Hanjue Xia, Johannes Horn, Monika J. Piotrowska, Konrad Sakowski, André Karch, Hannan Tahir, Mirjam Kretzschmar, Rafael Mikolajczyk
Abstract
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.
Introduction
Transfers of patients between hospitals have an important impact on transmission pathways of hospital-acquired infections (HAIs) [1–11]. In recent years, hospital discharge databases based on English, Dutch as well as French national medical registration datasets have been used to construct “healthcare networks” to provide insights into patient transfer management, hospital infection prevention and control [2, 3, 5, 7, 8, 11]. In these networks, nodes represent hospitals and edges between pairs of nodes represent patient transfers between the linked pairs of hospitals. Based on these data, network measures like degree, closeness, and also network density were calculated [4, 5, 8]. The networks were used for simulating the spread of HAIs, evaluating epidemic risk [1–3, 5–7, 9–11], and recommending control strategies [1, 5, 11, 12].
Materials and methods
Data processing
We used anonymized data from hospitalization databases, provided by three different German insurance companies, specific to each region: AOK (historically “general local health insurance company”, but currently only the abbreviation is used) Lower Saxony, AOK Bavaria, and AOK PLUS (merger of AOK Saxony and AOK Thuringia). AOKs are insurance companies, which historically exclusively insured persons from the federal states where they were founded. As a consequence, they have high coverage of the population in their own federal state and low coverage outside, and can thus be used to study regional networks. In each dataset, the following are available: anonymized patient ID, anonymized hospital facility ID, the federal state the hospital is located in, admission and discharge date, main diagnosis (ICD 10 GM code) as well as year of birth and sex of the patient.
Discussion
We demonstrated that while incomplete coverage of the population affected the studied network measures, this did not greatly bias the prevalence as a measure of epidemic spread until the level of incompleteness exceeded 90% (in relation to the original dataset). Scale-up by “cloning” the patients provided little improvement, unless for very high incompleteness levels.
Degree and closeness displayed strong bias of around 25% at incompleteness levels of 50%–60% and nearly 50% at an incompleteness level of 80%. This effect was mainly due to the removal of weaker links, caused by missing patient records with transfers between particular hospitals, which would underestimate the risk of pathogen transmission between those hospitals. In case of the network measure strength, this bias was reduced to 10% or less by adopting the scale-up method based on “cloning” of patients. However, since the scale-up could not impute lost edges, it provided little benefit with respect to the other measures.
Citation: Xia H, Horn J, Piotrowska MJ, Sakowski K, Karch A, Tahir H, et al. (2021) Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections. PLoS Comput Biol 17(5): e1008941. https://doi.org/10.1371/journal.pcbi.1008941
Editor: Benjamin Muir Althouse, Institute for Disease Modeling, UNITED STATES
Received: October 5, 2020; Accepted: April 6, 2021; Published: May 6, 2021.
Copyright: © 2021 Xia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The anonymized insurance data are owned by a third party (AOK Lower Saxony) and authors do not have permission to share them. These data may be requested from: AOK Bavaria, Carl-Wery- Straße 28, 81739 München; https://www.aok.de/pk/bayern/ AOK Lower Saxony: AOK Niedersachsen: Hildesheimer Straße 273, 30519 Hannover; https://niedersachsen.aok.de/ AOK PLUS, Sternplatz 7, 01067 Dresden; https://www.aok.de/pk/plus/.
Funding: This work was supported by grants from the following national funding agencies: Bundesministerium für Bildung und Forschung (BMBF), Germany, 01KI1704C (Martin-Luther-University Halle-Wittenberg, Medical Faculty, Institute of medical epidemiology, biostatistics and informatics) to RM and National Science Centre, Poland, Unisono: 2016/22/Z/ST1/00690 (University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Institute of Applied Mathematics and Mechanics) to MJP and KS and the Netherlands ZonMw grant number 547001005 (Julius Centre, University Medical Centre Utrecht) to MK within the 3rd JPI AMR framework (Joint Programming Initiative on Antimicrobial Resistance) cofound grant no 681055 for the consortium EMerGE-Net (Effectiveness of infection control strategies against intra- and inter-hospital transmission of MultidruG-resistant Enterobacteriaceae). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.