Prevalence of CKD in the United States: A Sensitivity Analysis Using the National Health and Nutrition Examination Survey (NHANES) 1999-2004

Abstract

Estimates of chronic kidney disease (CKD) in the United States using the continuous National Health and Nutrition Examination Survey (NHANES) data set 1999-2004 indicate that 13.1% of the population (26.3 million people based on the 2000 census) has CKD stages 1 to 4. We performed sensitivity analyses to highlight assumptions underlying these estimates and illustrate their robustness to varying assumptions. NHANES 1999-2004 was a nationally representative cross-sectional continuous survey of the civilian noninstitutionalized US population. Our sample included participants 20 years and older. Estimated glomerular filtration rate (GFR) less than 60 mL/min/1.73 m(2) defined from the 4-variable Modification of Diet in Renal Disease (MDRD) Study equation; albuminuria defined as persistence of urinary albumin-creatinine ratio greater than 30 mg/g. We compared prevalence estimates using the MDRD Study equation with 2 other GFR estimating equations (equation 5 by Rule et al from the Mayo Clinic Donors study; Cockcroft-Gault equation adjusted for body surface area and corrected for the bias in the MDRD Study sample), and sex-specific cutoff values to define albuminuria. We found CKD stages 1 to 4 prevalence estimates ranging from 11.7% to 24.9%, a more than 2-fold difference resulting in population estimates of 25.8 million to 54.0 million people using 2006 population estimates. Considering only stages 3 and 4, which are not affected by the choice of cutoff values to define albuminuria, prevalence estimates ranged from 6.3% to 18.6%, resulting in population estimates of 13.7 million to 40.3 million people, a nearly 3-fold difference. NHANES 1999-2004 is a cross-sectional survey and allows for GFR and albumin-creatinine ratio estimates at 1 point in time. NHANES does not account for seniors in long-term care facilities. Although CKD prevalence is high regardless of varying modeling assumptions, different assumptions yield large differences in prevalence estimates.