Unraveling the complexity of driving while intoxicated: a study into the prevalence of psychiatric and substance abuse comorbidity

Objective Research is beginning to provide an indication of the co-occurring substance abuse and mental health needs for the driving under the influence (DUI) population. This study aimed to examine the extent of such psychiatric problems among a large sample size of DUI offenders entering treatment in Texas. Methods This is a study of 36,373 past year DUI clients and 308,714 non-past year DUI clients admitted to Texas treatment programs between 2005 and 2008. Data were obtained from the State's administrative dataset. Results Analysis indicated that non-past year DUI clients were more likely to present with more severe illicit substance use problems, while past year DUI clients were more likely to have a primary problem with alcohol. Nevertheless, a cannabis use problem was also found to be significantly associated with DUI recidivism in the last year. In regards to mental health status, a major finding was that depression was the most common psychiatric condition reported by DUI clients, including those with more than one DUI offence in the past year. This cohort also reported elevated levels of Bipolar Disorder compared to the general population, and such a diagnosis was also associated with an increased likelihood of not completing treatment. Additionally, female clients were more likely to be diagnosed with mental health problems than males, as well as more likely to be placed on medications at admission and more likely to have problems with methamphetamine, cocaine, and opiates. Conclusions DUI offenders are at an increased risk of experiencing comorbid psychiatric disorders, and thus, corresponding treatment programs need to cater for a range of mental health concerns that are likely to affect recidivism rates.

Modeling crash types: New insights into the effects of covariates on crashes at rural intersections

Many studies focused on the development of crash prediction models have resulted in aggregate crash prediction models to quantify the safety effects of geometric, traffic, and environmental factors on the expected number of total, fatal, injury, and/or property damage crashes at specific locations. Crash prediction models focused on predicting different crash types, however, have rarely been developed. Crash type models are useful for at least three reasons. The first is motivated by the need to identify sites that are high risk with respect to specific crash types but that may not be revealed through crash totals. Second, countermeasures are likely to affect only a subset of all crashes—usually called target crashes—and so examination of crash types will lead to improved ability to identify effective countermeasures. Finally, there is a priori reason to believe that different crash types (e.g., rear-end, angle, etc.) are associated with road geometry, the environment, and traffic variables in different ways and as a result justify the estimation of individual predictive models. The objectives of this paper are to (1) demonstrate that different crash types are associated to predictor variables in different ways (as theorized) and (2) show that estimation of crash type models may lead to greater insights regarding crash occurrence and countermeasure effectiveness. This paper first describes the estimation results of crash prediction models for angle, head-on, rear-end, sideswipe (same direction and opposite direction), and pedestrian-involved crash types. Serving as a basis for comparison, a crash prediction model is estimated for total crashes. Based on 837 motor vehicle crashes collected on two-lane rural intersections in the state of Georgia, six prediction models are estimated resulting in two Poisson (P) models and four NB (NB) models. The analysis reveals that factors such as the annual average daily traffic, the presence of turning lanes, and the number of driveways have a positive association with each type of crash, whereas median widths and the presence of lighting are negatively associated. For the best fitting models covariates are related to crash types in different ways, suggesting that crash types are associated with different precrash conditions and that modeling total crash frequency may not be helpful for identifying specific countermeasures.

Investigation into the introduction of clonal strains of Pseudomonas aeruginosa to the cystic fibrosis population by consumption of raw salad vegetables

