How to assess the relative importance of different colonization routes of pathogens within hospital settings

Abstract

The emergence of antibiotic resistance among nosocomial pathogens has reemphasized the need for effective infection control strategies. The spread of resistant pathogens within hospital settings proceeds along various routes of transmission and is characterized by large fluctuations in prevalence, which are typical for small populations. Identification of the most important route of colonization (exogenous by cross-transmission or endogenous caused by the selective pressure of antibiotics) is important for the design of optimal infection control strategies. Such identification can be based on a combination of epidemiological surveillance and costly and laborious as well as time-consuming methods of genotyping. Furthermore, analysis of the effects of interventions is hampered by the natural fluctuations in prevalence. To overcome these problems, we introduce a mathematical algorithm based on a Markov chain description. The input is longitudinal prevalence data only. The output is estimates of the key parameters characterizing the two colonization routes. The algorithm is tested on two longitudinal surveillance data sets of intensive care patients. The quality of the estimates is determined by comparing them to accurate estimates based on additional information obtained by genotyping. The results warrant optimism that this algorithm may help to quantify transmission dynamics and can be used to evaluate the effects of infection control interventions more carefully.

Persistent colonization and the spread of antibiotic resistance in nosocomial pathogens: Resistance is a regional problem

“…These carriers increase colonization pressure; the number of patients who are shedding increases the risk that another patient becomes a carrier for ARB or acquires a resistant infection (7). Hospitals that reduce the incidence of resistance (the number of new cases) may see no reduction in overall prevalence (the fraction of patients with ARB), because these hospitals admit an increasing number of ARB carriers (8,9). Patients infected with ARB generally remain hospitalized until the symptoms are cured, but they may continue to carry and shed ARB for months or years.…”

On methods for studying stochastic disease dynamics

“…Second, it circumvents the necessity to choose between density-and frequency-dependent transmission (Begon et al 2002). Finally, a constant population size is reasonable for many applied problems, such as modelling the transmission of infection upon farms or within hospital wards, where there is generally little variation in the number of individuals in the population (Pelupessy et al 2002;Cooper & Lipsitch 2004;Viet & Medley 2006). It should be noted that while assuming a constant population size does not change the ODEs (3.1) (if, as is typical, we assume BZd ), however, it does change the Kolmogorov forward equations dp S;I dt Z½bðS C1ÞðI K1C3Þ p SC1;IK1 C½gðI C1Þ p S;I C1 C½dðN KðSK1ÞKI Þ p SK1;I C½dðI C1Þ p SK1;I C1 K½bSðI C3ÞCgI CdðN KSKI ÞCdI p S;I ; ð3:3Þ…”

“…Finally, the matrix formulation also allows exact evaluation of the likelihood for a sequence of data, and thus provides a framework for parameter estimation (Pelupessy et al 2002;Cooper & Lipsitch 2004;Ross et al 2006). Suppose there is a parameter (or set of parameters) q, we wish to estimate from some given data.…”

Modelling population processes with random initial conditions

“…We explain how their approach can be extended to allow initial state uncertainty to be incorporated in general population processes where a deterministic model is sufficient to describe the dynamics of the population, thus allowing one to model variability in dynamics, at any point in time, due solely to uncertainty in the initial state. For one-dimensional processes, such as those used for modelling sexually transmitted infections [2] and hospital-acquired infections [37], and a range of single-species population models, we demonstrate how this can be effected without the need to exhibit the trajectories explicitly. We illustrate our results with reference to several population models.…”