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Background: Regression-based research has successfully identified independent predictors of smoking cessation, both its initiation and maintenance. However, it is unclear how these various independent predictors interact with each other and conjointly influence smoking behaviour. As a proof-of-concept, this study used decision tree analysis (DTA) to identify the characteristics of smoker subgroups with high versus low smoking cessation initiation probability based on the conjoint effects of four predictor variables, and determine any variations by socio-economic status (SES)., Methods: Data come from the Australian arm of the ITC project, a longitudinal cohort study of adult smokers followed up approximately annually. Reported wanting to quit smoking, worries about smoking negative health impact, quitting self-efficacy and quit intentions assessed in 2005 were used as predictors and reported quit attempts at the 2006 follow-up survey were used as the outcome for the initial model calibration and validation analyses (n = 1475), and further cross-validated using the 2012-2013 data (n = 787)., Results: DTA revealed that while all four predictor variables conjointly contributed to the identification of subgroups with high versus low smoking cessation initiation probability, quit intention was the most important predictor common across all SES strata. The relative importance of the other predictors showed differences by SES., Conclusions: Modifiable characteristics of smoker subgroups associated with making a quit attempt and any variations by SES can be successfully identified using a decision tree analysis approach, to provide insights as to who might benefit from targeted intervention, thus, underscoring the value of this approach to complement the conventional regression-based approach., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Introduction: The objective was to compare the diagnostic accuracy of the decision tree analysis prediction model P1, which incorporates the human chorionic gonadotropin (hCG) ratio (hCG 48 hours/hCG 0 hour), and risk prediction model M4 in the management of women with pregnancy of unknown location (PUL)., Material and Methods: A retrospective diagnostic accuracy study was performed on PUL data collected between August 2011 and September 2018. Women with a PUL were prospectively managed according to the P1 prediction model, which utilizes the hCG ratio and, if necessary, a day (D) 7 hCG. We compared the performance of P1 with the M4 model, a logistic regression mathematical model using initial hCG and hCG ratio, to classify PULs as low risk (failed PUL [failed] or intrauterine pregnancy) or high risk (ectopic pregnancy or persistent PUL). The reference standard was defined as the final PUL outcome., Results: Transvaginal ultrasound was done in 3847 consecutive women for early pregnancy complications, 437 (11.3%) of whom were classified as PUL. Final analysis comprised 413 cases with complete data. Final PUL clinical outcomes were: 247 (59.8%) failed PUL, 94 (22.7%) intrauterine pregnancy, 49 (11.8%) ectopic pregnancy and 23 (5.5%) persistent PUL. The sensitivity of P1 and M4 in predicting high-risk PUL were 81.9% (95% confidence interval [CI] 71.1-90.0) and 80.6% (95% CI 69.5-88.9), respectively. The specificities were 74.5% (95% CI 69.5-79.1) and 75.6% (95% CI 70.7-80.1), respectively., Conclusions: P1 and M4 performed similarly with respect to diagnostic accuracy in predicting PUL outcome. P1 needs to be externally validated., (© 2019 Nordic Federation of Societies of Obstetrics and Gynecology.)

The aim of this study was to assess if tactical and technical performance indicators (PIs) could be used in combination to model match outcomes in Australian Football (AF). A database of 101 technical PIs and 14 tactical PIs from every match in the 2009-2016 Australian Football League (AFL) seasons was merged. Two outcome measures Win-loss and Score margin were used as dependent variables. The top 45 ranked technical and tactical PIs from a feature selection process were used to model match outcome using decision tree and Generalised Linear Models (GLMs). Of the top 45 selected features, this included seven tactical PIs. The Win-loss-based Decision tree model achieved a classification accuracy of 89.0% and GLM 93.2%. A Score margin-based GLM achieved a root mean squared error (RMSE) of 6.9 points. A combined approach to the classification of match outcomes provided no improvement in model accuracy compared with previous literature. However, this study has established the relative importance of technical and tactical measures of performance in relation to successful team performance in AF.

Background: While associations between specific risk factors and subsequent suicidal thoughts or behaviours have been widely examined, there is limited understanding of the interplay between risk factors in the development of suicide risk. This study used a decision tree approach to develop individual models of suicide risk and identify the risk factors for suicidality that are important for different subpopulations., Methods: In a population cohort of 6656 Australian adults, the study examined whether measures of mental health, physical health, personality, substance use, social support, social stressors and background characteristics were associated with suicidal ideation and suicidal behaviours after four-year follow-up., Results: Previous suicidality, anxiety symptoms, depression symptoms, neuroticism and rumination were the strongest predictors of suicidal ideation and behaviour after four years. However, divergent factors were predictive of suicidal thoughts and behaviours across the spectrum of mental health. In particular, substance use was only associated with suicidal thoughts and behaviours in those with moderate levels of anxiety or depression., Limitations: Most of the measurements were based on self-report. Further research is required to assess whether changes in risk factors lead to changes in suicidality., Conclusions: Examining suicide risk factors using decision trees is a promising approach for developing individualised assessments of suicide risk and tailored intervention programs., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Global declines in ecosystem extent and condition mean there is an increasing demand for recovery and conservation plans. Conservation plans for ecological communities require a management framework with measurable, time‐bound objectives. Efficient and structured processes that facilitate timely and comparable conservation plans are essential, especially where resources are constrained. We describe a process to streamline the development of conservation plans by combining functionally similar community sub‐types into a multi‐community State and Transition Model that can be used to guide conservation planning. We demonstrate this approach in a case study using eucalypt dominated woodlands of southern Australia—an ecosystem which occupies a vast geographical range across temperate Australia and includes many distinct vegetation communities, a growing number of which are endangered or threatened. Australian woodland ecologists (grouped according to their knowledge of three broad woodland sub‐types) were asked to develop causal chains to describe all factors associated with transitions among woodland condition states, and estimate the likelihoods associated with each transition at two time‐scales. The resultant State and Transition Model includes a set of eight general condition states that are common to eucalypt dominated woodlands, and 364 unique causal chains describing the drivers of all plausible transitions. We include an example of how the same information can be presented as a series of decision trees aimed at supporting on‐ground management decisions. The case study demonstrates that it is possible to construct a detailed State and Transition Model that synthesizes knowledge across multiple similar vegetation communities. To date, State and Transition Models focused on single communities or a smaller spatial scale, and this is the first attempt to construct a nationally relevant multi‐community State and Transition Model via a structured and participatory process. Synthesis and applications. This approach can be applied at multiple spatial scales to improve and streamline the development of robust conservation plans to improve how we plan for, implement and measure global biodiversity outcomes. [ABSTRACT FROM AUTHOR]

Background: Older people often take multiple medications. It is a policy priority to facilitate older people to stay at home longer. Three-quarters of nursing home placements in the US are preceded by a hospitalization., Objective: To investigate the association between polypharmacy and medication regimen complexity with hospital discharge destination among older people., Methods: This prospective cohort study comprised patients aged ≥70 years consecutively admitted to the Geriatric Evaluation and Management unit at a tertiary hospital in Adelaide, Australia, between October 2010 and December 2011. Medication regimen complexity at discharge was calculated using the 65-item validated Medication Regimen Complexity Index (MRCI). Unadjusted and adjusted relative risks (RRs) with 95 % confidence intervals (CIs) were calculated for medication-related factors associated with discharge directly to home versus non-community settings (rehabilitation, transition care, and residential aged care)., Results: From 163 eligible patients, 87 were discharged directly to home (mean age 84.6 years, standard deviation [SD] 6.9; mean MRCI 26.1, SD 9.7), while 76 were discharged to non-community settings (mean age 85.8 years, SD 5.8; mean MRCI 29.9, SD 13.2). After adjusting for age, sex, comorbidity, and activities of daily living, having a high medication regimen complexity (MRCI >35) was inversely associated with discharge directly to home (RR 0.39; 95 % CI 0.20-0.73), whereas polypharmacy (≥9 medications) was not significantly associated with discharge directly to home (RR 0.97; 95 % CI 0.53-1.58)., Conclusion: Having high medication regimen complexity was inversely associated with discharge directly to home, while polypharmacy was not associated with discharge destination.