Most salad vegetables are eaten fresh by consumers. However, raw vegetables may pose a risk of transmitting opportunistic bacteria to immunocompromised people, including cystic fibrosis (CF) patients. In particular, CF patients are vulnerable to chronic Pseudomonas aeruginosa lung infections and this organism is the primary cause of morbidity and mortality in this group. Clonal variants of P. aeruginosa have been identified as emerging threats to people afflicted with CF; however it has not yet been proven from where these clones originate or how they are transmitted. Due to the organisms‟ aquatic environmental niche, it was hypothesised that vegetables may be a source of these clones. To test this hypothesis, lettuce, tomatoes, mushrooms and bean sprout packages (n = 150) were analysed from a green grocer, supermarket and farmers‟ market within the Brisbane region, availability permitting. The internal and external surfaces of the vegetables were separately analysed for the presence of clonal strains of P. aeruginosa using washings and homogenisation techniques, respectively. This separation was in an attempt to establish which surface was contaminated, so that recommendations could be made to decrease or eliminate P. aeruginosa from these foods prior to consumption. Soil and water samples (n = 17) from local farms were also analysed for the presence of P. aeruginosa. Presumptive identification of isolates recovered from these environmental samples was made based on growth on Cetrimide agar at 42°C, presence of the cytochrome-oxidase enzyme and inability to ferment lactose. P. aeruginosa duplex real-time polymerase chain reaction assay (PAduplex) was performed on all bacterial isolates presumptively identified as P. aeruginosa. Enterobacterial repetitive intergenic consensus strain typing PCR (ERIC-PCR) was subsequently performed on confirmed bacterial isolates. Although 72 P. aeruginosa were isolated, none of these proved to be clonal strains. The significance of these findings is that vegetables may pose a risk of transmitting sporadic strains of P. aeruginosa to people afflicted with CF and possibly, other immunocompromised people.

Action inquiry into the use of standardized evaluation tools for music therapy : a real life journey within a parent-child community program

Sing & Grow is an early intervention music therapy project that provides community group music therapy programs to families with young children who encounter risk factors that may impact on parenting and optimal child develop variety of evaluation tools were devised and used over the first 3 years of the project. Upon the subsequent funding and expansion of the project at the end of this period, it was necessary to find, test and devise more rigorous, valid and reliable measures to withstand the scrutiny of researchers, and to combat the concerns and criticisms associated with the previous methods of data collection. An action inquiry project was therefore undertaken with two groups of project participants to trial the use of the Parenting Stress Index and Depression, Anxiety and Stress Scales, both recommended by leading psychologists. Key findings that will be discussed include the friction between the deficit-focussed nature of many psychometric tools and the strengths-based approach taken in service delivery, the level of difficulty in terms of literacy and comprehension for vulnerable respondents, and the lack of one tool with the ability to comprehensively measure all aspects of a broad scoping program. Keywords: music therapy, evaluation, PSI, DASS, action inquiry.

Using Bayesian methods for the estimation of uncertainty in complex statistical models

