Modeling fault among motorcyclists involved in crashes

Singapore crash statistics from 2001 to 2006 show that the motorcyclist fatality and injury rates per registered vehicle are higher than those of other motor vehicles by 13 and 7 times respectively. The crash involvement rate of motorcyclists as victims of other road users is also about 43%. The objective of this study is to identify the factors that contribute to the fault of motorcyclists involved in crashes. This is done by using the binary logit model to differentiate between at-fault and not-at-fault cases and the analysis is further categorized by the location of the crashes, i.e., at intersections, on expressways and at non-intersections. A number of explanatory variables representing roadway characteristics, environmental factors, motorcycle descriptions, and rider demographics have been evaluated. Time trend effect shows that not-at-fault crash involvement of motorcyclists has increased with time. The likelihood of night time crashes has also increased for not-at-fault crashes at intersections and expressways. The presence of surveillance cameras is effective in reducing not-at-fault crashes at intersections. Wet road surfaces increase at-fault crash involvement at non-intersections. At intersections, not-at-fault crash involvement is more likely on single lane roads or on median lane of multi-lane roads, while on expressways at-fault crash involvement is more likely on the median lane. Roads with higher speed limit have higher at-fault crash involvement and this is also true on expressways. Motorcycles with pillion passengers or with higher engine capacity have higher likelihood of being at-fault in crashes on expressways. Motorcyclists are more likely to be at-fault in collisions involving pedestrians and this effect is higher at night. In multi-vehicle crashes, motorcyclists are more likely to be victims than at fault. Young and older riders are more likely to be at-fault in crashes than middle-aged group of riders. The findings of this study will help to develop more targeted countermeasures to improve motorcycle safety and more cost-effective safety awareness program in motorcyclist training.

Developing targeted countermeasures to improve motorcycle safety

The fatality and injury rate of motorcyclists per registered vehicle are higher than those of other motor vehicles by 13 and 7 times respectively. The crash involvement rate of motorcyclists as a victim party is 58% at intersections and as an offending party is 67% at expressways. Previous research efforts showed that the motorcycle safety programs are not very effective in improving motorcycle safety. This is perhaps due to inefficient design of safety program as specific causal factors may not be well explored. The objective of this study is to propose more sophisticated countermeasures and awareness programs for improving motorcycle safety after analyzing specific causal factors for motorcycle crashes at intersections and expressways. Methodologically this study applies the binary logistic model to explore the at-fault or not-at-fault crash involvement of motorcyclists at those locations. A number of explanatory variables representing roadway characteristics, environmental factors, motorcycle descriptions, and rider demographics have been evaluated. Results shows that the night time crash occurrence, presence of red light camera, lane position, rider age, licence class, and multivehicle collision significantly affect the fault of motorcyclists involved in crashes at intersections. On the other hand, the night time crash occurrence, lane position, speed limit, rider age, licence class, engine capacity, riding with pillion passenger, foreign registered motorcycles, and multivehicle collision has been found to be significant at expressways. Legislate to wear reflective clothes and using reflective markings on the motorcycles and helmets are suggested as an effective countermeasure for reducing their vulnerability. The red light cameras at intersections reduce the vulnerability of motorcycles and hence motorcycle flow and motorcycle crashes should be considered during installation of red light cameras. At signalized intersections, motorcyclists may be taught to follow correct movement and queuing rather than weaving through the traffic as it leads them to become victims of other motorists. The riding simulators in the training centers can be useful to demonstrate the proper movement and queuing at junctions. Riding with pillion passenger and excess speed at expressways are found to significantly influence the at at-fault crash involvement of the motorcyclists. Hence the motorcyclists should be advised to concentrate more on riding while riding with pillion passenger and encouraged to avoid excess speed at expressways. Very young and very older group of riders are found to be at-fault than middle aged groups. Hence this group of riders should be targeted for safety improvement. This can be done by arranging safety talks and programs in motorcycling clubs in colleges and universities as well as community riding clubs with high proportion of elderly riders. It is recommended that the driving centers may use the findings of this study to include in licensure program to make motorcyclists more aware of the different factors which expose the motorcyclists to crash risks so that more defensive riding may be needed.