Study Design: This was a prospective observational study., Objectives: To review airway management of patients with acute cervical spinal cord injury (CSCI) who are admitted to the intensive care unit (ICU) and to develop a classification and regression tree (CART) to direct clinical decision making in airway management., Setting: This study was carried out in Australia., Methods: All patients with CSCI who required intubation and mechanical ventilation and who were admitted to ICU in three tertiary hospitals in Melbourne between October 2004 and May 2009 and two other interstate hospitals between December 2004 and December 2005 were included. Airway management was recorded., Results: A total of 114 patients were included. Tracheostomy insertion occurred in 68 patients (59.7%). Using CART analysis, it was found that the variables forced vital capacity, the volume of pulmonary secretion and gas exchange were predictive of airway management on 82.3% occasions with an 8.7% extubation failure rate., Conclusion: A CART can be useful in clinical decision making regarding airway management in CSCI.

Background: Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk., Methods: The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves., Results: The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors., Conclusion: The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

Objective: To develop a diagnostic algorithm (decision tree) to improve the ability to identify or predict medically important spider bites (funnel-web and redback spiders) from information about the circumstances and initial clinical effects of spider bites., Methods: A dataset of definite spider bites with expert identification of all spiders was used from a previous Australia-wide prospective study. Spider bites were categorized as: big black spider (BBS), redback spider (RED) and other spider (OTH). Big black spider included funnel-web spiders (most medically significant), but also other spiders of similar appearance. Fifteen predictor variables were based on univariate analysis from previous studies and clinical experience. They included information about the circumstances and early clinical effects of bites. The data were analyzed using CART (Classification and Regression Trees), a 'decision tree' algorithm used to create a tree-like structure to describe a data set., Results: Of 789 spider bites there were 49 (6.2%) bites by BBS, 68 (8.6%) bites by RED and 672 (85.2%) bites by OTH. A decision tree was developed that included six predictor variables (fang marks/bleeding; state/territory; local diaphoresis; month; time of day; and proximal or distal bite region). The decision tree accurately classified 47 out of the 49 (96%) BBS, and no funnel-web spiders were incorrectly classified (100% sensitivity). Two hundred and forty-four of 789 were classified as OTH and included no BBS., Conclusions: A decision tree based on a small amount of information about the circumstances and early clinical effects of spider bites safely predicted all funnel-web spider bites. Application of this algorithm would allow the early institution of appropriate treatment for funnel-web spider bites and the immediate discharge of 31% as other spider bites (reassurance only).

Financial cutbacks and budgetary constraints continue to be a part of government policy, and impact deeply on many aspects of daily life in Australia in the late 90s. Health care is not immune from these measures, and nurses and other allied health professionals are having to manage increasingly complex patient care with less funding and resources. Patients undergoing chemotherapy and radiotherapy can usually be managed on an outpatient basis, but need round the clock specialist nursing care at home following treatment which can have unpleasant side effects. Home nursing is an expensive option, but it has been shown in the US that a specialist nurse can triage by telephone, providing expert and timely advice and care to this vulnerable group, preventing admission to hospital and providing family support. The purpose of this paper is to demonstrate that, despite budget constraints, an improved cost effective service can offer clients expert care at a fraction of the cost of alternative services. By Sally Koodiaroff.

A method of costing clinical laboratory tests is described in which total laboratory costs are apportioned into 3 components: an 'organizational overhead cost' to describe the cost of running an organization; a 'cost per episode' to describe the costs incurred per request; and a 'cost per test' to describe the actual analytical costs of tests performed. A tree-like structural model is described which allows for distribution of this 'cost per test' component to the contributing tests and the calculation of the true 'cost per test'. The implementation of this model on a computer spreadsheet has been achieved. When combined with the other cost components, the true total costs of laboratory testing can be determined for any particular test. The model also allows calculation of the incremental and marginal costs of a test and permits optimization of laboratory test frequencies and work-load to minimize laboratory analytical costs.

Background: This study aimed to evaluate the cost-effectiveness of a telehealth coaching intervention to prevent gestational diabetes mellitus (GDM) and to calculate the breakeven point of preventing GDM. Methods: Data to inform the economic evaluation model was sourced directly from the large quaternary hospital in Brisbane, where the Living Well during Pregnancy (LWdP) program was implemented, and further supplemented with literature-based estimates where data had not been directly collected in the trial. A cost-effectiveness model was developed using a decision tree framework to estimate the potential for cost savings and quality of life improvement. A total of 1,315 pregnant women (49% with a BMI 25-29.9, and 51% with a BMI ≥ 30) were included in the analyses. Results: The costs of providing routine care and routine care plus LWdP coaching intervention to pregnant women were calculated to be AUD 20,933 and AUD 20,828, respectively. The effectiveness of the LWdP coaching program (0.894 utility) was slightly higher compared to routine care (0.893). Therefore, the value of the incremental cost-effectiveness ratio (ICER) was negative, and it indicates that the LWdP coaching program is a dominant strategy to prevent GDM in pregnant women. We also performed a probabilistic sensitivity analysis using Monte Carlo simulation through 1,000 simulations. The ICE scatter plot showed that the LWdP coaching intervention was dominant over routine care in 93.60% of the trials using a willingness to pay threshold of AUD 50,000. Conclusion: Findings support consideration by healthcare policy and decision makers of telehealth and broad-reach delivery of structured lifestyle interventions during pregnancy to lower short-term costs associated with GDM to the health system. [ABSTRACT FROM AUTHOR]

Assessment of the spatial distribution of potential pathways of sediment transport and the degree of linkage between sediment sources and the channel network within a watershed represents a valuable analysis for informing management decisions on sediment yield and transfer. Given the limitations of conventional methods for determining index of sediment connectivity (IC), there is a need to provide a flexible and efficient approach with the ability to apply different factors. In this regard, five decision tree-based machine learning models: M5 prime (M5P), random tree (RT), random forest (RF), alternating model tree (AMT), and reduced error pruning tree (REPT) were tested using geomorphic and climatic factors. Two databases were constructed with 200 and 1600 classes at 50 watersheds in Queensland, Australia. In these models, IC was assessed as an output parameter and six attributes that affect IC were assigned as input parameters (i.e., elevation, slope, area, length of stream channel, normalized difference vegetation index, and rainfall). Statistical validation and comparison of model predictions with calculated IC values based on the approach of Borselli et al. (Catena 75:268–277, 2008) were performed. Based on the statistical criteria, the RF model produced the most robust estimations of IC compared to other models and performed very well for IC modelling, especially in smaller subsections of watersheds. Accordingly, these findings can play an effective role for implementing watershed management and soil and water resources management measures. [ABSTRACT FROM AUTHOR]

