Forecasting the onset of the grass pollen season in Melbourne (Australia)

In Melbourne, a southern hemisphere city with a cool temperate climate, the grass pollen season has been monitored using a Burkard spore trap for 12 years (11 pollen seasons, which extend from October through January). The onset of the grass pollen season (OGPS) has been defined in various ways using both arbitrary cumulative scores (Sum 75, Sum 100) and percentages (10% Pollen Fly). OGPS, based on the forecast model of pollen season devised by Lejoly-Gabriel (Acta Geogr. Lovan., 13 (1978) 1–260) has been most widely used in efforts to forecast the beginning of the pollen season. OGPS occurred in Melbourne between 20 October to 24 November (average 6 November), a difference of 35 days. Duration of the pollen season ranged from 46 to 81 days, with a mean of 55 days, one of the longest reported. The relationships between onset and various weather parameters for July have enabled us to modify a model, using linear regression analysis, to predict onset. The prediction model is based on a negative correlation between date of onset and the sum of rainfall for July (a winter month). The error of prediction (Ep) is 24% and predicted day of OGPS was precisely predicted on 2 occasions, and on others with a range of accuracy of 3 to 14 days.

A randomised, feasibility trial of a tele-health intervention for acute coronary syndrome patients with depression (‘MoodCare’): study protocol

Background Coronary heart disease (CHD) and depression are leading causes of disease burden globally and the two often co-exist. Depression is common after Myocardial Infarction (MI) and it has been estimated that 15-35% of patients experience depressive symptoms. Co-morbid depression can impair health related quality of life (HRQOL), decrease medication adherence and appropriate utilisation of health services, lead to increased morbidity and suicide risk, and is associated with poorer CHD risk factor profiles and reduced survival. We aim to determine the feasibility of conducting a randomised, multi-centre trial designed to compare a tele-health program (MoodCare) for depression and CHD secondary prevention, with Usual Care (UC).

Methods Over 1600 patients admitted after index admission for Acute Coronary Syndrome (ACS) are being screened for depression at six metropolitan hospitals in the Australian states of Victoria and Queensland. Consenting participants are then contacted at two weeks post-discharge for baseline assessment. One hundred eligible participants are to be randomised to an intervention or a usual medical care control group (50 per group). The intervention consists of up to 10 × 30-40 minute structured telephone sessions, delivered by registered psychologists, commencing within two weeks of baseline screening. The intervention focuses on depression management, lifestyle factors (physical activity, healthy eating, smoking cessation, alcohol intake), medication adherence and managing co-morbidities. Data collection occurs at baseline (Time 1), 6 months (post-intervention) (Time 2), 12 months (Time 3) and 24 months follow-up for longer term effects (Time 4). We are comparing depression (Cardiac Depression Scale [CDS]) and HRQOL (Short Form-12 [SF-12]) scores between treatment and UC groups, assessing the feasibility of the program through patient acceptability and exploring long term maintenance effects. A cost-effectiveness analysis of the costs and outcomes for patients in the intervention and control groups is being conducted from the perspective of health care costs to the government.

Discussion This manuscript presents the protocol for a randomised, multi-centre trial to evaluate the feasibility of a tele-based depression management and CHD secondary prevention program for ACS patients. The results of this trial will provide valuable new information about potential psychological and wellbeing benefits, cost-effectiveness and acceptability of an innovative tele-based depression management and secondary prevention program for CHD patients experiencing depression.

Is there an interaction between socioeconomic status and FRAX 10-year fracture probability determined with and without bone density measures? Data from the Geelong Osteoporosis Study of female cohort.

