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

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.