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Exploring the coding of migration status in English primary care from 2011 to 2025 using OpenCodeCounts.

Yamina Boukari¹, Lucinda Hiam², James Scuffell³

¹Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK yamina.boukari@jesus.ox.ac.uk.

|February 26, 2026

Summary

This summary is machine-generated.

Migration codes in English primary care electronic health records (EHRs) are increasingly used, especially for language and country of birth. However, coding for immigration status is low, indicating potential biases for future research.

Keywords:

England SNOMED CT migration

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Published on: April 4, 2018

Area of Science:

Health Informatics
Public Health Research
Data Science in Healthcare

Background:

Migration status is inconsistently recorded in English primary care electronic health records (EHRs).
Codelist approaches offer a method to identify migrant populations within EHR data.
Understanding migration data in EHRs is crucial for public health and research.

Purpose of the Study:

To explore the utility of migration-related SNOMED CT codes for primary care research.
To assess the feasibility and potential biases of using codelists for migrant cohort identification.
To inform future research utilizing primary care data for migrant populations.

Main Methods:

Utilized the OpenCodeCounts tool to analyze SNOMED CT code usage in English primary care.
Developed migration-related codelists and tracked their usage from 2011 to 2025.
Compared codelist usage trends with Home Office and 2021 Census migration statistics.

Main Results:

Observed over 34 million uses of 1119 migration-related codes between 2011 and 2025.
Migration coding increased significantly, particularly for country-of-birth and language, with a notable rise from 2020.
Language coding constituted 65% of usage; country-of-birth coding showed variable agreement with census data; immigration legal status coding was predominantly for asylum/refugee status.

Conclusions:

Demonstrated the feasibility of using migration-related SNOMED CT codelists in primary care EHRs.
Highlighted potential biases in cohorts derived from these codelists, particularly regarding immigration legal status.
Emphasized the importance of considering these biases for accurate future research on migrant health.