Gascoigne, C;
Jeffery, A;
Shao, Z;
Geneletti, S;
Kirkbride, JB;
Baio, G;
Blangiardo, M;
(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
, Article 100662. 10.1016/j.sste.2024.100662.
Preview |
Text
ITS-universal-credit.pdf - Published Version Download (2MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | A Bayesian Interrupted Time Series framework for evaluating policy change on mental well-being: An application to England's welfare reform |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.sste.2024.100662 |
| Publisher version: | http://dx.doi.org/10.1016/j.sste.2024.100662 |
| Language: | English |
| Additional information: | © 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Bayesian hierarchical model, Interrupted time series, Mental well-being, Policy evaluation, Spatial random effect, Humans, Bayes Theorem, Mental Health, Social Welfare, Interrupted Time Series Analysis, Male, Longitudinal Studies, Female, England, Adult, Middle Aged, Socioeconomic Factors |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Division of Psychiatry UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Division of Psychiatry > Epidemiology and Applied Clinical Research |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10196530 |
Archive Staff Only
 |
View Item |
