A network analysis of housing quality indicators and depression in women

Faye Sanders; Lucy Waldren; Vilte Baltramonaityte; Alexandre A Lussier; Esther Walton

doi:10.1038/s41598-025-22353-z

A network analysis of housing quality indicators and depression in women

Faye Sanders, Lucy Waldren, Vilte Baltramonaityte, Alexandre A Lussier, Esther Walton

Research output: Contribution to journal › Article › peer-review

Abstract

Numerous studies have detected associations between poor housing quality and increased risk for mental illness. However, it currently remains unclear in associations between poor housing quality and increased risk for women’s mental illness which housing quality indicators drive this association and hence which specific indicators should be prioritised in housing quality assessments or improvements. In a sample of up to 9,669 pregnant women, we used a network analysis to investigate cross-sectional associations between poor housing quality indicators (e.g., house size, facilities, leaks or condensation/mould, decorations, and feelings towards the home) and depressive symptoms (assessed at age 28). All 36 edges showed non-zero associations, whereby when considering all poor housing quality indicators ‘feelings-towards-the-home’ had the strongest association with depressive symptoms, and ‘feelings-towards-the-home’, in turn, was most strongly associated with house problems, size, and facilities. Our findings highlight the importance of using multiple (or composite) person-centred measures of housing quality in the context of maternal mental health.

Original language	English
Article number	38745
Number of pages	8
Journal	Scientific Reports
Volume	15
Issue number	1
DOIs	https://doi.org/10.1038/s41598-025-22353-z
Publication status	Published - 5 Nov 2025

Data Availability Statement

Please note that the study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool ([http://www.bristol.ac.uk/alspac/researchers/our-data/](http:/www.bristol.ac.uk/alspac/researchers/our-data)). Access to ALSPAC data is through a system of managed open access ([http://www.bristol.ac.uk/alspac/researchers/access/](http:/www.bristol.ac.uk/alspac/researchers/access)).

Acknowledgements

We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses.

Funding

The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and FS and EW will serve as guarantors for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). EW and VB are funded from the European Union’s Horizon 2020 research and innovation programme (grant references: 848158, EarlyCause), and from UK Research and Innovation (UKRI) under the UK government’s Horizon Europe/ERC Frontier Research Guarantee [BrainHealth, grant number EP/Y015037/1]. EW and AAL are also funded from the National Institute of Mental Health of the National Institutes of Health (award number R01MH113930; PI Dunn). AAL is supported by an MQ Fellows Award from the MQ Foundation (MQF22\9). LHW is supported by the Leverhulme Trust Research Project Grant (RPG-2023-268). FS is supported by a scholarship from the EPSRC Centre for Doctoral Training in Advanced Automotive Propulsion Systems (AAPS), under the project EP/S023364/1.

Funders	Funder number
Medical Research Council
National Institute of Mental Health
UK Research and Innovation
University of Bristol
Horizon Europe
Leverhulme Trust	RPG-2023-268
European Union’s Horizon 2020 research and innovation programme	848158
EPSRC	EP/S023364/1
National Institutes of Health	R01MH113930
Wellcome	217065/Z/19/Z
MQ Foundation	MQF22\9
ERC Frontier Research Guarantee	EP/Y015037/1

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1038/s41598-025-22353-zLicence: CC BY

Cite this

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A network analysis of housing quality indicators and depression in women

Abstract

Data Availability Statement

Acknowledgements

Funding

UN SDGs

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