Table 1.
Descriptive data of participating hospitals.
Table 2.
A patient having any of the mentioned ICD-10 codes was considered as a high-risk patient.
Table 3.
Descriptive statistics for data included in the analysis for participating hospitals.
Fig 1.
(A) Box plots showing patient age distribution for each hospital and risk group, (B) Box plots for admission LOS distribution for each hospital and risk group. In A and B, purple lines in the boxes show mean value whereas the grey line in the boxes show median of the data. (C) Proportion of admissions versus LOS on log-linear scale for each risk group in every hospital. To better visualize the trends between the risk groups at smaller LOS, proportion of admissions with LOS over 100 days are not shown in C. There are however, few data points above 100 days LOS (see S1 Numerical Data for complete data).
Fig 2.
(A) Mean number of movements per day normalized by hospital size, number of departments and the number of admissions per year in each risk group for every hospital. (B) Mean number of movements per hospital admission for each risk group and for each hospital. Error bars show standard deviation. (C) Proportion of admissions versus number of movements in each risk group for every hospital on log-linear scale. It is worth noting here that major proportion of admissions in each hospital has zero movements (UMCU (Low-risk 86.62%, High-risk 87.06%), HUVM (Low-risk 89.05%, High-risk 83.79%), CUM (Low-risk 64.81%, High-risk 64.43%), BH (Low-risk 96.6%, High-risk 93.63%), and UKH (Low-risk 76.45%, High-risk 66.01%)). (D) Mean number of movements versus admission LOS for each risk group and every hospital. To better visualize the trends between the risk groups at smaller LOS, data over 100 days LOS are not shown in D. There are however, few data points above 100 days LOS (see S2 Numerical Data for complete data).
Fig 3.
Intra-hospital hospital networks of HUVM showing patient directed movements from one department to another.
Nodes represent departments and arrows represent patient movements between these departments. Color of the nodes was based on nodes degree whereas size of the nodes was based on nodes weighted degree. Arrows thickness is based on the directed number of transfers (weights) between the departments and color of an arrow is assigned similar to the node color from where the arrow is originating. For visualization of the clustering, a clustering layout of the complete HUVM network is also shown in the supplementary S1 Fig.
Table 4.
Intra-hospital hospital networks statistics for each risk group.
Complete data correspond to unstratified data including low-risk and high-risk groups.
Fig 4.
(A) Patient movements network generated from the ABM simulation, (B) Actual HUVM patient movement network without stratification of patients into low-risk and high-risk. Nodes represent departments and arrows represent patient movements between these departments. Color of the nodes is based on nodes degree and dark orange color refers to low values of degree whereas purple color refers to high degree. Size of the nodes is based on nodes weighted degree.
Fig 5.
(A) Distribution of daily departments’ prevalence of MDR-E for a period of 395 days for scenario 1 where a single colonized patient was admitted to the ICU on day 30. (B) Distribution of steady state daily departments’ prevalence of MDR-E (from 200 days onwards) for scenario 2, 3 & 4 which include 1%, 5% and 15% daily arrivals of colonized patients respectively from day 30 onwards. Yellow lines represent the mean prevalence per department for every scenario. For all results shown in Fig 5, transmission parameter β = 0.25 was used for all departments. Departments are ordered by size with internal medicine department being the largest department. Results shown in Fig 5 are based on 50 simulations for every scenario.
Fig 6.
Spearman’s rank correlation coefficient results between averaged network statistics from 50 simulations and departments’ prevalence of MDR-E.
(A) Correlation between departments’ degree and departments’ mean prevalence. (B) Correlation between departments’ weighted degree and departments’ mean prevalence. In all scenarios, different transmission parameter β values were tested. For Scenario 1, mean prevalence over a period of 395 days was used. For Scenario 2–4, steady state mean prevalence from 200 days onwards was used. Joining datapoints with lines was only done to improve readability but it does not show a functional relationship as x-axis is a categorical axis. For both A and B, errorbars represent 95% confidence intervals.
Table 5.
Top three departments of each participating hospital with simulated high mean prevalence based on network characteristics with respect to a hospital-wide MDR-E spread in the absence of interventions.
Fig 7.
Impact of interventions on transmissions percent reductions for different β values.
Error bars represent 95% confidence intervals.