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Biomedical subjects

A N Pettitt

Publications and source records attributed to A N Pettitt.

13 recordsLinked to original sources
PubMed2006

A stochastic mathematical model of methicillin resistant Staphylococcus aureus transmission in an intensive care unit: predicting the impact of interventions.

OBJECTIVES: To estimate the transmission rate of MRSA in an intensive care unit (ICU) in an 800 bed Australian teaching hospital and predict the impact of infection control interventions. METHODS: A mathematical model was developed which consisted of four compartments: colonised and uncolonised patients and contaminated and uncontaminated health-care workers (HCWs). Patient movements, MRSA acquisition and daily prevalence data were collected from an ICU over 939 days. Hand hygiene compliance and the probability of MRSA transmission from patient to HCW per discordant contact were measured during the study. Attack rate and reproduction ratio were estimated using Bayesian methods. The impact of a number of interventions on attack rate was estimated using both stochastic and deterministic versions of the model. RESULTS: The mean number of secondary cases arising from the ICU admission of colonised patients, also called the ward reproduction ratio, R(w), was estimated to be 0.50 (95% CI 0.39-0.62). The attack rate was one MRSA transmission per 160 (95% CI 130-210) uncolonised-patient days. Results were not sensitive to uncertainty in measured model parameters (hand hygiene rate and transmission probability per contact). Hand hygiene was predicted to be the most effective intervention. Decolonisation was predicted to be relatively ineffective. Increasing HCW numbers was predicted to increase MRSA transmission, in the absence of patient cohorting. The predictions of the stochastic model differed from those of the deterministic model, with lower levels of colonisation predicted by the stochastic model. CONCLUSIONS: The number of secondary cases of MRSA colonisation within the ICU in this study was below unity. Transmission of MRSA was sustained through admission of colonised patients. Stochastic model simulations give more realistic predictions in hospital ward settings than deterministic models. Increasing staff does not necessarily lead to reduced transmission of nosocomial pathogens.

Bayes Theorem↗
PubMed2006

Bayesian inference of hospital-acquired infectious diseases and control measures given imperfect surveillance data.

This paper describes a stochastic epidemic model developed to infer transmission rates of asymptomatic communicable pathogens within a hospital ward. Inference is complicated by partial observation of the epidemic process and dependencies within the data. The epidemic process of nosocomial communicable pathogens can be partially observed by routine swabs testing for the presence of the pathogen. False-negative swab results must be accounted for and make it difficult to ascertain the number of patients who were colonized. Reversible jump Markov chain Monte Carlo methods are used within a Bayesian framework to make inferences about the colonization rates and unknown colonization times. The methods are applied to routinely collected data concerning methicillin-resistant Staphylococcus Aureus in an intensive care unit to estimate the effectiveness of isolation on reducing transmission of the bacterium.

Bayes Theorem↗
PubMed2006

Bayesian modelling of an epidemic of severe acute respiratory syndrome.

This paper analyses data arising from a SARS epidemic in Shanxi province of China involving a total of 354 people infected with SARS-CoV between late February and late May 2003. Using Bayesian inference, we have estimated critical epidemiological determinants. The estimated mean incubation period was 5.3 days (95% CI 4.2-6.8 days), mean time to hospitalisation was 3.5 days (95% CI 2.8-3.6 days), mean time from symptom onset to recovery was 26 days (95% CI 25-27 days) and mean time from symptom onset to death was 21 days (95% CI 16-26 days). The reproduction ratio was estimated to be 4.8 (95% CI 2.2-8.8) in the early part of the epidemic (February and March 2003) reducing to 0.75 (95% CI 0.65-0.85) in the later part of the epidemic (April and May 2003). The infectivity of symptomatic SARS cases in hospital and in the community was estimated. Community SARS cases caused transmission to others at an estimated rate of 0.4 per infective per day during the early part of the epidemic, reducing to 0.2 in the later part of the epidemic. For hospitalised patients, the daily infectivity was approximately 0.15 early in the epidemic, but fell to 0.0006 in the later part of the epidemic. Despite the lower daily infectivity level for hospitalised patients, the long duration of the hospitalisation led to a greater number of transmissions within hospitals compared with the community in the early part of the epidemic, as estimated by this study. This study investigated the individual infectivity profile during the symptomatic period, with an estimated peak infectivity on the ninth symptomatic day.

Bayes Theorem↗
PubMed2006

The stimulus-response curve and motor unit variability in normal subjects and subjects with amyotrophic lateral sclerosis.

The behavior and stability of motor units (MUs) in response to electrical stimulation of different intensities can be assessed with the stimulus-response curve, which is a graphical representation of the size of the compound muscle action potential (CMAP) in relation to stimulus intensity. To examine MU characteristics across the whole stimulus range, the variability of CMAP responses to electrical stimulation, and the differences that occur between normal and disease states, the curve was studied in 11 normal subjects and 16 subjects with amyotrophic lateral sclerosis (ALS). In normal subjects, the curve showed a gradual increase in CMAP size with increasing stimulus intensity, although one or two discrete steps were sometimes observed in the upper half of the curve, indicating the activation of large MUs at higher intensities. In ALS subjects, large discrete steps, due to loss of MUs and collateral sprouting, were frequently present. Variability of the CMAP responses was greater than baseline variability, indicating variability of MU responses, and at certain levels this variability was up to 100 microVms. The stimulus-response curve shows differences between normal and ALS subjects and provides information on MU activation and variability throughout the curve.

