IJPDS International Journal of Population Data Science IJPDS 2399-4908 Swansea University 10.23889/ijpds.v10i3.3171 10:3:142 Understanding the quality of ethnicity data recorded in health-related administrative data sources compared with Census 2021 in England Razieh Cameron 1 2 Office for National Statistics, Newport, United Kingdom University of Leicester, Leicester, United Kingdom 01 06 2025 2025 8 4 3171 This work is licenced under a Creative Commons Attribution 4.0 International License. This article is available from the IJPDS website at: https://ijpds.org/article/view/3171 Objectives

Electronic health records (EHR) are increasingly used to investigate health inequalities across ethnic groups. Whilst there are some studies showing that the recording of ethnicity in EHR is imperfect, there is no robust evidence on the accuracy between the ethnicity information recorded in various real-world sources and census data.

 Methods

We linked primary and secondary care NHS England data sources with Census 2021 data and compared individual-level agreement of ethnicity recording in General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR), Hospital Episode Statistics (HES), Ethnic Category Information Asset (ECIA) and Talking Therapies for anxiety and depression (TT) with ethnicity reported in the census. Census ethnicity is self-reported and, therefore, regarded as the most reliable population-level source of ethnicity recording. We further assessed the impact of multiple approaches to assigning a person an ethnic category. The number of people that could be linked to census from ECIA, GDPPR, HES and TT were 47.4m, 43.5m, 47.8m and 6.3m, respectively.

 Results

Across all four data sources, the White British category had the highest level of agreement with census (≥96%), followed by the Bangladeshi category (≥93%). Levels of agreement for Pakistani, Indian, and Chinese categories were ≥87%, ≥83% and ≥80% across all sources. Agreement was lower for Mixed (≤75%) and Other (≤71%) categories across all data sources. The categories with the lowest agreement were Gypsy or Irish Traveller category (≤6%), Other Black (≤19%) and Any Other Ethnic Group (≤25%). Different methodologies to assign ethnic category resulted in different coverage between data sources, particularly in HES, with increased coverage seen in HES when reallocating Not Stated and Not Known categories.

 Conclusion

Certain ethnic categories across all data sources have high discordance with census ethnic categories. These differences may lead to biased estimates of differences in health outcomes between ethnic groups, a critical data point used when making health policy and planning decisions.