Meteorological phenomena is an area in which a large amount of data is generated and where it is more difficult to make predictions about events that will occur due to the high number of variables on which they depend. In general, for this, probabilistic models are used that offer predictions with a margin of error, so that in many cases they are not very good. Due to the aforementioned conditions, the use of machine learning algorithms can serve to improve predictions. This article describes an exploratory study of the use of machine learning to make predictions about the phenomenon of rain. To do this, a set of data was taken as an example that describes the measurements gathered on rainfall in the main cities of Australia in the last 10 years, and some of the main machine learning algorithms were applied (knn, decision tree, random forest, and neural networks). The results show that the best model is based on neural networks. [ABSTRACT FROM AUTHOR]

This study introduces data mining methods to accounting education scholarship to explore the relationship between accounting students' current academic performance (grades), demographic information, pre-university entrance scores and predicted academic performance. It adopts a C4.5 classification algorithm based on decision-tree analysis to examine 640 accounting students enrolled in an undergraduate accounting program at an Australian university. A significant contribution of this study is improved prediction of academic performance and identification of characteristics of students deemed to be at risk. By partitioning students into sub-groups based on tertiary entrance scores and employing clustering of study units, this study facilitates a more nuanced understanding of predictor attributes. Key findings were the dominance of a cluster of second year units in predicting students' later academic performance; that gender did not influence performance; and that performance in first year at university, rather than secondary school grades, was the most important predictor of subsequent academic performance. [ABSTRACT FROM AUTHOR]

Ex situ ('off‐site') management refers to keeping species in artificial conditions away from their natural habitat and includes captive breeding facilities, botanical gardens and seed banks. There is scope for ex situ programmes to be more commonly used for supplementing or establishing wild populations. However, undertaking ex situ management comes with risks, costs and uncertainties, which must be assessed in the context of available in situ ('on‐site') management options. The PACES (Planning and Assessment for Conservation through Ex situ management) tool tailors the principles of structured decision‐making to the specific problem of assessing and comparing ex situ and in situ management options. We applied the PACES tool to the mahogany glider (Petaurus gracilis), a threatened arboreal marsupial endemic to north Queensland, Australia. Through an expert elicitation process, we predicted the likely benefits of an ex situ and two in situ management options, as compared to a baseline 'do‐nothing' scenario. The 'in situ plus' alternative (where extra resources are dedicated to in situ management) was predicted to result in the largest population increase according to the participants' best estimates. However, this benefit came at a much larger cost than the ex situ alternative, and without the benefit of an ex situ insurance population. The PACES tool assessment allowed the Mahogany Glider Recovery Team to document and plan the financial costs, risks and benefits of potential future management options for the mahogany glider, laying a transparent basis for future assessment and decision‐making. [ABSTRACT FROM AUTHOR]

Annual turnover of home care workers represents a huge loss of revenue and is a key source of inefficiency in the home health care industry. In this article, we propose a data-driven approach to monitor employee churn and to capture the evolution of employee intent to leave. Unlike most papers in the literature, we use machine learning techniques to analyze over 2 million visits in the US, Canada, and Australia between 2016 and 2019. Results show that the gap between the number of hours worked and in the contract is the most important factor to predict employee intent to leave, which means an employee should be given as many hours as requested in the contract to improve retention. Secondary results show that having diverse shift lengths and continuity in services and patients seem to be associated with less turnover. [ABSTRACT FROM AUTHOR]

Although it is hard to predict wildfires, risky areas can be systematically assessed and managed. Some of the factors for decision-making are hazard, vulnerability, and risk maps, which are the end product of wildfire mapping. This study deals with wildfire risk analysis in Queensland, Australia. A review of the previous studies focusing on each aspect has been done and used with wildfire records from 2011 to 2019 in Queensland, Australia, to compile the required input models to detect risky wildfire regions. Machine learning (ML) methods of Decision Tree (DT) and Support Vector Machine (SVM) were used to perform hazard assessment. The reason was to select the most accurate outcomes for the rest of the analysis. Among accuracy assessment techniques, the Area Under Curvature (AUC) method was used to evaluate the hazard maps. Prediction rates of 89.21% and 83.78% were obtained for DT and SVM, respectively. The DT prediction value showed that the DT-hazard map was more accurate than the SVM-hazard map. Vulnerability analysis was implemented by assigning weights to each factor according to the literature. Lastly, in order to create the wildfire risk map, the hazard and vulnerability indices were combined. The risk map showed that particularly dense urbanization regions are under future wildfire risk. To perform preliminary land use planning, this output can be used by local governmental authorities. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the Canadian Syncope Risk Score (CSRS) in syncope patients presenting to the ED from an economic perspective, using very‐low and low‐risk patients (CSRS −3 to 0) as a threshold for avoiding hospital admissions. Methods: A decision‐analytic model, specifically a decision‐tree, was developed to evaluate application of the CSRS. A hypothetical cohort of 1000 patients was modelled based on characteristics and outcome of patients enrolled in a clinical validation study performed alongside this evaluation. Several analytic based approaches were used to handle model outputs and uncertainties. Results: For a cohort of 1000 patients, applying the CSRS was associated with 169 less inpatient admissions from the ED, when compared to usual care. There was also a cost‐saving of $8255 per admitted patient, when the CSRS was applied, compared to usual care. Adopting the CSRS was the optimal approach in all scenario analyses and was robust to changes in model parameters. More than three‐quarters (78.6%) of all model simulations showed that applying the CSRS is a cost‐saving approach to managing syncope. There was high confidence in all results, with the approach using the CSRS reducing the costs and number of syncope‐related hospital admissions. Conclusions: Compared to usual care, applying the CSRS appeared as a cost‐effective strategy. This new evidence will help decision‐makers choose cost‐effective approaches for the management of patients presenting to the ED with syncope, as they search for efficient ways to maximise health gain from a finite budget. [ABSTRACT FROM AUTHOR]

This study evaluates the ability of machine learning algorithms to classify tropical depressions (TDs) and tropical storms (TSs) in the western region of the southwest Pacific Ocean (SWPO). Decision rules are generated to predict the environment required for a depression to fully develop into a mature storm, and the most influential predictors in the classification decision are ranked. TD and TS are discriminated based on a maximum sustained wind speed threshold (≥17 ms−1). Various aerosol, thermodynamic, and dynamic parameters are extracted closest to the initiation point of each non‐developing and developing sample. The covariates associated with each labelled sample are used to train a decision tree and random forest model. Results using a testing dataset suggest the random forest approach more accurately distinguishes between non‐developing and developing samples. The classification accuracy of the decision tree and random forest are 72% and 91%, respectively. Random forest outperformed the decision tree by providing higher accuracy in test data. The most important variables for binary classification are sea salt aerosol optical depth (AOD), 1,000 mb relative humidity, and sea surface temperature. AOD is a quantitative estimate of the aerosols presents in the air through the extinction of a ray of light as it passes through the atmosphere. Mean composite maps constructed in an unsupervised manner have been created for the most important variables identified by the random forest classifier during TD and TS events to highlight the difference in geophysical and aerosol variables' climatology during the two different classifications. This work will advance the risk management strategies for northeastern Australia and other SWPO basin islands to control their tropical cyclone related losses through prioritizing forecasting variables that are the strongest predictors of the strengthening of tropical depressions into tropical cyclones. [ABSTRACT FROM AUTHOR]

