Importance: Person-to-person contact is important for the transmission of healthcare-associated pathogens. Quantifying these contact patterns is crucial for modeling disease transmission and understanding routes of potential transmission.
Objective: Generate and analyze the mixing matrices of hospital patients based on their contacts within hospital units.
Design, Setting, and Participants: The study was conducted in 24 hospitals in the Southeastern United States that were part of the Duke Antimicrobial Stewardship Outreach Network (DASON) between January 2015 and December 2017. There were a total of 1,569,413 patients and 299 hospital units.
Main Outcome and Measures: The mixing matrices of patients for each hospital unit using age, Elixhauser Score, and a measure of antibiotic exposure.
Results: Mixing matrices were calculated from a database of 24 hospitals, which included 2.9 million admission records for nearly 1.6 million patients. Some units had highly similar patterns across multiple hospitals although the number of patients might vary to a great extent. Within a period of 26 months (October 2015 and December 2017), the highest daily average is 765 patients in the ED of Hospital-12 and lowest daily average is only 2 patients in some of the smaller hospital units. For most of the adult inpatient units, frequent mixing was observed for older adult groups while outpatient units e.g. ED and Behavioral Health etc. units showed mixing between different age groups. From the mixing matrices by Elixhauser Score, we observed mixing between patients with relatively higher comorbidity index on the ICUs. Mixing matrices by Antibiotic Rank, a 4-point scale based on priority for antibiotic stewardship programs, resulted in six major distinct patterns due to the variation of the type of antibiotics used in different units.
Conclusions and Relevance: The mixing patterns of patients both within and between hospitals followed broadly expected patterns, though with a considerable amount of heterogeneity. These patterns can be used to evaluate the appropriateness of policies and guidelines for smaller community hospitals, as well as improve the design of interventions that rely on altering patient contact patterns.