Projecting ESRD incidence and prevalence in the United States through 2030
Open accessAbstract
Background: Population rates of obesity, hypertension, diabetes, age, and race can be used in simulation models to develop projections of ESRD incidence and prevalence. Such projections can inform long-range planning for ESRD resources needs. Methods: We used an open compartmental simulation model to estimate the incidence and prevalence of ESRD in the United States through 2030 on the basis of wide-ranging projections of population obesity and ESRD death rates. Population trends in age, race, hypertension, and diabetes were on the basis of data from the Centers for Disease Control and Prevention’s National Health and Nutrition Examination Survey and the US Census. Results: The increase in ESRD incidence rates within age and race groups has leveled off and/or declined in recent years, but our model indicates that population changes in age and race distribution, obesity and diabetes prevalence, and ESRD survival will result in a 11%–18% increase in the crude incidence rate from 2015 to 2030. This incidence trend along with reductions in ESRD mortality will increase the number of patients with ESRD by 29%–68% during the same period to between 971,000 and 1,259,000 in 2030. Conclusions: The burden of ESRD will increase in the United States population through 2030 due to demographic, clinical, and lifestyle shifts in the population and improvements in RRT. Planning for ESRD resource allocation should allow for substantial continued growth in the population of patients with ESRD. Future interventions should be directed to preventing the progression of CKD to kidney failure.
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Cardiovascular disease and chronic kidney disease are projected to rise and disproportionately impact racial and ethnic minority populations in the US.
“cardiovascular disease and chronic kidney disease are projected to rise and disproportionately impact racial and ethnic minority populations in the US.30,31”
From Race and ethnicity and pharmacy dispensing of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes (2024) · cites this paper as [31]
The Discussion (page not provided) projects increasing US ESRD incidence and prevalence through 2030 and says growth in the nonwhite population contributes to that trend. The paper does not project cardiovascular disease or CKD overall, and the cited passages do not establish that the projected increase will disproportionately affect racial and ethnic minority populations. (LLM, conf=0.88)
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Cites
Prevalence of Childhood and Adult Obesity in the United States, 2011-2012
Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014) · JAMA · doi:10.1001/jama.2014.732 · Cited by 8,072 · see citing works
Flagged first, then by citations
Research works printed but not linked: 22
- [1]United States Renal Data System : Medicare expenditures for persons with ESRD. In: 2017 USRDS Annual Data Report: Epidemiology of Kidney Disease in the United States, Bethesda, MD, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 2017. Available at: http://www.usrds.org. Accessed February 8, 2018 [Google Scholar]
- [2]United States Renal Data System : Medicare expenditures for persons with ESRD. In: 2015 USRDS Annual Data Report: Epidemiology of kidney disease in the United States, Bethesda, MD, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 2015. Available at: http://www.usrds.org. Accessed February 8, 2018 [Google Scholar]
- [3]United States Renal Data System : Incidence, prevalence, patient characteristics, and treatment modalities. In: 2015 USRDS Annual Data Report: Epidemiology of kidney disease in the United States, Bethesda, MD, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 2015. Available at: http://www.usrds.org. Accessed February 8, 2018 [Google Scholar]
- [5]Nelder JA, Mead R: A simplex method for function minimization. Comput J 7: 308–313, 1965 [Google Scholar]
- [11]Hales CM, Fryar CD, Carroll MD, Freedman DS, Ogden CL: Trends in obesity and severe obesity prevalence in US youth and adults by sex and age, 2007-2008 to 2015-2016. JAMA 319: 1723–1725, 2018 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14]United States Renal Data System: Mortality. In: 2017 USRDS Annual Data Report: Epidemiology of Kidney Disease in the United States, Bethesda, MD, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 2017. Available at: http://www.usrds.org. Accessed February 8, 2018 [Google Scholar]
- [15]Møller B, Fekjaer H, Hakulinen T, Sigvaldason H, Storm HH, Talbäck M, et al.: Prediction of cancer incidence in the Nordic countries: Empirical comparison of different approaches. Stat Med 22: 2751–2766, 2003 [DOI] [PubMed] [Google Scholar]
- [16]Port FK: End-stage renal disease: Magnitude of the problem, prognosis of future trends and possible solutions. Kidney Int Suppl 50[Suppl]: S3–S6, 1995 [PubMed] [Google Scholar]
- [17]Xue JL, Ma JZ, Louis TA, Collins AJ: Forecast of the number of patients with end-stage renal disease in the United States to the year 2010. J Am Soc Nephrol 12: 2753–2758, 2001 [DOI] [PubMed] [Google Scholar]
- [18]Gilbertson DT, Liu J, Xue JL, Louis TA, Solid CA, Ebben JP, et al.: Projecting the number of patients with end-stage renal disease in the United States to the year 2015. J Am Soc Nephrol 16: 3736–3741, 2005 [DOI] [PubMed] [Google Scholar]
- [19]United States Renal Data System: Incidence, prevalence, patient characteristics, and treatment modalities. In: 2016 USRDS Annual Data Report: Epidemiology of Kidney Disease in the United States, Bethesda, MD, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 2016. Available at: http://www.usrds.org. Accessed February 8, 2018 [Google Scholar]