Objective: Certification by treating physicians of fitness to drive in people with epilepsy creates a conflict of interest that may result in unsafe decisions, damage the doctor‐patient relationship, expose the physician to legal liability and prevent optimal treatment. Ideally, the treating physician should provide objective clinical information to the driver licensing authority (DLA), which then determines fitness or otherwise. However, DLAs in Australia do not employ medical staff and the national standards are complex. Fitness is determined by the treating physician, according to published national standards. The purpose of this study was to determine the feasibility of using a decision tree to determine fitness, according to the Australian standards. Methods: A decision tree was constructed to use clinical data to determine whether a patient met the national standard to drive a private motorcar, failed to meet it or required further assessment. A form was designed to collect the necessary clinical data from the treating physician. A computerized version of the decision tree was then used in a pilot in two Australian states in parallel with the existing certification system. Four hundred thirty‐nine drivers with declared epilepsy and their treating physicians were invited to participate when their annual driver licence review was due. Results: Two hundred fifty‐three (58%) forms were returned. All patients were considered fit to drive by their physician. Seventy‐six percent had not had a seizure for over two years. In 88.1%, there was agreement between the decision tree and treating physician, with 3.6% identified by the decision tree as requiring review. Although considered fit by their physician, 6.3% did not meet the national standard to drive. Significance: The decision tree model is a practical alternative to fitness certification by treating physicians. This Australian pilot can serve as a model for applying objective standards to driving assessments in other jurisdictions, using local driving standards. It has the potential to improve road safety by avoiding the negative effects of certification by treating physicians and can cope with complex standards. It is now in use in two states of Australia. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to determine the impacts of anterior cruciate ligament reconstruction (ACLR) and recent (< 12 months) hamstring strain injury (HSI) on (1) future HSI risk, and (2) eccentric knee flexor strength and between-limb imbalance during the Nordic hamstring exercise. A secondary goal was to examine whether eccentric knee flexor strength was a risk factor for future HSI in athletes with prior ACLR and/or HSI. Methods: In this prospective cohort study, 531 male athletes had preseason eccentric knee flexor strength tests. Injury history was also collected. The main outcome was HSI occurrence in the subsequent competitive season. Results: Overall, 74 athletes suffered at least one prospective HSI. Compared with control athletes, those with a lifetime history of ACLR and no recent HSI had 2.2 (95% confidence interval [CI] 1.1–4.4; p = 0.029) times greater odds of subsequent HSI while those with at least one HSI in the previous 12 months and no history of ACLR had 3.1 (95% CI 1.8–5.4; p < 0.001) times greater odds for subsequent HSI. Only athletes with a combined history of ACLR and recent HSI had weaker injured limbs (p = 0.001) and larger between-limb imbalances (p < 0.001) than uninjured players. An exploratory decision tree analysis suggested eccentric strength may protect against HSI after ACLR. Conclusion: ACLR and recent HSI were similarly predictive of future HSI. Lower levels of eccentric knee flexor strength and larger between-limb imbalances were found in athletes with combined histories of ACLR and recent HSI. These findings may have implications for injury rehabilitation. [ABSTRACT FROM AUTHOR]

Cust, EE, Elsworthy, N, and Robertson, S. Analysis of training loads in elite under 18 Australian rule football players. J Strength Cond Res 32(9): 2521–2528, 2017—Differences in training loads (TLs) between under 18 (U18) Australian rules football (AF) state academy–selected and state academy–nonselected players were investigated. Players were categorized relating to their highest representative level: state academy–selected (n = 9) and TAC cup–level players (n = 38). Data were obtained from an online training–monitoring tool implemented to collect player training and match information across a 20-week period during the regular season. Parameters modeled included AF skills, strength, and other sport training sessions. Descriptive statistics (mean ± SD) and between-group comparisons (Cohen's d) were computed. A J48 decision tree modeled which TL variables could predict selection level. Pooled data showed 60% of weekly training duration consisted of AF training sessions. Similar AF TL were reported between state academy and TAC cup players (1,578 ± 1,264 arbitrary units (AU) vs. 1,368 ± 872 AU; d = 0.05). Although higher TLs were reported for state-selected players comparative with TAC cup in total training (d = 0.20), core stability (d = 0.36), flexibility (d = 0.44), on-feet conditioning (d = 0.26), and off-feet conditioning (d = 0.26). Decision tree analysis showed core stability duration and flexibility TL, the most influential parameters in classifying group selection (97.7% accuracy TAC cup level; 35.8% accuracy state academy level). Insights of U18 AF players' weekly training structures, loads, and characteristics of higher achieving players are provided. This study supports the application of training diaries and session rating of perceived exertion for TL monitoring in junior athletes. [ABSTRACT FROM AUTHOR]

Background: The primary aim of this study was to determine the influence of task constraints, from an ecological perspective, on goal kicking performance in Australian football. The secondary aim was to compare the applicability of three analysis techniques; logistic regression, a rule induction approach and conditional inference trees to achieve the primary aim. In this study, an ecological perspective has been applied to explore the impact of task constraints on shots on goal in the Australian Football League, such as shot type, field location and pressure. Analytical techniques can increase the understanding of competition environments and the influence of constraints on skilled events. Differing analytical techniques can produce varying outputs styles which can impact the applicability of the technique. Logistic regression, Classification Based on Associations rules and conditional inference trees were conducted to determine constraint interaction and their influence on goal kicking, with both the accuracy and applicability of each approach assessed. Results: Each analysis technique had similar accuracy, ranging between 63.5% and 65.4%. For general play shots, the type of pressure and location particularly affected the likelihood of a shot being successful. Location was also a major influence on goal kicking performance from set shots. Conclusions: When different analytical methods display similar performance on a given problem, those should be prioritised which show the highest interpretability and an ability to guide decision-making in a manner similar to what is currently observed in the organisation. [ABSTRACT FROM AUTHOR]

Several credit-scoring models have been developed using ensemble classifiers in order to improve the accuracy of assessment. However, among the ensemble models, little consideration has been focused on the hyperparameters tuning of base learners, although these are crucial to constructing ensemble models. This study proposes an improved credit scoring model based on the extreme gradient boosting (XGB) classifier using Bayesian hyper-parameters optimization (XGB-BO). The model comprises two steps. Firstly, data pre-processing is utilized to handle missing values and scale the data. Secondly, Bayesian hyper-parameter optimization is applied to tune the hyper-parameters of the XGB classifier and used to train the model. The model is evaluated on four widely public datasets, i.e., the German, Australia, Lending club, and Polish datasets. Several state-of-the-art classification algorithms are implemented for predictive comparison with the proposed method. The results of the proposed model showed promising results, with an improvement in accuracy of 4.10%, 3.03%, and 2.76% on the German, Lending club, and Australian datasets, respectively. The proposed model outperformed commonly used techniques, e.g., decision tree, support vector machine, neural network, logistic regression, random forest, and bagging, according to the evaluation results. The experimental results confirmed that the XGB-BO model is suitable for assessing the creditworthiness of applicants. [ABSTRACT FROM AUTHOR]

Lightning-caused wildfires are a significant contributor to burned areas, with lightning ignitions remaining one of the most unpredictable aspects of the fire environment. There is a clear connection between fuel moisture and the probability of ignition; however, the mechanisms are poorly understood and predictive methods are underdeveloped. Establishing a lightning-ignition relationship would be useful in developing a model that would complement early warning systems designed for fire control and prevention. A machine learning (ML) approach was used to define a predictive model for wildfire ignition based on lightning forecasts and environmental conditions. Three different binary classifiers were adopted: a decision tree, an AdaBoost and a Random Forest, showing promising results, with both ensemble methods (Random Forest and AdaBoost) exhibiting an out-ofsample accuracy of 78%. Data provided by a Western Australia wildfire database allowed a comprehensive verification on over 145 lightning-ignited wildfires in regions of Australia during 2016. This highlighted that in a minimum of 71% of the cases the ML models correctly predicted the occurrence of an ignition when a fire was actually initiated. The super-learner developed is planned to be used in an operational context to the enhance information connected to fire management. [ABSTRACT FROM AUTHOR]

