1. Using data more effectively to describe ethnic health inequalities in the UK
Lynne Carter
NHS Equality and Diversity Manager and NIHR Knowledge Mobilisation Research Fellow

2. Lynne Carter was until recently a Knowledge Mobilisation Research Fellow supported by the National Institute for Health Research. The views expressed in this presentation are Lynne’s and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. Lynne’s fellowship was supported by the Collaboration for Leadership in Applied Health Research and Care - CLAHRC Yorkshire and Humber.

3. Background Knowledge Mobilisation Research Fellowship Case Studies
Evidence and Ethnicity in Commissioning research project (Salway et al, 2013)
Generation and use of evidence and knowledge is needed to raise awareness of the ethnic inequalities and to challenge and support key actors to find viable solutions
Equality and Diversity professionals and others advocating for action to improve race equality often lack skills and confidence in data analysis and in accessing, appraising and applying research evidence

4. Case Study – an NHS mental health trust
Reconfiguration of community based mental health services
Data from:
2011 UK census
Local Authority records
Trust’s patient records
Produced colour coded charts comparing the proportions of men and women of different ethnicities using different mental health services with the proportions of these groups of people in the local population

5. Feedback from stakeholders
√ Indication of under-usage and over-usage of different services √ Fine ethnic categories and differences for women and men √ Possible connections between different services √ Very varied levels of recording of ethnicity X No indication of statistical significance X No indication of need

6. Case study – stage 2
Including prevalence data from the Adult Psychiatric Morbidity Survey, we then produced new charts comparing need (local men and women of different ethnicities likely to have common mental disorders) with receipt of treatment (local men and women of different ethnicities accessing NHS Increasing Access to Psychological Therapies (IAPT) services)
Using the Chi Square test function in Microsoft Excel and broader ethnic categories we were able to show statistically significant differences
Finally, using IAPT data published by NHS Digital, we produced charts showing the proportions of men and women of different ethnicities who finish an IAPT course of treatment, reliably improve and move to recovery in this locality compared to the whole of England

7. Next steps
Some managers are now working to improve the recording of the ethnicity of their service users
Some teams have looked at the data charts and discussed the factors leading to the inequalities the data describes using a fishbone diagram
The local mental health commissioners will be receiving an update on this work

8. Learning from this stage of the case study
√ Accurate and local data which describes the gap between need and receipt of services for women and men of different ethnicities
√ Counter the frequently raised objection about lack of statistical significance
√ Compare local performance with national performance
X Broader ethnic categories may make understanding the causes of differences and identifying interventions more difficult

9. Case Study – Public Health’s BAME Health Needs Assessment
In a city with a lower proportion of Black, Asian and Minority Ethnic people the Public Health team in the Local Authority produced a BAME Health Needs Assessment and identified mental health as an initial implementation project.
Three different data sets showing population, prevalence and IAPT service usage & outcomes were brought together to describe for local people of different ethnicities:
“population” – numbers of people with expected common mental disorders
“reach” - numbers of these people being referred to the IAPT service
“impact” – numbers of these people who complete the programme of treatment
“outcome” – numbers of these people who reliably improve
Using broad ethnic categories, statistically significant differences in reach and outcome for BAME men and women were described

10. Next steps
An externally facilitated workshop for a range of stakeholders:
IAPT managers and staff
Mental Health commissioners
Public Health staff
Staff from a range of local third sector organisations
The workshop explored reasons for the disparities shown by these data using Leeds GATE’s Road, Bridges and Tunnels model
A plan to reduce the inequalities has been drawn up

11. Learning from this case study
√ Accurate and local data which describes the gap between need, receipt of services and outcomes for women and men of different ethnicities
√ Presented in easy-to-interpret charts
√ Data rang true with service providers and community groups and stimulated discussion
X Broader ethnic categories may make understanding the causes of differences and identifying interventions more difficult
X Need to involve service users

12. Learning about knowledge creation and mobilisation
Developing a methodology to create knowledge that is “good enough” – accurate and robust data – local, specific and easy to understand
A method that doesn’t require qualified data analysts, researchers and lots of time
Can potentially be used for other conditions:
Health checks
Hospital admissions
Cancer
Describing inequalities is only the first stage in reducing them!

13. Contact details
Look at data sets in detail and provide feedback at a workshop in Leeds, UK on 11th July 2018
To book a place and /or get more details contact
Thanks to my mentor, Professor Sarah Salway, and my case study partners, Liz Johnson, Susan Hampshaw and Laurie Mott