Five things the census longitudinal studies have taught us about health inequalities

The ONS Longitudinal Study and its sister studies in Scotland and Northern Ireland have shed light on a huge range of social issues: this series of Linking our Lives blogs looks back on the major contributions which have been made to different fields of research using these unique data resources. This blog is the fourth in the series, and highlights some of the key ways in which these population samples, which contain census and life event data, have contributed to the study of health inequalities. The series aims to highlight the breadth of further research which will become possible when data from the latest censuses becomes available.

1. Health inequalities and places

In 1980 the influential Black Report revived interest in the relationships between place and health, and in the ensuing decades, numerous research projects demonstrated that where we live is related to our health and our likelihood of dying.

Using 1991 Census cross-sectional microdata, in 2002 Paul Boyle and colleagues found that individuals who were well were more likely to migrate away from deprived areas and those who were ill were more likely to migrate towards them. They found migrants tended to be healthier than non-migrants, and long-distance migrants were less likely to suffer limiting long-term illnesses (LLTIs) than short-distance migrants. However, the cross-sectional data specification held this work back from its full potential.

This was the first in a series of studies by Boyle with Paul Norman, Frank Popham, Phil Rees and others. In 2004 these researchers used the ONS LS to delve further, tracking individuals in England and Wales between 1971 and 1991 to examine whether systematic movements between small areas contributed to health inequalities.

The results showed that among the young, migrants were generally healthier than non-migrants. Over 20 years, the predominant flow they found was of relatively healthy migrants moving from more deprived areas to less deprived areas. This raised ill-health and mortality rates in places of origin and lowered them in destinations. The study concluded that increases in health inequality were largely accounted for by migration rather than by changes in the deprivation of areas where non-migrants lived.

This work was taken further in a study which selected people living in less deprived areas who did not move between 1971 and 1991. It asked whether changes in deprivation in their areas influenced their health or mortality. It found changes in deprivation were related to both health and mortality but were more significant for illness, suggesting public health and regeneration programmes could affect the health of residents.

In 2009, research using ONS LS data from 1971 to 1991 asked what effect social mobility might have on health. It found those who were upwardly mobile tended to be healthier than the class they left and less healthy than the class they joined. This mitigated any social mobility health effect, but the study found that overall health inequalities had widened during the period.

A further study in 2014 used data from 1991 and 2001 to look into mortality rates and health inequality. It asked whether inequalities were greater for some age groups than for others, and found the greatest inequalities occurred during mid-life, with the young and the old suffering lesser effects. Area differences in health might be best highlighted through a focus on those aged between 30 and 60, it concluded.

2. The labour market

A 2015 study by Tom Clemens with Frank Popham and Paul Boyle looked at whether the association between unemployment and mortality was causal. This study used data from the SLS on working men and women aged 25-54 in 1991. It followed them through to 2001 to ask if they were by that time working or unemployed, and asked whether those who were unemployed in 2001 were more likely to die before 2010. The study found that even after prior health conditions were taken into account, unemployed men had a significantly higher risk of mortality than those who were employed. The effects for women were smaller and not statistically significant – though complexities with capturing female employment patterns in the census might have affected that result.

A  further strand of research using the ONS LS focused on whether tackling health inequalities could help older workers to stay in their jobs for longer. A major report in 2022 from the HOPE project aimed to shed light on the factors driving employment in older age, looking at how levels of poor health within a place were linked to the chances of residents being in paid work. It found older workers from the unhealthiest places were 60 per cent more likely to be out of work than those in the healthiest places. Historically disadvantaged areas continued to struggle, it found: areas where people left paid work at a younger age due to poor health in 1991 were much more likely to experience this trend in 2011 as well. This disproportionately affected those in manual occupations, who were much more likely to experience ill health and to expect fewer years of healthy life beyond age 50, compared with workers in administrative or professional roles.

Further work published in 2024 looked at these area level health effects in relation to employment outcomes for different occupational groups, finding the gap between the healthiest and unhealthiest areas was most marked for those in skilled trades; process, plant and machine operatives; and elementary occupations.

3. Pregnancy outcomes

Tom Clemens and Chris Dibben have been able to use the SLS to look at the ways in which where we live can affect pregnancy outcomes.  These effects relate both to the physical and to the social environment, with a 2015 studyfocusing on possible relationships between air pollution and births. They were able to place information on levels of sulphur dioxide, particulates and nitrogen dioxide in the places where pregnant women lived and worked across Scotland alongside information on whether their babies were born under-weight or pre-term. The study found women in areas with higher levels of pollution were at higher risk of low birth weight, though a raised risk of having a pre-term baby was not statistically significant.

In 2017 this work was taken further, using administrative data to look at the interplay between neighbourhood and smoking on birth effects. The study found that while the effects of pollution were significant overall, they were subsumed by the effects of smoking and were not significant for that group.