FRAX(©) evaluates 10-year fracture probabilities and can be calculated with and without bone mineral density (BMD). Low socioeconomic status (SES) may affect BMD, and is associated with increased fracture risk. Clinical risk factors differ by SES; however, it is unknown whether aninteraction exists between SES and FRAX determined with and without the BMD. From the Geelong Osteoporosis Study, we drew 819 females aged ≥50 years. Clinical data were collected during 1993-1997. SES was determined by cross-referencing residential addresses with Australian Bureau of Statistics census data and categorized in quintiles. BMD was measured by dual energy X-ray absorptiometry at the same time as other clinical data were collected. Ten-year fracture probabilities were calculated using FRAX (Australia). Using multivariable regression analyses, we examined whether interactions existed between SES and 10-year probability for hip and any major osteoporotic fracture (MOF) defined by use of FRAX with and without BMD. We observed a trend for a SES * FRAX(no-BMD) interaction term for 10-year hip fracture probability (p = 0.09); however, not for MOF (p = 0.42). In women without prior fracture (n = 518), we observed a significant SES * FRAX(no-BMD) interaction term for hip fracture (p = 0.03) and MOF (p = 0.04). SES does not appear to have an interaction with 10-year fracture probabilities determined by FRAX with and without BMD in women with previous fracture; however, it does appear to exist for those without previous fracture.

Differentially private random forest with high utility

Privacy-preserving data mining has become an active focus of the research community in the domains where data are sensitive and personal in nature. For example, highly sensitive digital repositories of medical or financial records offer enormous values for risk prediction and decision making. However, prediction models derived from such repositories should maintain strict privacy of individuals. We propose a novel random forest algorithm under the framework of differential privacy. Unlike previous works that strictly follow differential privacy and keep the complete data distribution approximately invariant to change in one data instance, we only keep the necessary statistics (e.g. variance of the estimate) invariant. This relaxation results in significantly higher utility. To realize our approach, we propose a novel differentially private decision tree induction algorithm and use them to create an ensemble of decision trees. We also propose feasible adversary models to infer about the attribute and class label of unknown data in presence of the knowledge of all other data. Under these adversary models, we derive bounds on the maximum number of trees that are allowed in the ensemble while maintaining privacy. We focus on binary classification problem and demonstrate our approach on four real-world datasets. Compared to the existing privacy preserving approaches we achieve significantly higher utility.

A review of vulnerability and risks for schizophrenia: beyond the two hit hypothesis

Schizophrenia risk has often been conceptualized using a model which requires two hits in order to generate the clinical phenotype-the first as an early priming in a genetically predisposed individual and the second a likely environmental insult. The aim of this paper was to review the literature and reformulate this binary risk-vulnerability model. We sourced the data for this narrative review from the electronic database PUBMED. Our search terms were not limited by language or date of publication. The development of schizophrenia may be driven by genetic vulnerability interacting with multiple vulnerability factors including lowered prenatal vitamin D exposure, viral infections, smoking intelligence quotient, social cognition cannabis use, social defeat, nutrition and childhood trauma. It is likely that these genetic risks, environmental risks and vulnerability factors are cumulative and interactive with each other and with critical periods of neurodevelopmental vulnerability. The development of schizophrenia is likely to be more complex and nuanced than the binary two hit model originally proposed nearly thirty years ago. Risk appears influenced by a more complex process involving genetic risk interfacing with multiple potentially interacting hits and vulnerability factors occurring at key periods of neurodevelopmental activity, which culminate in the expression of disease state. These risks are common across a number of neuropsychiatric and medical disorders, which might inform common preventive and intervention strategies across non-communicable disorders.

Local environmental covariates are important for predicting fire history from tree stem diameters

In fire-prone landscapes, knowing when vegetation was last burnt is important for understanding how species respond to fire and to develop effective fire management strategies. However, fire history is often incomplete or non-existent. We developed a fire-age prediction model for two mallee woodland tree species in southern Australia. The models were based on stem diameters from ∼1172 individuals surveyed along 87 transects. Time since fire accounted for the greatest proportion of the explained variation in stem diameter for our two mallee tree species but variation in mean stem diameters was also influenced by local environmental factors. We illustrate a simple tool that enables time since fire to be predicted based on stem diameter and local covariates. We tested our model against new data but it performed poorly with respect to the mapped fire history. A combination of different covariate effects, variation in among-tree competition, including above- and below-ground competition, and unreliable fire history may have contributed to poor model performance. Understanding how the influence of covariates on stem diameter growth varies spatially is critical for determining the generality of models that predict time since fire. Models that were developed in one region may need to be independently verified before they can be reliably applied in new regions.

