Projecting the Epidemiological and Economic Impact of Chronic Kidney Disease Using Patient-Level Microsimulation Modelling: Rationale and Methods of Inside CKD.
Burden of disease
Chronic kidney disease
Dialysis
Methodology
Microsimulation
Model
Policy
Prevalence
Journal
Advances in therapy
ISSN: 1865-8652
Titre abrégé: Adv Ther
Pays: United States
ID NLM: 8611864
Informations de publication
Date de publication:
01 2023
01 2023
Historique:
received:
17
08
2022
accepted:
06
10
2022
pubmed:
29
10
2022
medline:
25
1
2023
entrez:
28
10
2022
Statut:
ppublish
Résumé
Chronic kidney disease (CKD) is a serious condition associated with significant morbidity and healthcare costs. Despite this, early-stage CKD is often undiagnosed, and globally there is substantial variation in the effectiveness of screening and subsequent management. Microsimulations can estimate future epidemiological costs, providing useful insights for clinicians, policymakers and researchers. Inside CKD is a programme designed to analyse the projected prevalence and burden of CKD for countries across the world, and to simulate hypothetical intervention strategies that can then be assessed for potential impact on health and economic outcomes at a national and a global level. Inside CKD uses a population-based approach that creates virtual individuals for a given country, with this simulated population progressing through a microsimulation in 1-year increments. A series of data inputs derived from national statistics and key literature defined the likelihood of a change in health state for each individual. Input modules allow for the input of nationally specific demographic and CKD status (including prevalence, diagnosis rates, disease stage and likelihood of renal replacement therapy), disease progression, critical comorbidities, and mortality. Health economics are reflected in cost data and a flexible intervention module allows for the testing of hypothetical policies-such as screening strategies-that may alter disease progression and outcomes. Using input data from the UK as a case study and a 6-year simulation period, Inside CKD estimated a prevalence of 9.2 million individuals (both diagnosed and estimated undiagnosed) with CKD by 2027 and a 5.0% increase in costs for diagnosed CKD and renal replacement therapy. External validation and sensitivity analyses confirmed the observed trends, substantiating the robustness of the microsimulation. Using a microsimulation approach, Inside CKD extends the reach of current CKD policy analyses by factoring in multiple inputs that reflect national healthcare systems and enable analysis of the effect of multiple hypothetical screening scenarios on disease progression and costs.
Identifiants
pubmed: 36307575
doi: 10.1007/s12325-022-02353-5
pii: 10.1007/s12325-022-02353-5
pmc: PMC9616410
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Pagination
265-281Informations de copyright
© 2022. The Author(s).
Références
GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. 2020. https://doi.org/10.1016/S0140-6736(20)30045-3 .
Hill NR, Fatoba ST, Oke JL, et al. Global prevalence of chronic kidney disease—a systematic review and meta-analysis. PLoS ONE. 2016;11:e0158765.
doi: 10.1371/journal.pone.0158765
Murton M, Goff-Leggett D, Bobrowska A, et al. Burden of chronic kidney disease by KDIGO categories of glomerular filtration rate and albuminuria: a systematic review. Adv Ther. 2021;38:180–200.
doi: 10.1007/s12325-020-01568-8
Levey AS, de Jong PE, Coresh J, et al. The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report. Kidney Int. 2011;80:17–28.
doi: 10.1038/ki.2010.483
Xie Y, Bowe B, Mokdad AH, et al. Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int. 2018;94:567–81.
doi: 10.1016/j.kint.2018.04.011
Carpio EM, Ashworth M, Asgari E, et al. Hypertension and cardiovascular risk factor management in a multi-ethnic cohort of adults with CKD: a cross sectional study in general practice. J Nephrol. 2022;35:901–10.
doi: 10.1007/s40620-021-01149-0
Fletcher BR, Damery S, Aiyegbusi OL, et al. Symptom burden and health-related quality of life in chronic kidney disease: a global systematic review and meta-analysis. PLoS Med. 2022;19:e1003954.
doi: 10.1371/journal.pmed.1003954
Saran R, Robinson B, Abbott KC, et al. US renal data system 2019 annual data report: epidemiology of kidney disease in the United States. Am J Kidney Dis. 2020;75:A6–7.
