Dynamic changes in depressive symptoms at the onset of military conflict in a neighboring country: a cross sectional study

Artur Airapetian^1*Valentas Gruzauskas²Neringa Urbonaite³Benedikt Bachmetjev⁴Povilas Bernadickas⁴Laura Nedzinskiene⁵Rolandas Zablockis⁶

• ¹Faculty of Medicine, Vilnius University, M.K. Ciurlionio 21, LT-03101 Vilniu, Vilnius University, Vilnius, Lithuania
• ²Institute of Computer Science, Naugarduko g. 24, LT-03225, Vilnius, Lithuania, Vilnius University, Vilnius, Lithuania
• ³Institute of Data Science and Digital Technologies, Akademijos g. 4, LT-08412 Vilnius, Vilnius University, Vilnius, Lithuania
• ⁴Faculty of Medicine, Vilnius University, M.K. Ciurlionio 21, LT-03101 Vilnius,, Vilnius University, Vilnius, Lithuania
• ⁵Department of Anatomy, Histology and Anthropology, Institute of Biomedical Sciences, Faculty of Medicine, 03101 Vilnius, Lithuania, Vilnius University, Vilnius, Lithuania
• ⁶Clinic of Chest Diseases, Immunology and Allergology, Institute of Clinical Medicine, M.K. Ciurlionio 21, LT-03101 Vilnius, Lithuania, Vilnius University, Vilnius, Lithuania

Background/Objectives: Amidst global conflict, mental health issues have surged, with a notable increase in Disability-Adjusted Life Years (DALYs) due to mental disorders from 1990 to 2019. In Lithuania, a high prevalence of depression contrasts with the global average, with a substantial healthcare allocation to mental health. The study aims to analyze depression rates across Lithuanian regions, employing the PHQ-9 questionnaire for accurate assessment, and addressing mental health needs through region-specific strategies.A cross-sectional study conducted in Lithuania employed an anonymous internet survey to assess the impact of conflict on mental health based on the PHQ-9 scale. Data analysis utilized statistical tools like Statistical Package for the Social Sciences (SPSS) and R studio. The study employed the Shepard operator for data interpolation, visualized in a detailed map of Lithuania illustrating regional depression levels. This approach is innovative as it utilizes advanced interpolation methods to create a highly accurate and detailed geographic representation of mental health data. The precision of this map allows for precise, location-targeted interventions for mental health prevention. This targeted approach is crucial in efficiently addressing mental health issues on a regional scale, ensuring that interventions are both effective and resource efficient.Results: Key findings included an increase in PHQ-9 depression scores from an average of 7.28 before the war to 9.00 two weeks after the invasion began, suggesting a possible association between the onset of conflict and depressive symptoms. The study revealed a moderate correlation (r = 0.443) between pre-war and post-war PHQ-9 scores. A linear regression model indicated factors affecting depression scores, including age and gender, and spatial mapping showed regional variations in depression, highlighting the western part of Lithuania and the north-eastern central region as areas with higher depression levels.The study shows increased depression symptoms after conflict, with diverse reactions across demographics and locations in Lithuania. Persistent severe symptoms and the varying effects of education and age on stress responses were observed. The research emphasizes the need for customized mental health strategies, particularly near geopolitical hotspots, to effectively address these challenges.