{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T13:08:21Z","timestamp":1781010501682,"version":"3.54.1"},"reference-count":75,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:00:00Z","timestamp":1777334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:00:00Z","timestamp":1777334400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000092","name":"U.S. National Library of Medicine","doi-asserted-by":"publisher","award":["R01LM013989"],"award-info":[{"award-number":["R01LM013989"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Med Decis Making"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The data-generating mechanisms underlying health care data are infrequently considered, leading to inequitable equilibria being reinforced throughout the care continuum. As race-based criteria are reassessed, including in chronic kidney disease, the effect of those criteria on patterns of disease progression should also be reevaluated. We proposed a microsimulation model for attenuating societal bias in primary care chronic kidney disease data to study this.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a continuous-time, discrete-event individual-level simulation model of kidney function decline, measured by estimated glomerular filtration rate (eGFR). The model simulates individual eGFR trajectories over time and enables generating counterfactual outcome distributions that would have been observed in the absence of race-based diagnosis and treatment criteria. eGFR decline is accelerated by hypertension, diabetes, and reaching chronic kidney disease stage 3a and can be delayed by interventions, which are applied based on eGFR level, measured with or without an adjustment for Black race. A Bayesian calibration procedure was applied to identify rates of eGFR decline corresponding to stage distributions in the cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Under the counterfactual scenario without a race adjustment, Black individuals qualify for diagnosis earlier, and non-Black individuals later, than under the reference scenario with race adjustment. The difference was largest for earlier stages and smaller at each consecutive stage. We do not observe differences in life expectancy between the 2 scenarios.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Limitations<\/jats:title>\n                    <jats:p>Large variability in the prevalence of treatment and heterogeneity in treatment effectiveness may affect our results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Beyond estimating the clinical consequences of the eGFR equation change, our work offers an alternative to previously proposed data-debiasing approaches. The simulated data can be used to inform future interventions and policy decisions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Highlights<\/jats:title>\n                    <jats:list list-type=\"bullet\">\n                      <jats:list-item>\n                        <jats:p>We developed a microsimulation model of chronic kidney disease progression with primary care data that reflect the effect of removing race-based diagnostic and treatment criteria.<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:p>The removal of race-based diagnostic criteria in our simulations changed the timing of qualification for chronic kidney disease diagnosis, ranging from 0.6\u2009y to 9.6\u2009y, with opposite effects for Black and non-Black patients.<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:p>The simulated differences in expected survival after removing the race adjustment did not exceed 2\u2009mo among individuals who developed chronic kidney disease.<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:p>The explicit representation of the data-generation process can help anticipate the effect that policy changes can have on clinical data distributions.<\/jats:p>\n                      <\/jats:list-item>\n                    <\/jats:list>\n                  <\/jats:sec>","DOI":"10.1177\/0272989x261432162","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T05:12:06Z","timestamp":1777439526000},"page":"624-636","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Microsimulation-Based Approach for Mitigating Societal Bias in Chronic Kidney Disease Data"],"prefix":"10.1177","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8968-5805","authenticated-orcid":false,"given":"Agata","family":"Foryciarz","sequence":"first","affiliation":[{"name":"Department of Computer Science, Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5076-1172","authenticated-orcid":false,"given":"Fernando","family":"Alarid-Escudero","sequence":"additional","affiliation":[{"name":"Department of Health Policy, Stanford School of Medicine, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1973-4752","authenticated-orcid":false,"given":"Gabriela","family":"Basel","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1750-5246","authenticated-orcid":false,"given":"Marika M.","family":"Cusick","sequence":"additional","affiliation":[{"name":"Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7882-1560","authenticated-orcid":false,"given":"Robert L.","family":"Phillips","sequence":"additional","affiliation":[{"name":"The American Board of Family Medicine, Lexington, KY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6028-2279","authenticated-orcid":false,"given":"Andrew","family":"Bazemore","sequence":"additional","affiliation":[{"name":"The American Board of Family Medicine, Lexington, KY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alyce S.","family":"Adams","sequence":"additional","affiliation":[{"name":"Department of Health Policy, Stanford School of Medicine, Stanford, CA, USA"},{"name":"Department of Epidemiology and Population Health, Stanford School of Medicine, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9076-8472","authenticated-orcid":false,"given":"Sherri","family":"Rose","sequence":"additional","affiliation":[{"name":"Department of Health Policy, Stanford School of Medicine, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"666","article-title":"Unequal treatment: confronting racial and ethnic disparities in health care","volume":"94","author":"Nelson A.","year":"2002","unstructured":"Nelson A. Unequal treatment: confronting racial and ethnic disparities in health care. J Natl Med Assoc. 