Use and validation of public data files for identification of the diabeticpopulation in a Danish county

Introduction: This study aims to describe the process of identifying people known to have diabetes through public data files, to validate this method, and to describe models for optimization of such identification processes. Patients and methods: In a study population of 303,250 citizens, the diabet ics were identified by combining information From public data files with in formation from general practitioners. Data validity was checked by comparin g the results of data searches in public data files against information Fro m general practitioners and a random sample of diabetics. Two models were d efined to optimize the use of public data files fur identification of diabe tics. In model A the minimum number of parameters needed to obtain a sensit ivity as high as possible was identified. In model B the optimal combinatio n of parameters needed to obtain a high positive predictive value combined with a high sensitivity was identified. Results: A total of 5449 diabetics were identified. Of those 4438 (81%) wer e classified as Type 2 diabetics and 1011 (19%) were classified as Type 1 d iabetics. The data validation revealed that one person was misclassified as a diabetic and 93 persons were misclassified as non-diabetics. In model A the identification parameters included: "prescription", ''HbA1c". "chiropod ist service" and "glucose service". In model B the optimal combination of p arameters was identified as: minimum two HbA1c measurements, minimum one vi sit to a chiropodist, minimum one prescription or minimum one abnormal HbAl c during one pear. Conclusion. Public data files are suitable for identification of both Type 1 and Type 2 diabetics. Models have been developed to identify diabetics an d to promote the possibilities of long-term follow-up and quality assessmen t in an unselected diabetic population in a region.