Hospitalisation with Infection, Asthma and Allergy in Kawasaki Disease Patients and Their Families: Genealogical Analysis Using Linked Population Data

Background: Kawasaki disease results from an abnormal immunological response to one or more infectious triggers. We hypothesised that heritable differences in immune responses in Kawasaki disease-affected children and their families would result in different epidemiological patterns of other immune-related conditions. We investigated whether hospitalisation for infection and asthma/allergy were different in Kawasaki disease-affected children and their relatives. Methods/Major Findings: We used Western Australian population-linked health data from live births (1970-2006) to compare patterns of hospital admissions in Kawasaki disease cases, age- and sex-matched controls, and their relatives. There were 295 Kawasaki disease cases and 598 age- and sex-matched controls, with 1,636 and 3,780 relatives, respectively. Compared to controls, cases were more likely to have been admitted at least once with an infection (cases, 150 admissions (50.8%) vs controls, 210 admissions (35.1%); odds ratio (OR) = 1.9, 95% confidence interval (CI) 1.4-2.6, P = 7.2×10-6), and with asthma/allergy (cases, 49 admissions (16.6%) vs controls, 42 admissions (7.0%); OR = 2.6, 95% CI 1.7-4.2, P = 1.3×10-5). Cases also had more admissions per person with infection (cases, median 2 admissions, 95% CI 1-5, vs controls, median 1 admission, 95% CI 1-4, P = 1.09×10-5). The risk of admission with infection was higher in the first degree relatives of Kawasaki disease cases compared to those of controls, but the differences were not significant. Conclusion: Differences in the immune phenotype of children who develop Kawasaki disease may influence the severity of other immune-related conditions, with some similar patterns observed in relatives. These data suggest the influence of shared heritable factors in these families

A statistical model for ordinal categorical family data

© 2011 Dr. Sophie Georgina ZaloumisThe R scripts to fit models are contained in the attached zip file 'R Scripts to fit models'My thesis presents a novel statistical model to investigate: 1. the contribution of genes and environment to the variation in an ordinal phenotype; and 2. the impact measured genetic and/or environmental exposures have at each stage of the trait. Genetic epidemiological methods can be used to determine the extent to which genes influence the variation in outcomes measured in families - they do not require measured genetic markers, but ordinal trait data collected from families. The model I constructed to accomplish the aims of this thesis is a threshold model with latent data assumed to follow a multivariate logistic distribution and threshold parameters that are allowed to be dependent on measured exposures. This model provides: 1. a straightforward way of modelling the differing degrees of correlation between family members that is analogous to specifying a multivariate normal model for family data; and 2. a way of examining whether the effect of covariates depends on the category of an ordinal response that avoids the model fitting difficulties encountered with standard approaches

Sequential infection experiments for quantifying innate and adaptive immunity during influenza infection.

Laboratory models are often used to understand the interaction of related pathogens via host immunity. For example, recent experiments where ferrets were exposed to two influenza strains within a short period of time have shown how the effects of cross-immunity vary with the time between exposures and the specific strains used. On the other hand, studies of the workings of different arms of the immune response, and their relative importance, typically use experiments involving a single infection. However, inferring the relative importance of different immune components from this type of data is challenging. Using simulations and mathematical modelling, here we investigate whether the sequential infection experiment design can be used not only to determine immune components contributing to cross-protection, but also to gain insight into the immune response during a single infection. We show that virological data from sequential infection experiments can be used to accurately extract the timing and extent of cross-protection. Moreover, the broad immune components responsible for such cross-protection can be determined. Such data can also be used to infer the timing and strength of some immune components in controlling a primary infection, even in the absence of serological data. By contrast, single infection data cannot be used to reliably recover this information. Hence, sequential infection data enhances our understanding of the mechanisms underlying the control and resolution of infection, and generates new insight into how previous exposure influences the time course of a subsequent infection

Malaria parasite clearance: what are we really measuring?

Antimalarial drugs are vital for treating malaria and controlling transmission. Measuring drug efficacy in the field requires large clinical trials and thus we have identified proxy measures of drug efficacy such as the parasite clearance curve. This is often assumed to measure the rate of drug activity against parasites and is used to predict optimal treatment regimens required to completely clear a blood-stage infection. We discuss evidence that the clearance curve is not measuring the rate of drug killing. This has major implications for how we assess optimal treatment regimens, as well as how we prioritise new drugs in the drug development pipeline