In-situ evaluation of predictive models for H₂S gas emission and the performance of optimal dosage of suppressing chemicals in a laboratory-scale sewer

This in-situ analysis quantifies hydrogen sulfide gas emission from a simulated sewerage system, with varying slopes between 0.5% and 1.5%, under the dosing of certain mitigating chemicals. A portable H₂S gas detector (OdaLog) was employed to record the gaseous phase concentration of hydrogen sulfide. The investigation was comprised of three interrelated phases. In the first stage, precision of four prediction models for H₂S gas emission from a laboratory-synthesized wastewater was assessed. It was found that the model suggested by Lahav fitted the experimental results accurately. Second phase explorations included jar tests to obtain the optimal dosage of four hydrogen sulfide suppressing chemicals, being Mg(OH)₂, NaOH, Ca(NO₃)₂, and FeCl₂. In the third stage, the optimal dosage of chemicals was introduced into the experimental sewerage system, with the OdaLog continuously monitoring the H₂S gas emission. According to a baseline (experiments with no chemical addition), it was found that NaOH and Mg(OH)₂ performed very good in mitigating the release of H₂S gas, while Ca(NO₃)₂ was not effective most probably due to the absence of biological activity. Furthermore, interpretation of OdaLog data through the optimum emission prediction model revealed that higher sewer slope led to more emission.

Forecasting daily patient outflow from a ward having no real-time clinical data

OBJECTIVE: Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data.

METHODS: We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features.

RESULTS: Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014.

CONCLUSIONS: In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments.

Elite athletes as charitable ambassadors: risks associated with indiscretions

Elite athletes can reach a level of notoriety where media and fans are interested in various aspects of their lives beyond that of their on-field success or failure. By receiving this level of attention, these sporting celebrities attract sponsorships from commercial, fee-paying corporations. With considered alignment, manufacturers can enhance the visibility of their product with target audiences that consume every aspect of the lives of celebrity endorsers. While this form of commodification has been explored from the perspective of the private sector, there is limited research that reflects on the ambassador relationship between sport celebrities and charitable organisations.While a charity ambassador role omits financial support, a win-win outcome can be achieved. Enhanced visibility can benefit both parties: the sports celebrity adds another dimension to their personal brand portfolio, and the charitable organisation broadens awareness of their social issue. Retired athletes continue to harbour desirable brand equity; they have ongoing potential to reach to multiple stakeholders and act as important catalysts for social change.Whilst heightened visibility of an issue is desired, the immense stakeholder interest in the life of a successful athlete has a downside if the celebrity transgresses. Minor transgressions may pass with little impact, yet what constitutes a minor transgression for one set of stakeholders may result in reputational damage for both athlete and brand. Adopting a case study approach, this chapter investigates the construction of the sports celebrity persona at various stages of their career and the response by all actors to transgressions.Findings reveal that media framing of successful sports personalities can exacerbate future failings and heighten the impact on stakeholders, thus lessening their viability and longevity as positive social catalysts. Replicating actions adopted by the private sector, charitable​ organisations may respond to scandals by immediately severing the relationship, or at the other extreme, provide visible support as the celebrity seeks to repair and restore their image. The cases lead to a cohesive set of risk assessment considerations.

