973 resultados para cost prediction


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AIM: To assess the cost-effectiveness of an automated telephone-linked care intervention, Australian TLC Diabetes, delivered over 6 months to patients with established Type 2 diabetes mellitus and high glycated haemoglobin level, compared to usual care. METHODS: A Markov model was designed to synthesize data from a randomized controlled trial of TLC Diabetes (n=120) and other published evidence. The 5-year model consisted of three health states related to glycaemic control: 'sub-optimal' HbA1c ≥58mmol/mol (7.5%); 'average' ≥48-57mmol/mol (6.5-7.4%) and 'optimal' <48mmol/mol (6.5%) and a fourth state 'all-cause death'. Key outcomes of the model include discounted health system costs and quality-adjusted life years (QALYS) using SF-6D utility weights. Univariate and probabilistic sensitivity analyses were undertaken. RESULTS: Annual medication costs for the intervention group were lower than usual care [Intervention: £1076 (95%CI: £947, £1206) versus usual care £1271 (95%CI: £1115, £1428) p=0.052]. The estimated mean cost for intervention group participants over five years, including the intervention cost, was £17,152 versus £17,835 for the usual care group. The corresponding mean QALYs were 3.381 (SD 0.40) for the intervention group and 3.377 (SD 0.41) for the usual care group. Results were sensitive to the model duration, utility values and medication costs. CONCLUSION: The Australian TLC Diabetes intervention was a low-cost investment for individuals with established diabetes and may result in medication cost-savings to the health system. Although QALYs were similar between groups, other benefits arising from the intervention should also be considered when determining the overall value of this strategy.

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This paper examines the impact of allowing for stochastic volatility and jumps (SVJ) in a structural model on corporate credit risk prediction. The results from a simulation study verify the better performance of the SVJ model compared with the commonly used Merton model, and three sources are provided to explain the superiority. The empirical analysis on two real samples further ascertains the importance of recognizing the stochastic volatility and jumps by showing that the SVJ model decreases bias in spread prediction from the Merton model, and better explains the time variation in actual CDS spreads. The improvements are found particularly apparent in small firms or when the market is turbulent such as the recent financial crisis.

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Objectives Commercial sex is licensed in Victoria, Australia such that sex workers are required to have regular tests for sexually transmitted infections (STIs). However, the incidence and prevalence of STIs in sex workers are very low, especially since there is almost universal condom use at work. We aimed to conduct a cost-effectiveness analysis of the financial cost of the testing policy versus the health benefits of averting the transmission of HIV, syphilis, chlamydia and gonorrhoea to clients. Methods We developed a simple mathematical transmission model, informed by conservative parameter estimates from all available data, linked to a cost-effectiveness analysis. Results We estimated that under current testing rates, it costs over $A90 000 in screening costs for every chlamydia infection averted (and $A600 000 in screening costs for each quality-adjusted life year (QALY) saved) and over $A4 000 000 for every HIV infection averted ($A10 000 000 in screening costs for each QALY saved). At an assumed willingness to pay of $A50 000 per QALY gained, HIV testing should not be conducted less than approximately every 40 weeks and chlamydia testing approximately once per year; in comparison, current requirements are testing every 12 weeks for HIV and every 4 weeks for chlamydia. Conclusions Mandatory screening of female sex workers at current testing frequencies is not cost-effective for the prevention of disease in their male clients. The current testing rate required of sex workers in Victoria is excessive. Screening intervals for sex workers should be based on local STI epidemiology and not locked by legislation.

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The measurements of plasma natriuretic peptides (NT-proBNP, proBNP and BNP) are used to diagnose heart failure but these are expensive to produce. We describe a rapid, cheap and facile production of proteins for immunoassays of heart failure. DNA encoding N-terminally His-tagged NT-proBNP and proBNP were cloned into the pJexpress404 vector. ProBNP and NT-proBNP peptides were expressed in Escherichia coli, purified and refolded in vitro. The analytical performance of these peptides were comparable with commercial analytes (NT-proBNP EC50 for the recombinant is 2.6 ng/ml and for the commercial material is 5.3 ng/ml) and the EC50 for recombinant and commercial proBNP, are 3.6 and 5.7 ng/ml respectively). Total yield of purified refolded NT-proBNP peptide was 1.75 mg/l and proBNP was 0.088 mg/l. This approach may also be useful in expressing other protein analytes for immunoassay applications. To develop a cost effective protein expression method in E. coli to obtain high yields of NT-proBNP (1.75 mg/l) and proBNP (0.088 mg/l) peptides for immunoassay use.

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This work deals with estimators for predicting when parametric roll resonance is going to occur in surface vessels. The roll angle of the vessel is modeled as a second-order linear oscillatory system with unknown parameters. Several algorithms are used to estimate the parameters and eigenvalues of the system based on data gathered experimentally on a 1:45 scale model of a tanker. Based on the estimated eigenvalues, the system predicts whether or not parametric roll occurred. A prediction accuracy of 100% is achieved for regular waves, and up to 87.5% for irregular waves.

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In this paper, a method of thrust allocation based on a linearly constrained quadratic cost function capable of handling rotating azimuths is presented. The problem formulation accounts for magnitude and rate constraints on both thruster forces and azimuth angles. The advantage of this formulation is that the solution can be found with a finite number of iterations for each time step. Experiments with a model ship are used to validate the thrust allocation system.

