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A Microsimulation-Based Approach for Mitigating Societal Bias in Chronic Kidney Disease Data.

Agata Foryciarz1, Fernando Alarid-Escudero2, Gabriela Basel3

  • 1Department of Computer Science, Stanford University, Stanford, CA, USA.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
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Summary

Removing race-based criteria in chronic kidney disease (CKD) diagnosis simulation altered diagnosis timing for Black and non-Black patients. This microsimulation model offers a new approach to address societal bias in health data.

Keywords:
chronic kidney diseasedata biashealth equitymicrosimulation modelsrace-based criteria

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Area of Science:

  • Health Informatics
  • Biostatistics
  • Public Health

Background:

  • Societal biases in healthcare data, particularly race-based criteria, can perpetuate inequities.
  • Reassessing race-based criteria in chronic kidney disease (CKD) necessitates understanding their impact on disease progression.
  • Existing data-debiasing methods may not fully capture the nuances of these criteria.

Purpose of the Study:

  • To develop a microsimulation model to attenuate societal bias in primary care CKD data.
  • To evaluate the effect of removing race-based diagnostic and treatment criteria on CKD progression.
  • To generate counterfactual outcome distributions in the absence of race adjustments.

Main Methods:

  • Developed a continuous-time, discrete-event individual-level simulation model for kidney function decline (eGFR).
  • Simulated eGFR trajectories, incorporating factors like hypertension, diabetes, and CKD stage.
  • Applied Bayesian calibration to estimate eGFR decline rates and compared scenarios with and without race adjustment.

Main Results:

  • Removing race adjustment led to earlier CKD diagnosis for Black individuals and later diagnosis for non-Black individuals.
  • The timing differences ranged from 0.6 to 9.6 years, with greater disparities in earlier CKD stages.
  • No significant differences in life expectancy were observed between the race-adjusted and unadjusted scenarios (within 2 months).

Conclusions:

  • The microsimulation model provides an alternative to existing data-debiasing approaches for CKD.
  • Simulated data can inform policy decisions and the development of future interventions.
  • Explicitly modeling the data-generation process helps anticipate the impact of policy changes on clinical data.