Limits of patient isolation measures to control extended-spectrum beta-lactamase-producing Enterobacteriaceae: model-based analysis of clinical data in a pediatric ward

Abstract

Background: Extended-spectrum beta-lactamase-producing Enterobacteriaceae (ESBL-E) are a growing concern in hospitals and the community. How to control the nosocomial ESBL-E transmission is a matter of debate. Contact isolation of patients has been recommended but evidence supporting it in non-outbreak settings has been inconclusive.

Methods: We used stochastic transmission models to analyze retrospective observational data from a two-phase intervention in a pediatric ward, successively implementing single-room isolation and patient cohorting in an isolation ward, combined with active ESBL-E screening.

Results: For both periods, model estimates suggested reduced transmission from isolated/cohorted patients. However, most of the incidence originated from sporadic sources (i.e. independent of cross-transmission), unaffected by the isolation measures. When sporadic sources are high, our model predicted that even substantial efforts to prevent transmission from carriers would have limited impact on ESBL-E rates.

Conclusions: Our results provide evidence that, considering the importance of sporadic acquisition, e.g. endogenous selection of resistant strains following antibiotic treatment, contact-isolation measures alone might not suffice to control ESBL-E. They also support the view that estimating cross-transmission extent is key to predicting the relative success of contact-isolation measures. Mathematical models could prove useful for those estimations and guide decisions concerning the most effective control strategy.

Figure 1

Model representation. Compartments represent different epidemiological states. Parameters are defined in Table 1. Arrows indicate transitions between states, which occur at a rate given by the parameter. The term Δ(C_*,I) represents an isolation function, such as Δ(C_*,I)=δ_*C_* if I < N_I (at least one isolation bed available), 0 otherwise.

Figure 2

Time-series for point prevalence and incidence data. Point-prevalence numbers of colonized patients (upper panel) and cumulated weekly numbers of acquisitions (lower panel) are represented. The vertical dot-dashed line at week 37 indicates isolation-ward implementation.

Figure 3

Model fit to data. Mean (dashed lines) and 95% prediction intervals (dotted lines) are represented for point-prevalence and weekly incidence data. Point-overlaid continuous lines indicate observed values. The vertical dot-dashed line at week 37 indicates the isolation- ward implementation.

Figure 4

Predicted impact of multifaceted interventions. Contour plot of incidence is represented for different levels of isolation effectiveness (1 − β₂/β₁, x-axis) and sporadic acquisition rate (β₀, y-axis). Increasing incidence values are indicated by ever darker shades of gray, black lines delineate incidence contours with the corresponding threshold value (per 1 000 patient-days). For these simulations, β₁ was set to 0.006 per day, other model parameters were those in P₁ (Table 1).

Figure 5

Impact of screening at admission. Contour plot of incidence is represented for different levels of admission prevalence (σ, x-axis) and isolation rate of imported cases (δ₁, y-axis). Increasing incidence values are indicated by ever darker shades of gray, black lines delineate incidence contours with the corresponding threshold value (per 1 000 patient-days). For these simulations, model parameters values were those estimated or fixed in P₁ (Table 1).