Who Is Hospitalized With Whom? Inpatient Contact Networks and Mixing Patterns

ImportancePerson-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. ObjectiveGenerate and analyze the mixing matrices of hospital patients based on their contacts within hospital units. Design, Setting, and ParticipantsThe 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 MeasuresThe mixing matrices of patients for each hospital unit using age, Elixhauser Score, and a measure of antibiotic exposure. ResultsMixing 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 RelevanceThe 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. Key PointsO_ST_ABSQuestionC_ST_ABSWhat are the mixing patterns among hospitalized patients who could be susceptible to infection? FindingIn this study of 299 hospital units from 24 hospitals, we analyzed the mixing patterns between patients based on a number of variables namely Age, Antibiotic Ranks (4-point scale based on priority for antibiotic stewardships programs) and Elixhauser Comorbidity Score. While some units showed highly similar patterns across hospitals, variation has also been observed in concentration of mixing on different age groups and antibiotic usage among the patients who are coming into contact. MeaningHow patients mix, can impact their risk of acquiring an infection. While patterns followed what was expected heuristically, there is considerable between-hospital heterogeneity, which can help in risk assessments and modeling approaches.