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Applying human factors and mathematical modeling approaches to prevent transmission of high-consequence pathogens
Investigator (PI): Pronovost, Peter J
Performing Organization (PO): (Current): Johns Hopkins Medicine, Armstrong Institute for Patient Safety and Quality / (410) 637-6261
Supporting Agency (SA): Centers for Disease Control and Prevention (CDC)
Initial Year: 2015
Final Year: 2018
Record Source/Award ID: RePorter/U54CK
Funding: Total Award Amount: $2,445,400
2015 Award Amount: $2,199,350
2017 Award Amount: $246,050
Award Type: Grant
Award Information: Reports resulting from this project
Abstract: The purpose of this proposal is to translate basic, epidemiologic, and technologic discoveries into new strategies to prevent healthcare-associated transmission of Ebola and other high-consequence pathogens. Healthcare-associated transmission of high-consequence pathogens such as the Ebola virus, SARS coronavirus, MERS coronavirus, novel respiratory pathogens, multidrug resistant bacteria, and Clostridium difficile poses a significant risk to patients, families, and healthcare workers (HCWs), leading to morbidity and mortality and dramatically increased healthcare costs. Recent experience with Ebola has shown that current infection prevention strategies may be inadequate to prevent transmission of high-consequence pathogens in healthcare settings. Using our broad experience and expertise and our history of successful collaboration, we propose a multifaceted and innovative framework integrating infection prevention, human factors engineering, and mathematical modeling approaches to address knowledge gaps in current infection prevention methods and to develop new interventions to prevent the transmission of high-consequence pathogens in healthcare settings. We hypothesize that 1) self-contamination of HCW while doffing personal protective equipment (PPE) can be prevented by elucidating risk factors for HCW self-contamination and designing effective risk mitigation strategies to prevent HCW self-contamination events; 2) pathogen transmission from environmental sources can be effectively prevented by elucidating specific risk factors in environmental service (EVS) worker training, communication, culture, and processes and designing effective interventions to optimize environmental cleaning and disinfection; and 3) mathematical modeling will improve understanding of how resistance mechanisms impact transmission routes of carbapenem-resistant Enterobacteriaciae (CRE) in healthcare settings and the effectiveness of contact precautions for the prevention of CRE transmission. We will apply human factors and mathematical modeling (T0) approaches to gather data on risk factors and potential failure modes that contribute to pathogen transmission during HCW PPE doffing, EVS cleaning and disinfection of the environment, and the care of patients who are colonized or infected with CRE. We will then use the identified risk factors and epidemiologic transmission data to develop (T1), implement (T2) and assess new strategies within a large tertiary care hospital and across a health system of hospitals (T3). In addition to our expertise in infection prevention surveillance, intervention trials, and antimicrobial stewardship, in this proposal we demonstrate our capacity to integrate distinct scientific disciplines and introduce novel applications of evidence-based risk mitigation strategies to expand the science of infection prevention. The findings from this proposal will inform CDC guidance for frontline HCWs by providing concise and practical, evidence-based recommendations to prevent pathogen transmission in order to reduce harm to patients and healthcare workers.
MeSH Terms:
  • Carbapenems /chemistry
  • Centers for Disease Control and Prevention, U.S.
  • Communicable Disease Control /*methods
  • Coronavirus Infections /prevention & control
  • Cross Infection /*prevention & control
  • Drug Resistance, Multiple, Bacterial
  • Enterobacteriaceae
  • Evidence-Based Medicine
  • Health Personnel
  • Hemorrhagic Fever, Ebola /prevention & control
  • Humans
  • Infectious Disease Medicine /*methods
  • Models, Theoretical
  • Program Development
  • Protective Clothing
  • Risk
  • Risk Factors
  • Severe Acute Respiratory Syndrome /prevention & control
  • United States
Country: United States
State: Maryland
Zip Code: 21205
UI: 20174233
Project Status: Completed