Although scientific and technological advances have improved the health and wellbeing of the U.S. population overall, racial and ethnic minorities, socioeconomically disadvantaged, underserved rural and sexual and gender minority populations continue to experience a disproportionate share of many acute or chronic diseases and adverse health outcomes. It is critical to address health disparities and improve health for all Americans.
Several characteristics make addressing health disparities an especially challenging problem. The challenges lie in the interactions of influences at various levels (e.g., individual, interpersonal, family, community, societal), the diversity of the relevant mediators (e.g., exposures, resilience factors), and the multiple interacting mechanisms involved (e.g., biological, behavioral, environmental, sociocultural, and healthcare system). The array of determinants of health across levels and domains are depicted in the NIMHD Research Framework (https://www.nimhd.nih.gov/about/overview/research-framework.html). Systems science considers different components within complex systems across multiple levels to help understand their interactions and influences. The focus on single, independent risk factors often fails to capture the complexity of interactions between diverse factors in subtle, bidirectional, or non-linear ways to strongly influence overall behaviors and health outcomes. The dynamic relationship that unfolds when considering contextual factors that contribute to health inequities, such as neighborhood segregation, housing insecurity, food insecurity, neighborhood safety, social networks, and community disempowerment, cannot be fully captured with currently available data and analytic methods. Simulation Modeling and Systems Science (SMSS) provides avenues for modeling relevant multiple processes, testing plausible scenarios, understanding the magnitude of intended and unintended consequences of specific interventions, and having the option to adjust and refine simulated intervention designs prior to actual implementation testing in the real world. SMSS approaches have been used to guide interventions in clinical preventive care, disaster planning, and for analyzing national health reform strategies. They have also been used to model potential public health outcomes in cases where it is not feasible to test various intervention strategies on real populations, particularly where interventions may involve factors far upstream from health outcomes, such as societal causes embedded in political, legal, economic and cultural factors.
The importance of using SMSS to address population health has been highlighted in Institute of Medicine (IOM) reports, including: For the Public’s Health: The Role of Measurement in Action and Accountability (2011) and Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making (2010). Moreover, results from simulation models developed under NCI’s Cancer Intervention and Surveillance Modeling Network (CISNET) were used to inform guidelines issued by the U.S. Preventive Services Task Force (e.g., breast cancer screening and colorectal cancer screening). However, SMSS have not been widely adopted in health disparities research to help understand the causes of disparities, guide efficient interventions, and/or inform policy making.
Although no simulation models can replace real world settings or scenarios, many are becoming indispensable for decision making, such as national or local pandemic planning, and can have a profound impact on health policies relevant to minority health and health disparities. The field of SMSS may help to guide health disparities research, in identifying causal inference and what types of situations will be most amenable to research, policy, and practice interventions and in implicating where leverage may be best applied for any health disparity population. Electronic health records, mobile health technologies, smart devices, sensors, and high-end laboratory technologies have greatly expanded the availability of rich data for more accurate simulation and modeling under the systems perspective. Many innovative methods have been developed to help harmonize disparate data across diverse sources and guide informed decision making. Traditional study design and statistical methods need to be rethought in the context of big data and high-performance computing to tackle disparities among diverse populations including those with limited and small samples. Thus, it is important to advance SMSS using new big data technologies to understand the etiology of health disparities and guide intervention development and implementation.
SMSS are also highly relevant to late-stage translation research because they integrate information and evidence from various sources such as epidemiology, clinical guidelines, sociology, behavioral science, psychology, neuroscience, and economics, to formulate complex predictive models. The etiology, pathways, and mechanisms that result in health disparities mimic a complex adaptive system. Models of health disparities seek to illuminate critical elements and intervention points that can tip the system for improved health or provide insights into why health has not improved. Modeling multi-level interventions is important for addressing how the interactions and influences of health determinants function. SMSS offer an opportunity to explore the potentially complex influences on population health at each intervention level; within the classic implementation research paradigm this analysis is cost prohibitive. SMSS provide the platform to explore the space of possible combinations of interventions, integrate information, uncover synergies, and provide close-to-real-world predictions to guide decision making. Of importance is the ability to identify unanticipated implementation research strategies that may yield high return. SMSS approaches can answer the critical questions of what works, under what conditions, what strategies and combinations of strategies will yield innovative ways to address disparities. Also, other significant questions include: why something did not work as anticipated, and how could the intervention be modified to be more effective in addressing disparities.
- Foster trans-disciplinary partnerships and collaborations in understanding the etiology and causal pathways of health disparities using SMSS
- Use SMSS to identify modifiable barriers and cost-effective factors to reduce and eventually eliminate health disparities
- Provide evidence-based simulation or prediction of the impact of effective or ineffective health disparities interventions delivered in real-world settings
- Promote big data harmonization and novel analytic methods in SMSS to address minority health and health disparities
Examples of research methods could include, but are not limited to:
- System dynamics
- Network analysis
- Agent-based modeling
- Microsimulation
- Discrete event analysis
- Markov modeling
Deadlines:
- Non-AIDS proposals – January 10 2018; June 6, 2018; January 8, 2019; June 7, 2019
- AIDS proposals – January 7, 2018; September 7, 2018; January 7, 2019; September 7, 2019
- Letters of intent are due 30 days prior to the deadline
URL: https://grants.nih.gov/grants/guide/pa-files/PAR-18-331.html
Filed Under: Funding Opportunities