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Abstract: SA-PO841

Added Value of Census Tract Measures of Socioeconomic Status to Identify Patients at High Risk of CKD in the Twin Cities Metro Area

Session Information

Category: CKD (Non-Dialysis)

  • 2101 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention


  • Ghazi, Lama, University of Minnesota, Saint Paul, Minnesota, United States
  • Drawz, Paul E., University of Minnesota, Saint Paul, Minnesota, United States

CKD is associated with low socioeconomic status (SES); however, guidelines do not recommend screening low SES patients for CKD. Our objective was to assess whether adding census tract level SES status to the traditional CKD screening approach improves our ability to detect patients with CKD.


Electronic health records of 256,212 patients with outpatient serum creatinine available who received primary care at a health system serving the 7 county Minneapolis/St Paul metro area . CKD was defined as having an estimated glomerular filtration rate <60 ml/min/1.73m2 or proteinuria. We compared 3 screening approaches: Approach 1(traditional), screening patients with diabetes(DM) or hypertension(HTN); Approach 2A, DM, HTN, or low census tract SES-housing (quartile 1 of the median value of owner occupied housing units); Approach 2B, DM, HTN, or low census tract SES-education (quartile 1 of percent of residents >= 25 years with complete college education); Approach 3A, screening patients with low census tract SES-housing; Approach 3B: screening patients with low census tract SES-education.


In our cohort, 34,489 patients had CKD. Adding low census tract SES (Approach 2A and 2B), significantly increases the sensitivity of detecting CKD (Table1). Number needed to screen to detect 1 CKD case was 4, 5, 5, 8, and 7 for Approaches 1, 2A, 2B, 3A, and 3B, respectively.


Adding an individuals’ residence SES status to traditional risk factors improved our ability to detect individuals at risk of CKD who may benefit from interventions to reduce risk of cardiovascular disease and progression of CKD.

Table 1. The sensitivity, specificity, positive predictive and negative predictive value of the screening approaches for detecting CKD.
 Number of CKD cases detectedSensitivitySpecificityPositive Predictive ValueNegative Predictive Value
Approach 117,11750772591
Approach 2A18,76954692291
Approach 2B19,19256682191
Approach 3A3,43910901387
Approach 3B4,30313881487