Psychotic-like experiences in a community sample of adolescents: implications for the continuum model of psychosis and prediction of schizophrenia

Studies conducted in community samples suggest that psychotic-like experiences are common in the general population, leading to suggestions that they are either variations of normal personality or are different expressions of underlying vulnerability to psychotic disorder. Different types of psychotic symptoms may exist, some being normal variants and some having implications for mental health and functioning. The aim of the present study was to determine if different subtypes of psychotic-like experiences could be identified in a community sample of adolescents and to investigate if particular subtypes were more likely to be associated with psychosocial difficulties, that is, distress, depression and poor functioning, than other subtypes. Eight hundred and seventy-five Year 10 students from 34 schools participated in a cross-sectional survey that measured psychotic-like experiences using the Community Assessment of Psychic Experiences; depression using the Centre for Epidemiologic Studies Depression Scale; and psychosocial functioning using the Revised Multidimensional Assessment of Functioning Scale. Factor analysis was conducted to identify any subtypes of psychotic experiences. Four subtypes of psychotic-like experiences were identified: Bizarre Experiences, Perceptual Abnormalities, Persecutory Ideas, and Magical Thinking. Intermittent, infrequent psychotic experiences were common, but frequent experiences were not. Bizarre Experiences, Perceptual Abnormalities and Persecutory Ideas were strongly associated with distress, depression and poor functioning. Magical Thinking was only weakly associated with these variables. Overall these findings may suggest that infrequent psychotic-like experiences are unlikely to be a specific risk factor for onset of a psychotic disorder in community samples. Given that the different subtypes had varying associations with current difficulties it is suggested that not all subtypes confer the same risk for onset of psychotic disorder and poor outcome. Bizarre Experiences, Perceptual Abnormalities and Persecutory Ideas may represent expressions of underlying vulnerability to psychotic disorder, but Magical Thinking may be a normal personality variant.

Reasoned versus reactive prediction of behaviour: a meta-analysis of the prototype willingness model

The prototype willingness model (PWM) was designed to extend expectancy-value models of health behaviour by also including a heuristic, or social reactive pathway, to better explain health-risk behaviours in adolescents and young adults. The pathway includes prototype, i.e., images of a typical person who engages in a behaviour, and willingness to engage in behaviour. The current study describes a meta-analysis of predictive research using the PWM and explores the role of the heuristic pathway and intentions in predicting behaviour. Eighty-one studies met inclusion criteria. Overall, the PWM was supported and explained 20.5% of the variance in behaviour. Willingness explained 4.9% of the variance in behaviour over and above intention, although intention tended to be more strongly related to behaviour than was willingness. The strength of the PWM relationships tended to vary according to the behaviour being tested, with alcohol consumption being the behaviour best explained. Age was also an important moderator, and, as expected, PWM behaviour was best accounted for within adolescent samples. Results were heterogeneous even after moderators were taken into consideration. This meta-analysis provides support for the PWM and may be used to inform future interventions that can be tailored for at-risk populations.

Optimizing prediction of binge eating episodes : a comparison approach to test alternative conceptualizations of the affect regulation model

Background : Although a wealth of studies have tested the link between negative mood states and likelihood of a subsequent binge eating episode, the assumption that this relationship follows a typical linear dose–response pattern (i.e., that risk of a binge episode increases in proportion to level of negative mood) has not been challenged. The present study demonstrates the applicability of an alternative, non-linear conceptualization of this relationship, in which the strength of association between negative mood and probability of a binge episode increases above a threshold value for the mood variable relative to the slope below this threshold value (threshold dose response model).

Methods : A sample of 93 women aged 18 to 40 completed an online survey at random intervals seven times per day for a period of one week. Participants self-reported their current mood state and whether they had recently engaged in an eating episode symptomatic of a binge.

Results : As hypothesized, the threshold approach was a better predictor than the linear dose–response modeling of likelihood of a binge episode. The superiority of the threshold approach was found even at low levels of negative mood (3 out of 10, with higher scores reflecting more  negative mood). Additionally, severity of negative mood beyond this threshold value appears to be useful for predicting time to onset of a binge episode.

