PurposeThe 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. MethodsWe 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. ResultsUnder 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. LimitationsLarge variability in the prevalence of treatment and heterogeneity in treatment effectiveness may affect our results. ConclusionsBeyond 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.