Effects of Mobile Phone Distraction at the Onset of Amber Light: Analysis of Driving Simulator Data

Distraction whilst driving on an approach to a signalized intersection is particularly dangerous, as potential vehicular conflicts and resulting angle collisions tend to be severe. This study examines the decisions of distracted drivers during the onset of amber lights. Driving simulator data were obtained from a sample of 58 drivers under baseline and handheld mobile phone conditions at the University of IOWA - National Advanced Driving Simulator. Explanatory variables include age, gender, cell phone use, distance to stop-line, and speed. An iterative combination of decision tree and logistic regression analyses are employed to identify main effects, non-linearities, and interactions effects. Results show that novice (16-17 years) and younger (18-25 years) drivers’ had heightened amber light running risk while distracted by cell phone, and speed and distance thresholds yielded significant interaction effects. Driver experience captured by age has a multiplicative effect with distraction, making the combined effect of being inexperienced and distracted particularly risky. Solutions are needed to combat the use of mobile phones whilst driving.

Statistical analysis of conflict involvements in port water navigation

With increasing rate of shipping traffic, the risk of collisions in busy and congested port waters is likely to rise. However, due to low collision frequencies in port waters, it is difficult to analyze such risk in a sound statistical manner. A convenient approach of investigating navigational collision risk is the application of the traffic conflict techniques, which have potential to overcome the difficulty of obtaining statistical soundness. This study aims at examining port water conflicts in order to understand the characteristics of collision risk with regard to vessels involved, conflict locations, traffic and kinematic conditions. A hierarchical binomial logit model, which considers the potential correlations between observation-units, i.e., vessels, involved in the same conflicts, is employed to evaluate the association of explanatory variables with conflict severity levels. Results show higher likelihood of serious conflicts for vessels of small gross tonnage or small overall length. The probability of serious conflict also increases at locations where vessels have more varied headings, such as traffic intersections and anchorages; becoming more critical at night time. Findings from this research should assist both navigators operating in port waters as well as port authorities overseeing navigational management.

Helping understand nutritional gaps in the elderly (HUNGER) : a prospective study of patient factors associated with inadequate nutritional intake in older medical inpatients

BACKGROUND: Malnutrition, and poor intake during hospitalisation, are common in older medical patients. Better understanding of patient-specific factors associated with poor intake may inform nutritional interventions. AIMS: To measure the proportion of older medical patients with inadequate nutritional intake, and identify patient-related factors associated with this outcome. METHODS: Prospective cohort study enrolling consecutive consenting medical inpatients aged 65 years or older. Primary outcome was energy intake less than resting energy expenditure estimated using weight-based equations. Energy intake was calculated for a single day using direct observation of plate waste. Explanatory variables included age, gender, number of co-morbidities, number of medications, diagnosis, usual residence, nutritional status, functional and cognitive impairment, depressive symptoms, poor appetite, poor dentition, and dysphagia. RESULTS: Of 134 participants (mean age 80 years, 51% female), only 41% met estimated resting energy requirements. Mean energy intake was 1220 kcal/day (SD 440), or 18.1 kcal/kg/day. Factors associated with inadequate energy intake in multivariate analysis were poor appetite, higher BMI, diagnosis of infection or cancer, delirium and need for assistance with feeding. CONCLUSIONS: Inadequate nutritional intake is common, and patient factors contributing to poor intake need to be considered in nutritional interventions.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Examination of distracted driving and yellow light running : analysis of simulator data

Driving on an approach to a signalized intersection while distracted is particularly dangerous, as potential vehicular conflicts and resulting angle collisions tend to be severe. Given the prevalence and importance of this particular scenario, the decisions and actions of distracted drivers during the onset of yellow lights are the focus of this study. Driving simulator data were obtained from a sample of 58 drivers under baseline and handheld mobile phone conditions at the University of Iowa - National Advanced Driving Simulator. Explanatory variables included age, gender, cell phone use, distance to stop-line, and speed. Although there is extensive research on drivers’ responses to yellow traffic signals, the examination has been conducted from a traditional regression-based approach, which does not necessary provide the underlying relations and patterns among the sampled data. In this paper, we exploit the benefits of both classical statistical inference and data mining techniques to identify the a priori relationships among main effects, non-linearities, and interaction effects. Results suggest that novice (16-17 years) and young drivers’ (18-25 years) have heightened yellow light running risk while distracted by a cell phone conversation. Driver experience captured by age has a multiplicative effect with distraction, making the combined effect of being inexperienced and distracted particularly risky. Overall, distracted drivers across most tested groups tend to reduce the propensity of yellow light running as the distance to stop line increases, exhibiting risk compensation on a critical driving situation.