2002;94:666.","journal-title":"J Natl Med Assoc"},{"key":"e_1_3_3_3_2","volume-title":"Disparities in Health and Health Care: Five Key Questions and Answers","author":"Artiga S","unstructured":"Artiga S, Orgera K, Pham O. Disparities in Health and Health Care: Five Key Questions and Answers. Kaiser Family Foundation. Policy Brief; 2020. https:\/\/files.kff.org\/attachment\/Issue-Brief-Disparities-in-Health-and-Health-Care-Five-Key-Questions-and-Answers"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2021.01423"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cct.2023.107238"},{"key":"e_1_3_3_6_2","volume-title":"Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias","author":"Perets O","year":"2025","unstructured":"Perets O, Stagno E, Ben Yehuda E, et al. Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias. New York, NY: ACM Transactions on Intelligent Systems and Technology; 2025."},{"key":"e_1_3_3_7_2","article-title":"Ethical machine learning in healthcare","volume":"4","author":"Chen IY","year":"2021","unstructured":"Chen IY, Pierson E, Rose S, Joshi S, Ferryman K, Ghassemi M. Ethical machine learning in healthcare. Annu Rev Biomed Data Sci. 2021;4:123\u201344.","journal-title":"Annu Rev Biomed Data Sci"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1097\/PHH.0000000000001254"},{"key":"e_1_3_3_9_2","volume-title":"International Conference on Machine Learning","author":"Zemel R","year":"2013","unstructured":"Zemel R, Wu Y, Swersky K, et al. Learning fair representations. In: International Conference on Machine Learning. Atlanta (GA): PMLR; 2013. p 325\u201333."},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhealeco.2019.05.005"},{"key":"e_1_3_3_11_2","volume-title":"The International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery","author":"Zhang Z","year":"2022","unstructured":"Zhang Z, Wang S, Meng G. A review on pre-processing methods for fairness in machine learning. In: The International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. Fuzhou, China: Springer; 2022. p 1185\u201391."},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocaf009"},{"key":"e_1_3_3_13_2","volume-title":"Chronic Kidney Disease in the United States, 2023","author":"Centers for Disease Control and Prevention","unstructured":"Centers for Disease Control and Prevention. Chronic Kidney Disease in the United States, 2023. Atlanta (GA): US Department of Health and Human Services, Centers for Disease Control and Prevention."},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrneph.2015.85"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.kint.2023.10.018"},{"key":"e_1_3_3_16_2","first-page":"1","article-title":"Kidney disease: improving global outcomes (KDIGO) CKD work group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease","volume":"3","author":"Levin A","year":"2013","unstructured":"Levin A, Stevens PE, Bilous RW, et al. Kidney disease: improving global outcomes (KDIGO) CKD work group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 2013;3:1\u2013150.","journal-title":"Kidney Int Suppl"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.HYP.30.3.428"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.05300515"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.4239\/wjd.v8.i5.172"},{"key":"e_1_3_3_20_2","article-title":"Decline in the estimated glomerular filtration rate (eGFR) following metabolic control and its relationship with baseline eGFR in type 2 diabetes with microalbuminuria or macroalbuminuria","volume":"13","author":"Akazawa S","year":"2022","unstructured":"Akazawa S, Sadashima E, Sera Y, Koga N. Decline in the estimated glomerular filtration rate (eGFR) following metabolic control and its relationship with baseline eGFR in type 2 diabetes with microalbuminuria or macroalbuminuria. Diabetol Int. 2022;13:148\u201359.","journal-title":"Diabetol Int"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1093\/ndt\/gfl198"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjmed.2007.04.024"},{"key":"e_1_3_3_23_2","first-page":"9","article-title":"Relationship between nephrologist care and progression of chronic kidney disease","volume":"68","author":"Orlando LA","year":"2007","unstructured":"Orlando LA, Owen WF, Matchar DB. Relationship between nephrologist care and progression of chronic kidney disease. N C Med J. 2007;68:9\u201316.","journal-title":"N C Med J"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11606-015-3452-x"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1053\/j.ajkd.2016.12.004"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11606-020-06280-5"},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1681\/ASN.2016010027"},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.07830520"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41581-021-00501-8"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2021.27014"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2369-9-3"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1159\/000180580"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-130-6-199903160-00002"},{"key":"e_1_3_3_34_2","first-page":"155","article-title":"A simplified equation to predict glomerular filtration rate from serum creatinine","volume":"11","author":"Levey A","year":"2000","unstructured":"Levey A, Greene T, Kusek J, Beck GJ. A simplified equation to predict glomerular filtration rate from serum creatinine. J Am Soc Nephrol. 