A combined epidemiologic and genomic approach to identify and control transmission events of hospital associated infections

PROJECT SUMMARY/ABSTRACT This is an application for a K01 award for Dr. Lindsay Keegan, an infectious disease epidemiologist at the University of Utah. Dr. Keegan is establishing herself as a young investigator in infectious disease dynamics addressing public health relevant questions on the spread and control of infectious diseases. This K01 award will provide the support necessary to accomplish the following goals to develop: (1) expertise in genomic methods as it relates to epidemiology; (2) expertise in biostatistics particularly probability theory, network models, and Bayesian statistics; (3) expertise in healthcare epidemiology as it relates to pathogen transmission within healthcare facilities (HCF); and (4) and strengthen research leadership and management skills. To achieve these goals, Dr. Keegan has assembled a mentoring team comprised of: Dr. Matthew Samore (primary mentor), a healthcare epidemiologist and expert in mathematical modeling of healthcare associated infections; and Dr. Marc Lipsitch (co-mentor), an epidemiologist and a recognized leader in mathematical modeling of infectious diseases including modeling healthcare associated infections, and Dr. Michael Rubin (co-mentor), and infectious disease physician and an expert on translational science to support antimicrobial stewardship and infection prevention and leadership. Dr. Keegan has also assembled a team of four advisors with expertise in biostatistics, genomic epidemiology, mathematical modeling, and hospital epidemiology. Antibiotic resistant bacteria pose a significant public health threat, causing over 2.8 million infections and over 35,000 deaths each year in the United States. The burden of these infections is concentrated within HCFs; and how to control these pathogens remains the source of considerable debate. Based on data collected from a prior CDC study, Dr. Keegan’s central hypothesis is that healthcare associated pathogens are spreading primarily indirectly between patients and environmental surfaces via patient shedding and inadequate source control. By pursuing the following Specific Aims, Dr. Keegan will test her hypotheses and develop methods to apply to future data sets (for a future proposed R01 application during the K01 period). In Specific Aim 1, Dr. Keegan will test the hypothesis that there will be phylogenetic support for clustering between pathogens isolated from the environment and those isolated from patients. In Specific Aim 2 she will construct and validate a model that integrates contact network data with pathogen genomic data to probabilistically infer the direction of transmission events within a HCF. In Specific Aim 3, she will test the hypothesis that the majority of patient-to-patient transmission events are mediated by environmental surfaces. The proposed research is significant because it addresses a critical barrier to improving infection control: without quantifying the role of different sources for pathogen transmission, infection control practices cannot intentionally target the key transmission steps. The proposed research is innovative because a method to infer transmission events for bacteria does not currently exist despite its critical importance in infection control.