Novel methods and the maximum likelihood estimation technique for estimating traffic critical gap

Most unsignalised intersection capacity calculation procedures are based on gap acceptance models. Accuracy of critical gap estimation affects accuracy of capacity and delay estimation. Several methods have been published to estimate drivers’ sample mean critical gap, the Maximum Likelihood Estimation (MLE) technique regarded as the most accurate. This study assesses three novel methods; Average Central Gap (ACG) method, Strength Weighted Central Gap method (SWCG), and Mode Central Gap method (MCG), against MLE for their fidelity in rendering true sample mean critical gaps. A Monte Carlo event based simulation model was used to draw the maximum rejected gap and accepted gap for each of a sample of 300 drivers across 32 simulation runs. Simulation mean critical gap is varied between 3s and 8s, while offered gap rate is varied between 0.05veh/s and 0.55veh/s. This study affirms that MLE provides a close to perfect fit to simulation mean critical gaps across a broad range of conditions. The MCG method also provides an almost perfect fit and has superior computational simplicity and efficiency to the MLE. The SWCG method performs robustly under high flows; however, poorly under low to moderate flows. Further research is recommended using field traffic data, under a variety of minor stream and major stream flow conditions for a variety of minor stream movement types, to compare critical gap estimates using MLE against MCG. Should the MCG method prove as robust as MLE, serious consideration should be given to its adoption to estimate critical gap parameters in guidelines.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

Socioeconomic differences in takeaway food consumption among adults

Background In Australia and other developed countries, there are consistent and marked socioeconomic inequalities in health. Diet is a major contributing factor to the poorer health of lower socioeconomic groups: the dietary patterns of disadvantaged groups are least consistent with dietary recommendations for the prevention of diet-related chronic diseases compared with their more advantaged counterparts. Part of the reason that lower socioeconomic groups have poorer diets may be their consumption of takeaway foods. These foods typically have nutrient contents that fail to comply with the dietary recommendations for the prevention of chronic disease and associated risk factors. A high level of takeaway food consumption, therefore, may negatively influence overall dietary intakes and, consequently, lead to adverse health outcomes. Despite this, little attention has focused on the association between socioeconomic position (SEP) and takeaway food consumption, with the limited number of studies showing mixed results. Additionally, studies have been limited by only considering a narrow range of takeaway foods and not examining how different socioeconomic groups make choices that are more (or less) consistent with dietary recommendations. While a large number of earlier studies have consistently reported socioeconomically disadvantaged groups consume a lesser amount of fruit and vegetables, there is limited knowledge about the role of takeaway food in socioeconomic variations in fruit and vegetable intake. Furthermore, no known studies have investigated why there are socioeconomic differences in takeaway food consumption. The aims of this study are to: examine takeaway food consumption and the types of takeaway food consumed (healthy and less healthy) by different socioeconomic groups, to determine whether takeaway food consumption patterns explain socioeconomic variations in fruit and vegetable intake, and investigate the role of a range of psychosocial factors in explaining the association between SEP and takeaway food consumption and the choice of takeaway food. Methods This study used two cross-sectional population-based datasets: 1) the 1995 Australian National Nutrition Survey (NNS) which was conducted among a nationally representative sample of adults aged between 25.64 years (N = 7319, 61% response rate); and 2) the Food and Lifestyle Survey (FLS) which was conducted by the candidate and was undertaken among randomly selected adults aged between 25.64 years residing in Brisbane, Australia in 2009 (N = 903, 64% response rate). The FLS extended the NNS in several ways by describing current socioeconomic differences in takeaway food consumption patterns, formally assessing the mediated effect of takeaway food consumption to socioeconomic inequalities in fruit and vegetable intake, and also investigating whether (and which) psychosocial factors contributed to the observed socioeconomic variations in takeaway food consumption patterns. Results Approximately 32% of the NNS participants consumed takeaway food in the previous 24 hours and 38% of the FLS participants reported consuming takeaway food once a week or more. The results from analyses of the NNS and the FLS were somewhat mixed; however, disadvantaged groups were likely to consume a high level of �\less healthy. takeaway food compared with their more advantaged counterparts. The lower fruit and vegetable intake among lower socioeconomic groups was partly mediated by their high consumption of �\less healthy. takeaway food. Lower socioeconomic groups were more likely to have negative meal preparation behaviours and attitudes, and weaker health and nutrition-related beliefs and knowledge. Socioeconomic differences in takeaway food consumption were partly explained by meal preparation behaviours and attitudes, and these factors along with health and nutrition-related beliefs and knowledge appeared to contribute to the socioeconomic variations in choice of takeaway foods. Conclusion This thesis enhances our understanding of socioeconomic differences in dietary behaviours and the potential pathways by describing takeaway food consumption patterns by SEP, explaining the role of takeaway food consumption in socioeconomic inequalities in fruit and vegetable intake, and identifying the potential impact of psychosocial factors on socioeconomic differences in takeaway food consumption and the choice of takeaway food. Some important evidence is also provided for developing policies and effective intervention programs to improve the diet quality of the population, especially among lower socioeconomic groups. This thesis concludes with a discussion of a number of recommendations about future research and strategies to improve the dietary intake of the whole population, and especially among disadvantaged groups.