Mothers’ social environments also affected behaviour that could impact on babies’ health, Clemens and Dibben found. In a further 2015 paper they explored links between crime rates and birth outcomes, looking again at possible links to birth weight and prematurity. SLS data was linked to maternity inpatient data and crime rates according to residential postcode, revealing that there were strong links between higher crime and birth weight as well as with prematurity. These effects persisted even when information on smoking, ethnicity and other socio-economic variables was taken into account. The research concluded that crime might be a proxy for the level of threat and therefore stress that women experienced in their neighbourhoods, and that this appeared to be an important determinant of adverse birth outcomes.

4. Cancer outcomes

In the early 2000s, census data from ONS LS participants in 1971 and 1981 was used to examine socio-economic differences in cancer survival. Previous studies had shown those from poorer backgrounds had worse outcomes, but this research was able to produce a more nuanced picture.

Andrew Sloggett and Emily Grundy used the data to link census responses with records of cancer diagnoses between 1981 and 1997. Outcomes for those who had been diagnosed with a primary malignant cancer at age 45 or older were followed up to the year 2000.

This research was able to produce a measure which showed the relative cancer survival rates of different social groups when compared to the population as a whole. It showed that while the most commonly-used measure, the Carstairs index, was adequate for highlighting differential outcomes, a measure which factored in car access and housing tenure produced a more sensitive measure. Social class in isolation was a relatively weak indicator of survival differentials, it found.

Further work published in 2023 by Charlotte Sturley and colleagues focused on colorectal cancer, the third most commonly-diagnosed cancer in the world and second most common cause of cancer death. This study looked at individual LS members aged 50-plus, and asked if the likelihood of them being diagnosed with the cancer was linked to their educational attainment, social class, housing tenure or area deprivation quintile. Cancer incidence was looked at over a 15-year follow-up period from 2001. It was found to be lower among those with a degree and higher among those in manual occupations , though there was no clear link to area deprivation. However, disparities were greater for survival than for diagnosis. Among 5000 people diagnosed with colorectal cancer, the likelihood of dying from any cause was lower among those with a degree and higher among those employed in manual occupations or living in social-rented housing rather than being owner-occupiers. Those living in the most deprived areas had a higher probability of death than those in the least deprived areas.

5. Urban/rural effects on health

In recent years, work using SLS has focused on the possible health benefits of living in remote island communities as opposed to living in urban ones. Unpublished work by Tom Clemens, using the SLS and reported in 2015, looked at birth outcomes for mothers living in island communities as opposed to urban ones. It found maternal residence in an island community had a large protective effect on birth weight, and also that this effect appeared to be related to the ‘remoteness’ of island communities.

A 2023 study by Kathryn Halliday with Tom Clemens and Chris Dibben used the SLS to look at this ‘island effect’ in relation to mental health and found those living more remotely gained better outcomes, both from their rurality and from their residence on islands. It concluded that the unique physical geography of islands was bringing social benefits to residents.

Ongoing work

Evidence that health and wellbeing can be influenced by access to nature is the focus of a current project led by Catharine Ward Thompson at the University of Edinburgh. This research addresses the concerns of forestry agencies which need to invest scarce resources in ways that maximise benefits for both people and planet. It will use SLS data to link information on  anti-depressant prescriptions and children’s motor skills development with data on access to urban forests. The results will show whether, for example, the extent of new footpaths, improved forest entrances or activities to bring children and adults into the forest can make a difference to health and child development.

Two further projects in progress at the University of Edinburgh are using the SLS to focus on how access to tobacco and alcohol in local environments might affect health. A project led by Niamh Shortt will measure change in the availability of alcohol and tobacco in Scottish neighbourhoods over time and will ask how it relates to health outcomes. Further research by Annika Chambers will use linked data on maternity records, which includes information on maternal smoking and alcohol consumption, to examine how changes in the retail landscape may be linked to health behaviours during pregnancy.  The outcomes should shed light on whether an over-supply of alcohol and tobacco is linked to health effects from smoking and alcohol consumption.

References

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Boyle, P., Norman, P. and Rees, P. (2004). Changing places. Do changes in the relative deprivation of areas influence limiting long-term illness and mortality among non-migrant people living in non-deprived households? Social Science & Medicine, 58(12), pp.2459–2471. doi:https://doi.org/10.1016/j.socscimed.2003.09.011.

Boyle, P.J., Norman, P. and Popham, F. (2009). Social mobility: Evidence that it can widen health inequalities. Social Science & Medicine, 68(10), pp.1835–1842. doi:https://doi.org/10.1016/j.socscimed.2009.02.051.

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Clemens, T., Popham, F. and Boyle, P. (2014). What is the effect of unemployment on all-cause mortality? A cohort study using propensity score matching. European Journal of Public Health, 25(1), pp.115–121. doi:https://doi.org/10.1093/eurpub/cku136.

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Sturley, C., Norman, P., Morris, M. and Downing, A. (2023). Contrasting socio-economic influences on colorectal cancer incidence and survival in England and Wales. Social Science & Medicine, [online] 333, p.116138. doi:https://doi.org/10.1016/j.socscimed.2023.116138.

UKRI (2015). Using secondary data to examine whether a programme of physical and social interventions in urban forests enhances community health and wellbeing. [online] Ukri.org. Available at: https://gtr.ukri.org/projects?ref=ES%2FV002457%2F1 [Accessed 14 Mar. 2025].

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