Higher quality data needed to understand and address “stark and persistent” inequalities across the life course

The NHS will be “flying blind” in its attempts to meet its legal, and moral, obligation to eliminate ethnic inequalities in health and care until longstanding problems with the quality of ethnicity data are resolved, warns an expert in The BMJ today.

Inequalities in health and care between ethnic groups have been documented for decades, explains Sarah Scobie at the Nuffield Trust. But she argues that analysis by broad ethnic groups (white, Asian, black, and mixed) can mask substantial variation within them.

An accompanying infographic presents some of these disparities across a range of measures throughout the life course.

In the UK, there are stark and persistent inequalities in stillbirths and infant death rates for Asian, black, and mixed ethnic groups compared with white people, but this pattern is not consistent across all measures, she explains.

For example, death from any cause (“all cause mortality”) in England and Wales for people aged 10 and over between 2017 and 2019 was higher for the white ethnic group (1,058 per 100,000 population) than the black African group (645 per 100,000 population), according to data from the Office for National Statistics.

Wide variations in patterns of illness are also apparent, she adds. For instance, contacts with mental health services are lowest for Asian groups, but these groups have higher rates of cardiovascular disease.

And while evidence shows that health services have responded for some conditions, such as cardiovascular disease, she notes that there are persistent inequalities in mental health, and maternal and infant mortality, with black groups having the worst outcomes, pointing to entrenched challenges.

She explains that the causes of ethnic inequality are multifaceted and include inequalities in socioeconomic status and the effects of structural racism, affecting access to jobs, housing, and other resources, as well as differences in where people live, with ethnic groups concentrated in cities. For example, a fifth (20%) of Asian and black children are born in the most deprived 10% of neighbourhoods, compared with 12% of white children.

Ethnic inequalities are also intrinsically linked to deprivation, and there are major differences within broad ethnic groups, she adds. For example, only 7% of children of Indian ethnicity are eligible for free school meals, the lowest of any group, whereas 29% of children of Bangladeshi ethnicity are eligible. The group with the highest proportion of eligible children is white travellers of Irish heritage (63%), compared with 22% for white British children.

Scobie acknowledges that there are big gaps in what we know about ethnic variations in health, such as access to planned care and for some parts of the life course, including in young adults and for end-of-life care. 

“The stark differences in the health effects of the covid-19 pandemic drew attention to longstanding ethnic inequalities, giving hope that these would receive a stronger focus in health policy,” she writes. “But momentum to tackle health inequalities seems to be stalling.”

And while NHS England has adopted an approach to tackle inequalities that considers ethnic and socioeconomic differences, as well as other protected characteristics, vulnerable groups and clinical areas, Scobie believes that “longstanding problems with the quality of ethnicity data hamper progress to understand and tackle ethnic inequalities across healthcare.”

“Until this changes, the NHS will be “flying blind” in its attempts to meet its legal, and moral, obligation to eliminate ethnic inequalities in care,” she concludes.

A linked opinion article asks: How can we make better use of ethnicity data to improve healthcare services?


Notes for editors
Feature: Ethnic inequalities in health and care show diversity in need and disadvantage doi: 10.1136/bmj.p1281
Journal: The BMJ

Link to Academy of Medical Sciences press release labelling system: http://press.psprings.co.uk/AMSlabels.pdf

Externally peer reviewed? Yes
Evidence type: Data analysis, opinion
Subject: Health inequalities