This FOA encourages research applications to examine the differential risk factors of populations that lead to or are associated with increased vulnerability to exposures, diseases and other adverse health outcomes related to climate change. Applications may involve either applied research studies that address specific hypotheses about risk factors or population characteristics associated with increased vulnerability, or research projects to develop general models or methods for identifying and characterizing population vulnerability to climate change. The ultimate goal of this research program is to help inform climate change adaptation and public health interventions to reduce current and future vulnerability of various populations to the health effects of climate change. Applications are anticipated to involve a multidisciplinary research team, including experts in health sciences and climatology as well as geography, modeling, statistics, demography, and social and behavioral sciences as appropriate. In addition, partnerships with community-based or advocacy organizations, public health officials, urban planners and others are encouraged.
The National Library of Medicine (NLM) is interested in novel informatics approaches that support the goal of assessing and modeling population vulnerability to climate change. Examples include, but are not limited to:
Approaches to merging data from disparate sources (e.g., satellite imagery with genomic or phenomic data) to provide a more complete picture of the relationships between climate and disease for a specific population, location or health condition.
Novel data mining approaches for discovering, visualizing and testing hypotheses about climate effects on particular diseases or population groups.
Advanced applications of computational intelligence to support information integration, minimizing the human intervention needed to map and standardize data elements in large health, demographic or climate data sets.
Analysis of the role access to timely, relevant information plays in a population’s vulnerability to climate change.
Informatics for bio- surveillance and dynamic mapping of diseases sensitive to climate change.
