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Linking Our Lives



What can the health of nurses tell us about population health and inequalities?

By Chris A Garrington, on 25 June 2021

medical staff undertaking operation

by William P Ball

Again and again, research has shown that health outcomes vary between groups of people. These differences appear when we group people based on where they live, the type of job they do, their level of education, their sex or race/ethnicity. 

When these differences are systematic, avoidable, and unfair we call them inequalities.

Geographic inequalities, often measured by the level of deprivation experienced by groups of people in small residential areas, are clear. Recent figures show that life expectancy in the least deprived areas of England is considerably higher than in the most deprived areas – although women live longer, both sexes can expect to live into their seventies in the poorest areas and into their eighties in the richest.

Understanding the mechanisms which produce these inequalities is incredibly important for researchers and policymakers. If we understand what drives these avoidable and unfair differences in health, we can start to design policies which aim to reduce them.


My research project looks at the self-rated health of nurses  – understanding this group, and how they experiences inequalities, is important for a number of reasons. It’s useful to understand their health in terms of planning around health care workforce and service delivery, but also to understand factors influencing health for the rest of the population.

Nurses are the biggest health workforce in the UK, with over 680,000 currently registered with the Nursing and Midwifery Council, and theyare a central part of the health service. Even with recent growth in the profession, the NHS in the UK has around 40,000 vacancies for nurse jobs and the workforce is ageing. If we can understand the health of nurses and reduce the number leaving the profession due to ill health, that will contribute to maintaining and improving patient safety. In the current context of COVID-19, where the NHS and its workforce has experienced huge pressure, the relevance of studying the health of healthcare professionals, for their sake, has become even more clear.

But the health of nurses isn’t only relevant to nurses themselves. Nurses have socioeconomic characteristics which should protect their health and they are also rather similar to one another – roughly 90 per cent are female and they are generally in a stable job which requires degree-level education. They also have higher levels of home ownership and are less likely to live in the most deprived areas.

The design of my project uses this similarity in socioeconomic characteristics of nurses to act as a sort of control – reducing the impact of variation between individuals to better isolate the effects of other variables, like area deprivation, on health,. This is like studies looking at differences in health outcomes in siblings or twins, which assess the health trajectories of people living with similar characteristics, in very similar conditions. This type of design is chosen to reduce the effect of confounding, where other variables influence the exposures and outcomes being studied.

Nurses also have very high levels of health literacy and knowledge of healthy behaviours. Their curriculum includes teaching on modifiable risk factors for disease, such as smoking, or lack of exercise – and many Nurses see first-hand the outcomes which are caused by such behaviours. We might expect that such a population would generally have better health and that its characteristics would protect it against inequalities. We can also investigate a counterfactual question – what would population health look like if we all had similar characteristics as nurses?


The Office for National Statistics and Scottish Longitudinal Studies (ONS LS and SLS) offer the opportunity to use individual-level data from on Census records, which have been linked to administrative data such as births and causes of death. They offer detailed data on a large and representative sample of the British population which can also link individual records to other sources of information such as deprivation measures.

Although the data held in each LS is only accessible in safe settings due to data protection concerns, a process called eDatashield has been developed to allow researchers to simulate ‘combined’ analysis. Raw data cannot be removed from the Secure Research Service computers in the ONS building in London or the safe haven at the National Records for Scotland offices in Edinburgh. However, eDatashield allows some types of analysis to be conducted by passing anonymised summary statistics to a computer which holds none of the original data. This approach has been used in my project to conduct cross-national analysis, to understand the role area deprivation plays in health differences between Scotland and England & Wales.


Preliminary results from this study suggest that self-reported health is patterned by area deprivation for non-nurses and nurses alike and that inequalities are present for both groups.

Roughly 90% of nurses appeared in the highest grouping for a measure of educational level, largely due to the inclusion of professional registration in this group. Nurses also had higher representation in the least deprived areas, but crucially nurses still lived in areas covering all levels of deprivation. 58% of nurses stated they have ‘very good’ health, with only 51% of non-nurses replying the same. Nurses reported a higher proportion of ‘very good’ self-rated health at all levels of deprivation compared with non-nurses. However, both groups displayed a social gradient in this – the proportion self-reporting ‘very good’ health increased when comparing from most to least deprived quintile. However, the gradient for nurses in less ‘steep’, meaning the differences between most and least deprived areas is smaller.

From these early results we can see that nurses generally have better health outcomes than non-nurses. That makes sense given their individual characteristics and that levels of wealth in the area where we live also affects our health. The crucial finding though is that social inequalities in self-rated health, although reduced, still exist within the nursing profession – even in a highly educated group with high health literacy the conditions in which we live influence patterns in health.

The future

The challenge of health inequalities has been clear for decades and current trends in the United Kingdom are worrying. Improvements in life expectancy in the UK, growing consistently over the past century, began to stall from 2012 onwards. This pattern is largely a result of slower gains for those in the most disadvantaged groups, and we have even seen reductions in life expectancy for women in these groups. Reducing inequalities which are systematic, avoidable, and unfair is better for everyone, but especially so for the most vulnerable people in our society.

Highly detailed datasets like the ONS LS and SLS, which are largely representative of the total population and can be linked to other information, can play a central role in the future of health inequalities research. Their use also allows some innovative research design which can help us better understand population health in the UK and ultimately inform policy actions which can make our society fairer.


The permission of the Office for National Statistics to use the Longitudinal Study is gratefully acknowledged, as is the help provided by staff of the Centre for Longitudinal Study Information & User Support (CeLSIUS). CeLSIUS is supported by the ESRC Census of Population Programme under project ES/K000365/1. The authors alone are responsible for the interpretation of the data. This work contains statistical data from ONS which is Crown Copyright. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research datasets which may not exactly reproduce National Statistics aggregates.

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