Establishing risk factors and outcomes for congenital hypothyroidism with gland in situ using population-based data linkage methods: study protocol

Introduction There has been an increase in the birth prevalence of congenital hypothyroidism (CH) since the introduction of newborn screening, both globally and in the UK. This increase can be accounted for by an increase in CH with gland in situ (CH-GIS). It is not known why CH-GIS is becoming more common, nor how it affects the health, development and learning of children over the long term. Our study will use linked administrative health, education and clinical data to determine risk factors for CH-GIS and describe long-term health and education outcomes for affected children. Methods and analysis We will construct a birth cohort study based on linked, administrative data to determine what factors have contributed to the increase in the birth prevalence of CH-GIS in the UK. We will also set up a follow-up study of cases and controls to determine the health and education outcomes of children with and without CH-GIS. We will use logistic/multinomial regression models to establish risk factors for CH-GIS. Changes in the prevalence of risk factors over time will help to explain the increase in birth prevalence of CH-GIS. Multivariable generalised linear models or Cox proportional hazards regression models will be used to assess the association between type of CH and school performance or health outcomes. Ethics and dissemination This study has been approved by the London Queen Square Research Ethics Committee and the Health Research Authority's Confidentiality Advisory Group CAG. Approvals are also being sought from each data provider. Obtaining approvals from CAG, data providers and information governance bodies have caused considerable delays to the project. Our methods and findings will be published in peer-reviewed journals and presented at academic conferences.

Automated summary

This study aims to determine risk factors for congenital hypothyroidism with gland in situ (CH-GIS) and describe long-term health and education outcomes for affected children using linked administrative health, education and clinical data. Logistic/multinomial regression models will be used to identify risk factors for CH-GIS, while generalised linear models or Cox proportional hazards regression models will assess the association between type of CH and school performance or health outcomes. The study has received ethical approval and its findings will be disseminated through peer-reviewed journals and academic conferences.