Self-rated health inequalities in British nurses
Ball, W., Atherton, I. and Kyle, R. (2020) International Journal of Population Data Science, 5(5), [ONS LS]
Objectives and Approach: We seek to test whether Nurses experience health inequalities in Self-Rated Health comparable with the general population. We also aim to explore cross-national differences within the Nursing occupational group. We utilise data from Census-derived Longitudinal Studies in Scotland and England & Wales which are linked to an adjusted UK-consistent Multiple Deprivation measure. The databases can only be accessed securely, so an innovative method (eDatashield) has been used to conduct analysis as if the two were combined. Nurses are of interest as they are a large occupational group with potentially protective characteristics against inequalities including high health literacy and level of education. Socioeconomic homogeneity in this group may reduce the effect of confounding when exploring area-based deprivation measures.
Results: Comparing Nurses to Non-Nurses we found they have systematically different and more homogenous characteristics. Nurses are; older, have a higher level of education, are more likely to be female, own their home, are less likely to live in deprived areas and they report better Self-Rated Health. However, inequalities persist. Comparing Self-Rated Health of Scottish with English & Welsh Nurses will determine whether an ‘excess’ in worse health outcomes exists and if so, whether the UK- consistent Deprivation Measure can account for this. Full results will be cleared for dissemination through disclosure control, prior to the conference.
Conclusion / Implications: Even in a privileged group with characteristics which protect against poor health, inequalities remain. The methods applied here present an opportunity for improved cross-national comparison and address limitations in confounding when exploring inequalities based on area deprivation.
Available online: International Journal of Population Data Science,
Output from project: 1005034