A microsimulation-based framework for mitigating societal bias in primary care data.

ABSTRACT:

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 enables generating counterfactual outcome distributions that would have been observed in the absence of race-based diagnosis and treatment criteria. We developed a continuous-time, discrete-event individual-level simulation model of kidney function decline, measured by estimated glomerular filtration rate

SUBMITTER: Foryciarz A 

PROVIDER: S-EPMC12687816 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature