A Microsimulation-based Framework for Mitigating Societal Bias in Primary Care Data

Oregon Convention Center 

Primary care registry data can be invaluable for measuring quality of care and informing improvements in diagnosis and management of chronic diseases due to its scale, availability, and representativeness. However, the data-generating mechanism underlying those data is rarely examined – which can lead to reproducing outcome disparities. In chronic kidney disease (CKD), unequal standards of care, including race-based diagnostic criteria, result in faster disease progression and higher mortality for Black patients. As the use of race-based criteria is reassessed, it is important to consider the effect of those criteria on historical patterns of disease progression before the registry data is used to inform new policy decisions. We propose a novel microsimulation-based framework for attenuating societal bias in CKD progression data from a large primary care registry, which allows us to generate counterfactual outcome distributions, reflecting rates of end-stage renal disease that would have been observed in the absence of race-based diagnosis and treatment criteria. The framework developed here could flexibly be adapted to mitigate bias in other health data.