Hospital in the Home in NSW, CHERE Project Report No 16

Hospital in the home, NSW

Rheological characterization of mixed liquor in a submerged membrane bioreactor: Interest for process management

Rheological analyses of a submerged membrane bioreactor mixed liquor were performed in the aim of characterizing the mixed liquor present in the bioreactor and thus proposing a process management. These analyses pointed out that the mixed liquor was characterized by its viscoplastic property, which leads to a possible restructuring ability when a shear stress lower than the yield stress is applied. As the shear stress in the bioreactor is essentially generated by coarse bubbles, specific experiments were carried out in which coarse bubbles were injected in an intermittent way. The results of these experiments showed that this method could avoid damage to the mixed liquor. So working with intermittent coarse bubbles is useful to prevent fouling, keep good flocculation and minimize the energy cost

Forecasting stock index volatility - a comparison of models

This thesis explores the useof popularmachine learning algorithms(K-Nearest NeighborandRandom Forest)and compares them to traditional techniques (Random Walk, ARIMAand GARCH) for forecastingone-day, one-week, one-monthand one-quarter volatilityusingThe OsloStock ExchangeAll Share Index. A number of error metrics are applied(RMSE, MAE, MAPE and R-squared)in order to compare their results.Machine learning methods are shown to forecast thechanges in volatilityto some extent, however, evidence isfound favouringtheARIMAmodel when forecastingvolatility time series

When is an ounce of prevention worth a pound of cure: The case of cardiovascular disease?

Objective: To provide decision makers with a tool to inform resource allocation decisions at the local level, using cardiovascular disease prevention as an example. Method: Evidence from the international literature was extrapolated to estimate the health and financial impacts in Central Sydney Area Health Service (CSAHS) of three different prevention programs; smoking cessation; blood pressure reduction and cholesterol lowering. The cost-effectiveness analysis framework was reconfigured to 1) estimate the risk of CVD in the community using local risk factor data, 2) estimate the number of CVD events prevented through investment in preventive programs and 3) estimate the local financial flow-on effects of prevention on acute care services. The model developed here estimates an upper bound of what local decision makers could spend on preventive programs whilst remaining consistent with their willingness to pay for one additional life-year gained. Results: The model predicted that over a five-year period the cumulative impact of the three programs has the potential to save 1245 life-years in people aged 40-79 years living in CSAHS. If decision-makers are willing to invest in cost-saving preventive programs only, the model estimates that they can spend up $12 per person in the target group per year. However, if they are willing to spend$70,000 per life-year gained, this amount rises to $201. Conclusions: Modelling the impact of preventive activities on the acute care health system enables us to estimate the amount that can be spent on preventive programs. The model is flexible in terms of its ability to examine these impacts in a variety of settings and therefore has the potential to be a useful resource planning tool.Resource allocation, cardiovascular disease, health promotion, Australia

Aeration mode, shear stress and sludge rheology in a submerged membrane bioreactor: some keys of energy saving

Aeration mode, shear stress and sludge rheology in a submerged membrane bioreactor: some keys of energy savin

Catastrophic insurance: Impact of the Australian Medicare Safety Net on fees, service use and out-of-pocket costs, CHERE Working Paper 2006/9

Objectives: The Medicare Safety Net Policy was introduced in March 2004 to provide financial relief for those Australians who face high out-of-pocket (OOP) costs for outpatient medical services. This study evaluates the extent to which out-of-pocket costs have fallen since the introduction of the Safety Net and examines the impact of the policy on the level of service use, the amount of benefits paid by government and fees charged by medical providers. Methods: Regression modelling of time series data was used to examine whether there have been significant changes in levels of service use, fees charged and benefits paid for services provided by specialists in the two-year period following the introduction of the Safety Net. Four speciality fields were examined in this analysis: general specialists? consultations, obstetrics, pathology and diagnostic imaging. Results: The analysis indicates that the introduction of the Safety Net coincided with a substantial rise in public funding for Medicare services and a much smaller reduction in OOP costs. The policy has coincided with a small but significant change in the number of pathology and diagnostic imaging services used and in some specialty areas a substantial increase in the fees charged by providers. The net impact shows that for specialists? consultations every dollar spent on the Medicare Safety Net, $0.68 went towards higher fees and$0.32 went towards reducing OOP costs. The corresponding figures for diagnostic imaging were $0.74 and$0.26 respectively. Conclusions: The Safety Net was heralded by the government as a fundamental reform in Australia?s Medicare program. Whilst the Safety Net was introduced to help reduce out-of-pocket medical costs, this analysis shows that in its first two years of operation, there has been significant leakage of public funding towards higher provider fees. More research is needed using longer term data to assess the impact of the policy on patient and provider behaviour more widely, including examining the policy?s impact on those who did qualify for Safety Net and those who did not, as well as more disaggregated analysis of different Medicare services.Out-of-pocket costs; moral hazard; catastrophic insurance; health care financing; Australia