doi: 10.1053/j.ajkd.2019.09.003
Darlington O, Dickerson C, Evans M, et al. Costs and healthcare resource use associated with risk of cardiovascular morbidity in patients with chronic kidney disease: evidence from a systematic literature review. Adv Ther. 2021;38:994–1010.
doi: 10.1007/s12325-020-01607-4
Elshahat S, Cockwell P, Maxwell AP, Griffin M, O’Brien T, O’Neill C. The impact of chronic kidney disease on developed countries from a health economics perspective: a systematic scoping review. PLoS ONE. 2020;15:e0230512.
doi: 10.1371/journal.pone.0230512
Wyld ML, Lee CM, Zhuo X, et al. Cost to government and society of chronic kidney disease stage 1–5: a national cohort study. Intern Med J. 2015;45:741–7.
doi: 10.1111/imj.12797
National Institute for Health and Care Excellence (NICE). Chronic kidney disease in adults: assessment and management. 2015. PMID 32208570.
Levin A, Rigatto C, Brendan B, et al. Cohort profile: Canadian study of prediction of death, dialysis and interim cardiovascular events (CanPREDDICT). BMC Nephrol. 2013;14:121.
doi: 10.1186/1471-2369-14-121
Shlipak MG, Tummalapalli SL, Boulware LE, et al. The case for early identification and intervention of chronic kidney disease: conclusions from a Kidney Disease: Improving global outcomes (KDIGO) controversies conference. Kidney Int. 2021;99:34–47.
doi: 10.1016/j.kint.2020.10.012
Hirst JA, Hill N, O’Callaghan CA, et al. Prevalence of chronic kidney disease in the community using data from OxRen: a UK population-based cohort study. Br J Gen Pract. 2020;70:e285–93.
doi: 10.3399/bjgp20X708245
Virgitti JB, Moriyama T, Wittbrodt ET, et al. REVEAL-CKD: prevalence of undiagnosed early chronic kidney disease in France and Japan [Abstract]. J Am Soc Nephrol. 2021;32:715–6.
White SL, Polkinghorne KR, Cass A, Shaw J, Atkins RC, Chadban SJ. Limited knowledge of kidney disease in a survey of AusDiab study participants. Med J Aust. 2008;188:204–8.
doi: 10.5694/j.1326-5377.2008.tb01585.x
Szczech LA, Stewart RC, Su HL, et al. Primary care detection of chronic kidney disease in adults with type-2 diabetes: the ADD-CKD Study (awareness, detection and drug therapy in type 2 diabetes and chronic kidney disease). PLoS ONE. 2014;9:e110535.
doi: 10.1371/journal.pone.0110535
Bello AK, Levin A, Tonelli M, et al. Global Kidney Health Atlas: A report by the International Society of Nephrology on the current state of organization and structures for kidney care across the globe. Brussels, Belgium: International Society of Nephrology; 2017. pp. 107–137.
Nagib SN, Abdelwahab S, Amin GEE, Allam MF. Screening and early detection of chronic kidney disease at primary healthcare. Clin Exp Hypertens. 2021;43:416–8.
doi: 10.1080/10641963.2021.1896726
Rutter CM, Miglioretti DL, Savarino JE. Evaluating risk factor assumptions: a simulation-based approach. BMC Med Inform Decis Mak. 2011;11:55.
doi: 10.1186/1472-6947-11-55
Rutter CM, Zaslavsky AM, Feuer EJ. Dynamic microsimulation models for health outcomes: a review. Med Decis Making. 2011;31:10–8.
doi: 10.1177/0272989X10369005
Sugrue DM, Ward T, Rai S, McEwan P, van Haalen HGM. Economic modelling of chronic kidney disease: a systematic literature review to inform conceptual model design. Pharmacoeconomics. 2019;37:1451–68.
doi: 10.1007/s40273-019-00835-z
Kilpi F, Webber L, Musaigner A, et al. Alarming predictions for obesity and non-communicable diseases in the Middle East. Public Health Nutr. 2014;17:1078–86.