- [20]United States Renal Data System: Incidence, prevalence, patient characteristics, and treatment modalities. In: 2017 USRDS Annual Data Report: Epidemiology of Kidney Disease in the United States, Bethesda, MD, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 2017. Available at: http://www.usrds.org. Accessed February 8, 2018 [Google Scholar]
- [21]Wakasugi M, Kazama JJ, Narita I: Anticipated increase in the number of patients who require dialysis treatment among the aging population of Japan. Ther Apher Dial 19: 201–206, 2015 [DOI] [PubMed] [Google Scholar]
- [22]McFarlane PA, Pierratos A, Redelmeier DA: Cost savings of home nocturnal versus conventional in-center hemodialysis. Kidney Int 62: 2216–2222, 2002 [DOI] [PubMed] [Google Scholar]
- [23]Sinnakirouchenan R, Holley JL: Peritoneal dialysis versus hemodialysis: Risks, benefits, and access issues. Adv Chronic Kidney Dis 18: 428–432, 2011 [DOI] [PubMed] [Google Scholar]
- [24]Genovese G, Friedman DJ, Ross MD, Lecordier L, Uzureau P, Freedman BI, et al. : Association of trypanolytic ApoL1 variants with kidney disease in African Americans. Science 329: 841–845, 2010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [25]Pecoits-Filho R, Perkovic V: Are SGLT2 inhibitors ready for prime time for CKD? Clin J Am Soc Nephrol 13: 318–320, 2017 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26]Held PJ, McCormick F, Ojo A, Roberts JP: A cost-benefit analysis of government compensation of kidney donors. Am J Transplant 16: 877–885, 2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [27]Wolfe RA, Ashby VB, Milford EL, Ojo AO, Ettenger RE, Agodoa LYC, et al.: Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant. N Engl J Med 341: 1725–1730, 1999 [DOI] [PubMed] [Google Scholar]
- [28]Melanson TA, Hockenberry JM, Plantinga L, Basu M, Pastan S, Mohan S, et al.: New kidney allocation system associated with increased rates of transplants among black and Hispanic patients. Health Aff (Millwood) 36: 1078–1085, 2017 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [31]Noble HR, Agus A, Brazil K, Burns A, Goodfellow NA, Guiney M, et al.: PAlliative Care in chronic Kidney diSease: The PACKS study--quality of life, decision making, costs and impact on carers in people managed without dialysis. BMC Nephrol 16: 104, 2015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [32]Ljungvalla Å, Zimmerman FJ.: Bigger bodies: long-term trends and disparities in obesity and body-mass index among US adults, 1960–2008. Social science & medicine 75: 109–119, 2012 [DOI] [PubMed] [Google Scholar]
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Tsao, C. W., Aday, A. W., Almarzooq, Z. I., Anderson, C. A. M., Arora, P., Avery, C. L., Baker-Smith, C. M., Beaton, A. Z., Boehme, A. K., Buxton, A. E., et al. (2023) · Circulation · doi:10.1161/cir.0000000000001123 · Cited by 8,300 · see citing works
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Tsao, C. W., Aday, A. W., Almarzooq, Z. I., Alonso, A., Beaton, A. Z., Bittencourt, M. S., Boehme, A. K., Buxton, A. E., Carson, A. P., Commodore-Mensah, Y., et al. (2022) · Circulation · doi:10.1161/cir.0000000000001052 · Cited by 5,987 · see citing works
Global epidemiology of end-stage kidney disease and disparities in kidney replacement therapy
Thurlow, J. S., Joshi, M., Yan, G., Norris, K. C., Agodoa, L. Y., Yuan, C. M., & Nee, R. (2021) · American Journal of Nephrology · doi:10.1159/000514550 · Cited by 855 · see citing works
Rodriguez, L. A., Finertie, H., Neugebauer, R. S., Gosiker, B., Thomas, T. W., Karter, A. J., Gilliam, L. K., Oshiro, C., An, J., Simonson, G., et al. (2024) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2024.100759 · Cited by 31 · see citing works
“30,31 Our work supports prior fi ndings 5,6 and highlights the need to evaluate approaches to increase the use of these cardiorenal protective drugs in patients from racial and ethnic minority groups with type 2 diabetes to reduce adverse car-diorenal outcomes and improve health equity.”
- Authors
- McCullough, K. P., Morgenstern, H., Saran, R., Herman, W. H., & Robinson, B. M.
- Affiliations
- Arbor Research Collaborative for Health (US)
- Epidemiology and
- University of Michigan (US)
- Corresponding author
- McCullough, Keith P.
- Venue
- Journal of the American Society of Nephrology
- Published
- 2018-12-17
- Volume
- 30
- Issue
- 1
- Pages
- 127-135
- ISSN
- 1046-6673 (ISSN-L)
- 1533-3450
- PMC
- PMC6317596
- OpenAlex
- W2905034532
- Cited by
- 353
- References
- 1
- Citations by year
- 2026: 21 · 2025: 47 · 2024: 45 · 2023: 46 · 2022: 69 · 2021: 74 · 2020: 35 · 2019: 15 · 2018: 1
- FWCI
- 25.62
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of the American Society of Nephrology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Health and Human Services
- National Institutes of Health
- Centers for Disease Control and Prevention
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK092926)
- U.S. National Library of Medicine (HHSN276201400001C)
- Topics
- Health Sciences › Medicine › Nephrology › Dialysis and Renal Disease Management
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Health Sciences › Medicine › Hematology › Erythropoietin and Anemia Treatment
- Keywords
- end-stage renal disease, ESRD incidence, population projections, obesity, hypertension, diabetes mellitus, renal replacement therapy, chronic kidney disease progression, healthcare resource planning
- MeSH terms
- Aged, Aged, 80 and over, Cause of Death, Diabetes Mellitus, Female, Humans, Hypertension, Kidney Failure, Chronic, Male, Middle Aged, Obesity, Predictive Value of Tests, Time Factors, United States, Models, Statistical, Comorbidity, Incidence, Prevalence, Survival Analysis
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