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Background: The Pediatric Quality of Life Inventory™ 4.0 Short Form 15 Generic Core Scales (hereafter the PedsQL) and the Child Health Utility-9 Dimensions (CHU9D) are two generic instruments designed to measure health-related quality of life in children and adolescents in the general population and paediatric patient groups living with specific health conditions. Although the PedsQL is widely used among paediatric patient populations, presently it is not possible to directly use the scores from the instrument to calculate quality-adjusted life-years (QALYs) for application in economic evaluation because it produces summary scores which are not preference-based.Objective: This paper examines different econometric mapping techniques for estimating CHU9D utility scores from the PedsQL for the purpose of calculating QALYs for cost-utility analysis.Methods: The PedsQL and the CHU9D were completed by a community sample of 755 Australian adolescents aged 15-17 years. Seven regression models were estimated: ordinary least squares estimator, generalised linear model, robust MM estimator, multivariate factorial polynomial estimator, beta-binomial estimator, finite mixture model and multinomial logistic model. The mean absolute error (MAE) and the mean squared error (MSE) were used to assess predictive ability of the models.Results: The MM estimator with stepwise-selected PedsQL dimension scores as explanatory variables had the best predictive accuracy using MAE and the equivalent beta-binomial model had the best predictive accuracy using MSE.Conclusions: Our mapping algorithm facilitates the estimation of health-state utilities for use within economic evaluations where only PedsQL data is available and is suitable for use in community-based adolescents aged 15-17 years. Applicability of the algorithm in younger populations should be assessed in further research. [ABSTRACT FROM AUTHOR]

Background: Nurses working in intensive care units make autonomous decisions to manage high‐risk vasoactive medications in critically ill patients. Noradrenaline (norepinephrine) is a vasoactive medication commonly administered to patients in intensive care units. The influence of unit culture and environment on nurse‐decision‐making on noradrenaline (norepinephrine) management is unknown. Aims: The study aimed to investigate nurses' perceptions of the impact of interpersonal interactions, socialisation, and the intensive care environment on decision‐making when managing noradrenaline (norepinephrine). Materials & Methods: An exploratory qualitative study applied thematic analysis to focus group data. A purposive sample of nineteen nurses participated in four focus groups at two intensive care units in Melbourne, Australia, from March to June 2021. The COREQ checklist was used to guide study development and no patients or members of the public were involved in focus groups. Results: Three themes were generated from the researcher's interaction with data, Nursing and Medications; Culture and Decision‐making; and a Safe Practice Environment. Nurses reported decision‐making challenges associated with learning to manage noradrenaline (norepinephrine) early in their intensive care career and discussed feelings of isolation due to staffing resources, and the configuration of the intensive care environment. Nurses developed titration and weaning strategies to support decision‐making in the absence of evidence‐based algorithms. Discussion: Empathetic patient allocation early in nurses' intensive care careers facilitated a safer learning environment, and reduced isolation inherent in single room intensive care units. Nurses developed and used titration and weaning strategies, often learnt from other clinicians to manage practice uncertainty. Conclusions: Management of noradrenaline (norepinephrine) is core business for intensive care nurses worldwide. Development of titration and weaning strategies by nurses indicated unmet need for guidelines to support decision‐making. Identifying contextual elements that impact nurse management of high‐risk medications can guide development of environments, resources and policies that support nurse decision‐making, and reduce nurse anxiety and disempowerment. [ABSTRACT FROM AUTHOR]

Live streaming of child sexual abuse (CSA) involves the procurement and viewing of sexual abuse of children across the internet in real time, in exchange for money. These offenses leave little tangible evidence of the offense beyond a financial transaction, and metadata relating to the live-streaming session. This research analyzed the demographic, criminal history, and financial transaction characteristics of 209 individuals who live streamed child sexual abuse. A machine learning clustering technique was implemented to consider whether there were sub-groups present among these offenders, and in particular the prevalence of contact sexual offending among any detected sub-groups. Findings revealed that offenders tend to engage in live streaming around the same age, before making regular transactions with facilitators at brief intervals, with the majority of offenders featuring limited criminal history. This analysis identified a notable sub-group of live-streaming offenders that also engaged in contact sexual offending. This is the first study to empirically demonstrate an intersection between live streaming of CSA, and contact sexual offenses against children and adults. This research highlighted the importance of financial transactions data in detecting, and disrupting this crime type. Further, the identification of an intersection between live-streaming CSA offenders, and contact sexual offenders suggests that these individuals may pose a risk to both local and international communities. [ABSTRACT FROM AUTHOR]

