BACKGROUND: Awareness of chronic kidney disease (CKD), defined by kidney damage or reduced glomerular filtration rate, remains low in the United States, and few estimates of its future burden exist. STUDY DESIGN: We used the CKD Health Policy Model to simulate the residual lifetime incidence of CKD and project the prevalence of CKD in 2020 and 2030. The simulation sample was based on nationally representative data from the 1999 to 2010 National Health and Nutrition Examination Surveys. SETTING & POPULATION: Current US population. MODEL, PERSPECTIVE, & TIMELINE: Simulation model following up individuals from current age through death or age 90 years. OUTCOMES: Residual lifetime incidence represents the projected percentage of persons who will develop new CKD during their lifetimes. Future prevalence is projected for 2020 and 2030. MEASUREMENTS: Development and progression of CKD are based on annual decrements in estimated glomerular filtration rates that depend on age and risk factors. RESULTS: For US adults aged 30 to 49, 50 to 64, and 65 years or older with no CKD at baseline, the residual lifetime incidences of CKD are 54%, 52%, and 42%, respectively. The prevalence of CKD in adults 30 years or older is projected to increase from 13.2% currently to 14.4% in 2020 and 16.7% in 2030. LIMITATIONS: Due to limited data, our simulation model estimates are based on assumptions about annual decrements in estimated glomerular filtration rates. CONCLUSIONS: For an individual, lifetime risk of CKD is high, with more than half the US adults aged 30 to 64 years likely to develop CKD. Knowing the lifetime incidence of CKD may raise individuals' awareness and encourage them to take steps to prevent CKD. From a national burden perspective, we estimate that the population prevalence of CKD will increase in coming decades, suggesting that development of interventions to slow CKD onset and progression should be considered.
BACKGROUND: Awareness of chronic kidney disease (CKD), defined by kidney damage or reduced glomerular filtration rate, remains low in the United States, and few estimates of its future burden exist. STUDY DESIGN: We used the CKD Health Policy Model to simulate the residual lifetime incidence of CKD and project the prevalence of CKD in 2020 and 2030. The simulation sample was based on nationally representative data from the 1999 to 2010 National Health and Nutrition Examination Surveys. SETTING & POPULATION: Current US population. MODEL, PERSPECTIVE, & TIMELINE: Simulation model following up individuals from current age through death or age 90 years. OUTCOMES: Residual lifetime incidence represents the projected percentage of persons who will develop new CKD during their lifetimes. Future prevalence is projected for 2020 and 2030. MEASUREMENTS: Development and progression of CKD are based on annual decrements in estimated glomerular filtration rates that depend on age and risk factors. RESULTS: For US adults aged 30 to 49, 50 to 64, and 65 years or older with no CKD at baseline, the residual lifetime incidences of CKD are 54%, 52%, and 42%, respectively. The prevalence of CKD in adults 30 years or older is projected to increase from 13.2% currently to 14.4% in 2020 and 16.7% in 2030. LIMITATIONS: Due to limited data, our simulation model estimates are based on assumptions about annual decrements in estimated glomerular filtration rates. CONCLUSIONS: For an individual, lifetime risk of CKD is high, with more than half the US adults aged 30 to 64 years likely to develop CKD. Knowing the lifetime incidence of CKD may raise individuals' awareness and encourage them to take steps to prevent CKD. From a national burden perspective, we estimate that the population prevalence of CKD will increase in coming decades, suggesting that development of interventions to slow CKD onset and progression should be considered.
Keywords:
CKD Health Policy Model; Centers for Disease Control and Prevention CKD Initiative; Chronic kidney disease (CKD); United States; disease burden; disease trajectory; lifetime incidence; prevalence; public health; simulation model
Authors: Celine Baligand; Hecong Qin; Aisha True-Yasaki; Jeremy W Gordon; Cornelius von Morze; Justin Delos Santos; David M Wilson; Robert Raffai; Patrick M Cowley; Anthony J Baker; John Kurhanewicz; David H Lovett; Zhen Jane Wang Journal: NMR Biomed Date: 2017-07-14 Impact factor: 4.044
Authors: Robert M Carey; David A Calhoun; George L Bakris; Robert D Brook; Stacie L Daugherty; Cheryl R Dennison-Himmelfarb; Brent M Egan; John M Flack; Samuel S Gidding; Eric Judd; Daniel T Lackland; Cheryl L Laffer; Christopher Newton-Cheh; Steven M Smith; Sandra J Taler; Stephen C Textor; Tanya N Turan; William B White Journal: Hypertension Date: 2018-11 Impact factor: 10.190