Labour, social and health outcomes of immigrants in Australia : effects of language proficiency using the IV approach

Language has been of interest to numerous economists since the late 20th century, with the majority of the studies focusing on its effects on immigrants’ labour market outcomes; earnings in particular. However, language is an endogenous variable, which along with its susceptibility to measurement error causes biases in ordinary-least-squares estimates. The instrumental variables method overcomes the shortcomings of ordinary least squares in modelling endogenous explanatory variables. In this dissertation, age at arrival combined with country of origin form an instrument creating a difference-in-difference scenario, to address the issue of endogeneity and attenuation error in language proficiency. The first half of the study aims to investigate the extent to which English speaking ability of immigrants improves their labour market outcomes and social assimilation in Australia, with the use of the 2006 Census. The findings have provided evidence that support the earlier studies. As expected, immigrants in Australia with better language proficiency are able to earn higher income, attain higher level of education, have higher probability of completing tertiary studies, and have more hours of work per week. Language proficiency also improves social integration, leading to higher probability of marriage to a native and higher probability of obtaining citizenship. The second half of the study further investigates whether language proficiency has similar effects on a migrant’s physical and mental wellbeing, health care access and lifestyle choices, with the use of three National Health Surveys. However, only limited evidence has been found with respect to the hypothesised causal relationship between language and health for Australian immigrants.

Bayesian survival analysis using gene expression

This thesis developed and applied Bayesian models for the analysis of survival data. The gene expression was considered as explanatory variables within the Bayesian survival model which can be considered the new contribution in the analysis of such data. The censoring factor that is inherent of survival data has also been addressed in terms of its impact on the fitting of a finite mixture of Weibull distribution with and without covariates. To investigate this, simulation study were carried out under several censoring percentages. Censoring percentage as high as 80% is acceptable here as the work involved high dimensional data. Lastly the Bayesian model averaging approach was developed to incorporate model uncertainty in the prediction of survival.

End-of-life care in the critical care setting : nurses' practices and factors affecting these practices

