ArticleFrontiers in psychiatry2025
Dynamic changes in depressive symptoms at the onset of military conflict in a neighboring country: a cross-sectional study.
Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. Methods: 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. Conclusions: 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.