Hospitalisation dynamics.

Simulations of the hospitalisation evolution (solid lines) are compared to data from Fig 1 (diamonds). Simulations were performed with the new hospitalisation simulations from Fig 5F as input, and with hospital parameters extracted from the clinical data in Fig 6). The simulations after February 2021 show the hospitalisation progression in a scenario where the social restrictions from January 2021 would have persisted throughout 2021 without the impact of new viral strains and vaccinations. (A-D) Evolution of patients hospitalised (A), in ICU (B), deceased in ICU (C), and recovered patients that were released from hospitals (D). The simulated recovered are the sum of patients released from hospital compartments j = 3 and j = 5. (E) Age group dependent Infection Fatality Ratio (IFR) computed by dividing the cumulated deceased from (C) with the cumulated new infections from Fig 5A. The dashed lines indicate the analytic values 0.0005%, 0.019%, 0.24%, 0.81%, and 2.62% computed with the probabilities for asymptomatic to become symptomatic, for symptomatic to become hospitalised, to be transferred to ICU, and to die in ICU. For the mean IFR we find 0.2%.</p

Checkpoint events where we changed the social contact matrix.

Events that had a confining effect are marked with bold font, deconfining events with italic font. The simulation period starts at 01/01/2020.</p

Cost-Effectiveness Analysis of a Transparent Antimicrobial Dressing for Managing Central Venous and Arterial Catheters in Intensive Care Units

<div><p>Objective</p><p>To model the cost-effectiveness impact of routine use of an antimicrobial chlorhexidine gluconate-containing securement dressing compared to non-antimicrobial transparent dressings for the protection of central vascular lines in intensive care unit patients.</p><p>Design</p><p>This study uses a novel health economic model to estimate the cost-effectiveness of using the chlorhexidine gluconate dressing versus transparent dressings in a French intensive care unit scenario. The 30-day time non-homogeneous markovian model comprises eight health states. The probabilities of events derive from a multicentre (12 French intensive care units) randomized controlled trial. 1,000 Monte Carlo simulations of 1,000 patients per dressing strategy are used for probabilistic sensitivity analysis and 95% confidence intervals calculations. The outcome is the number of catheter-related bloodstream infections avoided. Costs of intensive care unit stay are based on a recent French multicentre study and the cost-effectiveness criterion is the cost per catheter-related bloodstream infections avoided. The incremental net monetary benefit per patient is also estimated.</p><p>Patients</p><p>1000 patients per group simulated based on the source randomized controlled trial involving 1,879 adults expected to require intravascular catheterization for 48 hours.</p><p>Intervention</p><p>Chlorhexidine Gluconate-containing securement dressing compared to non-antimicrobial transparent dressings.</p><p>Results</p><p>The chlorhexidine gluconate dressing prevents 11.8 infections /1,000 patients (95% confidence interval: [3.85; 19.64]) with a number needed to treat of 85 patients. The mean cost difference per patient of €141 is not statistically significant (95% confidence interval: [€-975; €1,258]). The incremental cost-effectiveness ratio is of €12,046 per catheter-related bloodstream infection prevented, and the incremental net monetary benefit per patient is of €344.88.</p><p>Conclusions</p><p>According to the base case scenario, the chlorhexidine gluconate dressing is more cost-effective than the reference dressing.</p><p>Trial Registration</p><p>This model is based on the data from the RCT registered with <a href="http://www.clinicaltrials.gov" target="_blank">www.clinicaltrials.gov</a> (<a href="https://clinicaltrials.gov/ct2/show/NCT01189682" target="_blank">NCT01189682</a>).</p></div

Occurrences per 1,000 patients as generated through 1,000 NH-MCMC of 1,000 patients in each dressing group, according to the base case scenario.

<p>CHG, Chlorhexidine Gluconate; CI, Confidence Interval; CRBSI, Catheter-related bloodstream infection; CT, Catheter; ICU, Intensive Care Unit; NH-MCMC, Non-Homogeneous Markov Chain Monte Carlo.</p><p>Occurrences per 1,000 patients as generated through 1,000 NH-MCMC of 1,000 patients in each dressing group, according to the base case scenario.</p

Health states defined from a multicentre randomized controlled trial [13].

<p>* New CT needed can mean either the replacement of the existing catheter, or the need for an additional catheter at a new site.</p><p>CRBSI, Catheter-related Bloodstream Infections; CT, Catheter (Central venous or radial / femoral arterial).</p><p>Health states defined from a multicentre randomized controlled trial [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130439#pone.0130439.ref013" target="_blank">13</a>].</p

Structure of the Markov Model showing the possible transition between health states from one Markov cycle to the next cycle.

<p>The costs per patient for each health state were calculated in both CHG and No-CHG dressing as respectively: State 1: €1,270 and €1,266; State 2: €1,364 and €1,361; State 3: €13,661 and €13,658; State 4: €13,756 and €13,752; State 5: €1,388 and €1,385; State 6: €1,266 and €1,266; State 7: €0 for both groups; State 8: €0 for both groups; CHG: chlorhexidine gluconate; CRBSI: catheter-related bloodstream infection; CT: catheter.</p

Cost-effectiveness results for the probabilistic sensitivity analysis.

<p>The analysis uses 1,000 non-homogeneous Markov-Chain Monte Carlo simulations of 1,000 patients for each dressing strategy. The x axis represents the difference in effectiveness (number of CRBSI events in CHG versus non CHG dressing) and the y axis represents the difference in cost (mean cost per patient with CHG versus non CHG dressing) in €2013. The (0,0)-point indicates the reference dressing strategy (Non-CHG group). Each point in the graph represents the Incremental Cost-Effectiveness Ratio (ICER) of CHG-dressing strategy versus reference dressing. All but three points are at the left side of the graph, showing that CHG dressing strategy was 99.7% more effective than the comparator at the same costs per patient. The squared point in the center of the cloud represents the average CE ratio of all 1,000 simulations. CHG: chlorhexidine gluconate; CRBSI: catheter-related bloodstream infection.</p

Mean Cost for one patient in each dressing group.

<p>Time Horizon: 30-days ICU—1,000 NH-MCMC simulations of 1,000 patients (€2013).</p><p>CHG: Chlorhexidine Gluconate; CI: Confidence Interval; ICU: Intensive Care Unit; NH-MCMC: Non-Homogeneous Markov-Chain Monte Carlo simulation</p><p>Mean Cost for one patient in each dressing group.</p

Cost-effectiveness results from observed data (CLEAN database)–Observed global patient—ICU-Time Horizon: 100 days.

<p>Cost-effectiveness results from observed data (CLEAN database)–Observed global patient—ICU-Time Horizon: 100 days.</p

Non-homogeneous Semi-Markov Chain simulation from observed data (CLEAN)–simulated global patient—ICU-time horizon: 100 days.

<p>Non-homogeneous Semi-Markov Chain simulation from observed data (CLEAN)–simulated global patient—ICU-time horizon: 100 days.</p