Attaching unit costs to Australia's National Survey of Mental Health and Wellbeing

Background: In mental health, policy-makers and planners are increasingly being asked to set priorities. This means that health economists, health services researchers and clinical investigators are being called upon to work together to define and measure costs. Typically, these researchers take available service utilisation data and convert them to costs, using a range of assumptions. There are inefficiencies, as individual groups of researchers frequently repeat essentially similar exercises in achieving this end. There are clearly areas where shared or common investment in the development of statistical software syntax, analytical frameworks and other resources could maximise the use of data. Aims of the Study: This paper reports on an Australian project in which we calculated unit costs for mental health admissions and community encounters. In reporting on these calculations, our purpose is to make the data and the resources associated with them publicly available to researchers interested in conducting economic analyses, and allow them to copy, distribute and modify them, providing that all copies and modifications are available under the same terms and conditions (i.e., in accordance with the 'Copyleft' principle), Within this context, the objectives of the paper are to: (i) introduce the 'Copyleft' principle; (ii) provide an overview of the methodology we employed to derive the unit costs; (iii) present the unit costs themselves; and (iv) examine the total and mean costs for a range of single and comorbid conditions, as an example of the kind of question that the unit cost data can be used to address. Method: We took relevant data from the Australian National Survey of Mental Health and Wellbeing (NSMHWB), and developed a set of unit costs for inpatient and community encounters. We then examined total and mean costs for a range of single and comorbid conditions. Results: We present the unit costs for mental health admissions and mental health community contacts. Our example, which explored the association between comorbidity and total and mean costs, suggested that comorbidly occurring conditions cost more than conditions which occur on their own. Discussion: Our unit costs, and the materials associated with them, have been published in a freely available form governed by a provision termed 'Copyleft'. They provide a valuable resource for researchers wanting to explore economic questions in mental health. Implications for Health Policies: Our unit costs provide an important resource to inform economic debate in mental health in Australia, particularly in the area of priority-setting. In the past, such debate has largely, been based on opinion. Our unit costs provide the underpinning to strengthen the evidence-base of this debate. Implications for Further Research: We would encourage other Australian researchers to make use of our unit costs in order to foster comparability across studies. We would also encourage Australian and international researchers to adopt the 'Copyleft' principle in equivalent circumstances. Furthermore, we suggest that the provision of 'Copyleft'-contingent funding to support the development of enabling resources for researchers should be considered in the planning of future large-scale collaborative survey work, both in Australia and overseas.

Construction of an Intraplate Fault System Model of South Australia, and Simulation Tool for the iSERVO Institute Seed Project

To foster ongoing international cooperation beyond ACES (APEC Cooperation for Earthquake Simulation) on the simulation of solid earth phenomena, agreement was reached to work towards establishment of a frontier international research institute for simulating the solid earth: iSERVO = International Solid Earth Research Virtual Observatory institute (http://www.iservo.edu.au). This paper outlines a key Australian contribution towards the iSERVO institute seed project, this is the construction of: (1) a typical intraplate fault system model using practical fault system data of South Australia (i.e., SA interacting fault model), which includes data management and editing, geometrical modeling and mesh generation; and (2) a finite-element based software tool, which is built on our long-term and ongoing effort to develop the R-minimum strategy based finite-element computational algorithm and software tool for modelling three-dimensional nonlinear frictional contact behavior between multiple deformable bodies with the arbitrarily-shaped contact element strategy. A numerical simulation of the SA fault system is carried out using this software tool to demonstrate its capability and our efforts towards seeding the iSERVO Institute.

A national survey of telehealth activity in Australian hospitals

We conducted a national survey of Australian hospitals to assess their use of telehealth. Information was sought from the 814 hospitals with 10 or more beds, excluding the small number that provided only day surgery and seven for which we could not identify a contact person. A total of 564 replies were received (a 69% response rate). Nationally, nearly half (49%) reported that they were engaged in some telehealth activity. However, there was a significant difference across jurisdictions. Hospitals in the public sector were significantly more likely to report the use of telehealth than those in the private sector (62% vs 14%). Hospital remoteness was measured according to the Accessibility/Remoteness Index of Australia (ARIA). The highest levels of use were reported by hospitals in 'very remote' and 'remote' areas (90% and 88%, respectively), with moderate levels of use in 'moderately accessible' and 'accessible' areas (67% and 52%, respectively) and the lowest level of use in 'highly accessible' areas (35%). This trend was significant.

Personality disorders in the community: Results from the Australian National Survey of Mental Health and Well-Being Part III - Relationships between specific type of personality disorder, Axis 1 mental disorders and physical conditions with disability and health consultations

Background The aims of this study were threefold. First, to ascertain whether personality disorder (PD) was a significant predictor of disability (as measured in a variety of ways) over and above that contributed by Axis I mental disorders and physical conditions. Second, whether the number of PD diagnoses given to an individual resulted in increasing severity of disability, and third, whether PD was a significant predictor of health and mental health consultations with GPs, psychiatrists, and psychologists, respectively, over the last 12 months. Method Data were obtained from the National Survey of Mental Health and Wellbeing, conducted between May and August 1997. A stratified random sample of households was generated, from which all those aged 18 and over were considered potential interviewees. There were 10 641 respondents to the survey, and this represented a response rate of 78%. Each interviewee was asked questions indexing specific ICD-10 PD criteria. Results Five measures of disability were examined. It was found that PD was a significant predictor of disability once Axis I and physical conditions were taken into account for four of the five disability measures. For three of the dichotomously-scored disability measures, odds ratios ranged from 1.88 to 6.32 for PD, whilst for the dimensionally-scored Mental Summary Subscale of the SF-12, a beta weight of -0.17 was recorded for PD. As regards number of PDs having a quasi-linear relationship to disability, there was some indication of this on the SF-12 Mental Summary Subscale and the two role functioning measures, and less so on the other two measures. As regards mental consultations, PD was a predictor of visits to GPs, psychiatrists and psychologists, over and above Axis I disorders and physical conditions. Conclusion The study reports findings from a nationwide survey conducted within Australia and as such the data are less influenced by the selection and setting bias inherent in other germane studies. However, it does support previous findings that PD is a significant predictor of disability and mental health consultations independent of Axis I disorders and physical conditions.