Biomedical subjects

Clare Marshall

Publications and source records attributed to Clare Marshall.

5 recordsLinked to original sources

PubMed2005

Bayesian population dynamics of interacting species: great gerbils and fleas in Kazakhstan.

We propose a discrete-time Bayesian hierarchical model for the population dynamics of the great gerbil-flea ecological system. The model accounts for the sampling variability arising from data originally collected for other purposes. The prior for the unknown population densities incorporates specific biological hypotheses regarding the interacting dynamics of the two species, as well as their life cycles, where density-dependent effects are included. Posterior estimates are obtained via Markov chain Monte Carlo. The variance of the observed density estimates is a quadratic function of the unknown density. Our study indicates the presence of a density-dependent growth rate for the gerbil population. For the flea population there is clear evidence of density-dependent over-summer net growth, which is dependent on the flea-to-gerbil ratio at the beginning of the reproductive summer. Over-winter net growth is favored by high density. We estimate that on average 35% of the gerbil population survives the winter. Our study shows that hierarchical Bayesian models can be useful in extracting ecobiological information from observational data.

Animals↗

PubMed2004

Bayesian statistics for parasitologists.

Bayesian statistical methods are increasingly being used in the analysis of parasitological data. Here, the basis of differences between the Bayesian method and the classical or frequentist approach to statistical inference is explained. This is illustrated with practical implications of Bayesian analyses using prevalence estimation of strongyloidiasis and onchocerciasis as two relevant examples. The strongyloidiasis example addresses the problem of parasitological diagnosis in the absence of a gold standard, whereas the onchocerciasis case focuses on the identification of villages warranting priority mass ivermectin treatment. The advantages and challenges faced by users of the Bayesian approach are also discussed and the readers pointed to further directions for a more in-depth exploration of the issues raised. We advocate collaboration between parasitologists and Bayesian statisticians as a fruitful and rewarding venture for advancing applied research in parasite epidemiology and the control of parasitic infections.

Animals↗

PubMed2003

Prediction of community prevalence of human onchocerciasis in the Amazonian onchocerciasis focus: Bayesian approach.

OBJECTIVE: To develop a Bayesian hierarchical model for human onchocerciasis with which to explore the factors that influence prevalence of microfilariae in the Amazonian focus of onchocerciasis and predict the probability of any community being at least mesoendemic (>20% prevalence of microfilariae), and thus in need of priority ivermectin treatment. METHODS: Models were developed with data from 732 individuals aged > or =15 years who lived in 29 Yanomami communities along four rivers of the south Venezuelan Orinoco basin. The models' abilities to predict prevalences of microfilariae in communities were compared. The deviance information criterion, Bayesian P-values, and residual values were used to select the best model with an approximate cross-validation procedure. FINDINGS: A three-level model that acknowledged clustering of infection within communities performed best, with host age and sex included at the individual level, a river-dependent altitude effect at the community level, and additional clustering of communities along rivers. This model correctly classified 25/29 (86%) villages with respect to their need for priority ivermectin treatment. CONCLUSION: Bayesian methods are a flexible and useful approach for public health research and control planning. Our model acknowledges the clustering of infection within communities, allows investigation of links between individual- or community-specific characteristics and infection, incorporates additional uncertainty due to missing covariate data, and informs policy decisions by predicting the probability that a new community is at least mesoendemic.

Adolescent↗

PubMed2003

Following Shipman: a pilot system for monitoring mortality rates in primary care.

As part of the investigations into the crimes of Harold Shipman, it has become clear that there is little monitoring of deaths in general practice. By use of data on annual deaths at family physician and practice level for five English health authorities for 1993-99, we investigate whether cumulative sum charts (a type of statistical process control chart) could be used to create a workable monitoring system. On such charts, thresholds for deaths can be set, which, if crossed, may indicate a potential problem. We chose thresholds based on empirical calculations of the probabilities of false and successful detection after allowing for multiple testing over physicians or practices. We also statistically adjusted the charts for extra-Poisson variation due to unmeasured case mix. Of 1009 family physicians, 33 (including Shipman) crossed the alarm threshold designed to detect a 2 SD increase in standardised mortality, with 97% successful detection and a 5% false-alarm rate. Poor data quality, plus factors such as the proportion of patients treated by these physicians in nursing homes or hospices are likely explanations for most of these additional alarms. If used appropriately, such charts represent a useful tool for monitoring deaths in primary care. However, improvement in data quality is essential.

Data Collection↗

PubMed2002

Frequency, severity, and duration of rhinovirus infections in asthmatic and non-asthmatic individuals: a longitudinal cohort study.

BACKGROUND: Rhinovirus infections cause exacerbations of asthma. We postulated that people with asthma are more susceptible to rhinovirus infection than people without the disease and compared the susceptibility of these groups. METHODS: We recruited 76 cohabiting couples. One person in every couple had atopic asthma and one was healthy. Participants completed daily diary cards of upper-respiratory-tract (URT) and lower-respiratory-tract (LRT) symptoms and measured peak expiratory flow twice daily. Every 2 weeks nasal aspirates were taken and examined for rhinovirus. Mixed models were used to compare risks of infection between groups. We also compared the severity and duration of infections. FINDINGS: We analysed 753 samples. Rhinovirus was detected in 10.1% (38/378) of samples from participants with asthma and 8.5% (32/375) of samples from healthy participants. After adjustment for confounding factors, asthma did not significantly increase risk of infection (odds ratio 1.15, 95% CI 0.71-1.87). Groups did not differ in frequency, severity, or duration of URT infections or symptoms associated with rhinovirus infection. First rhinovirus infection was associated more frequently with LRT infection in participants with asthma than in healthy individuals (12 of 28 infections vs four of 23, respectively, p=0.051). Symptoms of LRT associated with rhinovirus infection were significantly more severe (p=0.001) and longer-lasting in participants with asthma than in healthy participants (p=0.005). INTERPRETATION: People with atopic asthma are not at greater risk of rhinovirus infection than healthy individuals but suffer from more frequent LRT infections and have more severe and longer-lasting LRT symptoms.

Adult↗