Abstract: FR-OR87
Significant Variation in Kidney Disease Indicators Within Cook County, IL: Findings from Geospatial Analysis of 2022 National Laboratory Data
Session Information
- Navigating a Path to Diversity and Equity in Kidney Health
November 03, 2023 | Location: Room 107, Pennsylvania Convention Center
Abstract Time: 05:24 PM - 05:33 PM
Category: Diversity and Equity in Kidney Health
- 900 Diversity and Equity in Kidney Health
Authors
- Bragg-Gresham, Jennifer L., University of Michigan Medical School, Ann Arbor, Michigan, United States
- Fraunhofer, Linda, Laboratory Corporation of America Holdings, Burlington, North Carolina, United States
- Licon, Ana Laura, University of Michigan Medical School, Ann Arbor, Michigan, United States
- Veinot, Tiffany C., University of Michigan School of Information, Ann Arbor, Michigan, United States
- Ennis, Jennifer L., Laboratory Corporation of America Holdings, Burlington, North Carolina, United States
- Saran, Rajiv, University of Michigan Medical School, Ann Arbor, Michigan, United States
Background
Chronic Kidney Disease (CKD) is frequently diagnosed through routine laboratory tests. There is not currently a source for population-level data on kidney disease and it is therefore difficult to assess prevalence at the local level. We sought to assess the feasibility of analyzing data from one of the largest clinical laboratory networks in the US to measure the extent of variation in kidney disease indicators found within a single county by ZIP code.
Methods
Data from approximately 600,000 Labcorp tests, resulted in 2022, for estimated glomerular filtration rate (eGFR) and urine albumin:creatinine ratio (UACR) in the greater-Chicago area of Cook County, IL were utilized. The presence of a kidney disease indicator was defined as an eGFR < 60 ml/min/1.73m2 or a UACR > 30 mg/g. The overall ZIP code percentage of lab results with kidney disease indicators were visualized with ArcGIS, stratified by Jenks Natural Breaks (5 classes). ZIP codes with less than 10 results were suppressed. Optimized hotspot analyses were conducted with the Getis-Ord Gi* statistic and Moran’s I statistic was applied for the outlier analysis. The interstate system was included to aid visualization.
Results
Kidney disease indicators varied markedly at the ZIP code level across Cook County, IL. The ZIP code percentage of lab results with kidney disease indicators ranged from a low of 0% to a high of 67%. Hotspot analysis indicated a clear high-high cluster in south and southeastern sections of the county. The northeastern section of the county appeared to be a low-low cluster, or cold spot. Both high-low and low-high outliers were found.
Conclusion
We demonstrate the feasibility of utilizing a large national laboratory database for mapping kidney disease indicators and identification of hotspots of kidney disease within a county. This work has the potential to support CKD surveillance systems to guide area-level CKD prevention and population health improvement initiatives.
Funding
- Other U.S. Government Support