Geographic Variation and US County Characteristics Associated With Rapid Kidney Function Decline

Abstract

IntroductionGeographic variation in the prevalence of chronic kidney disease and incidence of end-stage renal disease has been previously reported. However, the geographic epidemiology of rapid estimated glomerular filtration rate (eGFR) decline has not been examined.MethodsWe built a longitudinal cohort of 2,107,570 US veterans to characterize the spatial epidemiology of and examine the associations between US county characteristics and rapid eGFR decline.ResultsThere were 169,029 (8.02%) with rapid eGFR decline (defined as eGFR slope < –5 ml/min per 1.73 m2/year). The prevalence of rapid eGFR decline adjusted for age, race, gender, diabetes, and hypertension varied by county from 4.10%–6.72% in the lowest prevalence quintile to 8.41%–22.04% in the highest prevalence quintile (P for heterogeneity < 0.001). Examination of adjusted prevalence showed substantial geographic variation in those with and without diabetes and those with and without hypertension (P for heterogeneity < 0.001). Cohort participants had higher odds of rapid eGFR decline when living in counties with unfavorable characteristics in domains including health outcomes (odds ratio [OR] = 1.15; confidence interval [CI] = 1.09–1.22), health behaviors (OR = 1.08; CI = 1.03–1.13), clinical care (OR = 1.11; CI = 1.06–1.16), socioeconomic conditions (OR = 1.15; CI = 1.09–1.22), and physical environment (OR = 1.15; CI = 1.01–1.20); living in counties with high percentage of minorities and immigrants was associated with rapid eGFR decline (OR = 1.25; CI = 1.20–1.31). Spatial analyses suggest the presence of cluster of counties with high prevalence of rapid eGFR decline.DiscussionOur findings show substantial geographic variation in rapid eGFR decline among US veterans; the variation persists in analyses stratified by diabetes and hypertension status; results show associations between US county characteristics in domains capturing health, socioeconomic, environmental, and diversity conditions, and rapid eGFR decline.

Particulate Matter Air Pollution and the Risk of Incident CKD and Progression to ESRD

“…Between-city risk estimates were slightly higher than withincity estimates (Supplemental Table 4). Because characteristics of geographies might confound the association between PM 2.5 and risk of kidney outcomes, 23 we curated the County Health Rankings datasets 24,25 and built analyses additionally controlling for 55 United States county-level variables in six domains, including demographics, physical environment, social and economic factors, health behaviors, clinical care, and health outcomes; the results remained consistent (Supplemental Table 5). We repeated the analyses using expanded and more sensitive definitions of hypertension and diabetes: the definition of hypertension included relevant diagnostic codes and average systolic BP (treated as a continuous variable) in the year before time zero (T 0 ); the definition of diabetes included relevant diagnostic codes as well as use of diabetic medications (including oral hypoglycemic agents and insulin) and hemoglobin A1c levels .6.4%.…”

“…23,29 It has been hypothesized that some of the geographic variation in kidney disease burden may be due to environmental factors. 23 Our study results suggest that particulate matter air pollution is an important, but yet unrecognized risk factor for kidney disease and its progression, which may explain some of the geographic variation in kidney disease burden in the United States. Examination of the effect of particulate matter on risk of incident kidney disease and its progression outside the United States, and particularly in areas with much higher levels of particulate matter air pollution is warranted.…”

“…We specifically note that the counties with the highest measures of PM 2.5 had higher population density and greater percentage of citizens living in poverty. Although we took care to develop analyses for the 100 most populated counties, built city-adjusted and within-city analyses, and also accounted for United States county-level characteristics 23 in domains including demographics, physical environment, social and economic conditions, health behaviors, clinical care, and health outcomes, it is possible that individual differences in genomic makeup, dietary habits, physical activity, other environmental or occupational attributes, variation in exposure to heavy metals, or other factors not captured in our analyses might explain the described results. To define covariates we relied on VA administrative databases, and although we used comorbidity definitions validated for use in administrative datasets, misclassification or inaccurate measurement of predictor variable is not impossible.…”

Association between Monocyte Count and Risk of Incident CKD and Progression to ESRD

“…The epidemiologic observations establish a clinical epidemiologic relevance of monocyte in kidney disease, further illuminate our understanding of this important field, and inform the discussion on filling the translational gap between experimental biology and clinical epidemiology-where a bidirectional flow of information from clinical epidemiologic observations informs biologic research (17,21,40). More experimental and clinical investigation is needed to better understand the biology of monocyte in kidney disease and translate the understanding into opportunities and therapeutic avenues to halt kidney disease progression (17,29).…”

“…We evaluated the robustness and consistency of study findings in a number of sensitivity analyses. High monocyte count was associated with higher risk of the alternative outcomes of severe CKD progression defined as eGFR loss .25 ml/min per 1.73 m 2 per year (HR, 1.32; 95% CI, 1.30 to 1.65) and ESRD, kidney transplant, or $50% decline in eGFR (HR, 1.23; 95% CI, 1.21 to 1.24) (Supplemental Tables 2 and 3) (15,17,21,(26)(27)(28)(29). When average monocyte count during the period at or before T 0 was used as the primary predictor variable, the results were consistent with those in the primary analyses (Supplemental Table 4).…”