Maurice Alan Brookhart1, Jonathan V Todd2, Xiaojuan Li2, B Diane Reams3, Virginia Pate2, Abhijit V Kshirsagar4. 1. Department of Epidemiology, UNC Gillings School of Global Public Health, UNC Chapel Hill, Chapel Hill, NC; Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill. Electronic address: abrookhart@unc.edu. 2. Department of Epidemiology, UNC Gillings School of Global Public Health, UNC Chapel Hill, Chapel Hill, NC. 3. Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill. 4. UNC School of Medicine, University of North Carolina Kidney Center, Chapel Hill.
Abstract
PURPOSE: To examine the extent to which commonly ordered laboratory values obtained from large health care databases are representative of the distribution of laboratory values from the general population as reflected in the National Health and Nutrition Examination Survey. METHODS: Means of test values from commercial insurance laboratory data and National Health and Nutrition Examination Survey data were compared. Inverse probability of selection weighting was used to account for possible selection bias and to create comparability between the two data sources. RESULTS: The average values of most of the laboratory results from routine care were very close to their population means as estimated from NHANES. Tests that were more selectively ordered tended to differ. The inverse probability of selection weighting approach generally had a small effect on the estimated means but did improve estimation of some of the more selected tests. CONCLUSIONS: Commonly ordered laboratory tests appear to be representative of values from the underlying population. This suggests that trends and other patterns in biomarker levels in the population may be reasonably studied using data collected during the routine delivery of medical care.
PURPOSE: To examine the extent to which commonly ordered laboratory values obtained from large health care databases are representative of the distribution of laboratory values from the general population as reflected in the National Health and Nutrition Examination Survey. METHODS: Means of test values from commercial insurance laboratory data and National Health and Nutrition Examination Survey data were compared. Inverse probability of selection weighting was used to account for possible selection bias and to create comparability between the two data sources. RESULTS: The average values of most of the laboratory results from routine care were very close to their population means as estimated from NHANES. Tests that were more selectively ordered tended to differ. The inverse probability of selection weighting approach generally had a small effect on the estimated means but did improve estimation of some of the more selected tests. CONCLUSIONS: Commonly ordered laboratory tests appear to be representative of values from the underlying population. This suggests that trends and other patterns in biomarker levels in the population may be reasonably studied using data collected during the routine delivery of medical care.
Authors: Seth S Martin; Michael J Blaha; Peter P Toth; Parag H Joshi; John W McEvoy; Haitham M Ahmed; Mohamed B Elshazly; Kristopher J Swiger; Erin D Michos; Peter O Kwiterovich; Krishnaji R Kulkarni; Joseph Chimera; Christopher P Cannon; Roger S Blumenthal; Steven R Jones Journal: Clin Cardiol Date: 2013-10-01 Impact factor: 2.882
Authors: Joseph Drozda; Joseph V Messer; John Spertus; Bruce Abramowitz; Karen Alexander; Craig T Beam; Robert O Bonow; Jill S Burkiewicz; Michael Crouch; David C Goff; Richard Hellman; Thomas James; Marjorie L King; Edison A Machado; Eduardo Ortiz; Michael O'Toole; Stephen D Persell; Jesse M Pines; Frank J Rybicki; Lawrence B Sadwin; Joanna D Sikkema; Peter K Smith; Patrick J Torcson; John B Wong Journal: Circulation Date: 2011-06-13 Impact factor: 29.690
Authors: Seth S Martin; Michael J Blaha; Mohamed B Elshazly; Eliot A Brinton; Peter P Toth; John W McEvoy; Parag H Joshi; Krishnaji R Kulkarni; Patrick D Mize; Peter O Kwiterovich; Andrew P Defilippis; Roger S Blumenthal; Steven R Jones Journal: J Am Coll Cardiol Date: 2013-03-21 Impact factor: 24.094
Authors: Aram V Chobanian; George L Bakris; Henry R Black; William C Cushman; Lee A Green; Joseph L Izzo; Daniel W Jones; Barry J Materson; Suzanne Oparil; Jackson T Wright; Edward J Roccella Journal: JAMA Date: 2003-05-14 Impact factor: 56.272
Authors: Sebastian Schneeweiss; Jeremy A Rassen; Robert J Glynn; Jerry Avorn; Helen Mogun; M Alan Brookhart Journal: Epidemiology Date: 2009-07 Impact factor: 4.822