Kidney Week

Abstract: FR-PO493

Spatial Analysis of CKD Prevalence in the US – A Joint Analysis of NHANES and KEEP

Session Information

• CKD: Epidemiology, Outcomes - Non-Cardiovascular - I
  November 03, 2017 | Location: Hall H, Morial Convention Center
  Abstract Time: 10:00 AM - 10:00 AM

Category: Chronic Kidney Disease (Non-Dialysis)

• 304 CKD: Epidemiology, Outcomes - Non-Cardiovascular

Authors

• Myers, Orrin, UNM Health Sciences Center, Albuquerque, New Mexico, United States

Orrin Myers,

• Pankratz, V. Shane, UNM Health Sciences Center, Albuquerque, New Mexico, United States

V. Shane Pankratz,

• Norris, Keith C., UCLA, Marina Del Rey, California, United States

Keith C. Norris,

• Vassalotti, Joseph A., Icahn School of Medicine at Mount Sinai, New York, New York, United States

Joseph A. Vassalotti,

• Unruh, Mark L., UNM Health Sciences Center, Albuquerque, New Mexico, United States

Mark L. Unruh,

• Argyropoulos, Christos, UNM Health Sciences Center, Albuquerque, New Mexico, United States

Christos Argyropoulos,

Background

Chronic Kidney Disease (CKD) is a public health concern in the US, but it lacks a nationwide surveillance system that can describe regional variation. We investigate the feasibility of estimating county-level CKD prevalence from the large-scale community disease detection Kidney Early Evaluation and Program (KEEP).

Methods

KEEP participants were recruited from two-thirds of the nation’s counties but were self-selected after targeted recruitment. We combined KEEP (N=127,149) and NHANES samples (N=27,565) from 2001–2012 to estimate sampling weights. The weights reduce self-selection bias in KEEP when estimating county-level prevalence of CKD (eGFR<60 mL/min/1.73m²).

Results

Nationwide prevalence of eGFR<60 was 8.9% (7.5-10.7) from KEEP and 6.8% (6.3-7.2) for NHANES. CKD prevalence was significantly higher in rural counties (Fig 1A), which also had higher uncertainty (Fig 1B).

Conclusion

A joint analysis of NHANES and KEEP produced estimates of eGFR<60 that are adjusted for selection bias. Our analysis found that CKD rates are higher in rural counties. This approach makes it possible to enhance spatial CKD surveillance systems.