Resilience and life events: Mental health in rural communities

Abstract Background Potentially traumatic events (PTEs) are associated with mental health (e.g. depression and resilience). Resilience may buffer these effects, evidence from rural populations remains scarce. This study investigates the association between PTEs with resilience and depression in a rural sample. Methods We analysed cross-sectional data from a population-based observational study of adults (aged 18+) in northern Germany. Resilience (BRS), depression (PHQ-9), anxiety (GAD-7) and PTEs (LEC-5) were assessed along with sociodemographic factors (age, gender, marital status, education level, employment status, household income). LEC-5 clusters were identified by principal component analysis (PCA). We used linear regression and moderation models to investigate associations. Results The sample (N = 354; n = 119, 33.6% male; n = 235, 66.4% female, mean age 52.3±17.7) reported moderate levels of depression (16.1%) and anxiety (11.9%). Resilience was reported low by 22.6% (n = 80), medium by 64.1% (n = 227), and high by 13.3% (n = 47). PCA identified four PTE clusters: accident/injury (n = 300, 84.7%), loss/life-threatening (n = 299, 84.5%), victimization (n = 218, 61.6%), and war/conflict-related (n = 88, 24.9%). Reporting more than four PTEs was significantly associated with higher depression scores (B = 1.658, 95%CI: 0.071-3.246). War/conflict-related PTEs were significantly associated with higher depression (B = 1.330, 95%CI: 0.197-2.463) and anxiety scores (B = 0.988, 95%CI: 0.024-1.952). Resilience significantly moderated the association between PTEs and depression at low and medium resilience levels. Conclusions PTEs, especially war/conflict-related, were associated with higher depression and anxiety scores. Our findings highlight resilience as a potential buffer for associations between PTEs and depression. To understand resilience trajectories, identify risk and protective factors for mental health and resilience, and develop interventions in rural populations, longitudinal data are needed.