Health disparities are differences in the incidence, prevalence, mortality, and burden of cancer and related adverse health conditions that exist among specific population groups. These population groups may be characterized by gender, age, ethnicity, education, income, social class, disability, geographic location, or sexual orientation.
The broad area of heath equity and disparities has benefitted significantly from transdisciplinary research teams in delineating the factors that contribute and exacerbate these inequities. Social determinants of health (SDOH) can be understood as conditions in which people are born, grow, live, work, and age, including the health system. These circumstances are shaped by the distribution of money, power, and resources at global, national, and local levels, which are themselves influenced by policy choices. SDOH are mostly responsible for health inequities – the unfair and avoidable factors in health status seen within and between countries. Since these factors are avoidable, they can be addressed through a complement of scientific activities that will reduce the burden of these factors and improve overall health.
Much of research generally in the area of SDOH focuses on delineating differences among racial/ethnic groups and understanding the barriers in care for specific underserved populations; and the subsequent development of behavioral interventions for these groups. These for large part have been partially successful but have not consistently sought to change the conditions in which people live, work, and play. Despite the growing evidence of the effects of SDOH on both short term and long term health outcomes, there is little emphasis on developing theory and evidence based multilevel and population (community) interventions, that target both structural and individual factors. For these interventions to be effective, it necessitates the development of new measures on inequity, social environment; the adaptation of existing measures of SDOH; and a comprehensive understanding of the pathways by which the social context affects health.
While focusing on improving the health of the population it is imperative that we also ensure that every sector of society benefits equally from the developments in health. In this regard, we need to encourage conducting research in small populations that are largely excluded from clinical trials and interventions due to the limited size of population (such as Asian Americans, Native Hawaiians and other Pacific Islanders, American Indians, Alaskan Natives, lesbian/gay/bisexual/transgender, African American subpopulations). Focusing on research in these small populations and subpopulations requires improved methodologies and selection of an appropriate sample size that will allow for generalizations to the subpopulation s across the US.
How can we develop models and tools for measuring the effects of social context (i.e., concept of place, built environment) on health among diverse populations?
How can we develop models to compare the effects of social context on health patterns and trends within various settings – including community and clinical settings? What are the common measures and how can there be greater interoperability?
How would real time data and the changing technology be garnered to have greater impact on health promotion and disease prevention in low income and ‘hard-to-reach’ populations?
How can use existing tools more efficiently to build large datasets to understand population health changes in building programs for health promotion and disease prevention?
How does social context contribute to disparities in cancer incidence and mortality?
How can increased knowledge of the global cancer burden contribute to our understanding of cancer disparities in the United States?
What are the key factors (including psychological, social, environmental, and policy-level) influencing cancer prevention strategies? How can these multilevel interventions be sustainable in real-life context?
How can we design and implement culturally appropriate interventions among indigent and medically underserved populations (including cancer survivors) to improve the health and quality of life of these populations?
To what extent are clinical and community-based intervention programs designed to address cancer disparities informed by evidence from science, practice, and policy?
Small Population/Subpopulation Research
What statistical tools are needed to analyze this kind of data?
What aspects of the subpopulation research in one community allow for generalizations to other African American and Native Hawaiian and other Pacific Islander sub-populations?
Can evidence-based interventions from other large populations be adapted to smaller diverse populations? What are the factors that need to be considered when testing and implementing multilevel interventions? Is there sufficient statistical power to assess the effects of each level and tease them apart?
Given the diverse locations of the subpopulations, how can concept of place be incorporated in these studies? Can these differences in communities be explained by the social, environmental or ecological factors? How can we account for these factors in interventions?