Modelling the effect of antimicrobial treatment on carriage of hospital pathogens with application to MRSA.

Numerous studies have sought to assess the effectiveness of control measures aimed at reducing the spread of pathogens such as Methicillin-resistant Staphylococcus aureus (MRSA) in hospital settings. Far less is known about possible short-term effects of antibiotics and other antimicrobial treatments on pathogen carriage in patients. This paper is concerned with developing and applying methods for the analysis of detailed data on hospital patients which include information on patient treatments and screening tests for the pathogen in question. The carriage status (colonized, or not) of each patient is modelled as a Markov chain, and models for both perfect and imperfect test sensitivity are developed. Goodness-of-fit procedures based on simulation are also proposed. The methods are illustrated using both simulated data and data on MRSA.

Modelling and inference for epidemic models featuring non-linear infection pressure.

We consider a Susceptible-Infective-Removed (SIR) stochastic epidemic model in which the infection rate is of the form ßN⁻¹X(t)Y(t)(α). It is demonstrated that both the threshold behaviour of this model and the behaviour of the corresponding deterministic model differ markedly from the standard SIR model (i.e. α=1). Methods of statistical inference for this model are described, given outbreak data, and the extent to which all three model parameters can be estimated is considered.

Effect of systemic antibiotics and topical chlorhexidine on meticillin-resistant Staphylococcus aureus carriage in intensive care unit patients.

Antibiotics and antiseptics have the potential to influence carriage and transmission of meticillin-resistant Staphylococcus aureus (MRSA), although effects are likely to be complex, particularly in a setting where multiple agents are used. Here admission and weekly MRSA screens and daily antibiotic and antiseptic prescribing data from 544 MRSA carriers on an intensive care unit (ICU) are used to determine the effect of these agents on short-term within-host MRSA carriage dynamics. Longitudinal data were analysed using Markov models allowing patients to move between two states: MRSA positive (detectable MRSA carriage) and MRSA negative (no detectable carriage). The effect of concurrent systemic antibiotic and topical chlorhexidine (CHX) on movement between these states was assessed. CHX targeted to MRSA screen carriage sites increased transition from culture positive to negative and there was also weaker evidence that it decreased subsequent transition from negative back to positive. In contrast, there was only weak and inconsistent evidence that any antibiotic influenced transition in either direction. For example, whereas univariate analysis found quinolones to be strongly associated with both increased risk of losing and then reacquiring MRSA carriage over time intervals of one day, no effect was seen with weekly models. Similar studies are required to determine the generalisability of these findings.

Quantifying uncertainty associated with microbial count data: a Bayesian approach.

We consider the problem of estimating bacterial concentration in a substance, given microbial count data. A Bayesian approach is proposed which naturally allows the incorporation of both plate-count data and extra information from confirmatory tests such as genotyping by polymerase chain reaction (PCR). The estimation methods yield posterior credible regions for bacterial concentration, in contrast to the previous methods, which generally only produce point estimates. The approach is illustrated with specific reference to the enumeration of the food-borne pathogen Escherichia coli O157 by spiral plating, although the methodology can be applied to any bacterium or counting method of interest. The results obtained provide guidance to the experimenter as to the number of confirmatory tests which should be performed, and also suggest that in the initial plate count one should err on the side of including rather than excluding colonies whose genotype seems unclear.

Bayesian methods for estimating pathogen prevalence within groups of animals from faecal-pat sampling.

Pathogens such as Escherichia coli O157:H7 and Campylobacter spp. have been implicated in outbreaks of food poisoning in the UK and elsewhere. Domestic animals and wildlife are important reservoirs for both of these agents, and cross-contamination from faeces is believed to be responsible for many human outbreaks. Appropriate parameterisation of quantitative microbial-risk models requires representative data at all levels of the food chain. Our focus in this paper is on the early stages of the food chain-specifically, sampling issues which arise at the farm level. We estimated animal-pathogen prevalence from faecal-pat samples using a Bayesian method which reflected the uncertainties inherent in the animal-level prevalence estimates. (Note that prevalence here refers to the percentage of animals shedding the bacteria of interest). The method offers more flexibility than traditional, classical approaches: it allows the incorporation of prior belief, and permits the computation of a variety of distributional and numerical summaries, analogues of which often are not available through a classical framework. The Bayesian technique is illustrated with a number of examples reflecting the effects of a diversity of assumptions about the underlying processes. The technique appears to be both robust and flexible, and is useful when defecation rates in infected and uninfected groups are unequal, where population size is uncertain, and also where the microbiological-test sensitivity is imperfect. We also investigated the determination of the sample size necessary for determining animal-level prevalence from pat samples to within a pre-specified degree of accuracy.

Inference for an epidemic when susceptibility varies.

A stochastic epidemic model featuring fixed-length latent periods, gamma-distributed infectious periods and randomly varying heterogeneity among susceptibles is considered. A Markov chain Monte Carlo algorithm is developed for performing Bayesian inference for the parameters governing the infectious-period length and the hyper-parameters governing the heterogeneity of susceptibility. This method of analysis applies to a wider class of diseases than methods proposed previously. An application to smallpox data confirms results about heterogeneity suggested by an earlier analysis that relied on less realistic assumptions.

On a branching model of division-within-division.

We consider a deterministic version of a stochastic model for division-within-division processes described by Kimmel (1997, In: Proceedings of the IMA Workshop 'Classical and Modern Branching Processes' (K. Arthreya and P. Jagers, eds.)???? :????). It is shown that the behaviour of the deterministic model can be analyzed by using an associated Markov chain, using the methods of Barbour et al.

Anterior crossbite and mobile lower central incisors in a 7-year-old patient: a case report.

Anterior crossbite, a permanent upper central or lateral incisor occluding lingually to an opposing tooth, can have serious dental health implications if left untreated. It usually becomes evident in the early mixed dentition, and all general dental practitioners should be aware of the condition and know how to manage it in order to prevent the possibly serious consequences.