A Population-based Ankylosing Spondylitis [PAS] cohort

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Studying long term conditions can be difficult as a result of the timescale involved and the difficulty in capturing associated non-medical costs such as disability or lack of earnings due to illness. A great deal of information is already regularly collected on all people using the NHS. However, this information is held in many different NHS systems, such as with the general practitioner, their specialist, the hospital and laboratories. By linking this information, people with specific conditions can be followed through the health system. In addition, these people can also contribute directly to the data collected by completing questionnaires about the effects of their disease, such as function or pain. This information can be fed back to their usual doctor, thereby contributing to their personal care, while also improving the quality and value of the data collected. In this study we intend to apply these strategies of linking data relating to people living in Wales with ankylosing spondylitis, the second most common inflammatory arthritis. This data will be collected and analysed in a secure and anonymised manner to help address important research questions about this condition. For example, this linked information will help us to better understand the variations in this lifelong condition; help to predict early in the disease which patients will develop the most disabling disease requiring more aggressive therapy; facilitate estimates of the true cost of this condition, thereby speeding up assessments of expensive new treatments as they become available; and help to better plan the pathway of these patients through the health system. This study will facilitate genetic and biological studies, as well as trials of new medications for ankylosing spondylitis, thereby further improving understanding of this condition and attracting high quality research to the NHS. The methods used here build on existing resources in the UK and can also be applied to many other conditions in the future.