Mean and Variance Modeling of Under-Dispersed and Over-Dispersed Grouped Binary Data

This article describes the R package BinaryEPPM and its use in determining maximum likelihood estimates of the parameters of extended Poisson process models for grouped binary data. These provide a Poisson process family of flexible models that can handle unlimited under-dispersion but limited over-dispersion in such data, with the binomial distribution being a special case. Within BinaryEPPM, models with the mean and variance related to covariates are constructed to match a generalized linear model formulation. Combining such under-dispersed models with standard over-dispersed models such as the beta binomial distribution provides a very general form of residual distribution for modeling grouped binary data. Use of the package is illustrated by application to several data-sets

Epidemiology of dengue in a high-income country: a case study in Queensland, Australia

Background: Australia is one of the few high-income countries where dengue transmission regularly occurs. Dengue is a major health threat in North Queensland (NQ), where the vector Aedes aegypti is present. Whether NQ should be considered as a dengue endemic or epidemic region is an ongoing debate. To help address this issue, we analysed the characteristics of locally-acquired (LA) and imported dengue cases in NQ through time and space. We describe the epidemiology of dengue in NQ from 1995 to 2011, to identify areas to target interventions. We also investigated the timeliness of notification and identified high-risk areas. Methods: Data sets of notified cases and viraemic arrivals from overseas were analysed. We developed a time series based on the LA cases and performed an analysis to capture the relationship between incidence rate and demographic factors. Spatial analysis was used to visualise incidence rates through space and time. Results: Between 1995 and 2011, 93.9% of reported dengue cases were LA, mainly in the 'Cairns and Hinterland' district; 49.7% were males, and the mean age was 38.0 years old. The sources of imported cases (6.1%) were Indonesia (24.6%), Papua New Guinea (23.2%), Thailand (13.4%), East Timor (8.9%) and the Philippines (6.7%), consistent with national data. Travellers importing dengue were predominantly in the age groups 30-34 and 45-49 years old, whereas the age range of patients who acquired dengue locally was larger. The number of LA cases correlated with the number of viraemic importations. Duration of viraemia of public health importance was positively correlated with the delay in notification. Dengue incidence varied over the year and was typically highest in summer and autumn. However, dengue activity has been reported in winter, and a number of outbreaks resulted in transmission year-round. Conclusions: This study emphasizes the importance of delay in notification and consequent duration of viraemia of public health importance for dengue outbreak duration. It also highlights the need for targeted vector control programmes and surveillance of travellers at airports as well as regularly affected local areas. Given the likely increase in dengue transmission with climate change, endemicity in NQ may become a very real possibility

Assessing the threat of chikungunya virus emergence in Australia

Background: Chikungunya virus (CHIKV) is a major threat to Australia given the distribution of competent vectors, and the large number of travellers returning from endemic regions. We describe current knowledge of CHIKV importations into Australia, and quantify reported viraemic cases, with the aim of facilitating the formulation of public health policy and ensuring maintenance of blood safety

Pathogen reduction/inactivation of products for the treatment of bleeding disorders:what are the processes and what should we say to patients?

Patients with blood disorders (including leukaemia, platelet function disorders and coagulation factor deficiencies) or acute bleeding receive blood-derived products, such as red blood cells, platelet concentrates and plasma-derived products. Although the risk of pathogen contamination of blood products has fallen considerably over the past three decades, contamination is still a topic of concern. In order to counsel patients and obtain informed consent before transfusion, physicians are required to keep up to date with current knowledge on residual risk of pathogen transmission and methods of pathogen removal/inactivation. Here, we describe pathogens relevant to transfusion of blood products and discuss contemporary pathogen removal/inactivation procedures, as well as the potential risks associated with these products: the risk of contamination by infectious agents varies according to blood product/region, and there is a fine line between adequate inactivation and functional impairment of the product. The cost implications of implementing pathogen inactivation technology are also considered

Mean and variance modeling of under- and overdispersed count data

This article describes the R package CountsEPPM and its use in determining maximum likelihood estimates of the parameters of extended Poisson process models. These provide a Poisson process based family of flexible models that can handle both underdispersion and overdispersion in observed count data, with the negative binomial and Poisson distributions being special cases. Within CountsEPPM models with mean and variance related to covariates are constructed to match a generalized linear model formulation. Use of the package is illustrated by application to several published datasets

Modeling the Dependence Between the Number of Trials and the Success Probability in Binary Trials

A model for binary trials based on a bivariate generalization of the Poisson process for both the number of successes and number of trials with the transition rates dependent on the accumulating numbers of successes and trials is used to reanalyze some recently published data of Zhu, Eickhoff, and Kaiser (2003, Biometrics59, 955–961). This modeling admits alternative distributions for the numbers of trials and the numbers of successes conditional on the number of trials which generalize the Poisson and binomial distributions, without some of the restrictions apparent in the beta-binomial-Poisson mixed modeling of Zhu et al. (2003). Some quite marked differences between the results of this analysis and those described in Zhu et al. (2003) are apparent