A national satellite-based land-use regression model for air pollution exposure assessment in Australia

Land-use regression (LUR) is a technique that can improve the accuracy of air pollution exposure assessment in epidemiological studies. Most LUR models are developed for single cities, which places limitations on their applicability to other locations. We sought to develop a model to predict nitrogen dioxide (NO2) concentrations with national coverage of Australia by using satellite observations of tropospheric NO2 columns combined with other predictor variables. We used a generalised estimating equation (GEE) model to predict annual and monthly average ambient NO2 concentrations measured by a national monitoring network from 2006 through 2011. The best annual model explained 81% of spatial variation in NO2 (absolute RMS error=1.4 ppb), while the best monthly model explained 76% (absolute RMS error=1.9 ppb). We applied our models to predict NO2 concentrations at the ~350,000 census mesh blocks across the country (a mesh block is the smallest spatial unit in the Australian census). National population-weighted average concentrations ranged from 7.3 ppb (2006) to 6.3 ppb (2011). We found that a simple approach using tropospheric NO2 column data yielded models with slightly better predictive ability than those produced using a more involved approach that required simulation of surface-to-column ratios. The models were capable of capturing within-urban variability in NO2, and offer the ability to estimate ambient NO2 concentrations at monthly and annual time scales across Australia from 2006–2011. We are making our model predictions freely available for research.

The influence of fleet safety climate on occupational driver behaviours

The concepts of traffic safety culture and climate hold considerable impact on road safety outcomes. Data sourced from four Australian organisations revealed a five factor structure that was consistent with previous research, which were: management commitment; work demands; relationships; appropriateness of rules; and communication. Correlation and regression analyses were conducted to identify which aspects of fleet safety climate were related to driver behaviours. The findings suggest that organisations may be able to reduce the likelihood of employees engaging in unsafe driving behaviours as a result of fatigue or distractions through increasing aspects of fleet safety climate, including: management commitment; level of trust; safety communication; appropriateness of work demands; and appropriateness of safety policies and procedures. To assist practitioners in enhancing fleet safety climate and managing occupational road risks, recommendations are made based on these findings, such as fostering a supportive environment of mutual responsibility.

Social support, self-efficacy, trauma and well-being in emergency medical dispatchers

Emergency Medical Dispatchers (EMDs) respond to crisis calls for ambulance; they dispatch paramedics and provide emotional and medical assistance to callers. Despite the stressful nature and exposure to potentially traumatising events in this role, there has been no published research specifically investigating well-being or posttraumatic growth among EMDs. Extrapolating from research conducted among other emergency services workers (e. g., paramedics, police), literature attests to the importance of self efficacy and social support in promoting mental health in emergency service workers. Therefore, this study assessed the impact of self efficacy, and giving and receiving social support on psychological well-being, posttraumatic growth (PTG), and symptoms of posttraumatic stress disorder (PTSD). Sixty EMDs (50% response rate) completed an online questionnaire. Three hierarchical multiple regression analyses were conducted to ascertain predictors of well-being, PTG and PTSD. Receiving social support emerged as a significant positive predictor of well-being and PTG, and a significant negative predictor of PTSD. Self efficacy was found to significantly and positively predict well-being, and shift-work was found to significantly and negatively predict PTSD. These results highlight that self efficacy and receiving social support are likely to be important for enhancing well-being within this population, and that receiving social support is also likely to facilitate positive post-trauma responses. Such findings have implications for the way emergency service personnel are educated with reference to aspects of mental health and how best to support personnel in order to achieve optimal mental health outcomes for all.

The psychology of containment : mis/representing emotional and behavioural difficulties in Australian schools

The number of students in special schools has increased at a rapid rate in some Australian states, due in part to increased enrolment under the categories of emotional disturbance (ED) and behaviour disorder (BD). Nonetheless, diagnostic distinctions between ED and BD are unclear. Moreover, despite international findings that students with particular backgrounds are over-represented in special schools, little is known about the backgrounds of students entering such settings in Australia. This study examined the government school enrolment data from New South Wales, the most populous of the Australian states. Linear and quadratic trends were used to describe the numbers and ages of students enrolled in special schools in the ED and BD categories. Changes between 1997 and 2007 were observed. Results showed an over-representation of boys that increased across the decade and a different pattern across age for boys and girls. Consistent with international findings, these results indicate that trends in special school placements are unrelated to disability prevalence in the population. Rather, it is suggested that schools act to preserve time and resources for others by removing their more challenging students: most typically, boys.

