The above examples demonstrate that linked routine data enables validation and clarification of patient reported data; the retrospective and prospective tracking of the patient healthcare utilization and pathways; and the referral history in a cohort of patients with AS. Such analysis makes it possible to deduce whether the anonymised individuals in question were suffering any common co-morbidities, in receipt of healthcare treatment prior to the occurrence of the reference event, whilst it also allows any frequent complications requiring medical attention in the days, months, years following the event (which could be an intervention or questionnaire) to be identified. From the methodological perspective, any linkage system would add new dimensions and perspective to traditional health related research (e.g. complement and enhance the results of RCTs), as a resource for clinical audits, and in a variety of health impact assessment exercises. For example, the longitudinal routine data would allow an assessment of the impact of specific healthcare interventions on subsequent healthcare utilisation (e.g. A&E visits or hospital admission). Important limitations of solely relying on questionnaire data include reliance on accurate patient recall and that the healthcare events of interest may not occur within the limited recall period (e.g. 3 months), but just before the recall period or after the completion date of the questionnaire. This makes extrapolation of the questionnaire data for an extended period of time unreliable. The longitudinal linked routine data comes into aid in this respect.
The linkage of the questionnaire data from the PAS patients with the GP data as shown above enhances and helps make sense of the rich information obtained from the GP Read codes. This constitutes a rich health history for these AS patients, for whom we can carry out patient pathway analysis from various clinical and economic aspects. In particular, in keeping with other AS cohorts, these patients had an average lag of about 8 years from symptom onset to AS diagnosis, on which we can conduct pre- and post-diagnosis analyses of health care utilisation.
Again, a matrix of traits based on the PAS questionnaire information linked with the SAIL data system will help profiling of AS patients for health and other related interventions. Table summarises the types of information gathered through the PAS questionnaires, which could all be linked with the routine data sources as well as various demographic, socio-economic, and environmental attributes of the patients.
The use of HERALD methodology can stratify groups of patients to identify the early characteristics of patients who subsequently develop severe disease, thus, enabling these patients to be targeted with early aggressive therapy in order to prevent severe damage and need for surgery. This profiling can be used to estimate the potential resource savings of focusing treatment on those patients with patterns of disease suggestive of the development of a severe outcome. All these will directly affect patient care for AS in terms of informing NHS service provision and NICE guidelines for the use of expensive biological therapies, and informing the process of assessment of cost-effectiveness. In principle, the methods developed for the PAS cohort and described here can be extrapolated to be used in other chronic disease conditions [6
]; thus improving patient care for all those conditions. Linked routine data provides many opportunities for enhanced healthcare research and allows evaluation of impacts beyond the limited primary outcomes of interventional studies. As an example, the expanding SAIL databank in Wales already holds over a billion anonymised records from various databases, which can be anonymously linked at the individual record level. The combination of routine data with information from patients and RCTs allows the validation of real-life data and its application for clinical research. These linkable databases provide factual and continuous information with rich clinical and non-clinical details, which offers wide ranging opportunities in the realm of conducting evaluative research, clinical epidemiology, trial recruitment, genetic research, basic research of biological markers, stratified medicine, post-trial surveillance, risk assessment, service delivery evaluation, resource use, decision analysis, identification of early disease predictors, and the identification of subjects for prospective studies [6
]. This data system also offers the opportunity for post-marketing surveillance and pharmacovigilance of new expensive, and often potentially dangerous, healthcare interventions in real-life settings. Complementing this resource with targeted health economic analysis, as proposed in the HERALD methodology, offers a unique opportunity to deliver the level of health economic data required to evaluate and drive forward cost-effective modern healthcare services.