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Earlier data are available but at lower quality. It holds routine EHR data from primary and secondary health care for about 5 million people from the turn of the century, including the active population of about 3.1 million people. In Wales, the Secure Anonymised Information Linkage (SAIL) Databank is the national repository of anonymised, person based, linkable data. At population level these data sources can provide large volumes of data and a broad longitudinal view of a condition over a period of time. There is increasing interest in using routinely collected, population-scale, electronic health record (EHR) administrative data, in order to conduct research. This retrospective review identifies some of the challenges in identifying CF cases and demonstrates the benefits of linking cases across multiple data sources to improve quality. Identifying health conditions in eHR data can be challenging, so data quality assurance and validation is important or the merit of the research is undermined. Over 98% of individuals identified as CF cases in all three eHR data sources were confirmed as true cases but this was only the case for 19.8% of those identified in a single data source.
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Subsequent validation using the UK CF Registry found that 257 (73%) of these were true cases. This was greater than expected based on historical incidence rates in Wales. We identified 352 children with CF in the three eHR data sources. The UK CF Registry was later acquired by SAIL and linked to the eHR cohort to validate the cases and explore the reasons for misclassifications. Three eHR data sources were used to identify children with CF born in Wales between 1 st January 1998 and 31 st August 2015 within the Secure Anonymised Information Linkage (SAIL) Databank. In particular, the UK CF Registry data, and to demonstrate the opportunity it represents as a resource for future CF research. To establish a proof of principle and provide insight into the merits of linked data in CF research and the benefits of access to multiple data sources. This paper describes the challenges of accurately identifying a cohort of children with Cystic Fibrosis (CF) using eHR and their validation against the UK CF Registry. The challenges in identifying a cohort of people with a rare condition can be addressed by routinely collected, population-scale electronic health record (eHR) data, which provide large volumes of data at a national level.