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Validation of the Danish National Diabetes Register

Authors Green A, Sorto C, Jenson P, Emneus M

Received 15 August 2014

Accepted for publication 26 September 2014

Published 18 December 2014 Volume 2015:7 Pages 5—15


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 3

Editor who approved publication: Professor Henrik Toft Sorensen

Anders Green,1,2 Camilla Sortsø,1,3 Peter Bjødstrup Jensen,2 Martha Emneus1

1Institute of Applied Economics and Health Research, Copenhagen, Denmark; 2Odense Patient Data Explorative Network, Odense University Hospital and University of Southern Denmark, 3Centre of Health Economics Research, Department of Business and Economics, University of Southern Denmark, Odense, Denmark

Abstract: The Danish National Diabetes Register (NDR) was established in 2006 and builds on data from Danish health registers. We validated the content of NDR, using full information from the Danish National Patient Register and data from the literature. Our study indicates that the completeness in NDR is ≥95% concerning ascertainment from data sources specific for diabetes, ie, prescriptions with antidiabetic drugs and diagnoses of diabetes in the National Patient Register. Since the NDR algorithm ignores diabetes-related hospital contacts terminated before 1990, the establishment of the date of inclusion is systematically delayed for ≥10% of the registrants in general and for ≥30% of the inclusions before 1997 in particular. This bias is enhanced for ascertainment by chiropody services and by frequent measurements of blood glucose because the date of reimbursement of services, rather than the date of encounter, has been taken as the date of inclusion in NDR. We also find that some 20% of the registrations in NDR may represent false positive inclusions of persons with frequent measurements of blood glucose without having diabetes. We conclude that NDR is a novel initiative to support research in the epidemiological and public health aspects of diabetes in Denmark, but we also present a list of recommended changes for improving validity, by reducing the impact of current sources of bias and misclassifications.

Keywords: diabetes mellitus, epidemiology, ascertainment, validity
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