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arXiv Open Access 2023

Bayesian Interrupted Time Series for evaluating policy change on mental well-being: an application to England's welfare reform

Connor Gascoigne Marta Blangiardo Zejing Shao Annie Jeffery Sara Geneletti +2 lainnya
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Abstrak

Factors contributing to social inequalities are also associated with negative mental health outcomes leading to disparities in mental well-being. We propose a Bayesian hierarchical model which can evaluate the impact of policies on population well-being, accounting for spatial/temporal dependencies. Building on an interrupted time series framework, our approach can evaluate how different profiles of individuals are affected in different ways, whilst accounting for their uncertainty. We apply the framework to assess the impact of the United Kingdoms welfare reform, which took place throughout the 2010s, on mental well-being using data from the UK Household Longitudinal Study. The additional depth of knowledge is essential for effective evaluation of current policy and implementation of future policy.

Topik & Kata Kunci

stat.ME

Penulis (7)

C

Connor Gascoigne

M

Marta Blangiardo

Z

Zejing Shao

A

Annie Jeffery

S

Sara Geneletti

J

James Kirkbride

G

Gianluca Baio

Format Sitasi

APA MLA BibTeX
Gascoigne, C., Blangiardo, M., Shao, Z., Jeffery, A., Geneletti, S., Kirkbride, J. et al. (2023). Bayesian Interrupted Time Series for evaluating policy change on mental well-being: an application to England's welfare reform. https://arxiv.org/abs/2306.15525

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Tahun Terbit
2023
Bahasa
en
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arXiv
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Open Access ✓