Short term variability in urinary bisphenol A in Australian children

Used frequently in food contact materials, bisphenol A (BPA) has been studied extensively in recent years, and ubiquitous exposure in the general population has been demonstrated worldwide. Characterising within- and between-individual variability of BPA concentrations is important for characterising exposure in biomonitoring studies, and this has been investigated previously in adults, but not in children. The aim of this study was to characterise the short-term variability of BPA in spot urine samples in young children. Children aged ≥2-<4 years (n = 25) were recruited from an existing cohort in Queensland Australia, and donated four spot urine samples each over a two day period. Samples were analysed for total BPA using isotope dilution online solid phase extraction-liquid chromatography-tandem mass spectrometry, and concentrations ranged from 0.53–74.5 ng/ml, with geometric mean and standard deviation of 2.70 ng/ml and 2.94 ng/ml, respectively. Sex and time of sample collection were not significant predictors of BPA concentration. The between-individual variability was approximately equal to the within-individual variability (ICC = 0.51), and this ICC is somewhat higher than previously reported literature values. This may be the result of physiological or behavioural differences between children and adults or of the relatively short exposure window assessed. Using a bootstrapping methodology, a single sample resulted in correct tertile classification approximately 70% of the time. This study suggests that single spot samples obtained from young children provide a reliable characterization of absolute and relative exposure over the short time window studied, but this may not hold true over longer timeframes.

Geometric sensitivity of patient-specific finite element models of the spine to variability in user-selected anatomical landmarks

Software to create individualised finite element (FE) models of the osseoligamentous spine using pre-operative computed tomography (CT) data-sets for spinal surgery patients has recently been developed. This study presents a geometric sensitivity analysis of this software to assess the effect of intra-observer variability in user-selected anatomical landmarks. User-selected landmarks on the osseous anatomy were defined from CT data-sets for three scoliosis patients and these landmarks were used to reconstruct patient-specific anatomy of the spine and ribcage using parametric descriptions. The intra-observer errors in landmark co-ordinates for these anatomical landmarks were calculated. FE models of the spine and ribcage were created using the reconstructed anatomy for each patient and these models were analysed for a loadcase simulating clinical flexibility assessment. The intra-observer error in the anatomical measurements was low in comparison to the initial dimensions, with the exception of the angular measurements for disc wedge and zygapophyseal joint (z-joint) orientation and disc height. This variability suggested that CT resolution may influence such angular measurements, particularly for small anatomical features, such as the z-joints, and may also affect disc height. The results of the FE analysis showed low variation in the model predictions for spinal curvature with the mean intra-observer variability substantially less than the accepted error in clinical measurement. These findings demonstrate that intra-observer variability in landmark point selection has minimal effect on the subsequent FE predictions for a clinical loadcase.

Public transport travel time variability definitions and monitoring

Public Transport Travel Time Variability (PTTV) is essential for understanding the deteriorations in the reliability of travel time, optimizing transit schedules and route choices. This paper establishes the key definitions of PTTV in which firstly include all buses, and secondly include only a single service from a bus route. The paper then analyzes the day-to-day distribution of public transport travel time by using Transit Signal Priority data. A comprehensive approach, using both parametric bootstrapping Kolmogorov-Smirnov test and Bayesian Information Creation technique is developed, recommends Lognormal distribution as the best descriptor of bus travel time on urban corridors. The probability density function of Lognormal distribution is finally used for calculating probability indicators of PTTV. The findings of this study are useful for both traffic managers and statisticians for planning and analyzing the transit systems.

Temperature variability and childhood pneumonia : an ecological study

Background Few data on the relationship between temperature variability and childhood pneumonia are available. This study attempted to fill this knowledge gap. Methods A quasi-Poisson generalized linear regression model combined with a distributed lag nonlinear model was used to quantify the impacts of diurnal temperature range (DTR) and temperature change between two neighbouring days (TCN) on emergency department visits (EDVs) for childhood pneumonia in Brisbane, from 2001 to 2010, after controlling for possible confounders. Results An adverse impact of TCN on EDVs for childhood pneumonia was observed, and the magnitude of this impact increased from the first five years (2001–2005) to the second five years (2006–2010). Children aged 5–14 years, female children and Indigenous children were particularly vulnerable to TCN impact. However, there was no significant association between DTR and EDVs for childhood pneumonia. Conclusions As climate change progresses, the days with unstable weather pattern are likely to increase. Parents and caregivers of children should be aware of the high risk of pneumonia posed by big TCN and take precautionary measures to protect children, especially those with a history of respiratory diseases, from climate impacts.

Seasonal amplitude of hemorrhagic fever with renal syndrome in China : a call for attention to neglected regions

Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne viral disease characterized by fever, hemorrhagic, kidney damage and hypotension, is caused by different species of hantaviruses [1]. Every year, HFRS affects thousands of people in Asia, and more than 90% of these cases are reported in China [2, 3]. Due to its high fatality, HFRS has attracted considerable research attention, and prior studies have predominantly focused on quantifying HFRS morbidity [4], identifying high risk areas [5] and populations [6], or exploring peak time of HFRS occurrence [3]. To date, no study has assessed the seasonal amplitude of HFRS in China, even though it reveals the seasonal fluctuation and thus may provide pivotal information on the possibility of HFRS outbreaks.

