This on-going PhD project uses nationally representative data to examine regional health disparities and explore what lies behind the differences in health outcomes across regions in the UK.
Adopting the World Health Organisation definition, the NHS England defines health inequalities as unfair and avoidable differences in health across the population and between different groups within society. Even though steady improvements in life expectancy and population health have been evident over recent decades, preventable inequalities in health persist within and between regions in the UK.
This project uses a nationally representative dataset for the UK, Understanding Society: the UK Household Longitudinal Study (UKHLS), to investigate the contribution of individual-level characteristics and the role of small area environment to health disparities in the UK. This research uses eight biomarkers relevant to critical chronic conditions which has profound implications for the population and life expectancy (body mass index [BMI], percentage body fat, heart rate, systolic blood pressure, triglycerides, cholesterol ratios, albumin, and estimated glomerular filtration rates).
This project examines the presence of regional variations in health at eleven Government office regions (GORs) of Great Britain (England (nine regions), Wales, and Scotland). Also, this research uses linear regressions estimated using Ordinary Least Squares (OLS). Beyond this analysis, Oaxaca-Blinder decomposition is used to decompose the contribution of individual variables in other to understand what drives these differences in health outcomes across the UK regions.
This research aims to address the following research questions:
- Are there regional inequalities in health in the UK?
- How does the level of health outcomes differ by government office regions?
- What lies behind these differences in health outcomes across government office regions in the UK?
Findings so far
This project is currently underway, but the research has already found that London has better health outcomes compared to the other regions, with respect to the eight biomarkers considered in the study. Additionally, initial analysis shows statistically significant (based on an F-test) regional disparity, even after adjusting for regional differences in age and gender.
From the Oaxaca-Blinder decomposition analysis results, it was found that the return of household income is the single most important contributor in the unexplained part, whilst differences in the level of education and house ownership are the highest contributors to regional disparities in the explained part.
- Godsfavour will apply for a special data licence from the United Kingdom Household Longitudinal Study (UKHLS). This special licence data holds the Lower Layer Super Output Area (LSOA) level. This data will allow exploration of smaller geographical areas, focusing on those most in need.
- Godsfavour will estimate a distributional analysis by applying quantile regression. This regression will provide evidence and the unconditional distribution of the biomarkers, important for designing health policies.
Who is involved
Principal Investigator: Godsfavour Ilori, PhD student, University of East Anglia (firstname.lastname@example.org)
Professor Tracey Sach, University of East Anglia
Dr Apostolos Davillas, Associate professor, HEDG, University of York and IZA, UEA.
- NHS England., 2017. Definitions for health inequalities. [online]. NHS England. [Viewed 15 March 2021]. Available from: https://www.england.nhs.uk/ltphimenu/definitions-for-health-inequalities/
- Oaxaca, R., 1973. Male-female wage differentials in urban labor markets. International economic review, pp.693-709.
- Blinder, A.S., 1973. Wage discrimination: reduced form and structural estimates. Journal of Human resources, pp.436-455.
- Davillas, A. and Jones, A.M., 2020. Regional inequalities in adiposity in England: distributional analysis of the contribution of individual-level characteristics and the small area obesogenic environment. Economics & Human Biology, 38, p.100887.
- Lee, J., McGovern, M.E., Bloom, D.E., Arokiasamy, P., Risbud, A., O’Brien, J., Kale, V. and Hu, P., 2015. Education, gender, and state-level disparities in the health of older Indians: Evidence from biomarker data. Economics & Human Biology, 19, pp.145-156.
- Di Paolo, A., Gil, J. and Raftopoulou, A., 2018. What drives regional differences in BMI? Evidence from Spain [WP]. AQR–Working Papers, 2018, AQR18/05.
- University of Essex, Institute for Social and Economic Research (2020). Understanding Society: Waves 1-10, 2009-2019 and Harmonised BHPS: Waves 1-18, 1991-2009. [data collection]. 13th Edition. UK Data Service. SN: 6614, http://doi.org/10.5255/UKDA-SN-6614-14.