Action Potentials↗
PubMed2004

Smoothing a discrete hazard function for the number of patients colonized with Methicillin-resistant Staphylococcus Aureus in an intensive care unit.

A new method for estimating the time to colonization of Methicillin-resistant Staphylococcus Aureus (MRSA) patients is developed in this paper. The time to colonization of MRSA is modelled using a Bayesian smoothing approach for the hazard function. There are two prior models discussed in this paper: the first difference prior and the second difference prior. The second difference prior model gives smoother estimates of the hazard functions and, when applied to data from an intensive care unit (ICU), clearly shows increasing hazard up to day 13, then a decreasing hazard. The results clearly demonstrate that the hazard is not constant and provide a useful quantification of the effect of length of stay on the risk of MRSA colonization which provides useful insight.

Bayes Theorem↗
PubMed2004

Use of a quantitative gene expression assay based on micro-array techniques and a mathematical model for the investigation of chlamydial generation time.

Chlamydia is an important pathogen which possesses a unique developmental cycle. We used real-time PCR technology to measure gene transcript levels in Chlamydia trachomatis strain L2. By measuring 16S rRNA transcript levels, and developing a mathematical model of the chlamydial developmental cycle fitting the data, we predict an average generation time of approximately 2.6 h. Additionally, potentially this modelling also provides the foundation for the application of emerging micro-array technology in which identification of the gene signals that trigger a chlamydial body to start replicating or transform to its infectious form can be made possible.

Chlamydia trachomatis↗
PubMed2003

'Online' monitoring and retrospective analysis of hospital outcomes based on a scan statistic.

Statistical tests based on the scan statistic are introduced for detecting possible increases in the occurrence of hospital events. The tests use a moving window and the theoretical aspects of the tests are investigated using Markov chain theory. The main objective of this study is to provide a statistical technique to assist hospital staff in deciding whether the variation they observe is greater than usually expected under random variation. In this paper we develop the test for Poisson data and apply the theory to monitor the occurrence of orthopaedic wound infection and Methicillin-resistant Staphylo- coccus aureus colonization. We find that this method is sensitive in detecting the change in the process parameter which may not be detected by standard control chart methods. Both online and retrospective analyses are considered.

Australia↗
PubMed2001

Bayesian analysis of a time series of counts with covariates: an application to the control of an infectious disease.

This paper presents a Bayesian analysis of a time series of counts to assess its dependence on an explanatory variable. The time series represented is the incidence of the infectious disease ESBL-producing Klebsiella pneumoniae in an Australian hospital and the explanatory variable is the number of grams of antibiotic (third generation) cephalosporin used during that time. We demonstrate that there is a statistically significant relationship between disease occurrence and use of the antibiotic, lagged by three months. The model used is a parameter-driven model in the form of a generalized linear mixed model. Comparison of models is made in terms of mean square error.

Journal Article↗
PubMed1984

Sleep deprivation and the physiological response to exercise under steady-state conditions in untrained subjects.

Seven physically untrained subjects underwent 72 h total sleep deprivation, followed a baseline day. Daily, at 0400 and 1600 h, subjects pedalled on a bicycle ergometer under individually set work loads of 40, 60, and 80% VO2max. This was not a study oriented towards endurance but towards capacity, requiring steady-state measurement. From assessments of heart rate, VO2 and VCO2 were calculated: VO2max, gross mechanical efficiency, VO2 at a heart rate of 150, and respiratory quotient. To assess possible training effects, a control group underwent identical procedures except that they slept at night and had the morning measure delayed until 0830 h. A series of statistical models were applied to the data, which centered on quantifying the inherent underlying variability, to estimate the level any main effect had to reach to become significant. the analysis showed that the noise level was small enough for any real effect of importance to have been detected, with a reasonably large probability. No statistically significant effects were found for any of the parameters with respect to conditions, days, and time. The main significant outcome was with mechanical efficiency, which displayed greater variability during sleep deprivation. Both groups displayed similar trends in training effects. It was concluded that the physiological ability to do work of the type and duration used here was not adversely affected by 72 h of sleep loss.

Adult↗
PubMed1984

Fitting a sinusoid to biological rhythm data using ranks.

Biological data are frequently collected with small numbers of observations from several subjects. Often a linear model is fitted to the data by averaging over subjects. Frequently there are considerable between-subject differences, and due allowance should be made for these when the model is fitted by parametric techniques. In this paper it is proposed that the model should be fitted by using the within-subject ranks of the observations. The technique is applied to the problem of estimating the 'cosinor' diagram for biological rhythm data. An example involving melatonin levels, in which there are considerable between-subject differences, is considered. The results obtained by use of the ranks are compared with those of a parametric analysis which makes allowance for the between-subject differences. The results are very similar, but the rank analysis requires much less computation.

Biometry↗
PubMed1983

A Bayesian analysis of capillary heterogeneity in the intact pig liver.

Published experimental data on the steady elimination of galactose by five isolated perfused pig livers are interpreted in terms of a model of hepatic uptake, in which the functional properties of the individual liver capillaries (hepatic sinusoids) are not identical. Kinetic parameters, including the Michaelis constant for the local enzyme-substrate interaction, are determined for each preparation on the basis of this model and are compared with previously obtained values based on an earlier model in which the sinusoids were assumed to be functionally identical. Posterior distributions of the degree of functional heterogeneity and the value of the Michaelis constant are given for the case where the livers are considered as a statistically homogeneous group. The degree of functional heterogeneity of the capillaries is found to be consistent with previous independent estimates.

Animals↗