Conclusions : Present findings suggest that simple dose–response formulations for the association between  negative mood and onset of binge episodes miss vital aspects of this relationship. Most  notably, the impact of mood on binge eating appears to depend on whether a threshold value  of negative mood has been breached, and elevation in mood beyond this point may be useful  for clinicians and researchers to identify time to onset.

Intensive case management for high-risk patients with first-episode psychosis: service model and outcomes

Background : The first episode of psychosis is a crucial period when early intervention can alter the trajectory of the young person's ongoing mental health and general functioning. After an investigation into completed suicides in the Early Psychosis Prevention and Intervention Centre (EPPIC) programme, the intensive case management subprogramme was developed in 2003 to provide assertive outreach to young people having a first episode of psychosis who are at high risk owing to risk to self or others, disengagement, or suboptimal recovery. We report intensive case management model development, characterise the target cohort, and report on outcomes compared with EPPIC treatment as usual.

Methods : Inclusion criteria, staff support, referral pathways, clinical review processes, models of engagement and care, and risk management protocols are described. We compared 120 consecutive referrals with 50 EPPIC treatment as usual patients (age 15–24 years) in a naturalistic stratified quasi-experimental real-world design. Key performance indicators of service use plus engagement and suicide attempts were compared between EPPIC treatment as usual and intensive case management, and psychosocial and clinical measures were compared between intensive case management referral and discharge.

Findings : Referrals were predominately unemployed males with low levels of functioning and educational attainment. They were characterised by a family history of mental illness, migration and early separation, with substantial trauma, history of violence, and forensic attention. Intensive case management improved psychopathology and psychosocial outcomes in high-risk patients and reduced risk ratings, admissions, bed days, and crisis contacts.

Interpretation : Characterisation of intensive case management patients validated the clinical research focus and identified a first episode of psychosis high-risk subgroup. In a real-world study, implementation of an intensive case management stream within a well-established first episode of psychosis service showed significant improvement in key service outcomes. Further analysis is needed to determine cost savings and effects on psychosocial outcomes. Targeting intensive case management services to high-risk patients with unmet needs should reduce the distress associated with pathways to care for patients, their families, and the community.

Prediction of stress-strain behaviors in steels using an integrated constitutive, FEM and ANN model

Austenitic steels with a carbon content of 0.0037 to 0.79 wt% C are torsion tested and modeled using a physically based constitutive model and an Integrated Phenomenological and Artificial neural Network (IPANN) model. The prediction of both the constitutive and IPANN models on steel 0.017 wt% C is then evaluated using a finite element (FEM) code ABAQUS with different reduction in the thickness after rolling through one roll stand. It is found that during the rolling process, the prediction accuracy of the reaction force from FEM simulation for both constitutive and IPANN models depends on the strain achieved (average reduction in thickness). By integrating FEM into IPANN model and introducing the product of strain and stress as an input of the ANN model, the accuracy of this integrated FEM and IPANN model is higher than either the constitutive or IPANN model.

Prediction intention to quit in the call centre industry: does the retail model fit?

Purpose – Models of workplace turnover are rarely assessed in contexts other than that in which they were developed. This reduces their generalizability and their usefulness in providing managers with guidance as to what they might do to reduce workers intentions to quit. The purpose of this study is to test a model derived from a study of shop floor retail salespeople in the call centre environment.

Design/methodology/approach – A questionnaire measuring the variables in the model was completed by 126 call centre representatives recruited from 11 call centres in Melbourne, Australia.

Findings – Although the model was supported, the interactions among the variables differed. In particular, stressors played a bigger, albeit indirect, role in the intention to quit.

Practical implications – Call centre managers need to consider carefully the aspects of the work environment that may be stressful. If appropriately addressed, turnover may be reduced, and productivity increased.

Originality/value – This paper demonstrates that the model of turnover derived from shop floor salespeople is generally robust in the call centre setting. It provides management of call centres with some guidance as to the factors associated with turnover and areas that can be addressed to reduce it.