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    As people with cystic fibrosis (CF) lead longer, healthier lives, educational qualifications and employment prospects are increasingly important. However, little is known about the social consequences of CF, in particular, any impact on educational achievements and the support children with CF receive in schools. To assess the educational achievements of children with CF in Wales compared to the general Welsh population, and the additional learning support children with CF receive in schools. We conducted a population-scale data linkage study of all children born in Wales using the Secure Anonymised Information Linkage (SAIL) Databank. We used anonymised individual-level population-scale health and administrative data sources to identify children with CF born between 2000 - 2015, linked to educational attainment records. We calculated the percentage of children that reached expected levels in statutory assessment at age 10-11, Key Stage 2 (KS2), and compared this to educational outcomes in the general population. We also assessed the percentage of children with CF that received extra learning support. Out of 150 eligible children, 119 had KS2 results. 77% (95% CI: 69%-84%) of children achieved expected levels in English, 81% (95% CI: 73% -87%) in Mathematics and 82% (95% CI: 75% - 88%) in Science. In the comparable general Welsh population, 83.4% to 91.1% achieved the expected level in English, 84.9% to 91.6% in Maths, and 87.1% to 92.2% in Science across the years of the study. 70% of children with CF received extra learning support. Children with CF in Wales may have worse educational achievements than the general population. More research is needed to inform policies and interventions to better support children with CF to reach their full educational potential and employment opportunities.


    Daniela K Schlüter, Rowena Griffiths, Ashley Akbari, David Taylor-Robinson. Educational achievements of children aged 10-11 years with cystic fibrosis. A data linkage study in Wales. International journal of population data science. 2022;7(1):1725

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    PMID: 35909577

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