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

Autor
Mikolajczyk, Rafael
Sakowski, Konrad
Kretzschmar, Mirjam
Piotrowska, Monika
Tahir, Hannan
Horn, Johannes
Karch, André
Xia, Hanjue
Data publikacji
2021
Abstrakt (EN)

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.

Dyscyplina PBN
matematyka
Czasopismo
PLoS Computational Biology
Tom
17
Zeszyt
5
Strony od-do
e1008941:1-18
ISSN
1553-734X
Data udostępnienia w otwartym dostępie
2021-05-06
Licencja otwartego dostępu
Uznanie autorstwa