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Examining the life course of patients with childhood onset rheumatic disease


   Faculty of Health and Life Science

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  Dr David Hughes, Dr Liza McCann  No more applications being accepted  Competition Funded PhD Project (UK Students Only)

About the Project

Juvenile Idiopathic arthritis (JIA) is the most common form of inflammatory arthritis in children, affecting around 1 in 1000 children. Lupus (jSLE, incidence=0.36-2.5/100,000/year) and Myositis (JDM, incidence=<4/million/year) are other rare but serious childhood inflammatory diseases associated with significant disability. People with inflammatory conditions are often at risk of developing other health conditions. Some of these health conditions may manifest in childhood. However, children with JIA/jSLE/JDM may be at additional risk of developing other health conditions later in adult life.

This project will utilise routinely collected GP data from the Clinical Practice Research Datalink (CPRD) to investigate patients who are diagnosed with JIA/jSLE/JDM as children and are followed up into adult life.

The project will first review and describe the kinds of comorbidities experienced by adults who were diagnosed with JIA/jSLE/JDM in childhood. It will then explore whether people with JIA/jSLE/JDM experience particular clusters of diseases, and investigate factors influencing these clusters. Finally, the project will determine whether changes in disease over time (for example, inflammatory markers) are predictive in the onset of the most common comorbidities.

This project is an exciting collaboration that will utilise and develop cutting edge statistical methodology for observational data that is collected repeatedly over time and also apply these methods to aid research into JIA in order to better inform patients with JIA/jSLE/JDM and their families of the risks of their condition, and aid clinicians in their treatment decisions.

Project aims and objectives

This PhD project has the following aims:

1.      Perform a systematic review to determine incidence, prevalence and impact of comorbidities in people with JIA/jSLE/JDM, through childhood and adulthood guided by PPIE responses.

2.      Perform feasibility assessment within CPRD database to understand whether the numbers of childhood onset non-JIA RhD (jSLE/JDM) are adequate to undertake cluster analysis methodology as per JIA.

3.      Describe the comorbidity burden, and links with deprivation in patients from the CPRD who were diagnosed with JIA/jSLE/JDM and compare comorbidity epidemiology with healthy controls with similar characteristics, from within CPRD

4.      Use cluster analysis methods to identify clusters of comorbidities commonly observed in people with JIA/jSLE/JDM, and assess patient characteristics associated with each cluster.

5.      Assess the association of longitudinal measurements of inflammatory disease markers such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) or clinical measures of disease activity / organ involvement, on the risk of developing common comorbidities in adults who developed JIA/jSLE/JDM as children.

Throughout the project the student will be encouraged to present their work both at local meetings and also at major statistical and clinical conferences.

This project will work closely with the Experimental Arthritis Treatment Centre for Children (EATC4Children) (www.EATC4Children.co.uk) based in Liverpool at Alder Hey Children’s NHS Foundation Trust and the University of Liverpool, and also with the Paediatric Rheumatology Clinical academic team at the National Institute of Health Research (NIHR) Alder Hey Clinical Research Facility.

For any enquiries please contact: Dr David Hughes on: [Email Address Removed]

Applications, including CV and cover letter, naming 2 referees to Dr David Hughes: [Email Address Removed] by 31st May 2021.


Funding Notes

This project is jointly funded by the Hugh Greenwood Foundation and the University of Liverpool, and is a collaborative project between Alder Hey Children’s Hospital and the Department of Health Data Science at the University of Liverpool. Funding covers tuition fees for 3 years, and an annual stipend, as well as support for training and consumable.
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