The effect of implementing a modified early warning scoring (MEWS) system on the adequacy of vital sign documentation

Introduction and objectives Early recognition of deteriorating patients results in better patient outcomes. Modified early warning scores (MEWS) attempt to identify deteriorating patients early so timely interventions can occur thus reducing serious adverse events. We compared frequencies of vital sign recording 24 h post-ICU discharge and 24 h preceding unplanned ICU admission before and after a new observation chart using MEWS and an associated educational programme was implemented into an Australian Tertiary referral hospital in Brisbane. Design Prospective before-and-after intervention study, using a convenience sample of ICU patients who have been discharged to the hospital wards, and in patients with an unplanned ICU admission, during November 2009 (before implementation; n = 69) and February 2010 (after implementation; n = 70). Main outcome measures Any change in a full set or individual vital sign frequency before-and-after the new MEWS observation chart and associated education programme was implemented. A full set of vital signs included Blood pressure (BP), heart rate (HR), temperature (T°), oxygen saturation (SaO2) respiratory rate (RR) and urine output (UO). Results After the MEWS observation chart implementation, we identified a statistically significant increase (210%) in overall frequency of full vital sign set documentation during the first 24 h post-ICU discharge (95% CI 148, 288%, p value <0.001). Frequency of all individual vital sign recordings increased after the MEWS observation chart was implemented. In particular, T° recordings increased by 26% (95% CI 8, 46%, p value = 0.003). An increased frequency of full vital sign set recordings for unplanned ICU admissions were found (44%, 95% CI 2, 102%, p value = 0.035). The only statistically significant improvement in individual vital sign recordings was urine output, demonstrating a 27% increase (95% CI 3, 57%, p value = 0.029). Conclusions The implementation of a new MEWS observation chart plus a supporting educational programme was associated with statistically significant increases in frequency of combined and individual vital sign set recordings during the first 24 h post-ICU discharge. There were no significant changes to frequency of individual vital sign recordings in unplanned admissions to ICU after the MEWS observation chart was implemented, except for urine output. Overall increases in the frequency of full vital sign sets were seen.

Gender differences in recreational and transport cycling : a cross-sectional mixed-methods comparison of cycling patterns, motivators, and constraints

Background: Gender differences in cycling are well-documented. However, most analyses of gender differences make broad comparisons, with few studies modeling male and female cycling patterns separately for recreational and transport cycling. This modeling is important, in order to improve our efforts to promote cycling to women and men in countries like Australia with low rates of transport cycling. The main aim of this study was to examine gender differences in cycling patterns and in motivators and constraints to cycling, separately for recreational and transport cycling. Methods: Adult members of a Queensland, Australia, community bicycling organization completed an online survey about their cycling patterns; cycling purposes; and personal, social and perceived environmental motivators and constraints (47% response rate). Closed and open-end questions were completed. Using the quantitative data, multivariable linear, logistic and ordinal regression models were used to examine associations between gender and cycling patterns, motivators and constraints. The qualitative data were thematically analysed to expand upon the quantitative findings. Results: In this sample of 1862 bicyclists, men were more likely than women to cycle for recreation and for transport, and they cycled for longer. Most transport cycling was for commuting, with men more likely than women to commute by bicycle. Men were more likely to cycle on-road, and women off-road. However, most men and women did not prefer to cycle on-road without designed bicycle lanes, and qualitative data indicated a strong preference by men and women for bicycle-only off-road paths. Both genders reported personal factors (health and enjoyment related) as motivators for cycling, although women were more likely to agree that other personal, social and environmental factors were also motivating. The main constraints for both genders and both cycling purposes were perceived environmental factors related to traffic conditions, motorist aggression and safety. Women, however, reported more constraints, and were more likely to report as constraints other environmental factors and personal factors. Conclusion: Differences found in men’s and women’s cycling patterns, motivators and constraints should be considered in efforts to promote cycling, particularly in efforts to increase cycling for transport.