Aim: To estimate the change in average cost and length of stay (LOS) for the neonatal birth admission resulting from use of the neonatal early‐onset sepsis (EOS) calculator compared to guideline‐based management, in an Australian perinatal health‐care setting. Methods: A decision‐analytic model (decision tree) was constructed to assess admission cost and LOS with EOS calculator use compared to guideline‐based management. Probabilities of clinical sepsis‐related outcomes were obtained via review of published literature. Costs and average LOS were obtained from Australia's Independent Hospital Pricing Authority. Results: EOS calculator use was associated with a reduction in costs of AUD$25806 and in average LOS of 25.4 days per 1000 babies born. Sensitivity analyses demonstrated greater net benefits could be expected for services where there is a higher baseline rate of antibiotic use. Conclusion: This model demonstrates a significant cost reduction for the neonatal birth admission, associated with use of the EOS calculator as compared to existing guidelines. The net benefit may be greater in Australia, where rates of empiric antibiotic use are reportedly high, compared to some European countries and the United States. Future research opportunities include prospective collection of economic data alongside the introduction of the EOS calculator. [ABSTRACT FROM AUTHOR]

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Background: A cluster randomised controlled trial (cRCT) performed from July 2018 to March 2019 demonstrated the clinical impact of a community pharmacist delivered minor ailment service (MAS) compared with usual pharmacist care (UC). MAS consisted of a technology-based face-to-face consultation delivered by trained community pharmacists. The consultation was guided by clinical pathways for assessment and management, and communication systems, collaboratively agreed with general practitioners. MAS pharmacists were trained and provided monthly practice support by a practice change facilitator. The objective of this study was to assess the cost utility of MAS, compared to UC. Methods: Participants recruited were adult patients with symptoms suggestive of a minor ailment condition, from community pharmacies located in Western Sydney. Patients received MAS (intervention) or UC (control) and were followed-up by telephone 14-days following consultation with the pharmacist. A cost utility analysis was conducted alongside the cRCT. Transition probabilities and costs were directly derived from cRCT study data. Utility values were not available from the cRCT, hence we relied on utility values reported in the published literature which were used to calculate quality adjusted life years (QALYs), using the area under the curve method. A decision tree model was used to capture the decision problem, considering a societal perspective and a 14-day time horizon. Deterministic and probabilistic sensitivity analyses assessed robustness and uncertainty of results, respectively. Results: Patients (n = 894) were recruited from 30 pharmacies and 82% (n = 732) responded to follow-up. On average, MAS was more costly but also more effective (in terms of symptom resolution and QALY gains) compared to UC. MAS patients (n = 524) gained an additional 0.003 QALYs at an incremental cost of $7.14 (Australian dollars), compared to UC (n = 370) which resulted in an ICER of $2277 (95% CI $681.49–3811.22) per QALY. Conclusion: Economic findings suggest that implementation of MAS within the Australian context is cost effective. Trial registration Registered with Australian New Zealand Clinical Trials Registry (ANZCTR) and allocated the ACTRN: ACTRN12618000286246. Registered on 23 February 2018. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to investigate the confusion among project management practitioners about the role of steering committees. Design/methodology/approach: Semi-structured interviews were conducted with highly experienced participants selected from a range of industries and disciplines in Queensland, Australia. Findings: Six separate confusions on the role of steering committees were identified within that practitioner community. However, despite participants expressing various opposing views, they had actually come to the same working arrangements for their committees; all that was missing was a common conceptualisation of these working arrangements and consistent terminology. Research limitations/implications: The paper provides clear evidence to the academic literature that confusion over the role of steering committees actually exists within the practitioner community and identifies six separate ways in which this occurs. It also identifies a problematic error in the widely used PRINCE2 governance model. Clarity in committee governance arrangements will facilitate future research endeavours through the removal of confusion surrounding committee labelling and accountability. Practical implications: A committee decision tree model that guards against all six confusions is proposed for practitioner use, providing a means of avoiding unnecessary internal conflict within organisational governance arrangements. It can be used to check terms of reference of existing or proposed committees, facilitating organisational efficiency and effectiveness. The suggested renaming of project control groups to project coordination groups, and discontinuance of the practice of labelling committees that cannot authorise their decisions as either steering committees or boards, further supports this. Social implications: Reconciliation of terminology with actual practice and the consequent clarity of governance arrangements can facilitate building social and physical systems and infrastructure, benefitting organisations, whether public, charitable or private. Originality/value: Clarity regarding committee accountability can avoid confusion, misunderstanding and their consequent waste of time, resources and money. [ABSTRACT FROM AUTHOR]

Aims: To provide herd managers with a set of decision rules allowing them to predict the likelihood that a juvenile bull is ready for Bull Breeding Soundness Evaluation (BBSE), or breeding, if bodyweight and scrotal circumference are known. Methods: This was a longitudinal study following two groups of young pasture-fed Holstein and Jersey bulls from northwest Tasmania, Australia. Individual scrotal circumference, bodyweight and semen characteristics were recorded at 6-8 weekly intervals, from 6-18 months of age. Classification and regression tree analyses were used to predict the probability that a bull had ≥70% normal sperm morphology based on scrotal circumference and bodyweight measurements. Results: Overall 1,661 scrotal circumference and bodyweight measurements were obtained, and 518 semen samples from 356 bulls were assessed for sperm morphology, from 16 examination sessions that took place between 29 May 2015 and 17 August 2016. Classification and regression tree analyses generated a decision tree for Holstein bulls with four node endpoints, and for Jersey bulls with three node endpoints. Diagnostic test performance showed that for Holstein bulls, using the node endpoints of scrotal circumference ≥27 cm and bodyweight ≥349 kg, 98% had ≥70% normal sperm (positive likelihood ratio 10.4; 95% CI = 2.7-41), and using the node endpoints of scrotal circumference ≥27 cm and bodyweight between 282-349 kg, 89% had ≥70% normal sperm (positive likelihood ratio 1.6; 95% CI = 0.9-2.6). For Jersey bulls, using the node endpoints of bodyweight ≥259 kg and scrotal circumference ≥29 cm, 88% had ≥70% normal sperm (positive likelihood ratio 3.4; 95% CI = 1.6-7.0). Conclusions: This study provides a set of relatively simple decision rules based on bodyweight and scrotal circumference measurements that allows herd managers to assess the likelihood that juvenile bulls are ready for BBSE or breeding. Abbreviations: BBSE: Bull breeding soundness evaluation; BRT: Boosted regression tree [ABSTRACT FROM AUTHOR]

The relationships between team performance indicators and match outcome have been examined in many team sports, however are limited in Australian Rules football. Using data from the 2013 and 2014 Australian Football League (AFL) regular seasons, this study assessed the ability of commonly reported discrete team performance indicators presented in their relative form (standardised against their opposition for a given match) to explain match outcome (Win/Loss). Logistic regression and decision tree (chi-squared automatic interaction detection (CHAID)) analyses both revealed relative differences between opposing teams for “kicks” and “goal conversion” as the most influential in explaining match outcome, with two models achieving 88.3% and 89.8% classification accuracies, respectively. Models incorporating a smaller performance indicator set displayed a slightly reduced ability to explain match outcome (81.0% and 81.5% for logistic regression and CHAID, respectively). However, both were fit to 2014 data with reduced error in comparison to the full models. Despite performance similarities across the two analysis approaches, the CHAID model revealed multiple winning performance indicator profiles, thereby increasing its comparative feasibility for use in the field. Coaches and analysts may find these results useful in informing strategy and game plan development in Australian Rules football, with the development of team-specific models recommended in future. [ABSTRACT FROM PUBLISHER]

