Feng, Shengxiao Vincent;
van den Boom, Willem;
De Iorio, Maria;
Thng, Gladi J;
Chan, Jerry KY;
Chen, Helen Y;
Tan, Kok Hian;
(2023)
Joint modelling of mental health markers through pregnancy: a Bayesian semi-parametric approach.
Journal of Applied Statistics
10.1080/02664763.2022.2154329.
(In press).
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Joint modelling of mental health markers through pregnancy: a Bayesian semi-parametric approach |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/02664763.2022.2154329 |
| Publisher version: | https://doi.org/10.1080/02664763.2022.2154329 |
| Language: | English |
| Additional information: | © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. |
| Keywords: | Bayesian non-parametrics, Dirichlet process, Gaussian process, mental health, pregnancy, trajectory clustering |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10163585 |
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