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      Association between maternal mental health-related hospitalisation in the 5 years prior to or during pregnancy and adverse birth outcomes: a population-based retrospective cohort data linkage study in the Northern Territory of Australia

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          Summary

          Background

          Mental health conditions prior to or during pregnancy that are not addressed can have adverse consequences for pregnancy and birth outcomes. This study aimed to determine the extent to which women's mental health-related hospitalisation (MHrH) prior to or during pregnancy was associated with a risk of adverse birth outcomes.

          Methods

          We linked the perinatal data register for all births in the Northern Territory, Australia, from the year 1999 to 2017, to hospital admissions records to create a cohort of births to women aged 15–44 years with and without MHrH prior to or during pregnancy. We used Modified Poisson Regression and Latent Class Analysis to assess the association between maternal MHrH and adverse birth outcomes (i.e., stillbirth, preterm birth, low birth weight, and short birth length). We explored a mediation effect of covariates on theoretical causal paths. We calculated the adjusted Population Attributable Fraction (PAF) and Preventive Fractions for the Population (PFP) for valid associations.

          Findings

          From 72,518 births, 70,425 births (36.4% for Aboriginal women) were included in the analysis. The Latent Class Analys identified two classes: high (membership probability of 10.5%) and low adverse birth outcomes. Births to Aboriginal women with MHrH were around two times more likely to be in the class of high adverse birth outcomes. MHrH prior to or during pregnancy increased the risk of all adverse birth outcomes in both populations with risk ranging from 1.19 (95% CI: 1.05, 1.35) to 7.89 (1.17, 53.37). Eight or more antenatal care visits and intrauterine growth restriction mostly played a significant mediation role between maternal MHrH and adverse birth outcomes with mediation effects ranging from 1.04 (1.01, 1.08) to 1.39 (1.14, 1.69). MHrH had a low to high population impact with a PAF ranging from 16.1% (5.1%, 25.7%) to 87.3% (14.3%, 98.1%). Eight or above antenatal care visits avert extra adverse birth outcomes that range from 723 (332–765) stillbirths to 3003 (1972–4434) preterm births.

          Interpretation

          Maternal MHrH is a modifiable risk factor that explained a low to moderate risk of adverse birth outcomes in the Northern Territory. The knowledge highlights the need for the development and implementation of preconception mental health care into routine health services.

          Funding

          The Child and Youth Development Research Partnership (CYDRP) data repository is supported by a grant from the doi 10.13039/501100022940, Northern Territory Government; .

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          Most cited references57

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          Sensitivity Analysis in Observational Research: Introducing the E-Value.

          Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
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            Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study

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              The Lancet Commission on global mental health and sustainable development

              The Lancet, 392(10157), 1553-1598
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                Author and article information

                Contributors
                Journal
                Lancet Reg Health West Pac
                Lancet Reg Health West Pac
                The Lancet Regional Health: Western Pacific
                Elsevier
                2666-6065
                16 April 2024
                May 2024
                16 April 2024
                : 46
                : 101063
                Affiliations
                [a ]Menzies School of Health Research, Charles Darwin University, Darwin, Australia
                [b ]Addis Continental Institute of Public Health, Addis Ababa, Ethiopia
                [c ]Royal Darwin Hospital, Tiwi, NT 0810, Australia
                [d ]Faculty of Health, Charles Darwin University, Darwin, Australia
                [e ]Faculty of Medicine and Health, The University of Sydney, Australia
                [f ]Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Sydney and St John of God Burwood Hospital, Sydney, Australia
                [g ]Faculty of Science, Medicine and Health, Graduate School of Medicine, University of Wollongong, Australia
                [h ]Faculty of Health, School of Psychology, Deakin University, Geelong, VIC, Australia
                [i ]Mater Research Institute, Aubigny Place, Raymond Terrace, South Brisbane, QLD, Australia
                [j ]School of Medicine, Charles Darwin University, Australia
                [k ]School of Medicine, The University of Sydney, Australia
                Author notes
                []Corresponding author. Menzies School of Health Research, Charles Darwin University, Darwin, Australia. abel.dadi@ 123456menzies.edu.au
                Article
                S2666-6065(24)00057-9 101063
                10.1016/j.lanwpc.2024.101063
                11040136
                38659431
                da339f2a-df84-43e5-a30c-0644748aeeeb
                Crown Copyright © 2024 Published by Elsevier Ltd.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 30 October 2023
                : 13 February 2024
                : 27 March 2024
                Categories
                Articles

                mental health-related hospitalization,latent class analysis,paf,ppf

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