The research objectives of this thesis were to contribute to Bayesian statistical methodology by contributing to risk assessment statistical methodology, and to spatial and spatio-temporal methodology, by modelling error structures using complex hierarchical models. Specifically, I hoped to consider two applied areas, and use these applications as a springboard for developing new statistical methods as well as undertaking analyses which might give answers to particular applied questions. Thus, this thesis considers a series of models, firstly in the context of risk assessments for recycled water, and secondly in the context of water usage by crops. The research objective was to model error structures using hierarchical models in two problems, namely risk assessment analyses for wastewater, and secondly, in a four dimensional dataset, assessing differences between cropping systems over time and over three spatial dimensions. The aim was to use the simplicity and insight afforded by Bayesian networks to develop appropriate models for risk scenarios, and again to use Bayesian hierarchical models to explore the necessarily complex modelling of four dimensional agricultural data. The specific objectives of the research were to develop a method for the calculation of credible intervals for the point estimates of Bayesian networks; to develop a model structure to incorporate all the experimental uncertainty associated with various constants thereby allowing the calculation of more credible credible intervals for a risk assessment; to model a single day’s data from the agricultural dataset which satisfactorily captured the complexities of the data; to build a model for several days’ data, in order to consider how the full data might be modelled; and finally to build a model for the full four dimensional dataset and to consider the timevarying nature of the contrast of interest, having satisfactorily accounted for possible spatial and temporal autocorrelations. This work forms five papers, two of which have been published, with two submitted, and the final paper still in draft. The first two objectives were met by recasting the risk assessments as directed, acyclic graphs (DAGs). In the first case, we elicited uncertainty for the conditional probabilities needed by the Bayesian net, incorporated these into a corresponding DAG, and used Markov chain Monte Carlo (MCMC) to find credible intervals, for all the scenarios and outcomes of interest. In the second case, we incorporated the experimental data underlying the risk assessment constants into the DAG, and also treated some of that data as needing to be modelled as an ‘errors-invariables’ problem [Fuller, 1987]. This illustrated a simple method for the incorporation of experimental error into risk assessments. In considering one day of the three-dimensional agricultural data, it became clear that geostatistical models or conditional autoregressive (CAR) models over the three dimensions were not the best way to approach the data. Instead CAR models are used with neighbours only in the same depth layer. This gave flexibility to the model, allowing both the spatially structured and non-structured variances to differ at all depths. We call this model the CAR layered model. Given the experimental design, the fixed part of the model could have been modelled as a set of means by treatment and by depth, but doing so allows little insight into how the treatment effects vary with depth. Hence, a number of essentially non-parametric approaches were taken to see the effects of depth on treatment, with the model of choice incorporating an errors-in-variables approach for depth in addition to a non-parametric smooth. The statistical contribution here was the introduction of the CAR layered model, the applied contribution the analysis of moisture over depth and estimation of the contrast of interest together with its credible intervals. These models were fitted using WinBUGS [Lunn et al., 2000]. The work in the fifth paper deals with the fact that with large datasets, the use of WinBUGS becomes more problematic because of its highly correlated term by term updating. In this work, we introduce a Gibbs sampler with block updating for the CAR layered model. The Gibbs sampler was implemented by Chris Strickland using pyMCMC [Strickland, 2010]. This framework is then used to consider five days data, and we show that moisture in the soil for all the various treatments reaches levels particular to each treatment at a depth of 200 cm and thereafter stays constant, albeit with increasing variances with depth. In an analysis across three spatial dimensions and across time, there are many interactions of time and the spatial dimensions to be considered. Hence, we chose to use a daily model and to repeat the analysis at all time points, effectively creating an interaction model of time by the daily model. Such an approach allows great flexibility. However, this approach does not allow insight into the way in which the parameter of interest varies over time. Hence, a two-stage approach was also used, with estimates from the first-stage being analysed as a set of time series. We see this spatio-temporal interaction model as being a useful approach to data measured across three spatial dimensions and time, since it does not assume additivity of the random spatial or temporal effects.

A human factors investigation into the unavailability of active warnings at railway level crossings

This paper discusses human factors issues of low cost railway level crossings in Australia. Several issues are discussed in this paper including safety at passive level railway crossings, human factors considerations associated with unavailability of a warning device, and a conceptual model for how safety could be compromised at railway level crossings following prolonged or frequent unavailability. The research plans to quantify safety risk to motorists at level crossings using a Human Reliability Assessment (HRA) method, supported by data collected using an advanced driving simulator. This method aims to identify human error within tasks and task units identified as part of the task analysis process. It is anticipated that by modelling driver behaviour the current study will be able to quantify meaningful task variability including temporal parameters, between participants and within participants. The process of complex tasks such as driving through a level crossing is fundamentally context-bound. Therefore this study also aims to quantify those performance-shaping factors that contribute to vehicle train collisions by highlighting changes in the task units and driver physiology. Finally we will also consider a number of variables germane to ensuring external validity of our results. Without this inclusion, such an analysis could seriously underestimate risk.

Controlled drift: an investigation into the controllability of underwater vehicles with minimal actuation

This paper is directed towards providing an answer to the question, ”Can you control the trajectory of a Lagrangian float?” Being a float that has minimal actuation (only buoyancy control), their horizontal trajectory is dictated through drifting with ocean currents. However, with the appropriate vertical actuation and utilising spatio-temporal variations in water speed and direction, we show here that broad controllabilty results can be met such as waypoint following to keep a float inside of a bay or out of a designated region. This paper extends theory experimen- tally evaluted on horizontally actuated Autonomous Underwater Vehicles (AUVs) for trajectory control utilising ocean forecast models and presents an initial investi- gation into the controllability of these minimally actuated drifting AUVs. Simulated results for offshore coastal and within highly dynamic tidal bays illustrate two tech- niques with the promise for an affirmative answer to the posed question above.

