The Epidemiology and Genomics Research Program (EGRP) has initiated a strategic planning effort to develop scientific priorities for cancer epidemiology research in the next decade in the midst of a period of great scientific opportunity but also of resource constraints. EGRP would like to engage the research community and other stakeholders in a planning effort that will include a workshop in December 2012 to help shape new foci for cancer epidemiology research.
EGRP Invites Your Feedback
To facilitate this process, we invite the research community to join in an ongoing Web-based conversation to develop priorities and influence the next generation of high-impact studies.
Our aim is to enhance the application of epidemiologic methods along the translational continuum from basic discoveries to population health impact.
This week, we address the use of epidemiologic research to bridge evidence gaps between scientific discoveries and population health impact, in particular, how we can use observational epidemiologic studies to supplement randomized clinical trials in advancing clinical and public health practice.
Translational research is a continuum from discovery to population health impact. Although this continuum is often considered a linear or unilateral process for laboratory scientists, the form shown in Figure 1 involves many iterations and feedback loops.
Within the translational research enterprise, epidemiology serves as a fundamental building block. The application of epidemiologic methods across all phases of translational research, T0 – T4, is known as translational epidemiology (Figure 1) and has been described in a 2010 publication in the American Journal of Epidemiology.
- T0, from population health measures to discoveries, consists of describing patterns of health outcomes by place, time, and person and finding determinants of health outcomes with observational studies.
- T1, from discoveries to promising applications, characterizes discovery and assesses potential health applications by using clinical and population studies.
- T2, from promising applications to evidence-based recommendations and policies, assesses the efficacy of interventions to improve health and prevent disease by using observational and experimental studies.
- T3, from evidence-based recommendations and policies to practice, assesses the implementation and dissemination of guidelines into practice.
- T4, from practice to population health measures, assesses the effectiveness of interventions on health outcomes.
The framework described above combines basic, clinical, and public health approaches to disease treatment, prevention, and control. It provides the necessary data to influence further research, policy, and practice by documenting what we know and do not know and what works and does not work. Translational epidemiology is an essential ingredient to accelerate the movement of discoveries from research into practice using evidence-based applications.
We would like to get your feedback on the following fundamental questions:
- What are new ways in which epidemiology can be used to fill evidence gaps between discoveries and population health impact in the cancer care continuum?
- How can observational epidemiology make the greatest scientific contributions in understanding cancer-related risk factors that cannot be studied through randomized clinical trials?
Please use the comment section below to share your perspectives.
We encourage you to be as specific as possible. You can use or be inspired by the NCI Provocative Questions exercise. Your comments will be used to shape the workshop discussion in December, aspiring to transform the future of cancer epidemiology in the next decade.
Comments are also still welcome in response to the first three questions posed as part of EGRP’s strategic planning series:
- June 4, 2012: What Scientific Questions Should Cancer Epidemiology Address in the Next Decade to Impact Public Health?
- July 20, 2012: How Should New Technologies Be Integrated into Cancer Epidemiology?
- August 27, 2012: What Have We Learned from Epidemiology Cohorts and Where Should We Go Next?