Distributional cost-effectiveness analysis (DCEA) supports equitable resource allocation by quantifying equity-efficiency trade-offs. DCEA may be particularly useful to understand equity impacts in the context of genomic medicine, a rapidly growing clinical area that has prompted concerns about its potential to exacerbate health inequities by differentially benefitting some population groups over others because of disparities in research inclusion and access to specialty care. This article critically examines the application of DCEA in the context of genomic medicine.
We articulate steps for distributional impact assessment in the context of genomic medicine by adapting an existing conceptual framework for understanding the causal pathway between a healthcare intervention and the distribution of costs and effects among social groups, the inequality staircase. We discuss related data equity considerations and evidence requirements specific to genomic medicine interventions.
The need for and receipt of a genomic medicine intervention, as well as an intervention’s short-term and long-term effects, may vary across equity-relevant subgroups. Research to enhance the relevance of DCEA in genomic medicine should avoid conflation of biological and social factors, empower populations that are underrepresented in genomics research, accurately assess variation in outcomes across equity-relevant subgroups, and develop methods for incorporation of nonhealth outcomes within a DCEA framework.
Best practice-aligned applications of DCEA may facilitate transparent discussions of health equity in coverage and implementation decisions. This article provides guidance to researchers on the use of DCEA in genomic medicine and other clinical areas with similarly complex considerations around equity.
This research can be used to improve health equity in genomic medicine. Genomic medicine, which includes advanced genetic testing and treatments, has the potential to enhance healthcare by offering personalized medical solutions. However, it also risks worsening health disparities because certain groups may benefit more due to differences in access to specialized care and research participation.
Distributional cost-effectiveness analysis is a method that helps balance the trade-offs between efficiency and equity when allocating healthcare resources. It enables policy makers to evaluate how different genomic interventions might affect various social groups, ensuring that these decisions are more equitable. The article emphasizes the importance of considering social factors alongside biological ones to advance health equity in genomic medicine.
Current challenges in genomic medicine include disparities in access and outcomes, which are influenced by the healthcare system, research designs, and data collection practices. To achieve health equity, it is important to provide fair and unbiased access to genomic services and to ensure data are collected on all who may be affected. Yet, policy makers often struggle with deciding whether to prioritize equal access over maximizing overall population health, which can lead to unequal health outcomes.
The article suggests several steps to enhance the relevance of distributional cost-effectiveness analysis in genomic medicine. These include empowering underrepresented populations in genomics research, collecting data to accurately assess variations in genomic medicine outcomes across different groups, and considering nonhealth outcomes that are significant to patients and families. Additionally, it calls for improved diversity in genomics research and workforce to ensure equitable representation and understanding of genetic diversity.
For policy makers and healthcare decision makers, the article provides guidance on using distributional cost-effectiveness analysis in genomic medicine to make informed decisions that promote health equity. By incorporating distributional cost-effectiveness analysis into health technology assessment processes, stakeholders can better address the equity implications of resource allocation decisions.
In conclusion, distributional cost-effectiveness analysis offers a promising approach to explicitly quantify the health equity impacts of genomic medicine interventions. While challenges remain, such as data collection and methodological advancements, applying distributional cost-effectiveness analysis can lead to more transparent discussions and decisions that prioritize equitable healthcare outcomes for all social groups.