Background and Objective: Many guidelines for clinical decisions are hierarchical and nonlinear. Evaluating if these guidelines are used in practice requires methods that can identify such structures and thresholds. Classification and regression trees (CART) were used to analyse prescribing patterns of Australian general practitioners (GPs) for the primary prevention of cardiovascular disease (CVD). Our aim was to identify if GPs use absolute risk (AR) guidelines in favour of individual risk factors to inform their prescribing decisions of lipid-lowering medications.Methods: We employed administrative prescribing information that is linked to patient-level data from a clinical assessment and patient survey (the AusHeart Study), and assessed prescribing of lipid-lowering medications over a 12-month period for patients (n = 1903) who were not using such medications prior to recruitment. CART models were developed to explain prescribing practice. Out-of-sample performance was evaluated using receiver operating characteristic (ROC) curves, and optimised via pruning.Results: We found that individual risk factors (low-density lipoprotein, diabetes, triglycerides and a history of CVD), GP-estimated rather than Framingham AR, and sociodemographic factors (household income, education) were the predominant drivers of GP prescribing. However, sociodemographic factors and some individual risk factors (triglycerides and CVD history) only become relevant for patients with a particular profile of other risk factors. The ROC area under the curve was 0.63 (95% confidence interval [CI] 0.60-0.64).Conclusions: There is little evidence that AR guidelines recommended by the National Heart Foundation and National Vascular Disease Prevention Alliance, or conditional individual risk eligibility guidelines from the Pharmaceutical Benefits Scheme, are adopted in prescribing practice. The hierarchy of conditional relationships between risk factors and socioeconomic factors identified by CART provides new insights into prescribing decisions. Overall, CART is a useful addition to the analyst's toolkit when investigating healthcare decisions. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to present a systematic analysis and review of the state of the art regarding the prediction of absenteeism and temporary incapacity using machine learning techniques. Moreover, the main contribution of this research is to reveal the most successful prediction models available in the literature. A systematic review of research papers published from 2010 to the present, related to the prediction of temporary disability and absenteeism in available in different research databases, is presented in this paper. The review focuses primarily on scientific databases such as Google Scholar, Science Direct, IEEE Xplore, Web of Science, and ResearchGate. A total of 58 articles were obtained from which, after removing duplicates and applying the search criteria, 18 have been included in the review. In total, 44% of the articles were published in 2019, representing a significant growth in scientific work regarding these indicators. This study also evidenced the interest of several countries. In addition, 56% of the articles were found to base their study on regression methods, 33% in classification, and 11% in grouping. After this systematic review, the efficiency and usefulness of artificial neural networks in predicting absenteeism and temporary incapacity are demonstrated. The studies regarding absenteeism and temporary disability at work are mainly conducted in Brazil and India, which are responsible for 44% of the analyzed papers followed by Saudi Arabia, and Australia which represented 22%. ANNs are the most used method in both classification and regression models representing 83% and 80% of the analyzed works, respectively. Only 10% of the literature use SVM, which is the less used method in regression models. Moreover, Naïve Bayes is the less used method in classification models representing 17%. [ABSTRACT FROM AUTHOR]

Remote sensing images classification is the key technology for monitoring forest changes. Texture features have been demonstrated to have better effectiveness than spectral features in the improvement of the classification accuracy. The accuracy of extracting texture information by window-based method depends on the choice of the window size. Moreover, the size should ideally match the spatial scale of the object or class under consideration. However, most of the existing texture feature extraction methods are all based on a single window and do not adequately consider the scale of different objects. Our first proposition is to use a composite window for extracting texture features, which is a small window surrounded by a larger window. Our second proposition is to reinforce the performance of the trained ensemble classifier by training it using only the most important features. Considering the advantages of random forest classifier, such as fast training speed and few parameters, these features feed this classifier. Measures of feature importance are estimated along with the growth of the base classifiers, here decision trees. We aim to classify each pixel of the forest images disturbed by hurricanes and fires in three classes, damaged, not damaged, or unknown, as this could be used to compute time-dependent aggregates. In this study, two research areas—Nezer Forest in France and Blue Mountain Forest in Australia—are utilized to validating the effectiveness of the proposed method. Numerical simulations show increased performance and improved monitoring ability of forest disturbance when using these two propositions. When compared with the reference methods, the best increase of the overall accuracy obtained by the proposed algorithm is 4.77% and 2.96% on the Nezer forest data and Blue Mountain forest data, respectively. [ABSTRACT FROM AUTHOR]

Context. The depth-to a constraint determines how much of the soil profile, and the water it contains, can be accessed by plant roots. Information describing the impacts of soil constraints on available water capacity (AWC) and yield is important for farm management, but is rarely considered in a spatial context. Aims and methods. The depth-to three yield-limiting constraints (sodicity, salinity, and alkalinity) was mapped across ~80 000 ha in northern New South Wales, Australia using machine learning and digital soil mapping techniques. Soil AWC was calculated using soil data and pedotransfer functions, and water use efficiency equations were used to determine potential yield loss due to the presence of soil constraints. From this, the most-limiting constraint to yield was mapped. Key results. One or more constraints were found to be present across 54% of the study area in the upper 1.2 m of the soil profile, overall reducing the AWC by ~50 mm and potential yield by an average of 1.1 t/ha for wheat and 0.8 bales/ha for cotton. Sodicity (Exchangeable Sodium Percentage > 15%) was identified as the most-limiting constraint to yield across the study area. Implications. The simplification of multiple sources of information into a single decision-making tool could prove valuable to growers and farm managers in managing soil constraints and understanding important interactions with available water and yield. [ABSTRACT FROM AUTHOR]

Predictive mapping of seabed sediments based on multibeam bathymetric (BM), and backscatter (BS) data is effective for mapping the spatial distribution of the substrate. A robust modeling technique, the random forest decision tree (RFDT), was used to predict the seabed sediments in an area of the Joseph Bonaparte Gulf, Northern Australia, using the multibeam data and seabed sediment samples collected simultaneously. The results showed that: (1) Using multibeam bathymetry data in addition to multibeam backscatter data improves the prediction performance of the RFDT. In comparison to only multibeam backscatter data, the prediction performance achieved a ~10% improvement in sediment properties; it achieved a ~44.45% improvement of overall accuracy in sediment types, and a ~0.55 improvement in Kappa. (2) The underlying relationships between sediment properties and multibeam data show that there is an opposite non-linear correlation between sediment property-BS and sediment property-BM. For example, there is an obvious negative relationship between %mud-BS at incidence angles of 13° and 21°, but the relationship between %mud-BM is positive. As such, the RFDT is a useful and well-performing method in predicting the relationship between sediment properties and multibeam data and in predicting the distribution of sediment properties and types. However, the sediment prediction method in deep-water areas with high gravel content needs to be further evaluated. [ABSTRACT FROM AUTHOR]

