Demand for public hospital emergency department services in Australia : 2000–2001 to 2009–2010

Objective: Hospital EDs are a significant and high-profile component of Australia’s health-care system, which in recent years have experienced considerable crowding. This crowding is caused by the combination of increasing demand, throughput and output factors. The aim of the present article is to clarify trends in the use of public ED services across Australia with a view to providing an evidence basis for future policy analysis and discussion. Methods: The data for the present article have been extracted, compiled and analysed from publicly available sources for a 10 year period between 2000–2001 and 2009–2010. Results: Demand for public ED care increased by 37% over the decade, an average annual increase of 1.8% in the utilization rate per 1000 persons. There were significant differences in utilization rates and in trends in growth among states and territories that do not easily relate to general population trends alone. Conclusions: This growth in demand exceeds general population growth, and the variability between states both in utilization rates and overall trends defies immediate explanation. The growth in demand for ED services is a partial contributor to the crowding being experienced in EDs across Australia. There is a need for more detailed study, including qualitative analysis of patient motivations in order to identify the factors driving this growth in demand.

Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data

In this paper we present a sequential Monte Carlo algorithm for Bayesian sequential experimental design applied to generalised non-linear models for discrete data. The approach is computationally convenient in that the information of newly observed data can be incorporated through a simple re-weighting step. We also consider a flexible parametric model for the stimulus-response relationship together with a newly developed hybrid design utility that can produce more robust estimates of the target stimulus in the presence of substantial model and parameter uncertainty. The algorithm is applied to hypothetical clinical trial or bioassay scenarios. In the discussion, potential generalisations of the algorithm are suggested to possibly extend its applicability to a wide variety of scenarios