Examining the predictive value of combining the theory of planned behaviour and the volunteer functions inventory

The functions of the volunteer functions inventory were combined with the constructs of the theory of planned behaviour (i.e., attitudes, subjective norms, and perceived behavioural control) to establish whether a stronger, single explanatory model prevailed. Undertaken in the context of episodic, skilled volunteering by individuals who were retired or approaching retirement (N = 186), the research advances on prior studies which either examined the predictive capacity of each model independently or compared their explanatory value. Using hierarchical regression analysis, the functions of the volunteer functions inventory (when controlling for demographic variables) explained an additional 7.0% of variability in individuals’ willingness to volunteer over and above that accounted for by the theory of planned behaviour. Significant predictors in the final model included attitudes, subjective norms and perceived behavioural control from the theory of planned behaviour and the understanding function from the volunteer functions inventory. It is proposed that the items comprising the understanding function may represent a deeper psychological construct (e.g., self-actualisation) not accounted for by the theory of planned behaviour. The findings highlight the potential benefit of combining these two prominent models in terms of improving understanding of volunteerism and providing a single parsimonious model for raising rates of this important behaviour.

Factors influencing bike share membership : an analysis of Melbourne and Brisbane

The number of bike share programs has increased rapidly in recent years and there are currently over 700 programs in operation globally. Australia’s two bike share programs have been in operation since 2010 and have significantly lower usage rates compared to Europe, North America and China. This study sets out to understand and quantify the factors influencing bike share membership in Australia’s two bike share programs located in Melbourne and Brisbane. An online survey was administered to members of both programs as well as a group with no known association with bike share. A logistic regression model revealed several significant predictors of membership including reactions to mandatory helmet legislation, riding activity over the previous month, and the degree to which convenience motivated private bike riding. In addition, respondents aged 18 - 34 and having docking station within 250m of their workplace were found to be statistically significant predictors of bike share membership. Finally, those with relatively high incomes increased the odds of membership. These results provide insight as to the relative influence of various factors impacting on bike share membership in Australia. The findings may assist bike share operators to maximize membership potential and help achieve the primary goal of bike share – to increase the sustainability of the transport system.

A Gaussian pseudolikelihood approach for quantile regression with repeated measurements

To enhance the efficiency of regression parameter estimation by modeling the correlation structure of correlated binary error terms in quantile regression with repeated measurements, we propose a Gaussian pseudolikelihood approach for estimating correlation parameters and selecting the most appropriate working correlation matrix simultaneously. The induced smoothing method is applied to estimate the covariance of the regression parameter estimates, which can bypass density estimation of the errors. Extensive numerical studies indicate that the proposed method performs well in selecting an accurate correlation structure and improving regression parameter estimation efficiency. The proposed method is further illustrated by analyzing a dental dataset.

Approximation of Bayesian predictive p-values with regression ABC

In the Bayesian framework a standard approach to model criticism is to compare some function of the observed data to a reference predictive distribution. The result of the comparison can be summarized in the form of a p-value, and it's well known that computation of some kinds of Bayesian predictive p-values can be challenging. The use of regression adjustment approximate Bayesian computation (ABC) methods is explored for this task. Two problems are considered. The first is the calibration of posterior predictive p-values so that they are uniformly distributed under some reference distribution for the data. Computation is difficult because the calibration process requires repeated approximation of the posterior for different data sets under the reference distribution. The second problem considered is approximation of distributions of prior predictive p-values for the purpose of choosing weakly informative priors in the case where the model checking statistic is expensive to compute. Here the computation is difficult because of the need to repeatedly sample from a prior predictive distribution for different values of a prior hyperparameter. In both these problems we argue that high accuracy in the computations is not required, which makes fast approximations such as regression adjustment ABC very useful. We illustrate our methods with several samples.

Liking for high fat foods in patients with Obstructive Sleep Apnoea

Excess weight and obesity are factors that are strongly associated with risk for Obstructive Sleep Apnoea (OSA).Weight loss has been associated with improvements in clinical indicators of OSA severity; however, patients’ beliefs about diet change have not been investigated. This study utilized a validated behaviour change model to estimate the relationship between food liking, food intake and indices of OSA severity. Two-hundred and six OSA patients recruited from a Sleep Disorders Clinic completed standardized questionnaires of: a) fat and fibre food intake, food liking, and food knowledge and; b) attitudes and intentions towards fat reduction. OSA severity and body mass index (BMI) were objectively measured using standard clinical guidelines. The relationship between liking for high fat food and OSA severity was tested with hierarchical regression. Gender and BMI explained a significant 20% of the variance in OSA severity, Fibre Liking accounted for an additional 6% (a negative relationship), and Fat Liking accounted for a further 3.6% of variance. Although the majority of individuals (47%) were currently “active” in reducing fat intake, overall the patients’ dietary beliefs and behaviours did not correspond. The independent relationship between OSA severity and liking for high fat foods (and disliking of high fibre foods) may be consistent with a two-way interaction between sleep disruption and food choice. Whilst the majority of OSA patients were intentionally active in changing to a healthy diet, further emphasis on improving healthy eating practices and beliefs in this population is necessary.

