Maternal depression and anxiety through pregnancy have lasting societal impacts. It is thus crucial to understand the trajectories of its progression from preconception to postnatal period, and the risk factors associated with it. Within the Bayesian framework, we propose to jointly model seven outcomes, of which two are physiological and five non-physiological indicators of maternal depression and anxiety over time. We model the former two by a Gaussian process and the latter by an autoregressive model, while imposing a multidimensional Dirichlet process prior on the subject-specific random effects to account for subject heterogeneity and induce clustering. The model allows for the inclusion of covariates through a regression term. Our findings reveal four distinct clusters of trajectories of the seven health outcomes, characterising women's mental health progression from before to after pregnancy. Importantly, our results caution against the loose use of hair corticosteroids as a biomarker, or even a causal factor, for pregnancy mental health progression. Additionally, the regression analysis reveals a range of preconception determinants and risk factors for depressive and anxiety symptoms during pregnancy.
Keywords: Bayesian non-parametrics; Dirichlet process; Gaussian process; mental health; pregnancy; trajectory clustering.
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.