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Store Locations. That health has many social determinants is well established and a myriad range of structural factors - social, cultural, political, economic, and environmental - are now known to impact on population well-being.

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Public health practice has started exploring and responding to a range of health-related challenges from a structural paradigm, including individual and population vulnerability to infection with HIV and AIDS, injury-prevention, obesity, and smoking cessation. As an example, between and , the Delaware Colorectal Cancer Coalition galvanized diverse policy, health care, and community stakeholders to sharply reduce or eliminate African American—White disparities in colorectal cancer screening, incidence, and mortality.


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Structural interventions that successfully address health disparities and improve minority health are disease-agnostic in their approach, enabling them to tackle common risk factors that lead to multiple health disparities, thereby altering the context s that yield social inequalities. Among these are policies and practices that focus on changing the mechanisms and trajectory of risk factors that lead to health disparities. For example, ParentCorps, which was designed to tackle gaps in academic achievement and mental health status among impoverished children in New York City, had a significant effect on reducing childhood obesity, anxiety, and depression in minority and low-income communities.

A crucial challenge is optimizing the timing and location of structural interventions to have the largest impact on reducing disparities. The Moving to Opportunity housing mobility experiment found that children from low-income and minority families who relocated to low-poverty areas had better long-term outcomes if the move occurred before age 13 years. Both Moving to Opportunity and the Earned Income Tax Credit demonstrated the profound effect of prenatal and childhood interventions on life course social, economic, and health trajectories.

Similarly, identifying geographic risks associated with residential neighborhood factors can inform local-area capacity building and propel cross-sectoral interventions that directly or indirectly reduce health disparities and improve health outcomes.

Structural Interventions to Reduce and Eliminate Health Disparities

The complex nature of structural interventions makes it important to examine their intended and unintended consequences—positive and negative—on health disparities and how to measure and interpret them. This concern is particularly important when one is evaluating how such programs and policies affect disparities. If population-wide health improvements disproportionately benefit the most advantaged members of society, disparities may widen among vulnerable underserved populations, as in the case of tobacco control in the United States, 27—30 which has been of less benefit to some minority communities compared with the general population.

Longitudinal analyses of Moving to Opportunity actually uncovered potential harms for some subgroups associated with this intervention, including social stressors that many low-income and minority families face regardless of neighborhood, the impact of multigenerational poverty and racism, and disrupted social ties engendered by the move to a new neighborhood.

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Discordance between interventions and local community cultures, norms, or other entrenched structures can also contribute to unintended consequences. Structural interventions must be developed and evaluated with sensitivity and appropriateness to existing local sociocultural structures, should be planned and tailored in collaboration with the communities directly impacted by the intervention, and should integrate the cultural, historical, and psychological factors that influence targeted behaviors. We identified several challenges to developing and deploying structural interventions that have the potential to reduce disparities.

To fill knowledge gaps, new research and policies are needed in several domains, including theoretical frameworks, measurement, study design, funding, evaluation, and dissemination. The task of identifying the distinct social-ecological factors that contribute to health risks and disparities and targeting these multiple contexts and levels of influence can be complex and pose several challenges to developing, implementing, and evaluating structural interventions.

These interacting factors complicate the measurement of individual and collective impacts, particularly over short timeframes, and potentially hinder the ability to prioritize meaningful solutions. Standard epidemiologic methods may not adequately measure the outcomes and the impact on disparities. For naturally occurring social experiments, such as universal pre-K in poor neighborhoods, tax credits, and food environment interventions, this challenge of attribution remains salient despite the emerging science in this area.

Better understanding of the mechanisms through which structural interventions succeed or fall short in improving minority health and reducing disparities is critical to informing and advancing the development, scalability, and sustainability of these programs and policies.

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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.


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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.

There is limited capacity and technical expertise for linking large data sets across multiple sectors to evaluate the impact of structural interventions. Related to this issue is the need for greater interoperability and harmonization across different data systems and for a common set of minority health and disparities-related data elements that can be captured across health and nonhealth sectors.

In this era of big data, a number of challenges continue to impede the culling of disparate data sets to meaningfully analyze community-wide and system-level interventions. Tackling structural determinants, such as a lack of affordable housing, poverty, and limited educational attainment, requires substantial investment from the national to the local community level across health and nonhealth sectors. However, funding is often siloed within sectors and allocated in tightly restricted ways that limit innovation and collaboration, even when organizations recognize the value of working collectively around shared goals and strategies.

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.

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. Although the evidence base for structural interventions to address health disparities is growing, evaluation data are still lacking on sustainability, scalability, and replicability of successful interventions.

The costs of structural interventions pose additional challenges as communities determine which interventions may offer the best return on investment in population health improvement and reduction in health disparities. There is a lack of clarity of the trade-offs for choosing one set of interventions versus another and how much those strategies cost per person in different communities.

Structural interventions are fundamentally rooted in understanding, and often altering, the contexts through which health disparities emerge and persist. They tackle complex combinations of structural determinants of health, including culture, social position, racism, environmental settings, and policies.