Context-aware rate-adaptive beaconing for efficient and scalable vehicular safety communication

Vehicular safety applications, such as cooperative collision warning systems, rely on beaconing to provide situational awareness that is needed to predict and therefore to avoid possible collisions. Beaconing is the continual exchange of vehicle motion-state information, such as position, speed, and heading, which enables each vehicle to track its neighboring vehicles in real time. This work presents a context-aware adaptive beaconing scheme that dynamically adapts the beaconing repetition rate based on an estimated channel load and the danger severity of the interactions among vehicles. The safety, efficiency, and scalability of the new scheme is evaluated by simulating vehicle collisions caused by inattentive drivers under various road traffic densities. Simulation results show that the new scheme is more efficient and scalable, and is able to improve safety better than the existing non-adaptive and adaptive rate schemes.

Developing guidelines for intervention to reduce risk of low-speed vehicle run-overs of young children

In Australia, research suggests that up to one quarter of child pedestrian hospitalisations result from driveway run-over incidents (Pinkney et al., 2006). In Queensland, these numbers equate to an average of four child fatalities and 81 children presenting at hospital emergency departments every year (The Commission for Children, Young People and Child Guardian). National comparison shows that these numbers represent a slightly higher per capita rate (23.5% of all deaths). To address this issue, the current research was undertaken with the aim to develop an educative intervention based on data collected from parents and caregivers of young children. Thus, the current project did not seek to use available intervention or educational material, but to develop a new evidence-based intervention specifically targeting driveway run-overs involving young children. To this end, general behavioural and environmental changes that caregivers had undertaken in order to reduce the risk of injury to any child in their care were investigated. Broadly, the first part of this report sought to: • develop a conceptual model of established domestic safety behaviours, and to investigate whether this model could be successfully applied to the driveway setting; • explore and compare sources of knowledge regarding domestic and driveway child safety; and • examine the theoretical implications of current domestic and driveway related behaviour and knowledge among caregivers. The aim of the second part of this research was to develop and test the efficacy of an intervention based on the findings in the first part of the research project. Specifically, it sought to: • develop an educational driveway intervention that is based on current safety behaviours in the domestic setting and informed by existing knowledge of driveway safety and behaviour change theory; and • evaluate its efficacy in a sample of parents and caregivers.

Quantitative studies of lower motor neuron degeneration in amyotrophic lateral sclerosis : Evidence for exponential decay of motor unit numbers and greatest rate of loss at the site of onset

Objective: To use our Bayesian method of motor unit number estimation (MUNE) to evaluate lower motor neuron degeneration in ALS. Methods: In subjects with ALS we performed serial MUNE studies. We examined the repeatability of the test and then determined whether the loss of MUs was fitted by an exponential or Weibull distribution. Results: The decline in motor unit (MU) numbers was well-fitted by an exponential decay curve. We calculated the half life of MUs in the abductor digiti minimi (ADM), abductor pollicis brevis (APB) and/or extensor digitorum brevis (EDB) muscles. The mean half life of the MUs of ADM muscle was greater than those of the APB or EDB muscles. The half-life of MUs was less in the ADM muscle of subjects with upper limb than in those with lower limb onset. Conclusions: The rate of loss of lower motor neurons in ALS is exponential, the motor units of the APB decay more quickly than those of the ADM muscle and the rate of loss of motor units is greater at the site of onset of disease. Significance: This shows that the Bayesian MUNE method is useful in following the course and exploring the clinical features of ALS. 2012 International Federation of Clinical Neurophysiology.

Consecutive days of cold water immersion: effects on cycling performance and heart rate variability.

