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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 stud...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130968/ https://www.ncbi.nlm.nih.gov/pubmed/33956787 http://dx.doi.org/10.1371/journal.pcbi.1008941 |
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author | Xia, Hanjue Horn, Johannes Piotrowska, Monika J. Sakowski, Konrad Karch, André Tahir, Hannan Kretzschmar, Mirjam Mikolajczyk, Rafael |
author_facet | Xia, Hanjue Horn, Johannes Piotrowska, Monika J. Sakowski, Konrad Karch, André Tahir, Hannan Kretzschmar, Mirjam Mikolajczyk, Rafael |
author_sort | Xia, Hanjue |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8130968 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81309682021-05-27 Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections Xia, Hanjue Horn, Johannes Piotrowska, Monika J. Sakowski, Konrad Karch, André Tahir, Hannan Kretzschmar, Mirjam Mikolajczyk, Rafael PLoS Comput Biol Research Article 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. Public Library of Science 2021-05-06 /pmc/articles/PMC8130968/ /pubmed/33956787 http://dx.doi.org/10.1371/journal.pcbi.1008941 Text en © 2021 Xia et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Xia, Hanjue Horn, Johannes Piotrowska, Monika J. Sakowski, Konrad Karch, André Tahir, Hannan Kretzschmar, Mirjam Mikolajczyk, Rafael Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections |
title | Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections |
title_full | Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections |
title_fullStr | Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections |
title_full_unstemmed | Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections |
title_short | Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections |
title_sort | effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130968/ https://www.ncbi.nlm.nih.gov/pubmed/33956787 http://dx.doi.org/10.1371/journal.pcbi.1008941 |
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