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Combining Simulation Model-Based Outcomes With County-Level Data for Geographic Health Equity Impact Evaluations of

Jeroen P Jansen1, Michael P Douglas2, Kathryn A Phillips1

  • 1Department of Clinical Pharmacy, UCSF Center for Translational and Policy Research on Precision Medicine (TRANSPERS), University of California, San Francisco, CA, USA; UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA; UCSF Philip R. Lee Institute for Health Policy, University of California, San Francisco, CA, USA.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|August 8, 2025
PubMed
Summary

This study presents a novel method to evaluate how new health technologies impact geographic health disparities using simulation models and county-level data. This approach helps identify areas most benefiting from interventions, promoting health equity.

Keywords:
distributional cost-effectiveness analysisgeographic datahealth equity impactsimulation modeling

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Area of Science:

  • Health Services Research
  • Health Economics
  • Public Health Policy

Background:

  • Persistent geographic health disparities exist in the US, necessitating evaluation of emerging technologies' impact on health equity.
  • Equitable health policies require understanding how new health interventions affect regional variations in health outcomes.

Purpose of the Study:

  • To introduce and demonstrate an approach for evaluating the geographic health equity impact of emerging health technologies.
  • To combine simulation model predictions with county-level demographic data for health equity assessment.

Main Methods:

  • Developed a method integrating simulation model outcomes (QALYs, costs) with US county-level data on equity-relevant subgroups.
  • Calculated county-specific QALYs, incremental net health benefits, and quality-adjusted life expectancy (QALE) with and without the technology.
  • Quantified inequality in QALYs and QALE between counties to assess health equity impact.

Main Results:

  • The approach was illustrated using liquid biopsy for non-small cell lung cancer first-line treatment.
  • Demonstrated the ability to quantify geographic disparities in health outcomes influenced by technology adoption.

Conclusions:

  • Combining simulation modeling with local demographic data offers a novel framework for health equity impact evaluations.
  • This approach aids in understanding the effects of new health technologies on geographic health disparities and identifying areas for targeted benefit.