Social support and post-discharge aggressive behavior among patients with mood disorders: a single-center analysis of recurrent aggressive events

Existing research on aggressive behavior among individuals with mood disorders remains limited, with most studies failing to account for the recurrent nature of such behavior. To address this gap, this study applied a recurrence data model, the Andersen-Gill model, to examine the association between social support and aggressive behavior among individuals with mood disorders. This prospective cohort study was conducted at Anding Psychiatric Hospital in Beijing, China, from December 2019 to September 2022. Newly admitted patients aged 18–64 years with mood disorders (ICD-10 diagnosis) and no prior systematic treatment were consecutively enrolled ( n = 388; 384 completed follow-up) and followed for 24 months post-discharge. Social support was assessed using the Social Support Rating Scale (SSRS) at baseline and categorized into quartiles (Q1-Q4). Aggressive behavior was evaluated using the Modified Overt Aggression Scale (MOAS) via telephone follow-ups every three months. Directed Acyclic Graphs were used to identify confounders for adjustment. The Andersen-Gill model was used to analyze the association between social support and recurrent aggressive behavior, with stratified analyses by disease type, gender, and age. Restricted cubic spline functions were used to explore dose-response relationships. Compared to the lowest quartile of social support (Q1), patients in the third and highest quartiles (Q3 and Q4) showed a significantly reduced risk of aggressive behavior (HR = 0.50, 95% CI: 0.25–0.97; HR = 0.40, 95% CI: 0.17–0.94, respectively). Stratified analyses revealed that the protective effect was most pronounced among patients with depressive disorder, males, and those aged ≥ 30 years. In contrast, among female patients, higher levels of social support were associated with an increased risk of aggressive behavior (Q3 vs. Q1: HR = 2.96, 95%CI: 1.05–8.37). Overall, a dose-response relationship was observed, with higher social support scores associated with lower risk of aggressive behavior. Social support appears to be protective against recurrent aggressive behavior in patients with mood disorders, particularly among those with depressive disorders, males, while a potential risk factor was observed among females.

Authors

Institutions

Publication Details

Journal
BMC Psychiatry
Published
2026-10-16
DOI
https://doi.org/10.1186/s12888-026-07984-4
Primary Topic
Bipolar Disorder and Treatment
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Social support and post-discharge aggressive behavior among patients with mood disorders: a single-center analysis of recurrent aggressive events

Junnan Xu, Peixia Cheng, Xinyi Yu, Qian Wang et al.
BMC Psychiatry
Bipolar Disorder and Treatment
article

Social support and post-discharge aggressive behavior among patients with mood disorders: a single-center analysis of recurrent aggressive events

Junnan Xu, Peixia Cheng, Xinyi Yu, Qian Wang, Hua Fan, Huiping Zhu, Qi Gao, Zexi Yang
article en

Abstract

Existing research on aggressive behavior among individuals with mood disorders remains limited, with most studies failing to account for the recurrent nature of such behavior. To address this gap, this study applied a recurrence data model, the Andersen-Gill model, to examine the association between social support and aggressive behavior among individuals with mood disorders. This prospective cohort study was conducted at Anding Psychiatric Hospital in Beijing, China, from December 2019 to September 2022. Newly admitted patients aged 18–64 years with mood disorders (ICD-10 diagnosis) and no prior systematic treatment were consecutively enrolled ( n = 388; 384 completed follow-up) and followed for 24 months post-discharge. Social support was assessed using the Social Support Rating Scale (SSRS) at baseline and categorized into quartiles (Q1-Q4). Aggressive behavior was evaluated using the Modified Overt Aggression Scale (MOAS) via telephone follow-ups every three months. Directed Acyclic Graphs were used to identify confounders for adjustment. The Andersen-Gill model was used to analyze the association between social support and recurrent aggressive behavior, with stratified analyses by disease type, gender, and age. Restricted cubic spline functions were used to explore dose-response relationships. Compared to the lowest quartile of social support (Q1), patients in the third and highest quartiles (Q3 and Q4) showed a significantly reduced risk of aggressive behavior (HR = 0.50, 95% CI: 0.25–0.97; HR = 0.40, 95% CI: 0.17–0.94, respectively). Stratified analyses revealed that the protective effect was most pronounced among patients with depressive disorder, males, and those aged ≥ 30 years. In contrast, among female patients, higher levels of social support were associated with an increased risk of aggressive behavior (Q3 vs. Q1: HR = 2.96, 95%CI: 1.05–8.37). Overall, a dose-response relationship was observed, with higher social support scores associated with lower risk of aggressive behavior. Social support appears to be protective against recurrent aggressive behavior in patients with mood disorders, particularly among those with depressive disorders, males, while a potential risk factor was observed among females.

BMC Psychiatry
Capital Medical University (CN), Beijing Anding Hospital (CN), Beijing Obstetrics and Gynecology Hospital (CN)
National Natural Science Foundation of China
Good health and well-being
Openalex Percentile: Top 13%
Bipolar Disorder and Treatment
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.