This paper analyses the relationship between health and socioeconomic disadvantage by adopting a dynamic approach accounting for spatial and temporal changes across ten domains including social isolation, environment, financial hardship and security. As a first step we develop a measure of overall multidimensional deprivation and undertake a decomposition analysis to explore the role of breadth and duration of deprivation on shaping the deprivation gradient in health. Subsequently, we employ unconditional quantile regression to conduct a distributional analysis of the gradient to understand how the gradient evolves for people with vulnerability in health. In contrast to the majority of existing studies, we capture health status using a range of nurse measured biomarkers, rather than self reported health measures, taken from the UKHLS and BHPS databases. The first main finding is that the socioeconomic gradient in most of our health measures is not solely attributed to income as it accounts for only 3.8% of total deprivation and thus it is important to account for other domains through a multidimensional deprivation measure in health gradient analysis. Our second finding is the existence of a systematic deprivation gradient for BMI, waist circumference, heart rate, C-reactive protein and HbA1c where evolution over time is an important factor particularly for individuals with greater burden of illness lying at the right tail of the biomarker distribution. Thus cost effective health policy would need to adopt targeted interventions prioritising people experiencing persistent deprivation in dimensions such as housing conditions and social isolation.
Bibliographical noteFunding Information:
Understanding Society is an initiative funded by the Economic and Social Research Council and various Government Departments, with scientific leadership by the Institute for Social and Economic Research, University of Essex, and survey delivery by NatCen Social Research and Kantar Public. The research data are distributed by the UK Data Service. The funders, data creators and UK Data Service have no responsibility for the contents of this paper.
Andrew Jones acknowledges funding from the Leverhulme Trust Major Research Fellowship (MRF – 2016-004). Kompal Sinha acknowledges funding from Macquarie University New Staff Grant (ID: 9201701164 ). The funders and UK Data Service have no responsibility for the contents of this paper. The authors would like to thank Editor, two anonymous referees, Guido Erreygers, conference participants at the Winter HESG meeting and Australian National University for comments and suggestions on previous drafts of this paper. The usual disclaimer applies.
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- Multidimensional deprivation
- Shapley decomposition
- Unconditional quantile regression