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Complex behaviour of air flow in the buildings makes it difficult to predict. Consequently, architects use common strategies for designing buildings with adequate natural ventilation. However, each climate needs specific strategies and there are not many heuristics for subtropical climate in literature. Furthermore, most of these common strategies are based on low-rise buildings and their performance for high-rise buildings might be different due to the increase of the wind speed with increase in the height. This study uses Computational Fluid Dynamics (CFD) to evaluate these rules of thumb for natural ventilation for multi-residential buildings in subtropical climate. Four design proposals for multi-residential towers with natural ventilation which were produced in intensive two days charrette were evaluated using CFD. The results show that all the buildings reach acceptable level of wind speed in living areas and poor amount of air flow in sleeping areas.

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This study evaluated the energy cost of walking (Cw) with knee flexion contractures (FC) simulated with a knee brace, in total knee arthroplasty (TKA) recipients (n=16) and normal controls (n=15), and compared it to baseline (no brace). There was no significant difference in Cw between the groups at baseline but TKA recipients walked slower (P=0.048) and with greater knee flexion in this condition (P=0.003). Simulated FC significantly increased Cw in both groups (TKA P=0.020, control P=0.002) and this occurred when FC exceeded 20° in the TKA group and 15° in the controls. Reported perceived exertion was only significantly increased by FC in the control group (control P<0.001, TKA P=0.058). Simulated knee FCs less than 20° do not increase Cw or perceived exertion in TKA recipients.

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In this article an alternate sensitivity analysis is proposed for train schedules. It characterises the schedules robustness or lack thereof and provides unique profiles of performance for different sources of delay and for different values of delay. An approach like this is necessary because train schedules are only a prediction of what will actually happen. They can perform poorly with respect to a variety of performance metrics, when deviations and other delays occur, if for instance they can even be implemented, and as originally intended. The information provided by this analytical approach is beneficial because it can be used as part of a proactive scheduling approach to alter a schedule in advance or to identify suitable courses of action for specific “bad behaviour”. Furthermore this information may be used to quantify the cost of delay. The effect of sectional running time (SRT) deviations and additional dwell time in particular were quantified for three railway schedule performance measures. The key features of this approach were demonstrated in a case study.

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Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3–6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Results Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Conclusions Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children.

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This paper uses transaction cost theory to study cloud computing adoption. A model is developed and tested with data from an Australian survey. According to the results, perceived vendor opportunism and perceived legislative uncertainty around cloud computing were significantly associated with perceived cloud computing security risk. There was also a significant negative relationship between perceived cloud computing security risk and the intention to adopt cloud services. This study also reports on adoption rates of cloud computing in terms of applications, as well as the types of services used.

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Background: It is important to identify patients who are at risk of malnutrition upon hospital admission as malnutrition results in poor outcomes such as longer length of hospital stay, readmission, hospitalisation cost and mortality. The aim of this study was to determine the prognostic validity of 3-Minute Nutrition Screening (3-MinNS) in predicting hospital outcomes in patients admitted to an acute tertiary hospital through a list of diagnosis-related groups (DRG). Methods: In this study, 818 adult patients were screened for risk of malnutrition using 3-MinNS within 24 hours of admission. Mortality data was collected from the National Registry with other hospitalisation outcomes retrieved from electronic hospital records. The results were adjusted for age, gender and ethnicity, and matched for DRG. Results: Patients identified to be at risk of malnutrition (37%) using 3-MinNS had significant positive association with longer length of hospital stay (6.6 ± 7.1 days vs. 4.5 ± 5.5 days, p<0.001), higher hospitalisation cost (S$4540 ± 7190 vs. S$3630 ± 4961, p<0.001) and increased mortality rate at 1 year (27.8% vs. 3.9%), 2 years (33.8% vs. 7.2%) and 3 years (39.1% vs. 10.5%); p<0.001 for all. Conclusions: The 3-MinNS is able to predict clinical outcomes and can be used to screen newly admitted patients for nutrition risk so that appropriate nutrition assessment and early nutritional intervention can be initiated.

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Hospitals invest considerable resources organizing operating suites and having surgeons and theatre staff available on an agreed schedule. A common impediment to efficiency is perioperative delay,including delays getting to the operating room or during the operation. Perioperative delays entail significant costs for hospitals,wasting staff time and operating theatre resources. They may also affect patient outcomes; prolonged surgery is a predictor for unanticipated admission following elective ambulatory surgery...

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With a focus to optimising the life cycle performance of Australian Railway bridges, new bridge classification and environmental classification systems are proposed. The new bridge classification system is mainly to facilitate the implementation of novel Bridge Management System (BMS) which optimise the life cycle cost both at project level and network level while environment classification is mainly to improve accuracy of Remaining Service Potential (RSP) module of the proposed BMS. In fact, limited capacity of the existing BMS to trigger the maintenance intervention point is an indirect result of inadequacies of the existing bridge and environmental classification systems. The proposed bridge classification system permits to identify the intervention points based on percentage deterioration of individual elements and maintenance cost, while allowing performance based rating technique to implement for maintenance optimisation and prioritisation. Simultaneously, the proposed environment classification system will enhance the accuracy of prediction of deterioration of steel components.