Developing a Shiny Cluster Identification Tool for Epidemiology

Environmental and occupational epidemiologic studies have previously linked some types of hematopoietic cancers—leukemias, lymphomas, and myelomas—to exposure to several types of pesticides. One of the more controversial actions taken based on this research is the International Agency for Research on Cancer (IARC) classifying glyphosate (also known as Roundup™) as “probably carcinogenic to humans”. Individual-level studies of case-control design have produced conflicting results, but there have been notably few ecological or population-based studies. This is an important omission because ecological studies can provide some advantages over casecontrol studies when examining temporal relationships between exposures and disease, and when investigating rare causes. Unfortunately, rigorous spatio-temporal statistical analysis is often time-consuming, requiring meticulous preparation of population data and mapping of potential exposures. In the proposed research, we will create a secure online cluster identification tool for ecological studies of disease using a development platform called Shiny, which integrates with the statistical software package, R. We will pre-load this system with population data and pesticide use data from the United States Geological Survey (USGS) National Air and Water Quality Assessment (NAWQA) project, and then use it to identify potential relationships between glyphosate use and incidence of specific hematopoietic cancers.