Unpacking gender-specific risk and protective factors for mental health status among Congolese refugees in Uganda.

Background: Research indicates refugees from the Democratic Republic of Congo (DRC), particularly females, have a higher incidence of mental health problems compared to the global norm for conflict-affected populations. Objective: This study aimed to unpack gender differences in the mental health of Congolese refugees by examining specific risk (trauma exposure, adaptation challenges, and discrimination) and protective factors (marital status, literacy, and social resources) in relation to posttraumatic stress symptoms (PTSS) and depression. Method: Utilizing systematic random sampling, we surveyed 667 adult refugees (370 females, 297 males) in the Kyaka II refugee settlement in Western Uganda. A multi-group path analysis was conducted, initially allowing for variance between genders and subsequently comparing a constrained model, where paths were set equal across groups. Results: The unconstrained model presented an excellent fit to the data. When paths were set to be equal across groups, the decline in model fit, confirmed by a chi-square difference test, indicated differences in the model for males and females. A series of Z-tests were used to compare individual paths. Experiencing discrimination was a stronger risk factor for depression among men, whereas a history of rape was more strongly associated with depression for females. Being literate and a member of a social group in the settlement were stronger protective factors for depression among men, whereas living with a partner and a felt sense of connection to their community was more important for women. Associations between risk and protective factors and PTSS were more similar across groups, only membership in a group was significantly moderated by gender; with group membership being more impactful for males. Conclusion: The results highlight similarities and differences in predictors of distress for male and female Congolese refugees and point to potential avenues for tailoring programming to be gender sensitive.