Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/118770
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Type: Journal article
Title: The impact of social housing on mental health: longitudinal analyses using marginal structural models and machine learning generated weights
Author: Bentley, R.
Baker, E.
Blakely, T.
SImons, K.
Simpson, J.
Citation: International Journal of Epidemiology, 2018; 47(5):1414-1422
Publisher: Oxford University Press
Issue Date: 2018
ISSN: 0300-5771
1464-3685
Statement of
Responsibility: 
Rebecca Bentley, Emma Baker, Koen Simons, Julie A Simpson, Tony Blakely
Abstract: Background: Social housing provides an affordable and secure residential environment, but is also associated with stigma, poor housing conditions and locational disadvantage. We examine the cumulative effect of additional years in social housing and number of transitions on mental health in a large cohort of lower-income Australians. Methods: We analyze a longitudinal panel survey that annually collected information on tenure and health from 2001 to 2013. To address the time varying effect of prior health on social housing occupancy, we used Marginal Structural Models. Stabilized inverse probabilities of treatment weights were generated using ensemble learning to improve prediction. To address remaining residual imbalance across covariates, double adjustment was made by additionally including baseline covariates in models. Mental health was measured using the Mental Health summary measure of the SF-36 (MH) and psychological distress was measured using the Kessler Psychological Distress Scale (K10). Results: People who had continuous exposure to social housing had worse mental health on average than people continuously occupying other tenures. The worst mental health outcomes, however, were observed for people who made multiple transitions. Mental health deteriorated and psychological distress increased with number of transitions: -1.04 (95% CI -2.16,0.08) for the MH and 0.56 (95%CI 0.13,0.99) for the K10. Estimates are in the order of 6% (MH) and 9% (K10) of one standard deviation for each measure. Conclusions: The more social housing transitions people made, the greater the impact on their mental health and psychological distress, supporting the case for provision of more stable forms of social housing.
Keywords: Social housing; mental health; marginal structural models; machine learning
Rights: © The Author(s) 2018; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association
RMID: 0030097609
DOI: 10.1093/ije/dyy116
Grant ID: http://purl.org/au-research/grants/arc/FT150100131
http://purl.org/au-research/grants/arc/FT140100872
http://purl.org/au-research/grants/nhmrc/1104975
http://purl.org/au-research/grants/arc/DP120102974
Appears in Collections:Public Health publications

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