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The Target Study: A Conceptual Model and Framework for Measuring Disparity.

John W Jackson1,2,3, Yea-Jen Hsu4, Raquel C Greer5

  • 1Departments of Epidemiology, Biostatistics, and Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.

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Summary
This summary is machine-generated.

This study introduces a novel sampling-based model to measure social disparity, even when groups are balanced on covariates. It addresses biases from sample selection and enrollment timing, offering a framework for health equity research.

Keywords:
Conceptual ModelDisparityEquityEthicsFairnessFrameworkTarget Study Emulation

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Equity Research

Background:

  • Measuring social disparity is complex due to confounding and selection biases.
  • Existing decomposition-based models may not adequately address these challenges in healthcare settings.

Purpose of the Study:

  • To present a conceptual sampling-based model for measuring disparity.
  • To extend the model for generalizing disparity under non-random selection and after interventions.
  • To provide a framework for implementing the model using various estimators.

Main Methods:

  • Developed a sampling design that does not alter group membership or covariates.
  • Incorporated methods to address non-random sample selection and differential enrollment timing.
  • Discussed study designs, data structures, and G-computation and weighting estimators.

Main Results:

  • The proposed model offers a framework to measure disparity by balancing covariates and enrollment time.
  • Methods are presented to transport or generalize disparity estimates.
  • The model was applied to measure health system disparities in hypertension control.

Conclusions:

  • The sampling-based model provides a robust approach to measuring social disparity in health.
  • It offers solutions for bias mitigation due to sample selection and enrollment timing.
  • The methods are applicable to real-world health equity research using electronic medical records.