Status and Predictors of Mental Health Symptoms Among Migrants and Refugees During the COVID-19 Pandemic

Overview

Migrants and refugees are vulnerable subgroups in general with regards to symptoms of psychopathology. Furthermore, recent calls for paper urge investigation on current mental health status of migrants and refugees during the pandemic, as different barriers such as lack of emotional support from relatives, in addition to language barriers potentially impairing comprehension about the pandemic having the chance to increase symptoms of psychopathology such as anxiety and depression. This study seeks to investigate the levels of mental health symptoms (i.e., depression, general anxiety, and health anxiety) among immigrants and refugees in Norway during the COVID-19 pandemic. Demographic factors will be investigated to identify subgroups with increased risk of meeting clinically significant depression and anxiety symptoms, as established by validated cut-offs to be elaborated below. Furthermore, transdiagnostic predictors which may prove as useful intervention targets will be investigated.

Research question What is the levels of mental health symptoms (i.e., depression, general anxiety, and health anxiety) among immigrants and refugees in Norway during the COVID-19 pandemic? The results will be benchmarked against the levels of mental symptoms in national and international surveys. Hypothesis: H1: There will be a significant decrease in the levels of health anxiety, depression, and general anxiety symptoms at the measurement period three months into the pandemic (T2) as compared to the baseline period (T1) with the strictest mitigation protocols in place during the first weeks of the pandemic. H2: Higher level at T1 and less reduction from T1 to T2 in positive metacognitions, negative metacognitions, unhelpful coping strategies, loneliness will be associated with less reduction in anxiety, depression and health anxiety from T1 to T2 above, and beyond age, gender, and education. Higher level at T1 and less reduction from T1 to T2 in emotional support will be associated with more reduction in anxiety, depression and health anxiety from T1 to T2. Statistical analysis: Repeated surveys like the present one typically have a lot of drop out and missing data. Therefore, we will use mixed models instead of paired t-tests, repeated measures ANOVAs, and ordinary linear regression to analyze the data. Mixed models use maximum likelihood estimation, which is the state of the art approach to handle missing data (Schafer \& Graham, 2002). Especially if data are missing at random, which is likely in our survey, mixed models give more unbiased results than the other analytic methods (O'Connel et al., 2017). In preliminary analyses, and for each of the dependent variables (GAD-7, PHQ-9) HAI, the combination of random effects and covariance structure of residuals that gives the best fit for the "empty" model (the model without fixed predictors except the intercept) will be chosen. Akaike's Information Criterion (AIC) will used to compare the fit of different models. Models that give a reduction in AIC greater than 2 will be considered better (Burnham \& Anderson, 2004). The program SPSS 25.0 will be used (IBM Corp, 2018). First, H1 about decrease in GAD-7, PHQ-9 and HAI will be tested by using anxiety or depression as dependent variable in a model using time (T1 period = 0, T2 period = 1) as a predictor. Second, demographic group variables will be added as predictors. Third, the initial (T1) levels of positive metacognitions, negative metacognitions and unhelpful coping strategies as measured with CAS-1, loneliness, and emotional support measured will be added, together with the interactions of these constant covariates with time. These interactions represent tests of H2 about the covariates predicting change in anxiety, depression and health anxiety. Finally, the T2 levels of positive metacognitions, negative metacognitions and unhelpful coping strategies as measured with CAS-1, loneliness, and emotional support as constant covariates will be added, together with the interactions of these constant covariates with time. These interactions represent tests of H2 about the change in the covariates from T1 to T2 predicting change in anxiety, depression and health anxiety from T1 to T2s. Inference criteria We pre-define the significance level: p \< 0.05 to determine significance. Sample size: The sample size at T1 included 574 participants. For the present study at T2, all participants will be invited to participate in accordance with the study plan. The data collection period will continue for up to three weeks until as many of the participants at baseline have responded. Measures: Positive metacognitions, negative metacognitions and unhelpful coping strategies will be measured with the CAS 1 (Wells, 2009): Emotional support will be measured with two items and loneliness is measured with the The UCLA Loneliness Scale-8 (ULS-8; Hays \& DiMatteo, 1987)which measures the frequency and intensity of aspects of the lonely experience. Possible transformations: All variables will be assessed in their original and validated format as is recommended practice, as long as this is possible with regards to statistical assumptions underlying the pre-defined analyses. However, if this is not possible with regards to the statistical assumptions behind the analyses, transformation (e.g., square root or log-transformations) may be needed to apply interval-based methods, alternatively the use of non-parametric tests. Missing data: Maximum likelihood Exploratory: Questions addressed in the future paper which is not pre-specified will be defined as exploratory.