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

Connor Gascoigne, Annie Jeffery, Zejing Shao, Sara Geneletti, James B. Kirkbride, Gianluca Baio and Marta Blangiardo

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: Bayesian hierarchical model; interrupted time series; mental well-being; policy evaluation; spatial random effect (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 10 pages
Date: 2024-08-31
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Published in Spatial and Spatio-temporal Epidemiology, 31, August, 2024, 50. ISSN: 1877-5845

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