The Copenhagen Primary Care Differential Count (CopDiff) database
Received 18 January 2014
Accepted for publication 4 April 2014
Published 12 June 2014 Volume 2014:6 Pages 199—211
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 5
Christen Lykkegaard Andersen,1,2 Volkert Dirk Siersma,1 Willy Karlslund,1 Hans Carl Hasselbalch,2 Peter Felding,3 Ole Weis Bjerrum,4 Niels de Fine Olivarius1
1The Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, 2Department of Hematology, Roskilde University Hospital, 3The Elective Laboratory of the Capital Region, 4Department of Hematology, Copenhagen University Hospital, Copenhagen, Denmark
Background: The differential blood cell count provides valuable information about a person's state of health. Together with a variety of biochemical variables, these analyses describe important physiological and pathophysiological relations. There is a need for research databases to explore associations between these parameters, concurrent comorbidities, and future disease outcomes.
Methods and results: The Copenhagen General Practitioners’ Laboratory is the only laboratory serving general practitioners in the Copenhagen area, covering approximately 1.2 million inhabitants. The Copenhagen General Practitioners’ Laboratory has registered all analytical results since July 1, 2000. The Copenhagen Primary Care Differential Count database contains all differential blood cell count results (n=1,308,022) from July 1, 2000 to January 25, 2010 requested by general practitioners, along with results from analysis of various other blood components. This data set is merged with detailed data at a person level from The Danish Cancer Registry, The Danish National Patient Register, The Danish Civil Registration System, and The Danish Register of Causes of Death.
Conclusion: This paper reviews methodological issues behind the construction of the Copenhagen Primary Care Differential Count database as well as the distribution of characteristics of the population it covers and the variables that are recorded. Finally, it gives examples of its use as an inspiration to peers for collaboration.
Keywords: differential leukocyte count, research, nationwide health registers
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Thorlund K, Druyts E, Aviña-Zubieta JA, et al. Biologics: Targets and Therapy. 2012;6:417–427.
On page 418, note that under Search strategy, Merck-Shire-Dome should have been listed as Merck, Sharp, and Dohme.
On page 421, Figure 2, the relative risk for golimumab versus placebo for the PsARC response was incorrectly listed as 2.45, and should be 3.45. The mean difference for etanercept versus placebo for the PASI response was incorrectly listed as 2.13, and should be 3.13. The x-axis of the PASI forest plot was incorrectly labeled with mean difference values of 1, 2, 3 and 4. The correct values are 3, 6, 9 and 12.
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