Investigation of barriers to entry into the design-build market in the People’s Republic of China (PRC)

Although the design-build (DB) system has been demonstrated to be an effective delivery method and has gained popularity worldwide, it has not gained the same popularity in the construction market of China. The objective of this study was, theretofore, to investigate the barriers to entry in the DB market. A total of 22 entry barriers were first identified through an open-ended questionnaire survey with 15 top construction professionals in the construction market of China. A broad questionnaire survey was further conducted to prioritize these entry barriers. Statistical analysis of responses shows that the most dominant barriers to entry into the DB market are, namely, lack of design expertise, lack of interest from owners, lack of suitable organization structure, lack of DB specialists, and lack of credit record system. Analysis of variance indicates that there is no difference of opinions among the respondent groups of academia, government departments, state-owned company, and private company, at the 5% significance level, on most of the barriers to entry. Finally, the underlying dimensions of barriers to entry in the DB market were investigated through factor analysis. The results indicate that there are six major underlying dimensions of entry barriers in DB market, which include, namely, the competence of design-builders, difficulty in project procurement, characteristics of DB projects, lack of support from public sectors, the competence of DB owners, and the immaturity of DB market. These findings are useful for both potential and incumbent design-builders to understand and analyze the DB market in China.

Case studies of statutory interventions into the common law concept of charity

This paper considers four examples of statutory interventions into the common law concept of charity, namely, those of Pennsylvania, Barbados, the definition recommended by the Report of the Inquiry into the Definition of Charities in Australia, and the Recreational Charities legislation of the United Kingdom. It comments on some issues affecting each style of intervention. The paper does not argue against statutory intervention but submits that legislative changes are best made by deeming a particular purpose to be charitable, or not charitable, so that, except to that extent, the common law concept remains intact – this is the approach adopted by the Recreational Charities legislation.

An investigation into the effect of amphiphilic siloxane oligomers on dermal fibroblasts

This study investigates the effect of well-defined poly(dimethylsiloxane)-poly(ethylene glycol) (PDMS-PEG) ABA linear block co-oligomers on the proliferation of human dermal fibroblasts. The co-oligomers assessed ranged in molecular weight (MW) from 1335 to 5208 Da and hydrophilic-lipophilic balance (HLB) from 5.9 to 16.6 by varying the number of both PDMS and PEG units. In general, it was found that co-oligomers of low MW or intermediate hydrophilicity significantly reduced fibroblast proliferation. A linear relationship between down-regulation of fibroblast proliferation, and the ratio HLB/MW was observed at concentrations of 0.1 and 1.0 wt % of the oligomers. This enabled the structures with highest efficiency to be determined. These results suggest the possible use of the PEG-PDMS-PEG block co-oligomers as an alternative to silicone gels for hypertrophic scar remediation.

Explorations into the Sociology of Criminal Justice and Punishment. Leaving the Modernist Project Behind

Law has been a close partner to sociology from its very beginning, and the partnership often has proven to be extremely prolific for sociology. Grand theories as well as vital conceptual tools can be counted among its offspring. Both disciplines share the common ground of socio-legal studies, which has developed into a nearly independent interdisciplinary enterprise where legal scholars and sociologists happily meander between the normative and the analytical. From the vast array of topics in the field of socio-legal studies I select the sociology of criminal justice and punishment in order to demonstrate the characteristics of this relationship. The partnership between sociology and law emerged as part of the modernization project in the 19th and 20th centuries, and the sociology of punishment was part of this endeavour. Rooted in a strong tradition of old (Durkheim) and new (Elias, Foucault) classics, recent developments in this field are leaving the idea of an `unproblematically modern punishment' (Whitman, 2005a) behind, and new fields of inquiry for comparative lawyers and sociologists are opening up.