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

TL;DR

This paper introduces a Bayesian hierarchical interrupted time series model to evaluate how policy changes, like the UK's welfare reform, impact mental well-being across different populations, accounting for uncertainties and dependencies.

Contribution

The paper presents a novel Bayesian hierarchical framework that assesses policy effects on mental well-being, incorporating spatial and temporal dependencies and individual profiles.

Findings

Welfare reform had measurable impacts on mental well-being.

The model effectively captures heterogeneity in policy effects.

Uncertainty quantification improves policy evaluation accuracy.

Abstract

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.