The National Mental Health Strategy and Geographical Access to Mental Health Services: Some empirical results

In 1992 the Australian Government adopted the National Mental Health Strategy in an attempt to improve the provision of mental health services. A component was to improve geographical access to hospital-based mental health services. This paper is concerned with determining if this objective has been achieved. Time-series data on patients (at a regional level) with mental illness in the State of Queensland are available for the years from 1968-69 to 2002-03. A change in regional classification by the Australian Bureau of Statistics complicates the analysis by precluding certain empirical tests such as converging utilisation rates by region. To overcome this problem, it was decided to apply concepts of concentration and equality that are commonly employed in industrial economics to the regional data. The empirical results show no evidence of improving regional access following the National Mental Health Strategy: in fact the statistical results show the opposite, i.e. declining regional access.

Ecoregion classification using a bayesian approach and centre-focused clusters

Ecological regions are increasingly used as a spatial unit for planning and environmental management. It is important to define these regions in a scientifically defensible way to justify any decisions made on the basis that they are representative of broad environmental assets. The paper describes a methodology and tool to identify cohesive bioregions. The methodology applies an elicitation process to obtain geographical descriptions for bioregions, each of these is transformed into a Normal density estimate on environmental variables within that region. This prior information is balanced with data classification of environmental datasets using a Bayesian statistical modelling approach to objectively map ecological regions. The method is called model-based clustering as it fits a Normal mixture model to the clusters associated with regions, and it addresses issues of uncertainty in environmental datasets due to overlapping clusters.