Gabriela Basel

Purpose: 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. Methods: 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. Results: 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. Limitations: Large variability in the prevalence of treatment and heterogeneity in treatment effectiveness may affect our results. Conclusions: 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. Highlights: 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. The removal of race-based diagnostic criteria in our simulations changed the timing of qualification for chronic kidney disease diagnosis, ranging from 0.6 y to 9.6 y, with opposite effects for Black and non-Black patients. The simulated differences in expected survival after removing the race adjustment did not exceed 2 mo among individuals who developed chronic kidney disease. The explicit representation of the data-generation process can help anticipate the effect that policy changes can have on clinical data distributions.

Purpose 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, the effect of those criteria on patterns of disease progression should also be reevaluated. We proposed a novel microsimulation-based framework for attenuating societal bias in primary care registry data to study this. Methods Our data transformation framework allows us to generate counterfactual outcome distributions that would have been observed in the absence of race-based diagnosis and treatment criteria. We developed a continuous-time microsimulation model of kidney function decline, measured by estimated glomerular filtration rate (eGFR). The model simulates individual eGFR trajectories over time. 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. Results 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 two scenarios. Limitations Large variability in the prevalence of treatment and heterogeneity in treatment effectiveness may impact our results. Conclusions Our data transformation framework demonstrates how the explicit representation of the data generation process could inform the effect of policy changes on clinical data distributions. The framework can flexibly be adapted to mitigate bias in other health data. Highlights We developed a novel data transformation framework for attenuating societal biases in data using microsimulation models in a study of chronic kidney disease progression with primary care data. The removal of race-based criteria changed the timing of qualification for chronic kidney disease diagnosis, with opposite effects for Black and non-Black patients. However, our simulation analyses did not identify a difference in life expectancy after the removal of the race adjustment. The explicit representation of the data generation process can help anticipate the effect that policy changes can have on clinical data distributions.