284 Using administrative linked cohorts to investigate migrant health inequalities in childhood and adolescent health: where is migration in your causal diagram?

Abstract WKS 41: Using administrative linked cohorts to investigate migrant health inequalities in childhood and adolescent health: Where is migration in your causal diagram?, B203 (FCSH), September 5, 2025, 11:30 - 12:30 Rationales and Purpose Cohorts made up of linked administrative records have been increasingly used in research due to the availability of large and often nationally representative data, and the unique opportunity to study health inequalities from birth. However, migration variables are often not adequately considered using a causal framework, with consideration of migration as a complex and interlinked social determinant of health. Failing to account for selection bias, confounding or linked errors and no contextualisation of what is being measured (i.e., effects of migration-led inequalities versus the “healthy migrant effect”, or comparing migrants to non-migrants in the place of arrival or of origin), can lead to inaccurate conclusions of the effect of migration on health. At this 2nd World Congress on Migration, we critically engaged with causal modelling to examine migrant health inequalities, while also exploring the new opportunities afforded by administrative data resources. Methods/Process of Workshop The workshop showcased a selection of cohort studies currently being undertaken on child and adolescent migrant health across the UK and Brazil. These were followed by a discussion on the key advantages, pitfalls and potential biases associated with the use of linked data, and the use of causal diagrams to aid in the process of conducting migrant health research. Projected Learning Outcomes This seminar aimed to equip participants with tools to assess which research questions can be answered using linked cohort studies; and to apply the best epidemiological and statistical methods to investigate the causal effects of migration when using observational data.