A Bayesian interrupted time series framework for evaluating policy change on mental well-being: an application to England’s welfare reform

Publication type

Journal Article

Authors

Publication date

June 11, 2024

Summary:

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.

Published in

Spatial and Spatio-temporal Epidemiology

DOI

https://doi.org/10.1016/j.sste.2024.100662

ISSN

18775845

Subjects

Notes

Online Early

Open Access

Under a Creative Commons license

© 2024 The Author(s). Published by Elsevier Ltd.


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