2000;11:155.","journal-title":"J Am Soc Nephrol"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-145-4-200608150-00004"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-150-9-200905050-00006"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1114248"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1053\/j.ajkd.2013.12.006"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2019.5774"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMms2004740"},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2102953"},{"key":"e_1_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcin.2023.07.031"},{"key":"e_1_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2020.34004"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2022.15404"},{"key":"e_1_3_3_45_2","doi-asserted-by":"publisher","DOI":"10.1093\/clinchem\/hvad116"},{"key":"e_1_3_3_46_2","article-title":"Algorithmic changes are not enough: Evaluating the removal of race adjustment from the eGFR equation","volume":"248","author":"Cusick MM","year":"2024","unstructured":"Cusick MM, Chertow GM, Owens DK, Williams MY, Rose S. Algorithmic changes are not enough: Evaluating the removal of race adjustment from the eGFR equation. Proc Mach Learn Res. 2024;248:644\u201382.","journal-title":"Proc Mach Learn Res"},{"key":"e_1_3_3_47_2","doi-asserted-by":"publisher","DOI":"10.3122\/jabfm.2017.04.170193"},{"key":"e_1_3_3_48_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00428-1"},{"key":"e_1_3_3_49_2","first-page":"6048919","article-title":"Highest recorded serum creatinine","volume":"2021","author":"Persaud C","year":"2021","unstructured":"Persaud C, Sandesara U, Hoang V, Tate J, Latack W, Dado D. Highest recorded serum creatinine. Case Rep Nephrol. 2021;2021:6048919.","journal-title":"Case Rep Nephrol"},{"key":"e_1_3_3_50_2","doi-asserted-by":"publisher","DOI":"10.2105\/AJPH.2015.302955"},{"key":"e_1_3_3_51_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1475-6773.2012.01449.x"},{"key":"e_1_3_3_52_2","doi-asserted-by":"publisher","DOI":"10.57761\/75cc-1t35"},{"key":"e_1_3_3_53_2","doi-asserted-by":"publisher","DOI":"10.1097\/01.mnh.0000444822.25991.f6"},{"key":"e_1_3_3_54_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40273-019-00835-z"},{"key":"e_1_3_3_55_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-018-0678-7"},{"key":"e_1_3_3_56_2","doi-asserted-by":"publisher","DOI":"10.1136\/heartjnl-2016-310970"},{"key":"e_1_3_3_57_2","doi-asserted-by":"publisher","DOI":"10.2165\/11536310-000000000-00000"},{"key":"e_1_3_3_58_2","doi-asserted-by":"publisher","DOI":"10.1053\/j.ajkd.2009.11.016"},{"key":"e_1_3_3_59_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.290.23.3101"},{"key":"e_1_3_3_60_2","doi-asserted-by":"publisher","DOI":"10.1681\/ASN.2021070988"},{"key":"e_1_3_3_61_2","volume-title":"National Health and Nutrition Examination Survey Data","author":"Centers for Disease Control and Prevention","year":"2020","unstructured":"Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey Data. Hyattsville (MD): US Department of Health and Human Services, Centers for Disease Control and Prevention; 2020."},{"key":"e_1_3_3_62_2","volume-title":"National Diabetes Statistics Report, 2020","author":"Centers for Disease Control and Prevention","year":"2020","unstructured":"Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2020. Atlanta (GA): Centers for Disease Control and Prevention, US Dept of Health and Human Services; 2020."},{"key":"e_1_3_3_63_2","doi-asserted-by":"publisher","DOI":"10.2105\/AJPH.78.6.676"},{"issue":"19","key":"e_1_3_3_64_2","first-page":"1","article-title":"United States life tables, 2019","volume":"70","author":"Arias E","year":"2022","unstructured":"Arias E, Xu J. United States life tables, 2019. Natl Vital Stat Rep. 2022;70(19):1\u201359.","journal-title":"Natl Vital Stat Rep"},{"key":"e_1_3_3_65_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(12)61350-6"},{"key":"e_1_3_3_66_2","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.2000.10485979"},{"key":"e_1_3_3_67_2","first-page":"395","volume-title":"Bayesian Statistics 3. Proceedings of the Third Valencia International Meeting","author":"Rubin D.","year":"1987","unstructured":"Rubin D. Using the SIR algorithm to simulate posterior distributions. In: Bayesian Statistics 3. Proceedings of the Third Valencia International Meeting; June 1\u20135, 1987. Oxford (UK): Clarendon Press; 1988. p 395\u2013402."},{"key":"e_1_3_3_68_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2022-067386"},{"key":"e_1_3_3_69_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjmed.2007.05.012"},{"key":"e_1_3_3_70_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2011.451"},{"key":"e_1_3_3_71_2","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.02800315"},{"key":"e_1_3_3_72_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12882-015-0154-x"},{"key":"e_1_3_3_73_2","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.10000721"},{"key":"e_1_3_3_74_2","doi-asserted-by":"publisher","DOI":"10.1093\/ndt\/gfac197"},{"key":"e_1_3_3_75_2","doi-asserted-by":"publisher","DOI":"10.1093\/ckj\/sfv103"},{"key":"e_1_3_3_76_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12325-023-02482-5"}],"container-title":["Medical Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0272989X261432162","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/0272989X261432162","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0272989X261432162","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T12:39:34Z","timestamp":1781008774000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/0272989X261432162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,28]]},"references-count":75,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["10.1177\/0272989X261432162"],"URL":"https:\/\/doi.org\/10.1177\/0272989x261432162","relation":{},"ISSN":["0272-989X","1552-681X"],"issn-type":[{"value":"0272-989X","type":"print"},{"value":"1552-681X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,28]]}}}