*2.2. Measures*

#### 2.2.1. European Quality of Life Five Dimensions Five Level (EQ–5D–5L)

The EQ–5D–5L is a well-known indirect, generic measure of HRQoL consisting of five domains: mobility, self-care, usual activities, pain/discomfort and anxiety/depression [27]. For each domain, participants endorse one of five levels of functioning ranging from no problems (=1), slight problems (=2), moderate problems (=3), severe problems (=4) to unable to/extreme problems (=5). A full, 5-digit scoring profile, or health state, is created by putting each domain score after one another in the order outlined above (starting with mobility and ending with anxiety/depression). For example, someone reporting 'no problems' on all five domains has a profile of 11,111, whereas someone with 'extreme problems' in the domains usual activities and anxiety/depression and 'no problems' in the other three domains has a profile of 11515. In total, there are 3125 possible combinations of domains scores and thus 3125 possible health states. In order to compare the perceived relative value or utility of different states, preference data are obtained from people experiencing these states by asking their willingness to trade time in their current state for time in 'perfect health' (time trade-off method), and this preference is converted to a number between 0 and 1. People who are perfectly happy with their health will not be willing to trade off any time and they define the 'perfect health'-end of the spectrum and are given an index score of 1 (10/10 = 1). People who struggle with severe health issues may be willing to trade, say, 10 years in their current health for 1 year in perfect health (hypothetically), and are given an index score of 0.1 (1/10 = 0.1). The closer the index score is to 1, the better the associated health and HRQoL. A value set for a population is a complete coding scheme where each of the possible 3125 health states have an assigned index score based on preference data from the population in question. Using the example above, the profile 11111 will have an index score of 1.0 (perfect health), whereas the profile 11515 may have an index score of, say, 0.70 (this depends on how the people with this profile in the population responded to the time trade-off question). The present study uses a value set from the Swedish general population to estimate HRQoL index scores for respondents [28].

#### 2.2.2. Post-Migration Factors

The Refugee Post-Migration Stress Scale was used to measure four domains of postmigration strain related to resettlement in the host country: (1) financial strain; (2) social strain; (3) competency strain; (4) perceived discrimination [29]. Financial strain relates to material and economic hardship that could affect integrity, independence, dignity and well-being (example: 'Worry about unstable financial situation'). Social strain relates to social hardship, e.g., feeling isolated or frustrated due to loss of status (example: 'Feeling excluded or isolated in the Swedish society'). Competency strain relates to feelings of inadequacy of host-country specific skills needed to successfully navigate and function in daily life (example: 'Bothering difficulties communicating in Swedish'). Perceived discrimination asks about experiences of unfair treatment in Sweden, either verbally or nonverbally, on the basis of prejudice (example: 'Feeling disrespected due to my national background'). All domains are comprised of three items, except perceived discrimination

that has four items. Respondents were asked to indicate how frequently they experience each item on a scale ranging from 1 = never to 5 = very often. Please refer to Figure S1 for the distribution of responses on individual items. Respondents were categorized into low, medium and high strain for each of the four domains. The low-strain group had a maximum score of 2 = seldom for all items within a given domain. The high-strain group answered 4 = often or 5 = very often on all items within a given domain, and the mediumstrain group consisted of the remaining. Cronbach's alpha for the four domains ranged between 0.80 and 0.84.

#### 2.2.3. Sociodemographic and Pre-Migration Trauma Exposure

Potential confounders included were sociodemographic variables, e.g., sex, age, education, civil status and year of immigration to Sweden. Potentially traumatic experiences (PTEs) related to before (pre-flight) or during (peri-flight) migration were measured with the Refugee Trauma History Checklist, tested and validated in a sample of Syrian asylum seekers in Sweden [30]. The scale asks about eight PTEs prior to flight, and the same eight during flight, for a total of 16 PTE items (e.g., 'War at close quarter' and 'Forced separation from family or close friends'). A PTE adversity ratio (PTE-AR) introduced by Steel et al., was calculated as the number of endorsed PTEs divided by the total number of PTEs inquired about and categorized into: <0.2; 0.2–0.29; 0.3–0.39; ≥0.4 [2].

## *2.3. Statistical Analyses*

Data were inspected for errors, outliers and missing values. Frequency distributions, simple summary statistics and cross tabulations were used to make the descriptive table. Sociodemographic variables were modelled as in prior studies by our group to facilitate comparison. Unadjusted and adjusted logistic regression were used to explore the association between post-migration stressors and the five domains in the EQ5 scale. Each domain was analyzed separately and answer choices were dichotomized with choice 1 (i.e., 'no problem') as the reference category and choices 2–5 (i.e., 'slight problems' or higher) defined as a case. In the adjusted analysis, missing was handled through listwise deletion with the total number contributing data to full models indicated in the relevant table. Odds ratios (ORs) with 95% confidence interval (95% CI) and associated *p*-values are presented.

Unadjusted and adjusted linear regression was used to investigate the association between post-migration stressors and HRQoL, with results reported through unstandardized regression coefficients with 95% CI and associated *p*-values. Standard regression coefficients were also estimated to make it easier to compare regression coefficients for different post-migration stressors. The standardized coefficients are only commented on in the text in order not to clutter tables. Missing values were handled through listwise deletion as in logistic regression (total number included in models is denoted in the table). Linear regression was deemed appropriate even if the outcome was skewed due to the large sample size (central limit theorem).