The role of resilience and mTBI as predictors of postconcussional disorder symptomatology

Objective Resilience is 1 of several factors that are thought to contribute to outcome following mild traumatic brain injury (mTBI). This study explored the predictors of the postconcussional syndrome (PCS) symptoms that can occur following mTBI. We hypothesized that a reported recent mTBI and lower psychological resilience would predict worse reported PCS symptomatology. Method 233 participants completed the Neurobehavioral Symptom Inventory (NSI) and the Brief Resilience Scale (BRS). Three NSI scores were used to define PCS symptomatology. A total of 35 participants reported an mTBI (as operationally defined by the World Health Organization) that was sustained between 1 and 6 months prior to their participation (positive mTBI history); the remainder reported having never had an mTBI. Results Regression analyses revealed that a positive reported recent mTBI history and lower psychological resilience were significant independent predictors of reported PCS symptomatology. These results were found for the 3 PCS scores from the NSI, including using a stringent caseness criterion, p < .05. Demographic variables (age and gender) were not related to outcome, with the exception of education in some analyses. Conclusion The results demonstrate that: (a) both perceived psychological resilience and mTBI history play a role in whether or not PCS symptoms are experienced, even when demographic variables are considered, and; (b) of these 2 variables, lower perceived psychological resilience was the strongest predictor of PCS-like symptomatology.

Primary and secondary neural networks of auditory prepulse inhibition: a functional magnetic resonance imaging study of sensorimotor gating of the human acoustic startle response

Feedforward inhibition deficits have been consistently demonstrated in a range of neuropsychiatric conditions using prepulse inhibition (PPI) of the acoustic startle eye-blink reflex when assessing sensorimotor gating. While PPI can be recorded in acutely decerebrated rats, behavioural, pharmacological and psychophysiological studies suggest the involvement of a complex neural network extending from brainstem nuclei to higher order cortical areas. The current functional magnetic resonance imaging study investigated the neural network underlying PPI and its association with electromyographically (EMG) recorded PPI of the acoustic startle eye-blink reflex in 16 healthy volunteers. A sparse imaging design was employed to model signal changes in blood oxygenation level-dependent (BOLD) responses to acoustic startle probes that were preceded by a prepulse at 120 ms or 480 ms stimulus onset asynchrony or without prepulse. Sensorimotor gating was EMG confirmed for the 120-ms prepulse condition, while startle responses in the 480-ms prepulse condition did not differ from startle alone. Multiple regression analysis of BOLD contrasts identified activation in pons, thalamus, caudate nuclei, left angular gyrus and bilaterally in anterior cingulate, associated with EMGrecorded sensorimotor gating. Planned contrasts confirmed increased pons activation for startle alone vs 120-ms prepulse condition, while increased anterior superior frontal gyrus activation was confirmed for the reverse contrast. Our findings are consistent with a primary pontine circuitry of sensorimotor gating that interconnects with inferior parietal, superior temporal, frontal and prefrontal cortices via thalamus and striatum. PPI processes in the prefrontal, frontal and superior temporal cortex were functionally distinct from sensorimotor gating.

Functional brain maps of Tower of London performance : a positron emission tomography and functional magnetic resonance imaging study

Regional cerebral blood flow (rCBF) and blood oxygenation level-dependent (BOLD) contrasts represent different physiological measures of brain activation. The present study aimed to compare two functional brain imaging techniques (functional magnetic resonance imaging versus [15O] positron emission tomography) when using Tower of London (TOL) problems as the activation task. A categorical analysis (task versus baseline) revealed a significant BOLD increase bilaterally for the dorsolateral prefrontal and inferior parietal cortex and for the cerebellum. A parametric haemodynamic response model (or regression analysis) confirmed a task-difficulty-dependent increase of BOLD and rCBF for the cerebellum and the left dorsolateral prefrontal cortex. In line with previous studies, a task-difficulty-dependent increase of left-hemispheric rCBF was also detected for the premotor cortex, cingulate, precuneus, and globus pallidus. These results imply consistency across the two neuroimaging modalities, particularly for the assessment of prefrontal brain function when using a parametric TOL adaptation.