TY - JOUR
T1 - Network analysis of depressive symptoms in Hong Kong residents during the COVID-19 pandemic
AU - The International Research Collaboration on COVID-19
AU - Cheung, Teris
AU - Jin, Yu
AU - Lam, Simon
AU - Su, Zhaohui
AU - Hall, Brian J.
AU - Xiang, Yu Tao
AU - Suen, Lorna Kwai Ping
AU - Chan, Shun
AU - Ho, Hilda Sze Wing
AU - Lam, Kin Bong Hubert
AU - Huang, Emma Yun zhi
AU - Xiao, Ying
AU - Pereira-Ávila, Fernanda Maria Vieira
AU - Gir, Elucir
AU - Yildirim, Menevse
AU - Intepeler, Seyda Seren
AU - Lantta, Tella
AU - Lee, Kyungmi
AU - Shin, Nayeon
AU - Parial, Laurence Lloyd
AU - Rossing, Tor Michael
AU - Hon, Ching Yuk
AU - Tsang, Merissa
AU - Poeys, Jessica P.Braz
AU - Fong, Tommy Kwan Hin
N1 - Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - In network theory depression is conceptualized as a complex network of individual symptoms that influence each other, and central symptoms in the network have the greatest impact on other symptoms. Clinical features of depression are largely determined by sociocultural context. No previous study examined the network structure of depressive symptoms in Hong Kong residents. The aim of this study was to characterize the depressive symptom network structure in a community adult sample in Hong Kong during the COVID-19 pandemic. A total of 11,072 participants were recruited between 24 March and 20 April 2020. Depressive symptoms were measured using the Patient Health Questionnaire-9. The network structure of depressive symptoms was characterized, and indices of “strength”, “betweenness”, and “closeness” were used to identify symptoms central to the network. Network stability was examined using a case-dropping bootstrap procedure. Guilt, Sad Mood, and Energy symptoms had the highest centrality values. In contrast, Concentration, Suicide, and Sleep had lower centrality values. There were no significant differences in network global strength (p = 0.259), distribution of edge weights (p = 0.73) and individual edge weights (all p values > 0.05 after Holm–Bonferroni corrections) between males and females. Guilt, Sad Mood, and Energy symptoms were central in the depressive symptom network. These central symptoms may be targets for focused treatments and future psychological and neurobiological research to gain novel insight into depression.
AB - In network theory depression is conceptualized as a complex network of individual symptoms that influence each other, and central symptoms in the network have the greatest impact on other symptoms. Clinical features of depression are largely determined by sociocultural context. No previous study examined the network structure of depressive symptoms in Hong Kong residents. The aim of this study was to characterize the depressive symptom network structure in a community adult sample in Hong Kong during the COVID-19 pandemic. A total of 11,072 participants were recruited between 24 March and 20 April 2020. Depressive symptoms were measured using the Patient Health Questionnaire-9. The network structure of depressive symptoms was characterized, and indices of “strength”, “betweenness”, and “closeness” were used to identify symptoms central to the network. Network stability was examined using a case-dropping bootstrap procedure. Guilt, Sad Mood, and Energy symptoms had the highest centrality values. In contrast, Concentration, Suicide, and Sleep had lower centrality values. There were no significant differences in network global strength (p = 0.259), distribution of edge weights (p = 0.73) and individual edge weights (all p values > 0.05 after Holm–Bonferroni corrections) between males and females. Guilt, Sad Mood, and Energy symptoms were central in the depressive symptom network. These central symptoms may be targets for focused treatments and future psychological and neurobiological research to gain novel insight into depression.
UR - http://www.scopus.com/inward/record.url?scp=85114750044&partnerID=8YFLogxK
U2 - 10.1038/s41398-021-01543-z
DO - 10.1038/s41398-021-01543-z
M3 - Journal article
C2 - 34489416
AN - SCOPUS:85114750044
VL - 11
JO - Translational Psychiatry
JF - Translational Psychiatry
SN - 2158-3188
IS - 1
M1 - 460
ER -