Regional patient transfer patterns matter for the spread of hospital-acquired pathogens.

Pathogens typically responsible for hospital-acquired infections (HAIs) constitute a major threat to healthcare systems worldwide. They spread via hospital (or hospital-community) networks by readmissions or patient transfers. Therefore, knowledge of these networks is essential to develop and test strategies to mitigate and control the HAI spread. Until now, no methods for comparing healthcare networks across different systems were proposed. Based on healthcare insurance data from four German federal states (Bavaria, Lower Saxony, Saxony and Thuringia), we constructed hospital networks and compared them in a systematic approach regarding population, hospital characteristics, and patient transfer patterns. Direct patient transfers between hospitals had only a limited impact on HAI spread. Whereas, with low colonization clearance rates, readmissions to the same hospitals posed the biggest transmission risk of all inter-hospital transfers. We then generated hospital-community networks, in which patients either stay in communities or in hospitals. We found that network characteristics affect the final prevalence and the time to reach it. However, depending on the characteristics of the pathogen (colonization clearance rate and transmission rate or even the relationship between transmission rate in hospitals and in the community), the studied networks performed differently. The differences were not large, but justify further studies.

Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections.

In the year 2020, there were 105 different statutory insurance companies in Germany with heterogeneous regional coverage. Obtaining data from all insurance companies is challenging, so that it is likely that projects will have to rely on data not covering the whole population. Consequently, the study of epidemic spread in hospital referral networks using data-driven models may be biased. We studied this bias using data from three German regional insurance companies covering four federal states: AOK (historically "general local health insurance company", but currently only the abbreviation is used) Lower Saxony (in Federal State of Lower Saxony), AOK Bavaria (in Bavaria), and AOK PLUS (in Thuringia and Saxony). To understand how incomplete data influence network characteristics and related epidemic simulations, we created sampled datasets by randomly dropping a proportion of patients from the full datasets and replacing them with random copies of the remaining patients to obtain scale-up datasets to the original size. For the sampled and scale-up datasets, we calculated several commonly used network measures, and compared them to those derived from the original data. We found that the network measures (degree, strength and closeness) were rather sensitive to incompleteness. Infection prevalence as an outcome from the applied susceptible-infectious-susceptible (SIS) model was fairly robust against incompleteness. At incompleteness levels as high as 90% of the original datasets the prevalence estimation bias was below 5% in scale-up datasets. Consequently, a coverage as low as 10% of the local population of the federal state population was sufficient to maintain the relative bias in prevalence below 10% for a wide range of transmission parameters as encountered in clinical settings. Our findings are reassuring that despite incomplete coverage of the population, German health insurance data can be used to study effects of patient traffic between institutions on the spread of pathogens within healthcare networks.

Relevance of intra-hospital patient movements for the spread of healthcare-associated infections within hospitals - a mathematical modeling study.

The aim of this study is to analyze patient movement patterns between hospital departments to derive the underlying intra-hospital movement network, and to assess if movement patterns differ between patients at high or low risk of colonization. For that purpose, we analyzed patient electronic medical record data from five hospitals to extract information on risk stratification and patient intra-hospital movements. Movement patterns were visualized as networks, and network centrality measures were calculated. Next, using an agent-based model where agents represent patients and intra-hospital patient movements were explicitly modeled, we simulated the spread of multidrug resistant enterobacteriacae (MDR-E) inside a hospital. Risk stratification of patients according to certain ICD-10 codes revealed that length of stay, patient age, and mean number of movements per admission were higher in the high-risk groups. Movement networks in all hospitals displayed a high variability among departments concerning their network centrality and connectedness with a few highly connected departments and many weakly connected peripheral departments. Simulating the spread of a pathogen in one hospital network showed positive correlation between department prevalence and network centrality measures. This study highlights the importance of intra-hospital patient movements and their possible impact on pathogen spread. Targeting interventions to departments of higher (weighted) degree may help to control the spread of MDR-E. Moreover, when the colonization status of patients coming from different departments is unknown, a ranking system based on department centralities may be used to design more effective interventions that mitigate pathogen spread.