Sediment concentration prediction and statistical evaluation for annual load estimation

We consider the development of statistical models for prediction of constituent concentration of riverine pollutants, which is a key step in load estimation from frequent flow rate data and less frequently collected concentration data. We consider how to capture the impacts of past flow patterns via the average discounted flow (ADF) which discounts the past flux based on the time lapsed - more recent fluxes are given more weight. However, the effectiveness of ADF depends critically on the choice of the discount factor which reflects the unknown environmental cumulating process of the concentration compounds. We propose to choose the discount factor by maximizing the adjusted R-2 values or the Nash-Sutcliffe model efficiency coefficient. The R2 values are also adjusted to take account of the number of parameters in the model fit. The resulting optimal discount factor can be interpreted as a measure of constituent exhaustion rate during flood events. To evaluate the performance of the proposed regression estimators, we examine two different sampling scenarios by resampling fortnightly and opportunistically from two real daily datasets, which come from two United States Geological Survey (USGS) gaging stations located in Des Plaines River and Illinois River basin. The generalized rating-curve approach produces biased estimates of the total sediment loads by -30% to 83%, whereas the new approaches produce relatively much lower biases, ranging from -24% to 35%. This substantial improvement in the estimates of the total load is due to the fact that predictability of concentration is greatly improved by the additional predictors.

Sequential allocation in clinical trials

Suppose two treatments with binary responses are available for patients with some disease and that each patient will receive one of the two treatments. In this paper we consider the interests of patients both within and outside a trial using a Bayesian bandit approach and conclude that equal allocation is not appropriate for either group of patients. It is suggested that Gittins indices should be used (using an approach called dynamic discounting by choosing the discount rate based on the number of future patients in the trial) if the disease is rare, and the least failures rule if the disease is common. Some analytical and simulation results are provided.

The effect of ground leases on house prices in Helsinki

One imperfection in housing markets is imperfect knowledge about legal interests such as ground leases. Both actual reduced legal interest as well as uncertainty surrounding rights and future lease payments for houses constructed on leased land may affect prices relative to houses built on freehold land. We use regression analysis of sales prices of condominium transactions in Helsinki to examine the effect ground leases have on house prices. We find that prices on condominiums constructed on leased lots are discounted at least 5 %, on average. In addition, we see that the announcement of potentially large increases in base rents upon renewal contributes to the discount.

A Lidl bit of grocery competition could cause a Coles rethink

Australian shoppers have inadvertently invited global discount grocers to our shores by demonstrating their readiness to adopt private labels. In 2001, German discounter Aldi opened its first store in Sydney. The impact this business format would have on the Australian grocery sector was underestimated.

Move over Aldi, Lidl may be next for Australian market

If speculation that German discount supermarket, Lidl, is preparing to launch into the Australian market is correct, it will be the biggest shake up in the grocery sector since Aldi’s arrival in 2001. With potentially five viable combatants in the mix, the way we shop and how supermarkets and suppliers compete, will fundamentally change.

Making decisions about antibiotic use in the Australian primary healthcare sector

Background Australia has one of the highest rates of antibiotic use amongst OECD countries. Data from the Australian primary healthcare sector suggests unnecessary antibiotics were prescribed for self-resolving conditions. We need to better understand what drives general practitioners (GPs) to prescribe antibiotics, consumers to seek antibiotics, and pharmacists to fill repeat antibiotic prescriptions. It is also not clear how these individuals trade-off between the possible benefits that antibiotics may provide in the immediate/short term, against the longer term societal risk of antimicrobial resistance. This project investigates what factors drive decisions to use antibiotics for GPs, pharmacists and consumers, and how these individuals discount the future. Methods Factors will be gleaned from published literature and from semi-structured interviews, to inform the development of Discrete Choice Experiments (DCEs). Three DCEs will be constructed – one for each group of interest – to allow investigation of which factors are more important in influencing (a) GPs to prescribe antibiotics, (b) consumers to seek antibiotics, and (c) pharmacists to fill legally valid but old or repeat prescriptions of antibiotics. Regression analysis will be conducted to understand the relative importance of these factors. A Time Trade Off exercise will be developed to investigate how these individuals discount the future. Results Findings from the DCEs will provide an insight into which factors are more important in driving decision making in antibiotic use for GPs, pharmacists and consumers. Findings from the Time Trade Off exercise will show what individuals are willing to trade for preserving the miracle of antibiotics. Conclusion Research findings will contribute to existing national programs to bring about a reduction in inappropriate use of antibiotic in Australia. Specifically, influencing how key messages and public health campaigns are crafted, and clinical education and empowerment of GPs and pharmacists to play a more responsive role as stewards of antibiotic use in the community.