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Joint modelling of mental health markers through pregnancy: a Bayesian semi-parametric approach

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dc.contributor.authorFeng, Shengxiao Vincent
dc.contributor.authorvan den Boom, Willem
dc.contributor.authorDe Iorio, Maria
dc.contributor.authorThng, Gladi
dc.contributor.authorChan, Jerry
dc.contributor.authorChen, Helen
dc.contributor.authorTan, Kok Hian
dc.contributor.authorKee, Michelle
dc.date.accessioned2024-05-22T19:40:46Z
dc.date.available2024-05-22T19:40:46Z
dc.date.issued2023-01-13
dc.description.abstractMaternal 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.en
dc.identifier.citationJournal of Applied Statistics
dc.identifier.doi10.1596/41584
dc.identifier.issn0266-4763 (print)
dc.identifier.issn1360-0532 (online)
dc.identifier.urihttps://hdl.handle.net/10986/41584
dc.language.isoen_US
dc.publisherTaylor and Francis
dc.rightsCC BY-NC-ND 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/igo/
dc.subjectBAYESIAN NON-PARAMETRICS
dc.subjectDIRICHLET PROCESS
dc.subjectGAUSSIAN PROCESS
dc.subjectMENTAL HEALTH
dc.subjectPREGNANCY
dc.subjectTRAJECTORY CLUSTERING
dc.titleJoint modelling of mental health markers through pregnancyen
dc.title.subtitlea Bayesian semi-parametric approachen
dc.typeJournal Article
dspace.entity.typePublication
okr.associatedcontenthttps://www.tandfonline.com/doi/full/10.1080/02664763.2022.2154329 Journal website (version of record)
okr.crossref.titleJoint modelling of mental health markers through pregnancy: a Bayesian semi-parametric approach
okr.date.disclosure2023-01-13
okr.doctypePublications & Research
okr.doctypePublications & Research::Journal Article
okr.externalcontentExternal Content
okr.identifier.doi10.1080/02664763.2022.2154329
okr.identifier.doihttps://doi.org/10.1596/41584
okr.pagenumber388-405
okr.peerreviewAcademic Peer Review
okr.region.geographicalWorld
okr.topicHealth, Nutrition and Population::Mental Health
okr.volume51(2)
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