EGRP News Flash - July 8, 2011

Three Funding Opportunity Announcements Issued for Research on Spatial Uncertainty: Data, Modeling, and Communication

The NCI's Division of Cancer Control and Population Sciences (DCCPS) is co-sponsoring three Funding Opportunity Announcements (FOAs) inviting applications to support innovative research that identifies sources of spatial uncertainty (i.e., inaccuracy or instability of spatial or geographic information) in public health data, incorporates the inaccuracy or instability into statistical methods, and develops novel tools to visualize the nature and consequences of spatial uncertainty. The Program Announcements (PAs) invite applications using the Research Project Grant (R01), Small Grant Program (R03), and the Exploratory/Developmental Research Grant Award (R21) mechanisms.

Spatial uncertainty is the lack of, or the error in, knowledge about an object's geographic position, which leads to uncertainty about the spatial relationship among its neighbors. For example, an error in a patient's residential address will introduce spatial uncertainty about where the patient lives, and this error will further bias any association between the patient's health status and specific environmental exposure. Spatial uncertainty in public health information is ever present from data collection and model specification to interpretation, visualization, and communication. Estimates of disease patterns or trends contain a certain degree of uncertainty. Bias may be introduced if the uncertainty is ignored or misunderstood.

Each PA requires a team of epidemiologists, statisticians, and experts in data visualization or health communication to attack the spatial uncertainty issue thoroughly. These PAs will facilitate multidisciplinary collaborations among scientists to promote research in identifying, quantifying, reducing, and communicating spatial uncertainty in health research to improve disease control and prevention. They also will facilitate integration of data collection, information technology, visualization tools, statistical models, and health communication to reduce spatial uncertainty in planning, implementing, and evaluating disease control programs.

NCI is interested in general methodology of spatial statistical models and visualization tools that are applicable to disease control and prevention especially as related to cancer and cancer patients.

For NCI, programmatic inquiries may be directed to Li Shu, Ph.D., Statistical Methodology and Applications Branch (SMAB), Surveillance Research Program (SRP), DCCPS.

The three PAs also are co-sponsored with several other NIH Institutes. Refer to the PAs for specific areas of interest and contact information.

Access the NIH Guide for Grants and Contracts for details:

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