1. A Bayesian Interrupted Time Series framework for evaluating policy change on mental well-being: An application to England's welfare reform.
- Author
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Gascoigne C, Jeffery A, Shao Z, Geneletti S, Kirkbride JB, Baio G, and Blangiardo M
- Subjects
- Humans, Male, Longitudinal Studies, Female, England, Adult, Middle Aged, Socioeconomic Factors, Bayes Theorem, Mental Health, Social Welfare, Interrupted Time Series Analysis
- 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
- Published
- 2024
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