Background. The true incidence and prevalence of liver disease is difficult to ascertain because there are few, if any, population-based registers of liver disease available to ensure proper case and comparator selection. The epidemiology of liver disease in Tayside (ELDIT) is a specially built register of liver disease for a well-defined geographical area of Scotland. Aims. This paper describes the electronic linkage of multiple data sources to form ELDIT and provides initial results from the database. Patients. All subjects resident in Tayside and registered with a general practitioner in the study period 1980-1999, approximately 400,000 people. Methods. Electronic record-linkage techniques were employed to include anonymised data from primary and secondary sources. Hospital admissions, dispensed medication, and laboratory results from immunology, virology, and biochemistry were used to identify cases of liver disease. Diagnostic algorithms were used to verify and classify subjects with liver disease. A validation of the algorithms against the clinical diagnosis was used to determine the measure of agreement (true positive rate) of ELDIT. Results. At present approximately 10,000 subjects have been identified with liver disease or abnormal liver function. The data set is nearing completion with cases of rarer liver disease being identified last. Incidence densities for the population were calculated. From the validation study, agreement between electronic and clinical diagnosis was 0.98 and positive predictive value was 0.83 showing electronic diagnostic algorithms are sensitive enough to identify liver disease using para-clinical data. Conclusions. ELDIT demonstrates how clinical information can be harnessed electronically to provide a better understanding of liver disease in a population. (C) 2002 Elsevier Science (USA). All rights reserved.
Steinke, D. T., Weston, T. L., Morris, A. D., MacDonald, T. M., & Dillon, J. F. (2002). The epidemiology of liver disease in Tayside database: a population-based record-linkage study. Journal of Biomedical Informatics, 35(3), 186-193. https://doi.org/10.1016/S1532-0464(02)00526-9