Qualitative interviews to understand methods and systems used to collect ethnicity information in health administrative data sources in England

• Published in: Wellcome open research Vol. 8; p. 265
• Main Authors: Quayle, Gemma, Jones, Bethan, Atkins, Jessica, Shannon, Caitriona, Smith, Roxanne, Tabor, David, Bałabuch, Zuzanna, Cox, Courtney, Horsell, Sarah, John, Marie, McGrail White, Tomas, Vickers, Sophie, Whittinger, Sophia, Bannister, Neil, Raleigh, Veena, Mateen, Bilal, Drummond, Rosemary
• Format: Journal Article
• Language: English
• Published: London, UK F1000 Research Limited 2023; Wellcome
• Subjects: data quality; eng; ethnic minority; Ethnicity; health disparities; health inequality; race
• ISSN: 2398-502X; 2398-502X
• DOI: 10.12688/wellcomeopenres.19262.1

Background:
 This article is one of a series aiming to inform analytical methods to improve comparability of estimates of ethnic health disparities based on different sources. This article explores the quality of ethnicity data and identifies potential sources of bias when ethnicity information is collected in three key NHS data sources. Future research can build on these findings to explore analytical methods to mitigate biases.
Methods:
 Thematic analysis of semi-structured qualitative interviews to explore potential sources of error and bias in the process of collecting ethnicity information across three NHS data sources: General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR), Hospital Episode Statistics (HES) and Improving Access to Psychological Therapies (IAPT). The study included feedback from 22 experts working on different aspects of health admin data collection for England (including staff from NHS Digital, IT system suppliers and relevant healthcare service providers).
Results:
 Potential sources of error and bias were identified across data collection, data processing and quality assurance processes. Similar issues were identified for all three sources. Our analysis revealed three main themes which can result in bias and inaccuracies in ethnicity data recorded: data infrastructure challenges, human challenges, and institutional challenges.
Conclusions:
 Findings highlighted that analysts using health admin data should be aware of the main sources of potential error and bias in health admin data, and be mindful that the main sources of error identified are more likely to affect the ethnicity data for ethnic minority groups. Where possible, analysts should describe and seek to account for this bias in their research.