Background: Electronic medical records are increasingly used for research with limited external validation of their data. Objective: This study investigates the validity of electronic medical data (EMD) for estimating diabetes prevalence in general practitioner (GP) patients by comparing EMD with national Bettering the Evaluation and Care of Health (BEACH) data. Method: A "decision tree" was created using inclusion/exclusion of pre-agreed variables to determine the probability of diabetes in absence of diagnostic label, including diagnoses (coded/free-text diabetes, polycystic ovarian syndrome, impaired glucose tolerance, impaired fasting glucose), diabetic annual cycle of care (DACC), glycated haemoglobin (HbA1c) > 6.5%, and prescription (metformin, other diabetes medications). Via SQL query, cases were identified in EMD of five Illawarra and Southern Practice Network practices (30,007 active patients; from 2 years to January 2015). Patient-based Supplementary Analysis of Nominated Data (SAND) sub-studies from BEACH investigating diabetes prevalence (1172 GPs; 35,162 patients; November 2012 to February 2015) were comparison data. SAND results were adjusted for number of GP encounters per year, per patient, and then age–sex standardised to match age–sex distribution of EMD patients. Cluster-adjusted 95% confidence intervals (CIs) were calculated for both datasets. Results: EMD diabetes prevalence (T1 and/or T2) was 6.5% (95% CI: 4.1–8.9). Following age–sex standardisation, SAND prevalence, not significantly different, was 6.7% (95% CI: 6.3–7.1). Extracting only coded diagnosis missed 13.0% of probable cases, subsequently identified through the presence of metformin/other diabetes medications (*without other indicator variables) (6.1%), free-text diabetes label (3.8%), HbA1c result* (1.6%), DACC* (1.3%), and diabetes medications* (0.2%). Discussion: While complex, proxy variables can improve usefulness of EMD for research. Without their consideration, EMD results should be interpreted with caution. Conclusion: Enforceable, transparent data linkages in EMRs would resolve many problems with identification of diagnoses. Ongoing data quality improvement remains essential. [ABSTRACT FROM AUTHOR]

Rationale Little scrutiny has been applied to how 'cost per fall' values have been calculated and applied. This paper presents discourse discussing how the cost of fall statistic could potentially be misleading when applied to inpatient health or residential care settings and provides estimates of the cost of falls and cost of falls per person. Method Burden of disease was estimated using a decision tree approach. Data informing the decision tree were drawn from a retrospective audit of 545 falls in a residential care facility, a visual observation study of 46 residents from the same facility and a cohort study of 186 residents from nine different facilities in Australia. Acute care and transportation costs were extracted from the retrospective audit using incident reports and care note review. The distribution of falls per person and associations between falls, fractures and change in resident mobility were extracted from the cohort study. The association between resident mobility and the amount of time required to perform toileting, transfer and dressing activities was extracted from the visual observational study. Results The minimum 'cost per fall' was estimated to be $AUD 841 and the maximum was $AUD 1024. The 'cost of falls per person' estimate was $AUD 1887 (2008 base year). Conclusions This cost per fall estimate was substantially lower than three previous internationally derived estimates possibly as a consequence of how fall data were collected and modelled in these studies. Cost of falls per person may be a preferable statistic for future use. [ABSTRACT FROM AUTHOR]

Young drivers remain overrepresented in road crashes around the world, with road injury the leading cause of death among adolescents. In addition, the majority of road traffic crashes, fatalities and injuries occur in low- and middle-income countries. All young drivers are at risk due to a breadth of age- and inexperience-related factors; however it is well recognised that young drivers may also intentionally engage in risky driving behaviours which increase their crash risk. The aim of this paper is to examine the self-reported risky driving behaviour of young drivers in Australia, New Zealand (high-income countries), and Colombia (middle-income country), and to explore the utility of a crash risk assessment model in these three countries. Young drivers aged 16–25 years completed the Behaviour of Young Novice Drivers Scale (BYNDS), in addition to self-reporting crash involvement and driving offences. A hierarchical segmentation analysis via decision trees was used to study the relationship between self-reported crashes and risky driving. Young drivers in Colombia reported more risky driving than young drivers in New Zealand, and considerably more risky driving than young drivers in Australia. Significant differences among and across countries in individual BYNDS items were found, and 23.5% of all participants reported they had been involved in a road crash. Handheld mobile phone usage was the strongest predictor of crashes, followed by driving after drinking alcohol, and carrying friends as passengers. Country of origin predicted mobile phone usage, with New Zealand and Colombia grouped in the same decision tree branch which implies no significant differences in the behaviour between these countries. Despite cultural differences in licensing programs and enforcement, young drivers reported engaging in a similar breadth of risky behaviours. Road crashes were explained by mobile phone usage, drink driving and driving with passengers, suggesting interventions should target these three risk factors. Whilst New Zealand and Australia have implemented graduated driver licensing programs, are geographical neighbours, and are high-income countries, the finding that behaviours of young drivers in New Zealand and Colombia were more similar than those of young drivers in New Zealand and Australia merits further investigation. [ABSTRACT FROM AUTHOR]

The authors reflect on pharmacist medication review services. They suggest that pharmacists use clinical pharmacy services to minimize the risks associated with the use of medicines, to increase consumer safety and to optimize patient health outcomes. They argue that medication review services should be structured, funded and reviewed as a suite of interlinked programs.

Sustainable Development Goals (SDGs) are a set of priorities the United Nations and World Bank have set for countries to reach in order to improve quality of life and environment globally by 2030. Free satellite images have been identified as a key resource that can be used to produce official statistics and analysis to measure progress towards SDGs, especially those that are concerned with the physical environment, such as forest, water, and crops. Satellite images can often be unusable due to missing data from cloud cover, particularly in tropical areas where the deforestation rates are high. There are existing methods for filling in image gaps; however, these are often computationally expensive in image classification or not effective at pixel scale. To address this, we use two machine learning methods—gradient boosted machine and random forest algorithms—to classify the observed and simulated 'missing' pixels in satellite images as either grassland or woodland. We also predict a continuous biophysical variable, Foliage Projective Cover (FPC), which was derived from satellite images, and perform accurate binary classification and prediction using only the latitude and longitude of the pixels. We compare the performance of these methods against each other and inverse distance weighted interpolation, which is a well-established spatial interpolation method. We find both of the machine learning methods, particularly random forest, perform fast and accurate classifications of both observed and missing pixels, with up to 0.90 accuracy for the binary classification of pixels as grassland or woodland. The results show that the random forest method is more accurate than inverse distance weighted interpolation and gradient boosted machine for prediction of FPC for observed and missing data. Based on the case study results from a sub-tropical site in Australia, we show that our approach provides an efficient alternative for interpolating images and performing land cover classifications. [ABSTRACT FROM AUTHOR]

Purpose: Pain management within emergency departments (ED) remains challenging. Given that unrelieved pain in children is linked to a number of negative physiological and psychological consequences, optimal management of children's pain is paramount. Many studies exploring children's pain have adopted quantitative methods or sought the perspectives of adults. Compared to adults, studies examining children's views on pain and pain management are limited. This study aimed to explore children's pain experiences, their perception of pain management and expectations of the role of the nurse. Design: This was a qualitative descriptive study using an inductive approach. Methods: Fifteen children, aged 4–8 years who presented to the ED of an Australian tertiary pediatric hospital in acute pain participated. Data were collected using draw, write, and tell (DWT) technique and analyzed using thematic analysis. Results: Three themes emerged (1) "Security," (2) "My pain" with subthemes: "The pain feelings" and "My sad/happy feelings," (3) "Comfort and relief" with subthemes: "Taking my mind off it," "Resting" and "Hospital things." When in pain children needed to feel secure. Parents and nurses were important in fostering a secure environment for children. Children were capable of describing their pain and identified nonpharmacological strategies to help their pain. Practice implications: Children as young as 4 years old can provide detailed accounts of their pain, which extends beyond physical dimensions to include visual, auditory, and sensory features. Nurses need to listen, be honest, and develop trust with children to be helpful. Nonpharmacological pain‐relieving strategies can be implemented by parents and nurses in collaboration with the child. Fostering a secure environment is essential. [ABSTRACT FROM AUTHOR]

