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

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 progres-sion 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 (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 diagnostic criteria in our simulations changed the timing of qualification for chronic kidney disease diagnosis, ranging from 0.6 years to 9.6 years, with opposite effects for Black and non-Black patients.

• The simulated differences in expected survival after removing the race adjustment did not exceed 2 months among individuals who developed CKD.

• The explicit representation of the data generation process can help anticipate the effect that policy changes can have on clinical data distributions.