Background: Critical care units are designed and resourced to save lives, yet the provision of end-of-life care is a significant component of nursing work in these settings. Limited research has investigated the actual practices of critical care nurses in the provision of end-of-life care, or the factors influencing these practices. To improve the care that patients at the end of life and their families receive, and to support nurses in the provision of this care, further research is needed. The purpose of this study was to identify critical care nurses' end-of-life care practices, the factors influencing the provision of end-of-life care and the factors associated with specific end-of-life care practices. Methods: A three-phase exploratory sequential mixed-methods design was utilised. Phase one used a qualitative approach involving interviews with a convenience sample of five intensive care nurses to identify their end-of-life care experiences and practices. In phase two, an online survey instrument was developed, based on a review of the literature and the findings of phase one. The survey instrument was reviewed by six content experts and pilot tested with a convenience sample of 28 critical care nurses (response rate 45%) enrolled in a postgraduate critical care nursing subject. The refined survey instrument was used in phase three of this study to conduct a national survey of critical care nurses. Descriptive analyses, exploratory factor analysis and univariate general linear modelling was undertaken on completed survey responses from 392 critical care nurses (response rate 25%). Results: Six end-of-life care practice areas were identified in this study: information sharing, environmental modification, emotional support, patient and family-centred decision making, symptom management and spiritual support. The items most frequently identified as always undertaken by critical care nurses in the provision of end-of-life care were from the information sharing and environmental modification practice areas. Items least frequently identified as always undertaken included items from the emotional support practice area. Eight factors influencing the provision of end-of-life care were identified: palliative values, patient and family preferences, knowledge, preparedness, organisational culture, resources, care planning, and emotional support for nurses. Strong agreement was noted with items reflecting values consistent with a palliative approach and inclusion of patient and family preferences. Variation was noted in agreement for items regarding opportunities for knowledge acquisition in the workplace and formal education, yet most respondents agreed that they felt adequately prepared. A context of nurse-led practice was identified, with variation in access to resources noted. Collegial support networks were identified as a source of emotional support for critical care nurses. Critical care nurses reporting values consistent with a palliative approach and/or those who scored higher on support for patient and family preferences were more likely to be engaged in end-of-life care practice areas identified in this study. Nurses who reported higher levels of preparedness and access to opportunities for knowledge acquisition were more likely to report engaging in interpersonal practices that supported patient and family centred decision making and emotional support of patients and their families. A negative relationship was identified between the explanatory variables of emotional support for nurses and death anxiety, and the patient and family centred decision making practice area. Contextual factors had a limited influence as explanatory variables of specific end-of-life care practice areas. Gender was identified as a significant explanatory variable in the emotional and spiritual support practice areas, with male gender associated with lower summated scores on these practice scales. Conclusions: Critical care nurses engage in practices to share control with and support inclusion of families experiencing death and dying. The most frequently identified end-of-life care practices were those that are easily implemented, practical strategies aimed at supporting the patient at the end of life and the patient's family. These practices arguably require less emotional engagement by the nurse. Critical care nurses' responses reflected values consistent with a palliative approach and a strong commitment to the inclusion of families in end-of-life care, and these factors were associated with engagement in all end-of-life care practice areas. Perceived preparedness or confidence with the provision of end-of-life care was associated with engagement in interpersonal caring practices. Critical care nurses autonomously engage in the provision of end-of-life care within the constraints of an environment designed for curative care and rely on their colleagues for emotional support. Critical care nurses must be adequately prepared and supported to provide comprehensive care in all areas of end-of-life care practice. The findings of this study raise important implications, and informed recommendations for practice, education and further research.

Efficiency and productivity of Singapore's manufacturing sector 2001-2010 : an analysis using bootstrapped truncated approach

This paper seeks to explain the lagging productivity in Singapore’s manufacturing noted in the statements of the Economic Strategies Committee Report 2010. Two methods are employed: the Malmquist productivity to measure total factor productivity (TFP) change and Simar and Wilson’s (2007) bootstrapped truncated regression approach which first derives bias-corrected efficiency estimates before being regressed against explanatory variables to help quantify sources of inefficiencies. The findings reveal that growth in total factor productivity was attributed to efficiency change with no technical progress. Sources of efficiency were attributed to quality of worker and flexible work arrangements while the use of foreign workers lowered efficiency.

High risk feet in subacute rehabilitation facilities : how many are there?

Background Australian subacute rehabilitation facilities face significant challenges from the ageing population with increased burden of chronic disease. High risk foot complications are a negative consequence of many chronic diseases. With the rapid expansion of subacute services, it seems imperative to investigate the prevalence of foot complications in this population. The primary aim of this study was to quantify the high risk foot complication prevalence in a subacute rehabilitation population. Methods Eligible participants were all adults admitted overnight, over two 4 week periods, into a large Australian subacute rehabilitation facility. Consenting participants underwent a short non-invasive foot examination by a podiatrist. The standard Queensland Health High Risk Foot Form collected data on age, sex, co-morbidities and foot complications. Descriptive statistics, logistic regression and odds ratios were used to determine the prevalence of foot complications and associations with explanatory variables. Results Overall, 85 of 97 eligible participants consented; mean age 80(9) and 71% were female. At least one foot complication was present in 56.5% participants; including 21.2% defined as high risk and 11.8% current foot ulcer. A previous diagnosis of neuropathy increased the risk of presenting with a high risk foot by 13-fold (OR 13.504, p = 0.001). Conclusion This study highlights the significance of foot complications in the subacute population. It appears that one in every two patients present with a foot complication and one in eight with a foot ulcer. It is suggested all patients admitted to subacute rehabilitation services should be screened for foot complications.