We investigated performance and heart rate (HR) variability (HRV) over consecutive days of cycling with post-exercise cold water immersion (CWI) or passive recovery (PAS). In a crossover design, 11 cyclists completed two separate 3-day training blocks (120 min cycling per day, 66 maximal sprints, 9 min time trialling [TT]), followed by 2 days of recovery-based training. The cyclists recovered from each training session by standing in cold water (10 °C) or at room temperature (27 °C) for 5 min. Mean power for sprints, total TT work and HR were assessed during each session. Resting vagal-HRV (natural logarithm of square-root of mean squared differences of successive R-R intervals; ln rMSSD) was assessed after exercise, after the recovery intervention, during sleep and upon waking. CWI allowed better maintenance of mean sprint power (between-trial difference [90 % confidence limits] +12.4 % [5.9; 18.9]), cadence (+2.0 % [0.6; 3.5]), and mean HR during exercise (+1.6 % [0.0; 3.2]) compared with PAS. ln rMSSD immediately following CWI was higher (+144 % [92; 211]) compared with PAS. There was no difference between the trials in TT performance (-0.2 % [-3.5; 3.0]) or waking ln rMSSD (-1.2 % [-5.9; 3.4]). CWI helps to maintain sprint performance during consecutive days of training, whereas its effects on vagal-HRV vary over time and depend on prior exercise intensity.

The effect of post-exercise hydrotherapy on subsequent exercise performance and heart rate variability

We investigated the effect of hydrotherapy on time-trial performance and cardiac parasympathetic reactivation during recovery from intense training. On three occasions, 18 well-trained cyclists completed 60 min high-intensity cycling, followed 20 min later by one of three 10-min recovery interventions: passive rest (PAS), cold water immersion (CWI), or contrast water immersion (CWT). The cyclists then rested quietly for 160 min with R-R intervals and perceptions of recovery recorded every 30 min. Cardiac parasympathetic activity was evaluated using the natural logarithm of the square root of mean squared differences of successive R-R intervals (ln rMSSD). Finally, the cyclists completed a work-based cycling time trial. Effects were examined using magnitude-based inferences. Differences in time-trial performance between the three trials were trivial. Compared with PAS, general fatigue was very likely lower for CWI (difference [90% confidence limits; -12% (-18; -5)]) and CWT [-11% (-19; -2)]. Leg soreness was almost certainly lower following CWI [-22% (-30; -14)] and CWT [-27% (-37; -15)]. The change in mean ln rMSSD following the recovery interventions (ln rMSSD(Post-interv)) was almost certainly higher following CWI [16.0% (10.4; 23.2)] and very likely higher following CWT [12.5% (5.5; 20.0)] compared with PAS, and possibly higher following CWI [3.7% (-0.9; 8.4)] compared with CWT. The correlations between performance, ln rMSSD(Post-interv) and perceptions of recovery were unclear. A moderate correlation was observed between ln rMSSD(Post-interv) and leg soreness [r = -0.50 (-0.66; -0.29)]. Although the effects of CWI and CWT on performance were trivial, the beneficial effects on perceptions of recovery support the use of these recovery strategies.

Are you telling the truth? Psychopathy assessment and impression management in a community sample

Objectives: Researchers have suggested that approximately 1% of individuals within the community have psychopathic tendencies (Neumann and Hare, 2008), although confirmatory evidence is scant. Design: The current study aimed to extend previous research beyond university student samples to explore the effect of impression management and self-deception on the identification of psychopathic traits. Methods: A non-incarcerated community sample comprising of 300 adults completed the Self-Reported Psychopathy scale – version 3 (SRP-III; Paulhus, Hemphill & Hare, in press) as well as the Paulhus Deception Scales (PDS; Paulhus, 1998). Results: Results indicated that at least 1% of the current community sample had clear psychopathic tendencies, and that such tendencies were found in younger males who mis-used alcohol. Conclusions: Importantly, individuals with psychopathic traits did not present with an inflated propensity to distort assessment responses, which provides support for future research endeavours that aim to conduct larger-scale psychopathy assessments within the community. This paper further outlines the study implications in regards to the practical assessment of psychopathy.

Jackknife for bias reduction in predictive regressions

One of the fundamental econometric models in finance is predictive regression. The standard least squares method produces biased coefficient estimates when the regressor is persistent and its innovations are correlated with those of the dependent variable. This article proposes a general and convenient method based on the jackknife technique to tackle the estimation problem. The proposed method reduces the bias for both single- and multiple-regressor models and for both short- and long-horizon regressions. The effectiveness of the proposed method is demonstrated by simulations. An empirical application to equity premium prediction using the dividend yield and the short rate highlights the differences between the results by the standard approach and those by the bias-reduced estimator. The significant predictive variables under the ordinary least squares become insignificant after adjusting for the finite-sample bias. These discrepancies suggest that bias reduction in predictive regressions is important in practical applications.