This study aims to understand the effects that role‐diverse online communities have on informal carers, particularly in providing support. Australian Facebook communities used to support those involved in the National Disability Insurance Scheme (NDIS) were explored. Social network analysis of an NDIS‐centred community was conducted, based on 909 publicly visible interactions that occurred in May–June and August–September 2019. Two managers of informal NDIS communities were interviewed, the transcripts of which were analysed using NVivo. Results from the two analyses suggest that both an individual carer's attributes and the collective attributes of the network defined the capability of the network to support the carer, often depending on the experiences and expertise of those offering support. Support was unconstrained by role, though differing goals and expectations often impeded collaboration between roles. The outcomes of support provision were shown to affect not only individuals but also the collective network. However, while effective, community spaces currently lack organisational backing and resources available to informal communities are constrained. Findings drawn from this study, which we believe are applicable to a broader, international context, are three‐fold. Firstly, it is recommended that informal support communities clearly define purpose and create multiple channels to ensure that all participants can meet their needs. Secondly, the benefits of participation to organisations should be further explored. Finally, the use of social network analysis as a method in this study has provided significant insights into the communication patterns and activities of the community under study. Future use of SNA in similar studies may provide further insight into the effectiveness and interactions of community‐based support methods. [ABSTRACT FROM AUTHOR]

Background and aims: Clinical staff are typically poor at predicting alcohol dependence treatment outcomes. Machine learning (ML) offers the potential to model complex clinical data more effectively. This study tested the predictive accuracy of ML algorithms demonstrated to be effective in predicting alcohol dependence outcomes, compared with clinical judgement and traditional linear regression. Design Prospective study. ML models were trained on 1016 previously treated patients (training‐set) who attended a hospital‐based alcohol and drug clinic. ML models (n = 27), clinical psychologists (n = 10) and a 'traditional' logistic regression model (n = 1) predicted treatment outcome during the initial treatment session of an alcohol dependence programme. Setting: A 12‐week cognitive behavioural therapy (CBT)‐based abstinence programme for alcohol dependence in a hospital‐based alcohol and drug clinic in Australia. Participants: Prospective predictions were made for 220 new patients (test‐set; 70.91% male, mean age = 35.78 years, standard deviation = 9.19). Sixty‐nine (31.36%) patients successfully completed treatment. Measurements Treatment success was the primary outcome variable. The cross‐validated training‐set accuracy of ML models was used to determine optimal parameters for selecting models for prospective prediction. Accuracy, sensitivity, specificity, area under the receiver operator curve (AUC), Brier score and calibration curves were calculated and compared across predictions. Findings The mean aggregate accuracy of the ML models (63.06%) was higher than the mean accuracy of psychologist predictions (56.36%). The most accurate ML model achieved 70% accuracy, as did logistic regression. Both were more accurate than psychologists (P < 0.05) and had superior calibration. The high specificity for the selected ML (79%) and logistic regression (90%) meant they were significantly (P < 0.001) more effective than psychologists (50%) at correctly identifying patients whose treatment was unsuccessful. For ML and logistic regression, high specificity came at the expense of sensitivity (26 and 31%, respectively), resulting in poor prediction of successful patients. Conclusions: Machine learning models and logistic regression appear to be more accurate than psychologists at predicting treatment outcomes in an abstinence programme for alcohol dependence, but sensitivity is low. [ABSTRACT FROM AUTHOR]

Introduction: The detection of harmful alcohol use and the delivery of brief advice in primary care are less than optimal. Given limited health care resources, deciding where best to allocate funding to optimize health outcomes is imperative. A simple data-based tool could be useful when access to specialist health economic advice is unavailable. This study aimed to examine the utility of a simple data-based calculator to facilitate priority setting in general practice for reducing alcohol-related harm.Methods: A simple algorithm was developed within Microsoft Excel to allow comparison of hypothetical intervention scenarios that aimed to increase detection and brief advice for harmful alcohol use in general practice. The calculator accommodated varying implementation costs, size of effect and reach for each scenario created. The incremental costs of the intervention scenarios, the incremental number of successes (i.e. abstinence or drinking at safe levels) and the incremental costs-effectiveness ratio (ICER) were calculated for each hypothetical scenario and compared with a usual care scenario.Results: In the hypothetical scenarios modelled, increasing both the detection of harmful alcohol consumption and the provision of brief advice produced the greatest number of incremental successes above baseline. Increasing detection alone produced fewer incremental successes but was the most cost-effective approach, as indicated by the lowest ICER.Discussion: The data-based calculator provides a simple method of exploring reach and cost-effectiveness outcomes without the need for any specific skills. Although this approach has limitations, the calculator can be used by decision makers to guide intervention planning. [ABSTRACT FROM AUTHOR]

Background: Missing data is a common problem in epidemiological studies, and is particularly prominent in longitudinal data, which involve multiple waves of data collection. Traditional multiple imputation (MI) methods (fully conditional specification (FCS) and multivariate normal imputation (MVNI)) treat repeated measurements of the same time-dependent variable as just another 'distinct' variable for imputation and therefore do not make the most of the longitudinal structure of the data. Only a few studies have explored extensions to the standard approaches to account for the temporal structure of longitudinal data. One suggestion is the two-fold fully conditional specification (two-fold FCS) algorithm, which restricts the imputation of a time-dependent variable to time blocks where the imputation model includes measurements taken at the specified and adjacent times. To date, no study has investigated the performance of two-fold FCS and standard MI methods for handling missing data in a time-varying covariate with a non-linear trajectory over time - a commonly encountered scenario in epidemiological studies.Methods: We simulated 1000 datasets of 5000 individuals based on the Longitudinal Study of Australian Children (LSAC). Three missing data mechanisms: missing completely at random (MCAR), and a weak and a strong missing at random (MAR) scenarios were used to impose missingness on body mass index (BMI) for age z-scores; a continuous time-varying exposure variable with a non-linear trajectory over time. We evaluated the performance of FCS, MVNI, and two-fold FCS for handling up to 50% of missing data when assessing the association between childhood obesity and sleep problems.Results: The standard two-fold FCS produced slightly more biased and less precise estimates than FCS and MVNI. We observed slight improvements in bias and precision when using a time window width of two for the two-fold FCS algorithm compared to the standard width of one.Conclusion: We recommend the use of FCS or MVNI in a similar longitudinal setting, and when encountering convergence issues due to a large number of time points or variables with missing values, the two-fold FCS with exploration of a suitable time window. [ABSTRACT FROM AUTHOR]

