Jen Murphy1, Mark Elliot1. 1. School of Social Sciences, University of Manchester, Oxford Road, Manchester, M21 9DN Greater Manchester UK.
Abstract
Purpose: We investigated the trajectory of wellbeing over the course of the first wave and sought to determine whether the change in wellbeing is distributed equally across the population. Specifically we investigated pre-existing medical conditions, social isolation, financial stress and deprivation as a predictor for wellbeing and whether there were community level characteristics which protect against poorer wellbeing. Methods: Using online survey responses from the COVID-19 modules of Understanding society, we linked 8379 English cases across five waves of data collection to location based deprivation statistics. We used ordinary least squares regression to estimate the association between deprivation, pre-existing conditions and socio-demographic factors and the change in well-being scores over time, as measured by the GHQ-12 questionnaire. Results: A decline in wellbeing was observed at the beginning of the first lock down period at the beginning of March 2020. This was matched with a corresponding recovery between April and July as restrictions were gradually lifted. There was no association between the decline and deprivation, nor between deprivation and recovery. The strongest predictor of wellbeing during the lock down, was the baseline score, with the counterintuitive finding that for those will pre-existing poor wellbeing, the impact of pandemic restrictions on mental health were minimal, but for those who had previously felt well, the restrictions and the impact of the pandemic on well-being were much greater. Conclusions: These data show no evidence of a social gradient in well-being related to the pandemic. In fact, well-being was shown to be highly elastic in this period indicating a national level of resilience which cut across the usually observed health inequalities.
Purpose: We investigated the trajectory of wellbeing over the course of the first wave and sought to determine whether the change in wellbeing is distributed equally across the population. Specifically we investigated pre-existing medical conditions, social isolation, financial stress and deprivation as a predictor for wellbeing and whether there were community level characteristics which protect against poorer wellbeing. Methods: Using online survey responses from the COVID-19 modules of Understanding society, we linked 8379 English cases across five waves of data collection to location based deprivation statistics. We used ordinary least squares regression to estimate the association between deprivation, pre-existing conditions and socio-demographic factors and the change in well-being scores over time, as measured by the GHQ-12 questionnaire. Results: A decline in wellbeing was observed at the beginning of the first lock down period at the beginning of March 2020. This was matched with a corresponding recovery between April and July as restrictions were gradually lifted. There was no association between the decline and deprivation, nor between deprivation and recovery. The strongest predictor of wellbeing during the lock down, was the baseline score, with the counterintuitive finding that for those will pre-existing poor wellbeing, the impact of pandemic restrictions on mental health were minimal, but for those who had previously felt well, the restrictions and the impact of the pandemic on well-being were much greater. Conclusions: These data show no evidence of a social gradient in well-being related to the pandemic. In fact, well-being was shown to be highly elastic in this period indicating a national level of resilience which cut across the usually observed health inequalities.
Authors: Helena Tunstall; Richard Mitchell; Julia Gibbs; Stephen Platt; Danny Dorling Journal: J Epidemiol Community Health Date: 2007-04 Impact factor: 3.710
Authors: David Reeves; Christian Blickem; Ivaylo Vassilev; Helen Brooks; Anne Kennedy; Gerry Richardson; Anne Rogers Journal: PLoS One Date: 2014-06-02 Impact factor: 3.240