Linking 1.7 million non-EU migrants and refugees to hospital data in England: linkage process and quality
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University College London, London, United Kingdom
Publication date: 2023-04-27
Popul. Med. 2023;5(Supplement):A1850
Background and Objective: Difficulties identifying migrants in national data sources such as hospital records has limited large-scale evaluation of migrant healthcare needs and use of health services in European countries like the UK. We aim to describe the novel linkage process and quality of linkage of 1.7 million non-EU migrants and resettled refugees within national health service (NHS) hospital care enabling research into the relationship between migration and health for a large cohort of international migrants in England. Methods: We use stepwise deterministic linkage algorithms to link longitudinal records from non-EU migrants and refugees to records in the NHS personal demographics services (PDS; linkage stage 1), and HES-ONS (linkage stage 2). We calculated linkage rates and compared migrant characteristics in linked and unlinked samples for each stage of linkage. Results of the 1,799307 unique migrant records, 1,134007 (63%) linked to an NHS number and 451916 (25%) linked to a hospital record in England. Individuals on settlement and dependent visas and refugees had the highest odds of having linked to a hospital record, compared to those on work, student, or working holiday visas. Migrants from the middle east and north Africa and South Asia had four times the odds of having at least one hospital record, compared to those from East Asia and the pacific. Differences in migrant characteristics between linked and unlinked samples were moderate to small. Conclusions: We linked over half of the migrants to an NHS number and one fourth to a hospital record. This linked dataset represents a unique opportunity to explore hospitalization rates in migrants. However, missed links disproportionately affected individuals on shorter-term visas and could be mitigated by increasing the quality and completeness of identifiers recorded in administrative data.
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