Their common features that have successfully mitigated or eliminated disparities include accounting for the social and physical contexts that produce or perpetuate disparities, authentic engagement and integration of community and other stakeholders in all phases of the research process, and taking a disease-agnostic approach to promote disparities reduction across different conditions and at multiple levels. Furthermore, effective implementation and evaluation require close attention to the timing and location of the intervention and both intended and unintended outcomes.

However, significant gaps remain in our knowledge. The following sections and the box on this page present recommendations for reducing these knowledge gaps and advancing the science of health disparities research. A key element of successful structural interventions is the critical role of community and stakeholder engagement in identifying the needs of disparity populations and communities, developing shared goals, and supporting meaningful, sustainable, and scalable interventions.

The development, implementation, evaluation, translation, and dissemination of structural interventions require early and continuous input by the communities who bear the disproportionate burden of disease and by the stakeholders who are instrumental in efforts to sustain and scale successful practices. Community-engaged approaches result in the development of strategies that have direct relevance and practical benefits to local communities, leading to better integration of science, practice, and policy.

Stakeholder engagement also informs the development of strategic partnerships across a range of sectors such as housing, food systems, transportation, criminal justice, and health care to address domains that contribute to health disparities at each level of influence. As noted earlier, few interventions aimed at structural determinants are guided by scientific frameworks. The effectiveness of these real-world efforts may be influenced by intersecting political, legal, economic, cultural, and biomedical factors that should be considered and accounted for in their design and evaluation.

A critical step in addressing the inherent complexity of structural interventions is developing and adopting a scientifically credible conceptual framework or theory of change that incorporates these diverse factors and the roles they are anticipated to play in health disparities. Robust evaluation designs and measures—derived from a broad range of disciplines and capable of harnessing big data across sectors—are needed to address the evidence gap in our understanding of the impact and reproducibility of structural interventions developed to reduce health disparities.

Big data science is rapidly evolving and should engage health disparities researchers who have expertise in social and structural determinants of health. Addressing measurement and data collection challenges requires broad-based strategies, among them, mixed-method evaluation, stakeholder involvement in designing the intervention and evaluation, multisector agreement on common nomenclature and measurement, standardized measurement systems, effective methods to harmonize disparate data, and novel modeling strategies Some structural interventions may not be suited to traditional research designs because of cost and time constraints, ethical considerations, an inability to randomize sites or individuals, or the continually evolving nature of the intervention.

Robust and validated approaches, such as stepped-wedge or staggered interventions, interrupted time-series, quasi-experimental designs, and cluster or group-randomized trials can facilitate rigorous evaluation at the individual, interpersonal, community, and societal levels. Optimally, studies should be prospective and should include data from multiple sectors that allow examination of the impact of structural interventions on population health and health disparities.

Historical data from various sectors may provide important insights into the development and evaluation of long-term impact of structural interventions by identifying simultaneous changes in social and health indicators over time that are associated with health disparities.

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Predictive analytics and other robust methods can inform decision-making on the nature and scope of various structural interventions and strategies to optimize health impact and disparities reduction. Understanding how structural interventions that were successfully developed in one community might be adapted, scaled up, and transferred to another setting is of critical importance. Promising and evidence-based structural interventions do not easily translate into improved health and reduced health disparities because it takes time, resources, and multidisciplinary teams to improve the relevance, uptake, and implementation of evidence-based interventions in real-world settings.

Dissemination and implementation research training has great potential to improve the reach and impact of structural interventions on minority health and health disparities.

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Structural Approaches in Public Health | Taylor & Francis Group

Advances in several disciplines are rapidly changing the ability to design, implement, and evaluate structural interventions. Technological advances, including big data science, can be mobilized to explain and address disparities. Many health system interventions have made innovative use of electronic health records as tools to characterize social and structural characteristics of populations, identify targets for intervening, and deploy interventions.

Geographic information system platforms can link individuals to social and structural risks and resources. Similarly, advances in fields such as personalized medicine, personalized public and population health, systems science, and computational biology may result in powerful predictive tools to identify those at highest risk for disparities and link them to appropriate structural interventions. To conduct well-designed structural interventions and robust evaluations, resources are needed to promote interactions among stakeholders from disparate sectors to plan and develop large-scale meaningful structural interventions that can effectively reduce health disparities in populations and communities.

Trans—federal agency collaborations and public—private partnerships may be promising approaches to intervening on health disparities. Standardizing approaches for motivating multistakeholder collaborations is a critical need in disparities research. Future efforts should focus on building partnerships among sectors in the earliest phases of intervention design by providing resources to support multisector partners.

These early partnerships can plan effectively by using a collective impact framework and group facilitation to support consensus building that effectively translates evidence into practice. In addition, these collaborations should have an explicit emphasis on informing the collection of shared metrics and facilitating opportunities to support interoperable data systems to access large classes of epidemiological, environmental, social, and biological data to determine the impact on advancing health equity and improving population health.