Objective To quantify the disparity in incidence of hepatitis B between indigenous and non-indigenous people in Australia, and to estimate the potential impact of a hepatitis B immunization programme targeting non-immune indigenous adults. Methods Using national data on persons with newly acquired hepatitis B disease notified between 2005 and 2012, we estimated incident infection rates and rate ratios comparing indigenous and non-indigenous people, with adjustments for underreporting. The potential impact of a hepatitis B immunization programme targeting non-immune indigenous adults was projected using a Markov chain Monte Carlo simulation model. Findings Of the 54 522 persons with hepatitis B disease notified between 1 January 2005 and 31 December 2012, 1953 infections were newly acquired. Acute hepatitis B infection notification rates were significantly higher for indigenous than non-indigenous Australians. The rates per 100 000 population for all ages were 3.6 (156/4 368 511) and 1.1 (1797/168 449 302) for indigenous and non-indigenous people respectively. The rate ratio of age-standardized notifications was 4.0 (95% confidence interval: 3.7-4.3). If 50% of non-immune indigenous adults (20% of all indigenous adults) were vaccinated over a 10-year programme a projected 527-549 new cases of acute hepatitis B would be prevented. Conclusion There continues to be significant health inequity between indigenous and non-indigenous Australians in relation to vaccine-preventable hepatitis B disease. An immunization programme targeting indigenous Australian adults could have considerable impact in terms of cases of acute hepatitis B prevented, with a relatively low number needed to vaccinate to prevent each case. [ABSTRACT FROM AUTHOR]

Abstract: To date, the development of fisheries harvest policies has typically focused on the use of indices estimated from quantitative stock assessment models. Where does this leave the small-scale and spatially complex resources for which costs and logistics prevent adequate data collection for the construction of quantitative assessment models at scales appropriate for the component populations being fished? This paper presents a novel harvest policy framework developed in Australia by the Victorian Western Zone Abalone Diver’s Association (WADA) to assess and manage their resource at the scale of component abalone reefs (100s–1000sm). This novel harvest policy framework uses a rapid visual evaluation of population fecundity based on shell shape and appearance. Codified and applied using a decision tree, it is being used in Australia by an increasing number of zonal abalone industry associations. Abalone populations on reef complexes are being assessed and managed with voluntary catch caps and voluntary minimum lengths larger than legal minimum lengths. It will probably be decades before a satisfactory quantitative analysis on the efficacy of the approach will be possible, but positive anecdotal accounts and the relatively rapid spread of the approach suggest industry members believe they are seeing benefits. [Copyright &y& Elsevier]

Critical care admission may be necessary for surgical patients requiring organ support or invasive monitoring in the peri-operative period. Unplanned critical care admission poses a potential risk to patients and pressure on services. Existing guidelines base admission criteria on predicted risk of 30-day mortality; however, this may not provide the best predictor of which patients would benefit from this service, and how unplanned admission might be avoided. A systematic review of MEDLINE, Embase, CINAHL, Web of Science, the Cochrane database and the grey literature identified 44 studies assessing risk factors for unplanned critical care admission in adult populations undergoing non-cardiac, non-thoracic and non-neurological surgery. Comparative, quantitative analysis of the admission criteria was not feasible due to heterogeneity in study design. Age, anaemia, ASA physical status, body mass index, comorbidity burden, emergency surgery, high-risk surgery, male sex, obstructive sleep apnoea, increased blood loss and operative duration were all independent risk factors for unplanned critical care admission. Age, body mass index, comorbidity extent and emergency surgery were the most common independent risk factors identified in the USA, UK, Asia and Australia. These risk factors could be used in the development of a risk tool or decision tree for determining which patients might benefit from planned critical care admission. Future work should involve testing the sensitivity and specificity of these measures, either alone or in combination, to guide planned critical care admission, reduce patient deterioration and unplanned admissions. [ABSTRACT FROM AUTHOR]

Active warming reduces risk of surgical complications. Implementation of a perioperative thermal care bundle increased use of active warming for surgical patients. This study aimed to determine if implementing a thermal care bundle to prevent inadvertent perioperative hypothermia is cost-effective. A model-based cost-effectiveness analysis was undertaken using Monte Carlo simulations from input distributions to estimate costs and effects. Hospitals undertaking between 5,000 and 40,000 surgeries per year, which either implemented or did not implement the thermal care bundle, were modelled. The decision tree guiding the structure of the model was populated with clinical outcomes (surgical site infection, blood transfusion requirement and morbid cardiac events) of a hypothetical cohort of surgical patients. Implementation or non-implementation of the thermal care bundle. Net monetary benefit was calculated by multiplying the health benefits (quality-adjusted life years) by the willingness-to-pay threshold minus the cost. We tested a range of values for willingness to pay per quality-adjusted life year thresholds and plotted results for expected incremental benefits and probability of cost-effectiveness. The incremental cost-effectiveness ratio was also calculated. Thermal care bundle implementation simultaneously reduced costs and increased quality-adjusted life years in the majority of simulations (88.1%). The average cost reduction was $689,659 (95% credible intervals spanned from a $2,718,364 decrease in costs to $379,826 increase in costs) and average difference in quality-adjusted life years was 54 (95% CI = 0.4 less to 176 more). This equated to an incremental cost-effectiveness ratio of $12747 saved per quality-adjusted life year gained. It is likely that increasing use of active warming by implementing the thermal care bundle would generate cost-savings and improve the quality of life for surgical patients. It would be good value for hospitals with similar characteristics to those included in our model to allocate the extra resources required for implementation. [ABSTRACT FROM AUTHOR]

Catheter associated urinary tract infections are one of the most common infections acquired in hospital. A recent randomised control study demonstrated the benefit of using chlorhexidine (0.1%) for meatal cleaning prior to urinary catheter insertion, by reducing both catheter associated asymptomatic bacteriuria and infection. These findings raise the important question of whether a decision to switch from saline to chlorhexidine was likely to be cost-effective. The aim of this paper was to evaluate the cost-effectiveness of adopting routine use of chlorhexidine for meatal cleaning prior to urinary catheter insertion The outcomes of this cost-effectiveness study are changes to health service costs in $AUD and changes to quality adjusted life years from a decision to adopt 0.1% chlorhexidine for meatal cleaning prior to urinary catheter insertion as compared to saline. Effectiveness outcomes for this study were taken from a 32 week stepped wedge randomised controlled study conducted in three Australian hospitals. The changes in health costs from switching from saline to 0.1% chlorhexidine per 100,000 catheterisations would save hospitals AUD$387,909 per 100,000 catherisations, prevent 70 cases of catheter associated urinary tract infections, release 282 bed days and provide a small improvement in health benefits of 1.43 quality adjusted life years. Using a maximum willingness to pay for a marginal quality adjusted life year threshold of AUD$28,000 per 100,000 catherisations, suggests that adopting chlorhexidine would be cost effective and potentially cost-saving. The findings from our work provide evidence to health system administrators and those responsible for drafting catheter associated urinary tract infections prevention guidelines that investing in switching from saline to chlorhexidine is not only clinically effective but also a sensible decision in the context of allocating finite healthcare resources. [ABSTRACT FROM AUTHOR]