Outbreak detection algorithms for seasonal disease data : a case study using Ross River virus disease

Background Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens. Methods We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms. Results The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs. Conclusions This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.

Interrupting malaria transmission: quantifying the impact of interventions in regions of low to moderate transmission

Malaria has been eliminated from over 40 countries with an additional 39 currently planning for, or committed to, elimination. Information on the likely impact of available interventions, and the required time, is urgently needed to help plan resource allocation. Mathematical modelling has been used to investigate the impact of various interventions; the strength of the conclusions is boosted when several models with differing formulation produce similar data. Here we predict by using an individual-based stochastic simulation model of seasonal Plasmodium falciparum transmission that transmission can be interrupted and parasite reintroductions controlled in villages of 1,000 individuals where the entomological inoculation rate is <7 infectious bites per person per year using chemotherapy and bed net strategies. Above this transmission intensity bed nets and symptomatic treatment alone were not sufficient to interrupt transmission and control the importation of malaria for at least 150 days. Our model results suggest that 1) stochastic events impact the likelihood of successfully interrupting transmission with large variability in the times required, 2) the relative reduction in morbidity caused by the interventions were age-group specific, changing over time, and 3) the post-intervention changes in morbidity were larger than the corresponding impact on transmission. These results generally agree with the conclusions from previously published models. However the model also predicted changes in parasite population structure as a result of improved treatment of symptomatic individuals; the survival probability of introduced parasites reduced leading to an increase in the prevalence of sub-patent infections in semi-immune individuals. This novel finding requires further investigation in the field because, if confirmed, such a change would have a negative impact on attempts to eliminate the disease from areas of moderate transmission.

Climate variability, climate change and land degradation

Effective response by government and individuals to the risk of land degradation requires an understanding of regional climate variations and the impacts of climate and management on condition and productivity of land and vegetation resources. Analysis of past land degradation and climate variability provides some understanding of vulnerability to current and future climate changes and the information needs for more sustainable management. We describe experience in providing climate risk assessment information for managing for the risk of land degradation in north-eastern Australian arid and semi-arid regions used for extensive grazing. However, we note that information based on historical climate variability, which has been relied on in the past, will now also have to factor in the influence of human-induced climate change. Examples illustrate trends in climate for Australia over the past decade and the impacts on indicators of resource condition. The analysis highlights the benefits of insights into past trends and variability in rainfall and other climate variables based on extended historic databases. This understanding in turn supports more reliable regional climate projections and decision support information for governments and land managers to better manage the risk of land degradation now and in the future.

Pasture Degradation and Recovery in Australia's Rangelands : Learning from History

"The extended drought periods in each degradation episode have provided a test of the capacity of grazing systems (i.e. land, plants, animals, humans and social structure) to handle stress. Evidence that degradation was already occurring was identified prior to the extended drought sequences. The sequence of dry years, ranging from two to eight years, exposed and/or amplified the degradation processes. The unequivocal evidence was provided by: (a) the physical 'horror' of bare landscapes, erosion scalds and gullies and dust storms; (b) the biological devastation of woody weeds and animal suffering/deaths or forced sales, and; (c) the financial and emotional plight of graziers and their families due to reduced production in some cases leading to abandonment of properties or, sadly, deaths (e.g. McDonald 1991, Ker Conway 1989)."--Publisher website

Seasonal factors influencing the failure of buried water reticulation pipes

While the use of environmental factors in the analysis and prediction of failures of buried reticulation pipes in cold environments has been the focus of extensive work, the same cannot be said for failures occurring on pipes in other (non-freezing) environments. A novel analysis of pipe failures in such an environment is the subject of this paper. An exploratory statistical analysis was undertaken, identifying a peak in failure rates during mid to late summer. This peak was found to correspond to a peak in the rate of circumferential failures, whilst the rate of longitudinal failures remained constant. Investigation into the effect of climate on failure rates revealed that the peak in failure rates occurs due to differential soil movement as the result of shrinkage in expansive soils.

Investigations on road noise level spatial variability within a specially designed acoustic balcony

An investigation into the spatial distribution of road traffic noise levels on a balcony is conducted. A balcony constructed to a special acoustic design due to its elevation above an 8 lane motorway is selected for detailed measurements. The as-constructed balcony design includes solid parapets, side walls, ceiling shields and highly absorptive material placed on the ceiling. Road traffic noise measurements are conducted spatially using a five channel acoustic analyzer, where four microphones are located at various positions within the balcony space and one microphone placed outside the parapet at a reference position. Spatial distributions in both vertical and horizontal planes are measured. A theoretical model and prediction configuration is presented that assesses the acoustic performance of the balcony under existing traffic flow conditions. The prediction model implements a combined direct path, specular reflection path and diffuse reflection path utilizing image source and radiosity techniques. Results obtained from the prediction model are presented and compared to the measurement results. The predictions are found to correlate well with measurements with some minor differences that are explained. It is determined that the prediction methodology is acceptable to assess a wider range of street and balcony configuration scenarios.