Identifying asymptomatic spreaders of antimicrobial-resistant pathogens in hospital settings

• Published in: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Vol. 118; no. 37; pp. 1 - 8
• Main Authors: Pei, Sen, Liljeros, Fredrik, Shaman, Jeffrey
• Format: Journal Article Publication
• Language: English
• Published: United States National Academy of Sciences 14.09.2021
• Subjects: Anti-Infective Agents - pharmacology; Antiinfectives and antibacterials; antimicrobial resistance; Asymptomatic; asymptomatic colonization; Biological Sciences; Carrier State - diagnosis; Carrier State - drug therapy; Carrier State - epidemiology; Carrier State - microbiology; Colonization; Contact tracing; Cross Infection - diagnosis; Cross Infection - drug therapy; Cross Infection - epidemiology; Cross Infection - microbiology; Disease Outbreaks - prevention & control; Drug resistance; Health care; Health care facilities; healthcare associated infections; Hospitals; Hospitals - standards; Humans; infection control; mathematical model; Methicillin; Methicillin-Resistant Staphylococcus aureus - physiology; Patients; Physical Sciences; Public health; Silica; Silicon dioxide; Spreaders; Staphylococcal Infections - diagnosis; Staphylococcal Infections - drug therapy; Staphylococcal Infections - epidemiology; Staphylococcal Infections - microbiology; Staphylococcus aureus; Staphylococcus infections; Sweden - epidemiology; Sweden; mathematical model; asymptomatic colonization; infection control; antimicrobial resistance; healthcare-associated infections
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2111190118

Antimicrobial-resistant organisms (AMROs) can colonize people without symptoms for long periods of time, during which these agents can spread unnoticed to other patients in healthcare systems. The accurate identification of asymptomatic spreaders of AMRO in hospital settings is essential for supporting the design of interventions against healthcare-associated infections (HAIs). However, this task remains challenging because of limited observations of colonization and the complicated transmission dynamics occurring within hospitals and the broader community. Here, we study the transmission of methicillin-resistant Staphylococcus aureus (MRSA), a prevalent AMRO, in 66 Swedish hospitals and healthcare facilities with inpatients using a data-driven, agent-based model informed by deidentified real-world hospitalization records. Combining the transmission model, patient-to-patient contact networks, and sparse observations of colonization, we develop and validate an individual-level inference approach that estimates the colonization probability of individual hospitalized patients. For both model-simulated and historical outbreaks, the proposed method supports the more accurate identification of asymptomatic MRSA carriers than other traditional approaches. In addition, in silica control experiments indicate that interventions targeted to inpatients with a high-colonization probability outperform heuristic strategies informed by hospitalization history and contact tracing.