Combining simulation model-based outcomes with county-level data for geographic health equity impact evaluations of new interventions.

Objective: Geographic health disparities persist across the United States, with substantial variations in health outcomes between regions. Evaluating how emerging health technologies might affect these disparities is crucial for developing equitable health policies. This paper introduces an approach for geographic health equity impact evaluation by combining predicted outcomes by equity-relevant subgroup from a simulation model with US county-level data on subgroup proportions.

Methods: The approach involves the following steps: 1) Create a dataset with county-level information on equity-relevant factors and lifetime risk of the target indication; 2) Estimate QALYs and costs with and without the intervention for different combinations of equity-relevant factors with the simulation model; 3) Calculate expected and incremental QALYs in target population, incremental net health benefits per 100,000 general population, and quality adjusted life expectancy at birth (QALEs) without and with LB for each county based on its distribution of equity-relevant factors and step 2 estimates; and 4) Quantify inequality in QALYs and QALEs between counties with and without the technology and the corresponding health equity impact.

Results: We illustrate the approach using liquid biopsy for first-line treatment in non-small cell lung cancer. Future applications should incorporate more detailed information on the equity-relevant groups by county.

Conclusion: Combining simulation model outcomes with county-level data on equity-relevant subgroups provides a novel approach for health equity impact evaluations of new interventions. It facilitates examining how introducing a new health technology can impact geographic disparities in health, and can help identify areas that may benefit most from a new intervention.