doi: 10.1017/S1368980013000840
Knuchel-Takano A, Hunt D, Jaccard A, et al. Modelling the implications of reducing smoking prevalence: the benefits of increasing the UK tobacco duty escalator to public health and economic outcomes. Tob Control. 2018;27:e124–9.
doi: 10.1136/tobaccocontrol-2017-053860
Pimpin L, Cortez-Pinto H, Negro F, et al. Burden of liver disease in Europe: epidemiology and analysis of risk factors to identify prevention policies. J Hepatol. 2018;69:718–35.
doi: 10.1016/j.jhep.2018.05.011
Pineda E, Sanchez-Romero LM, Brown M, et al. Forecasting future trends in obesity across Europe: the value of improving surveillance. Obes Facts. 2018;11:360–71.
doi: 10.1159/000492115
Webber L, Xu M, Graff H. Modelling the long-term health impacts of changing exposure to NO2 and PM2.5 in London. 2020. https://www.london.gov.uk/sites/default/files/modelling_the_long-term_health_impacts_of_changing_exposure_to_no2_and_pm2.5_in_london_final_250220_-4.pdf . Accessed 22 Oct 2022.
United Nations. World Population Prospects 2022. https://population.un.org/wpp/ . Accessed 14 July 2022.
Pecoits-Filho R, James G, Carrero JJ, et al. Methods and rationale of the DISCOVER CKD global observational study. Clin Kidney J. 2021;14:1570–8.
doi: 10.1093/ckj/sfab046
George LK, Koshy SKG, Molnar MZ, et al. Heart failure increases the risk of adverse renal outcomes in patients with normal kidney function. Circ Heart Fail. 2017;10:2.
doi: 10.1161/CIRCHEARTFAILURE.116.003825
Go AS, Yang J, Tan TC, et al. Contemporary rates and predictors of fast progression of chronic kidney disease in adults with and without diabetes mellitus. BMC Nephrol. 2018;19:146.
doi: 10.1186/s12882-018-0942-1
International Monetary Fund. International Monetary Fund Data. https://www.imf.org/en/Data . Accessed 14 July 2022.
Cooper JT, Lloyd A, Sanchez JJG, Sörstadius E, Briggs A, McFarlane P. Health related quality of life utility weights for economic evaluation through different stages of chronic kidney disease: a systematic literature review. Health Qual Life Outcomes. 2020;18:310.
doi: 10.1186/s12955-020-01559-x
Levey AS, Eckardt KU, Dorman NM, et al. Nomenclature for kidney function and disease: report of a kidney disease: improving Global Outcomes (KDIGO) Consensus Conference. Kidney Int. 2020;97:1117–29.
doi: 10.1016/j.kint.2020.02.010
Eddy DM, Hollingworth W, Caro JJ, et al. Model transparency and validation: a report of the ISPOR-SMDM modeling good research practices task force-7. Med Decis Making. 2012;32:733–43.
doi: 10.1177/0272989X12454579
Hounkpatin HO, Harris S, Fraser SDS, et al. Prevalence of chronic kidney disease in adults in England: comparison of nationally representative cross-sectional surveys from 2003 to 2016. BMJ Open. 2020;10: e038423.
doi: 10.1136/bmjopen-2020-038423
Public Health England. Chronic Kidney Disease (CKD) prevalence model. October 2014 PHE publications gateway number: 2014386. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/612303/ChronickidneydiseaseCKDprevalencemodelbriefing.pdf . Accessed 14 July 2022.
Kerr M, Bray B, Medcalf J, O’Donoghue DJ, Matthews B. Estimating the financial cost of chronic kidney disease to the NHS in England. Nephrol Dial Transplant. 2012;27(Suppl 3):73–80.
doi: 10.1093/ndt/gfs269
UK Kidney Association. UK Renal Registry. https://ukkidney.org/about-us/who-we-are/uk-renal-registry . Accessed 14 July 2022.
Chung EYM, Palmer SC, Natale P, et al. Incidence and outcomes of COVID-19 in people with CKD: a systematic review and meta-analysis. Am J Kidney Dis. 2021;78:804–15.
doi: 10.1053/j.ajkd.2021.07.003