Depression-Anxiety Comorbidity Across Sex During COVID-19: An Evaluation of Partial Invariance with Invariance Partial Pruning
Abstract
Depression and anxiety co-occur so frequently that comorbidity is more prevalent than the occurrence of either in isolation. The conventional latent-variable perspective offers limited insight into comorbidity because its inherent causal assumptions overlook causal relations among observable symptoms that are meaningful for explaining comorbidity. At the same time, comorbidity studies based on the network perspective have largely relied on cross-sectional data, which cannot capture the dynamic nature of mental disorders, or on two-wave panel data, which does not separate between- and within-variance and biases the estimation of dynamic processes. Therefore, we present a dynamic network analysis of depression-anxiety comorbidity with the panel graphical vector autoregressive model, using invariance partial pruning to consider possible network structural differences between biological sex. Results revealed the central role of fear in predicting depressive moods and anhedonia as a possible explanation of comorbidity. Anxiety symptoms temporally preceded depression symptoms. In the contemporaneous networks, nervousness and fear were connected to depressive mood. Furthermore, worry and rumination were more associated with depression, whereas beliefs about the uncontrollability of worry and threat monitoring were more associated with anxiety. No sex difference was discovered.
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BibTeX
@misc{du2026depressionanxiety,
title = {{Depression-Anxiety Comorbidity Across Sex During COVID-19: An Evaluation of Partial Invariance with Invariance Partial Pruning}},
author = {Xinkai Du and Asle Hoffart and Sacha Epskamp and Helga Ask and Ragnar Nesvåg and Sverre Urnes Johnson},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/gwnav_v1},
url = {https://osf.io/preprints/psyarxiv/gwnav_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Depression-Anxiety Comorbidity Across Sex During COVID-19: An Evaluation of Partial Invariance with Invariance Partial Pruning
AU - Xinkai Du
AU - Asle Hoffart
AU - Sacha Epskamp
AU - Helga Ask
AU - Ragnar Nesvåg
AU - Sverre Urnes Johnson
PY - 2026
DA - 2026/10/31
PB - PsyArXiv
DO - 10.31234/osf.io/gwnav_v1
UR - https://osf.io/preprints/psyarxiv/gwnav_v1/
AB - Depression and anxiety co-occur so frequently that comorbidity is more prevalent than the occurrence of either in isolation. The conventional latent-variable perspective offers limited insight into comorbidity because its inherent causal assumptions overlook causal relations among observable symptoms that are meaningful for explaining comorbidity. At the same time, comorbidity studies based on the network perspective have largely relied on cross-sectional data, which cannot capture the dynamic nature of mental disorders, or on two-wave panel data, which does not separate between- and within-variance and biases the estimation of dynamic processes. Therefore, we present a dynamic network analysis of depression-anxiety comorbidity with the panel graphical vector autoregressive model, using invariance partial pruning to consider possible network structural differences between biological sex. Results revealed the central role of fear in predicting depressive moods and anhedonia as a possible explanation of comorbidity. Anxiety symptoms temporally preceded depression symptoms. In the contemporaneous networks, nervousness and fear were connected to depressive mood. Furthermore, worry and rumination were more associated with depression, whereas beliefs about the uncontrollability of worry and threat monitoring were more associated with anxiety. No sex difference was discovered.
ER -