A Bayesian Interrupted Time Series framework for evaluating policy change on mental well-being: an application to England’s welfare reform
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Published version
Author(s)
Type
Journal Article
Abstract
Factors contributing to social inequalities are associated with negative mental health outcomes and disparities in mental well-being. We propose a Bayesian hierarchical controlled interrupted time series to evaluate the impact of policies on population well-being whilst accounting for spatial and temporal patterns. Using data from the UKs Household Longitudinal Study, we apply this framework to evaluate the impact of the UKs welfare reform implemented in the 2010s on the mental health of the participants, measured using the GHQ-12 index. Our findings indicate that the reform led to a 2.36% (95% CrI: 0.57%–4.37%) increase in the national GHQ-12 index in the exposed group, after adjustment for the control group. Moreover, the geographical areas that experienced the largest increase in the GHQ-12 index are from more disadvantage backgrounds than affluent backgrounds.
Date Issued
2024-08
Date Acceptance
2024-05-23
Citation
Spatial and Spatio-temporal Epidemiology, 2024, 50
ISSN
1877-5845
Publisher
Elsevier
Journal / Book Title
Spatial and Spatio-temporal Epidemiology
Volume
50
Copyright Statement
© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
http://dx.doi.org/10.1016/j.sste.2024.100662
Publication Status
Published
Article Number
100662
Date Publish Online
2024-06-11