet]. 2013 [cited 2025 Jun 28];(121):38.
25. Akinsulure-Smith AM. Displaced African Female Survivors of Conflict-Related Sexual Violence: Challenges for Mental Health Providers. Violence Women [Internet]. 2014 [cited 2025 May 9];20(6):677–694.
26. Freedman, J; Crankshaw, TL; Mutambara, VM. Sexual and reproductive health of asylum seeking and refugee women in South africa: understanding the determinants of vulnerability. Sex Reprod Health Matters; 2020; 28,
27. McCann, TV; Renzaho, A; Mugavin, J et al. Stigma of mental illness and substance misuse in sub-Saharan African migrants: a qualitative study. Int J Ment Health Nurs; 2018; 27,
28. Tol, WA; Ager, A; Bizouerne, C et al. Improving mental health and psychosocial wellbeing in humanitarian settings: reflections on research funded through R2HC. Confl Health; 2020; [DOI: https://dx.doi.org/10.1186/s13031-020-00317-6] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33292413][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7602334]
29. Siriwardhana, C; Ali, SS; Roberts, B et al. A systematic review of resilience and mental health outcomes of conflict-driven adult forced migrants. Confl Health; 2014; 8,
30. Ey L. Forcibly Displaced People (Internally Displaced People, Refugees, Asylum Seekers) and Their Mental Health Situation. Risk Manag Terror Induc Stress [Internet]. IOS Press; 2020 [cited 2025 May 9]. pp. 104–112. Available from: https://ebooks.iospress.nl/doi/https://doi.org/10.3233/NHSDP200018
31. Deighton, J; Croudace, T; Fonagy, P et al. Measuring mental health and wellbeing outcomes for children and adolescents to inform practice and policy: a review of child self-report measures. Child Adolesc Psychiatry Ment Health; 2014; 8, 14. [DOI: https://dx.doi.org/10.1186/1753-2000-8-14] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24834111][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4022575]
32. Marlow, M; Skeen, S; Grieve, CM et al. Detecting depression and anxiety among adolescents in South Africa: validity of the isiXhosa Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7. J Adolesc Health; 2023; 72,
33. Stockton, MA; Mazinyo, EW; Mlanjeni, L et al. Validation of screening instruments for common mental disorders and suicide risk in South African primary care settings. J Affect Disord; 2024; 362, pp. 161-8. [DOI: https://dx.doi.org/10.1016/j.jad.2024.06.071] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38908555][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316636]
34. Lovero, KL; Adam, SE; Bila, CE et al. Validation of brief screening instruments for internalising and externalising disorders in Mozambican adolescents. BMC Psychiatry; 2022; 22,
35. Niwenahisemo LC, Hong S, Kuang L. Assessing anxiety symptom severity in Rwandese adolescents: cross-gender measurement invariance of GAD-7. Front Psychiatry [Internet]. 2024 [cited 2025 Jun 28];15.
36. Adjorlolo, S. Generalised anxiety disorder in adolescents in ghana: examination of the psychometric properties of the generalised anxiety Disorder-7 scale. Afr J Psychol Assess [Internet]; 2019; 1,
37. Pingani, L; Sampogna, G; Evans-Lacko, S et al. How to measure knowledge about mental disorders? Validation of the Italian version of the MAKS. Community Ment Health J; 2019; 55,
38. Kwak, C; Clayton-Matthews, A. Multinomial logistic regression. Nurs Res; 2002; 51,
39. Chen, Z; Kuo, L. A note on the estimation of the multinomial logit model with random effects. Am Stat; 2001; 55,
40. Canini F, El-Hage W, Garcia R. L’ABC des psychotraumas: approches biologiques et cliniques des psychotraumas. Editions Ellipses; 2024.
41. Braun-Lewensohn, O; Abu-Kaf, S; Al-Said, K. Women in refugee camps: which coping resources help them to adapt?. Int J Environ Res Public Health; 2019; 16,
42. Amede ES, Tesfaye E, Ahmed G. Prevalence and associated factors of depression and anxiety among Sudanese refugees at Bambasi camp in Northwest ethiopia: a cross-sectional study. Front Psychiatry [Internet]. 2024 [cited 2025 Jun 10];15.
43. Albert, PR. Why is depression more prevalent in women?. J Psychiatry Neurosci; 2015; 40,
44. Sabin, M; Lopes Cardozo, B; Nackerud, L et al. Factors associated with poor mental health among Guatemalan refugees living in Mexico 20 years after civil conflict. JAMA; 2003; 290,
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