Boukari, Y;
Hiam, L;
Scuffell, J;
Tamborska, A;
Burns, R;
Wiedemann, M;
Campos-Matos, I;
Aldridge, RW;
Walsh, P;
Hargreaves, S;
et al.
Boukari, Y; Hiam, L; Scuffell, J; Tamborska, A; Burns, R; Wiedemann, M; Campos-Matos, I; Aldridge, RW; Walsh, P; Hargreaves, S; Pathak, N; Goldacre, B; Hulme, WJ
(2026)
Exploring the coding of migration status in English primary care from 2011 to 2025 using OpenCodeCounts.
BJGP Open.
BJGPO.2025.0138-BJGPO.2025.0138.
ISSN 2398-3795
https://doi.org/10.3399/bjgpo.2025.0138
SGUL Authors: Hargreaves, Sally
Abstract
Background
The migration status of the 9.8 million migrants living in England is not consistently recorded in primary care electronic health records (EHRs). Codelist approaches enable creation of cohorts of individuals who have had a predefined, optional migration-related code (e.g. “refugee”) added to their EHR.
Aims
We aimed to explore the use of migration-related SNOMED CT codes to inform future research using primary care data.
Design & setting
We used our OpenCodeCounts tool to explore data published by NHS England on SNOMED CT code usage in English primary care.
Method
We created migration-related codelists and described their use from 1st August 2011 to 31st July 2025. To understand code usage in the context of known information on migrants in England, we compared code usage to trends in migration-related statistics from the Home Office and the 2021 Census.
Results
There were 34.2 million uses of 1119 migration-related codes from 2011 to 2025. Migration-related coding increased over time, generally exceeding the increase observed for coding overall, with a sharp increase from 2020, particularly for country-of-birth and language. Language-related coding represented 65% of code usage and where country of birth was recorded, there was mixed agreement with the Census. Coding of immigration legal statuses was low and overwhelmingly about asylum/refugee status.
Conclusion
Utilising OpenCodeCounts, we demonstrate the feasibility of using migration-related SNOMED CT codelists within primary care EHRs and highlight some of the potential biases that cohorts created based on these codelists may have to inform future research.
Statistics
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