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

Gascoigne, Connor, Jeffery, Annie, Shao, Zejing, Geneletti, Sara ORCID: 0000-0001-6456-7258, Kirkbride, James B., Baio, Gianluca and Blangiardo, Marta (2024) A Bayesian interrupted time series framework for evaluating policy change on mental well-being: an application to England's welfare reform. Spatial and Spatio-temporal Epidemiology, 50. ISSN 1877-5845

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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.

Item Type:	Article
Additional Information:	© 2024 The Author(s)
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics R Medicine > RA Public aspects of medicine
Date Deposited:	11 Oct 2024 14:42
Last Modified:	12 Dec 2024 04:31
URI:	http://eprints.lse.ac.uk/id/eprint/125699

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