Associations between area-level disadvantage and DMFT among a birth cohort of Indigenous Australians

Background Individual-level factors influence DMFT, but little is known about the influence of community environment. This study examines associations between community-level influences and DMFT among a birth cohort of Indigenous Australians aged 16–20 years. Methods Data were collected as part of Wave 3 of the Aboriginal Birth Cohort study. Fifteen community areas were established and the sample comprised 442 individuals. The outcome variable was mean DMFT with explanatory variables including diet and community disadvantage (access to services, infrastructure and communications). Data were analysed using multilevel regression modelling. Results In a null model, 13.8% of the total variance in mean DMFT was between community areas, which increased to 14.3% after adjusting for sex, age and diet. Addition of the community disadvantage variable decreased the variance between areas by 4.8%, indicating that community disadvantage explained one-third of the area-level variance. Residents of under-resourced communities had significantly higher mean DMFT (β=3.86, 95% CI 0.02¬, 7.70) after adjusting for sex, age and diet. Conclusions Living in under-resourced communities was associated with greater DMFT among this disadvantaged population, indicating that policies aiming to reduce oral health-related inequalities among vulnerable groups may benefit from taking into account factors external to individual-level influences.

Partitioning the variation in stream fish assemblages within a spatio-temporal hierarchy

This paper describes the relative influence of: (i) landscape scale environmental and hydrological factors; (ii) local scale environmental conditions including recent flow history, and; (iii) spatial effects (proximity of sites to one another) on the spatial and temporal variation in local freshwater fish assemblages in the Mary River, south-eastern Queensland, Australia. Using canonical correspondence analysis, each of the three sets of variables explained similar amounts of variation in fish assemblages (ranging from 44 to 52%). Variation in fish assemblages was partitioned into eight unique components: pure environmental, pure spatial, pure temporal, spatially structured environmental variation, temporally structured environmental variation, spatially structured temporal variation, the combined spatial/temporal component of environmental variation and unexplained variation. The total variation explained by these components was 65%. The combined spatial/temporal/environmental component explained the largest component (30%) of the total variation in fish assemblages, whereas pure environmental (6%), temporal (9%) and spatial (2%) effects were relatively unimportant. The high degree of intercorrelation between the three different groups of explanatory variables indicates that our understanding of the importance to fish assemblages of hydrological variation (often highlighted as the major structuring force in river systems) is dependent on the environmental context in which this role is examined.

Identifying the spatial scale of land use that most strongly influences overall river ecosystem health score

Catchment and riparian degradation has resulted in declining ecosystem health of streams worldwide. With restoration a priority in many regions, there is an increasing interest in the scale at which land use influences stream ecosystem health. Our goal was to use a substantial data set collected as part of a monitoring program (the Southeast Queensland, Australia, Ecological Health Monitoring Program data set, collected at 116 sites over six years) to identify the spatial scale of land use, or the combination of spatial scales, that most strongly influences overall ecosystem health. In addition, we aimed to determine whether the most influential scale differed for different aspects of ecosystem health. We used linear-mixed models and a Bayesian model-averaging approach to generate models for the overall aggregated ecosystem health score and for each of the five component indicators (fish, macroinvertebrates, water quality, nutrients, and ecosystem processes) that make up the score. Dense forest close to the survey site, mid-dense forest in the hydrologically active nearstream areas of the catchment, urbanization in the riparian buffer, and tree cover at the reach scale were all significant in explaining ecosystem health, suggesting an overriding influence of forest cover, particularly close to the stream. Season and antecedent rainfall were also important explanatory variables, with some land-use variables showing significant seasonal interactions. There were also differential influences of land use for each of the component indicators. Our approach is useful given that restoring general ecosystem health is the focus of many stream restoration projects; it allowed us to predict the scale and catchment position of restoration that would result in the greatest improvement of ecosystem health in the regions streams and rivers. The models we generated suggested that good ecosystem health can be maintained in catchments where 80% of hydrologically active areas in close proximity to the stream have mid-dense forest cover and moderate health can be obtained with 60% cover.