Health disparities research in the United States over the past 2 decades has yielded considerable progress and contributed to a developing evidence base for interventions that tackle disparities in health status and access to care. However, health disparity interventions have focused primarily on individual and interpersonal factors, which are often limited in their ability to yield sustained improvements.
Health disparities emerge and persist through complex mechanisms that include socioeconomic, environmental, and system-level factors. To accelerate the reduction of health disparities and yield enduring health outcomes requires broader approaches that intervene upon these structural determinants. Although an increasing number of innovative programs and policies have been deployed to address structural determinants, few explicitly focused on their impact on minority health and health disparities.
Structural interventions often evolve in response to emerging policy, funding, or political priorities, and thus may be implemented in an iterative, discontinuous manner. Another challenge is the long follow-up periods required to observe and measure health outcomes, and especially to document decreases in health disparities, thus necessitating prolonged, multilevel evaluations that extend far beyond typical funding cycles. Structural interventions may require years, sometimes decades, of follow up before improvements in health outcomes can be observed. Most research grants are between 3 and 5 years, a timeline too short to assess long-term impact on reducing health disparities.
The examples of ParentCorps and the Earned Income Tax Credit illustrate the need in disparities research for long-term interventions to understand downstream effects of these structural interventions on minority health and health disparities.
Many local communities recognize the role of structural determinants on health and social outcomes but face the challenge of identifying the interventions most likely to influence their populations’ social and health outcomes. An emerging solution is the collation of evidence-based interventions in registries and reports, among them the National Registry of Evidence-Based Programs and Practices, the What Works Clearinghouse, and the Community Guide, along with an increasing number of databases that aim to help local communities examine health and social indicators at the state, county, and local level, such as the 500 Cities: New Data for Better Health and County Rankings Projects and The Opportunity Atlas.
NIMHD is currently developing an Intervention Portal to serve as a repository for interventions that have successfully improved minority health or reduced health disparities. This portal is part of HDPulse an ecosystem that provides access to data and resources to design, implement, and evaluate evidence-based interventions to improve minority health and reduce health disparities. There is potential to harness expertise in predictive modeling and analytics to help local communities and states determine which structural interventions may yield the most meaningful reductions in health disparities.
Measurement and methodological issues are critical to narrowing the evidence gap and elucidating the role of structural interventions in reducing and eliminating health disparities. The literature reviewed for this article revealed that interventions targeting social and, specifically, structural determinants represent a broad class of strategies and approaches that cut across multiple sectors and domains of influence. As described in the previous section, these interventions target a range of issues, from early childhood education, fiscal and tax policies, housing access, and neighborhood environments, to structural racism.
Although individual interventions may have positive effects, the lack of standardized definitions of structural factors and consistent criteria for classifying different sets of relevant interventions and the limited inclusion of process and outcome measures related to health in many of these interventions impede opportunities to compare and evaluate their impact on a range of health disparities.
Despite opportunities for analyzing and linking existing data across systems, such as electronic health records, registry data, and non–health sector data, there are limitations in utilizing these data for evaluating structural interventions. Investigators and evaluators may not have contributed to intervention design, implementation, or evaluation; therefore, the measures needed to determine causal inferences are lacking or unavailable. Consistent and valid measurement across different sectors is also a concern if, for example, important variables such as race or ethnicity are inadequately measured or specified.