Prevalence and associated factors for poor mental health among young migrants in Sweden: a cross-sectional study

Abstract

Background Young migrants face multiple challenges that can affect their mental, sexual and reproductive health. Objective To assess the prevalence of self-reported poor mental health and its associated demographic, post-migration and sexual risk behaviour factors among young migrants (aged 15–25) in Sweden. Methods Data were drawn from a cross-sectional survey conducted with migrants aged 15–65 years old in Sweden between December 2018 and November 2019 ( n = 6449). Among these, 990 participants aged 15–25 were eligible for the study. Mental health was measured using the Refugee Health Screener-13. Missing data indicator analysis and multivariable logistic regression models were conducted to estimate the association between mental health, sexual risk behaviour, demographic and migration-related variables. Results Of the 990 participants, 59% reported poor mental health. Participants reporting poor mental health were more likely to be female (AOR:1.63, 95% CI:1.18–2.25), to have lived in Sweden more than three years (AOR:2.16, 95% CI:1.17–3.97), to engage in any sexual risk behaviour (AOR:1.99, 95% CI:1.25–3.17), and to live alone (AOR:1.95, 95% CI:1.25–3.03) or with friends they already knew (AOR:1.60, 95% CI:1.37–4.91). People arriving from the Americas (AOR:0.54, 95% CI:0.33–0.88), Asia (AOR:0.44, 95% CI:0.22–0.86), Europe (AOR:0.30, 95% CI:0.14–0.61) and Africa (AOR 0.37, 95% CI: 0.23–0.60) had lower odds of poor mental health than those arriving from Syria. Conclusion The prevalence of poor mental health among young migrants in Sweden was high, with specific subgroups (women, asylum seekers, people arriving from Syria, and those residing longer in Sweden) being particularly vulnerable. Our results indicate the interconnectedness between poor mental health and sexual risk behaviour in this population. Thus, policies targeting young migrants should ensure that healthcare services screen for both poor sexual and mental health at the same time.

Effective analysis of job satisfaction among medical staff in Chinese public hospitals: a random forest model

“…Building an optimal calculation model is the prerequisite for influencing factor analysis, which is crucial to ensure the robustness and accuracy of the analysis results. Multiple logistic regression models are widely used in related research due to their relatively simple theoretical assumptions ( 26 ). Regression models have greater advantages over OLS models in probability prediction ( 27 ).…”