The coding of migration status in English primary care from 2011 to 2024: a pilot use of Open Code Counts

Authors:
Preprint
Publisher website
Google Scholar
Abstract
Background: The migration status of the 9.8 million migrants living in England is not systematically recorded in primary care electronic health records (EHRs). Codelist approaches enable us to create 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 and Setting: We used our Open Code Counts tool and R package 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 2024. We compared code usage to trends in migration-related statistics from the Home Office and the 2021 Census. Results: There were 29.1 million uses of 1,114 migration-related codes from 2011 to 2024. Migration-related coding increased over time, exceeding the increase observed for coding overall, with a sharp increase from 2020, particularly for country-of-birth and language. Language coding represented 71% of code usage and where country of birth was recorded, there was mixed agreement with the census estimates. Coding of immigration legal statuses was low and overwhelmingly about asylum or refugee status. Conclusion: Rapid assessment of migration-related coding using Open Code Counts highlights that a non-English first language is the most strongly represented characteristic in migrant cohorts in English primary care EHRs, which should be considered when interpreting future research findings. How this fits in: This study offers population-wide insights into migration-related SNOMED CT coding in primary care in England from 2011 to 2024 using our new open-source tool, Open Code Counts. Here, we show that a first language that is not English is the most commonly recorded aspect of migration, which must be considered when interpreting results from studies that use this methodology for researching migrants’ health in primary care EHRs. We also show that migration-related coding has increased, particularly after the start of the pandemic and for country-of-birth and language codes. The increased use of these code types offers the opportunity for GP practices to better identify patients requiring language support and potential screening and service needs based on their country of birth. Summary sentence: Language is the most commonly coded aspect of migration in primary care, which must be considered when interpreting primary care data studies.

This publication has 17 references indexed in Scilit: