Post-COVID Mood Disorders and Their Risk Factors: A Cross-Sectional Study in a Caucasian Cohort

Post-acute COVID-19 syndrome has been associated with persistent psychological symptoms, including affective disturbances. This study assesses the severity of mood alterations and examines associated demographic and clinical risk factors in individuals who have recovered from COVID-19. A cross-sectional study was conducted among 200 individuals from the Caucasian region, 3 to 24 months post-COVID-19 recovery. Affective symptom burden was assessed using the PHQ-9 and Beck Depression Inventory (BDI). Key variables included age, sex, physical health, hospitalisation, quarantine history, vaccination status, and biomarkers (CRP, IL-6). Descriptive trends indicated elevated affective symptom scores among women, middle-aged participants, and those with poor physical health. However, statistical significance was not achieved (PHQ-9: p > 0.5; BDI: p > 0.2). PHQ-9 and BDI scores correlated positively, validating consistent mood-related symptomatology across tools. Variables such as duration since COVID-19, economic impact, and sleep patterns are under further investigation. Conclusion: Although not statistically significant, observed trends suggest specific demographic groups may be more vulnerable to post-COVID depressive symptoms. These findings underscore the need for targeted mental health follow-up and future longitudinal studies to validate and further explore these trends.

Authors

Institutions

Publication Details

Journal
GEORGIAN SCIENTISTS
Published
2025-05-15
DOI
https://doi.org/10.52340/gs.2025.07.02.29
Primary Topic
COVID-19 and Mental Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Post-COVID Mood Disorders and Their Risk Factors: A Cross-Sectional Study in a Caucasian Cohort

Marina Janelidze, Gucha Kobaidze, Tamar Sakvarelidze, Shota Kepuladze
1 links
GEORGIAN SCIENTISTS
COVID-19 and Mental Health
article

Post-COVID Mood Disorders and Their Risk Factors: A Cross-Sectional Study in a Caucasian Cohort

Marina Janelidze, Gucha Kobaidze, Tamar Sakvarelidze, Shota Kepuladze
article en

Abstract

Post-acute COVID-19 syndrome has been associated with persistent psychological symptoms, including affective disturbances. This study assesses the severity of mood alterations and examines associated demographic and clinical risk factors in individuals who have recovered from COVID-19. A cross-sectional study was conducted among 200 individuals from the Caucasian region, 3 to 24 months post-COVID-19 recovery. Affective symptom burden was assessed using the PHQ-9 and Beck Depression Inventory (BDI). Key variables included age, sex, physical health, hospitalisation, quarantine history, vaccination status, and biomarkers (CRP, IL-6). Descriptive trends indicated elevated affective symptom scores among women, middle-aged participants, and those with poor physical health. However, statistical significance was not achieved (PHQ-9: p > 0.5; BDI: p > 0.2). PHQ-9 and BDI scores correlated positively, validating consistent mood-related symptomatology across tools. Variables such as duration since COVID-19, economic impact, and sleep patterns are under further investigation. Conclusion: Although not statistically significant, observed trends suggest specific demographic groups may be more vulnerable to post-COVID depressive symptoms. These findings underscore the need for targeted mental health follow-up and future longitudinal studies to validate and further explore these trends.

GEORGIAN SCIENTISTS
Tbilisi State Medical University (GE)
Openalex Percentile: Top 84%
COVID-19 and Mental Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.