Micro-sensors were used to quantify macro kinematics of classical cross-country skiing techniques and measure cycle rates and cycle lengths during on-snow training. Data were collected from seven national level participants skiing at two submaximal intensities while wearing a micro-sensor unit (MinimaxX™). Algorithms were developed identifying double poling (DP), diagonal striding (DS), kick-double poling (KDP), tucking (Tuck), and turning (Turn). Technique duration (T-time), cycle rates, and cycle counts were compared to video-derived data to assess system accuracy. There was good reliability between micro-sensor and video calculated cycle rates for DP, DS, and KDP, with small mean differences (Mdiff% = −0.2 ± 3.2, −1.5 ± 2.2 and −1.4 ± 6.2) and trivial to small effect sizes (ES = 0.20, 0.30 and 0.13). Very strong correlations were observed for DP, DS, and KDP for T-time (r = 0.87–0.99) and cycle count (r = 0.87–0.99), while mean values were under-reported by the micro-sensor. Incorrect Turn detection was a major factor in technique cycle misclassification. Data presented highlight the potential of automated ski technique classification in cross-country skiing research. With further refinement, this approach will allow many applied questions associated with pacing, fatigue, technique selection and power output during training and competition to be answered. [ABSTRACT FROM PUBLISHER]

Introduction and aims: Illicit methamphetamine (MA) use is an important public health concern. There is a dearth of knowledge about effective and cost-effective treatments for methamphetamine (MA) dependence in Australia. This article evaluates the cost-effectiveness of counselling as a treatment option for illicit MA use compared with no treatment option. Design and methods: Data are from 501 individuals recruited into Methamphetamine Treatment Evaluation Study (MATES). The population of MA users from MATES is extrapolated to a total number of 1000 MA users in the intervention group (counselling treatment) and control group (non-treatment group). A decision analytic model is developed that examines the costs and health outcomes [measures as quality adjusted life years (QALYs) gained] for the treatment and comparison group over a 3-year period. A societal perspective is adopted and model inputs are subject to sensitivity and uncertainty analysis to test the robustness of results to parameter variability. Results are discounted by using 3% discount rate and expressed in 2011 Australian dollars. Results: The incremental cost-effectiveness analysis suggests that counselling is a dominant health care intervention, i.e. saves money and is more effective than a do nothing intervention. The incremental difference in costs is −AU$18.36 million (95% CI −AU$22.80 million to −AU$14.31 million) and the incremental difference in QALY is 107 (95% CI −640 to 820) with a probability of 78.64% of counselling being a dominant and cost-effective treatment within the acceptable incremental cost-effectiveness ratio (ICER) of $63 832 per QALY in the Australian society. The results of the sensitivity analysis show that the ICER is most sensitive to change in five major inputs: baseline utility, utility at 3 months, dealing crime costs, property crime costs and fraud crime costs. Discussion and Conclusions: The economic evaluation of the cost-effectiveness of counselling for MA dependence, as a first cost-effectiveness study to assess psychosocial treatment options for MA dependence, shows that greater investment in this cost-effective strategy will produce significant cost-savings and improve health outcomes as well as improve a lot of externality issues associated with drug use. [ABSTRACT FROM AUTHOR]

Objective. To determine the economic feasibility in Australian general practices of using a practice nurse (PN)-led care model of chronic disease management. Methods. A cost-analysis of item numbers from the Medicare Benefit Schedule (MBS) was performed in three Australian general practices, one urban, one regional and one rural. Patients (n =254; >18 years of age) with chronic conditions (type 2 diabetes, hypertension, ischaemic heart disease) but without unstable or major health problems were randomised into usual general practitioner (GP) or PN-led care for management of their condition over a period of 12 months. After the 12-month intervention, total MBS item charges were evaluated for patients managed for their stable chronic condition by usual GP or PN-led care. Zero-skewness log transformation was applied to cost data and log-linear regression analysis was undertaken. Results. There was an estimated A$129 mean increase in total MBS item charges over a 1-year period (controlled for age, self-reported quality of life and geographic location of practice) associated with PN-led care. The frequency of GP and PN visits varied markedly according to the chronic disease. Conclusions. Medicare reimbursements provided sufficient funding for general practices to employ PNs within limits of workloads before the new Practice Nurse Incentive Program was introduced in July 2012. [ABSTRACT FROM AUTHOR]