Use of Contact Networks to Estimate Potential Pathogen Risk Exposure in Hospitals.

Importance: Person-to-person contact is important for the transmission of health care-associated pathogens. Quantifying these contact patterns is crucial for modeling disease transmission and understanding routes of potential transmission. Objective: To generate and analyze the mixing matrices of hospital patients based on their contacts within hospital units. Design, Setting, and Participants: In this quality improvement study, mixing matrices were created using a weighted contact network of connected hospital patients, in which contact was defined as occupying the same hospital unit for 1 day. Participants included hospitalized patients at 299 hospital units in 24 hospitals in the Southeastern United States that were part of the Duke Antimicrobial Stewardship Outreach Network between January 2015 and December 2017. Analysis was conducted between October 2021 and February 2022. Main Outcomes and Measures: The mixing matrices of patients for each hospital unit were assessed using age, Elixhauser Score, and a measure of antibiotic exposure. Results: Among 1 549 413 hospitalized patients (median [IQR] age, 44 [26-63] years; 883 580 [56.3%] women) in 299 hospital units, some units had highly similar patterns across multiple hospitals, although the number of patients varied to a great extent. For most of the adult inpatient units, frequent mixing was observed for older adult groups, while outpatient units (eg, emergency departments and behavioral health units) showed mixing between different age groups. Most units mixing patterns followed the marginal distribution of age; however, patients aged 90 years or older with longer lengths of stay created a secondary peak in some medical wards. From the mixing matrices by Elixhauser Score, mixing between patients with relatively higher comorbidity index was observed in intensive care units. Mixing matrices by antibiotic spectrum, a 4-point scale based on priority for antibiotic stewardship programs, resulted in 6 major distinct patterns owing to the variation of the type of antibiotics used in different units, namely those dominated by a single antibiotic spectrum (narrow, broad, or extended), 1 pattern spanning all antibiotic spectrum types and 2 forms of narrow- and extended-spectrum dominant exposure patterns (an emergency room where patients were exposed to one type of antibiotic or the other and a pediatric ward where patients were exposed to both types). Conclusions and Relevance: This quality improvement study found that the mixing patterns of patients both within and between hospitals followed broadly expected patterns, although with a considerable amount of heterogeneity. These patterns could be used to inform mathematical models of health care-associated infections, assess the appropriateness of both models and policies for smaller community hospitals, and provide baseline information for the design of interventions that rely on altering patient contact patterns, such as practices for transferring patients within hospitals.