Analysis of the AOK Plus data and derived hospital network

Lonc, Agata; Piotrowska, Monika Joanna; Sakowski, Konrad

doi:10.14708/ma.v47i1.6497

Cited by 5 publications

(7 citation statements)

References 6 publications

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“…To provide further information about the data, we showed also the distribution of LOS in S2 – S4 Figs, of hospitalizations in S5 Fig , of network weights in S6 Fig , of in- and out-degrees of each hospital in S7 and S8 Figs, and of in- and out-strengths in S9 and S10 Figs. More detailed information about the AOK data can be also accessed in our previous work [ 23 , 24 ].…”

Section: Resultsmentioning

confidence: 99%

“…We also excluded all hospitals located outside the respective federal state as they did not have the same local coverage of the studied population in their catchment area. The datasets contained various types of overlaps, which suggested that a patient stayed in multiple hospitals on a given day [ 23 , 24 ]. Overlaps can happen due to several factors.…”

Section: Methodsmentioning

confidence: 99%

“…This network had N nodes and e directed weighted edges. In this network, nodes represented the hospitals and edges starting from node i towards node j with weight indicating the total number of patient transfers from hospital i to hospital j during the whole time period [ 28 ]. From this static network, we further extracted two different kinds of subnetworks: The first one consisted only of patient transfers from one hospital to another with no stay at home in between; we referred to this as the “direct hospital network”.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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“…In case of the network derived from the provided data, the regularity of matrix A was checked numerically by empirical verification ( A 5 has positive elements only), but this result is dependent on numerical errors. Nevertheless, we can use a lemma providing an analytical argument, for details see [ 14 ] or S1 Appendix . A non-zero pattern of matrix A is presented in Fig 5b .…”

Section: Methodsmentioning

confidence: 99%

Modelling pathogen spread in a healthcare network: Indirect patient movements

Piotrowska

Sakowski

Karch³

et al. 2020

PLoS Comput Biol

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Inter-hospital patient transfers (direct transfers) between healthcare facilities have been shown to contribute to the spread of pathogens in a healthcare network. However, the impact of indirect transfers (patients re-admitted from the community to the same or different hospital) is not well studied. This work aims to study the contribution of indirect transfers to the spread of pathogens in a healthcare network. To address this aim, a hybrid network–deterministic model to simulate the spread of multiresistant pathogens in a healthcare system was developed for the region of Lower Saxony (Germany). The model accounts for both, direct and indirect transfers of patients. Intra-hospital pathogen transmission is governed by a SIS model expressed by a system of ordinary differential equations. Our results show that the proposed model reproduces the basic properties of healthcare-associated pathogen spread. They also show the importance of indirect transfers: restricting the pathogen spread to direct transfers only leads to 4.2% system wide prevalence. However, adding indirect transfers leads to an increase in the overall prevalence by a factor of 4 (18%). In addition, we demonstrated that the final prevalence in the individual healthcare facilities depends on average length of stay in a way described by a non-linear concave function. Moreover, we demonstrate that the network parameters of the model may be derived from administrative admission/discharge records. In particular, they are sufficient to obtain inter-hospital transfer probabilities, and to express the patients’ transfers as a Markov process. Using the proposed model, we show that indirect transfers of patients are equally or even more important as direct transfers for the spread of pathogens in a healthcare network.

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“…Figure 6: (a) In-degree and out-degree for each hospital node of the network (self-loops not included); (b) out-degree for each community node of the network (self-loops not included), in-degree for all community nodes are equal to 1. the regularity of matrix A was checked numerically by empirical verification (A 5 has positive elements only), but this result is dependent on numerical errors. Nevertheless, we can use a lemma providing an analytical argument, for details see [14] or Appendix 5. A non-zero pattern of matrix A is presented in Figure 5.…”

Section: 4)mentioning

confidence: 99%

Modelling pathogen spread in a healthcare network: indirect patient movements

Piotrowska,

Sakowski,

Karch

et al. 2020

Preprint

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A hybrid network-deterministic model for simulation of multiresistant pathogen spread in a healthcare system is presented. The model accounts for two paths of pathogen transmission between the healthcare facilities: inter-hospital patient transfers (direct transfers) and readmission of colonized patients (indirect transfers). In the latter case, the patients may be readmitted to the same facility or to a different one. Intra-hospital pathogen transmission is governed by a SIS model expressed by a system of ordinary differential equations.Using a network model created for a Lower Saxony region (Germany), we showed that the proposed model reproduces the basic properties of healthcare-associated pathogen spread. Moreover, it shows the important contribution of the readmission of colonized patients on the prevalence of individual hospitals as well as of overall healthcare system: it can increase the overall prevalence by the factor of 4 as compared to inter-hospital transfers only. The final prevalence in individual healthcare facilities was shown to depend on average length of stay by a non-linear concave function.Finally, we demonstrated that the network parameters of the model may be derived from administrative admission/discharge records. In particular, they are sufficient to obtain interhospital transfer probabilities, and to express the patients' transfer as a Markov process.

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Analysis of the AOK Plus data and derived hospital network

Cited by 5 publications

References 6 publications

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

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

Modelling pathogen spread in a healthcare network: Indirect patient movements

Modelling pathogen spread in a healthcare network: indirect patient movements

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