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BACKGROUND AND AIMS: Young people accessing alcohol and other drug (AOD) treatment experience high rates of treatment disengagement, contributing to poorer outcomes. To improve outcomes, it is important to identify factors associated with treatment retention. This study measured the relationships between client characteristics, treatment characteristics, clinical severity measures and completion of treatment among young people. DESIGN, SETTING AND PARTICIPANTS: This study was a retrospective analysis of routinely collected data set in residential- and community-based AOD services in New South Wales, Australia. Routinely collected data from the Network of Alcohol and Other Drug Agencies' (NADA) database were used. Included individuals were aged 10-24 years and accessed treatment between 2012 and 2023 (n = 17 474). MEASUREMENTS: Variables included client-related characteristics, service characteristics and baseline measures of clinical severity [Kessler-10 (K10), EUROHIS-QoL, severity of dependence scale (SDS)]. Multivariable binary logistic regression models assessed the relationships between these characteristics and treatment completion. FINDINGS: Rates of treatment completion were highest among adolescents in community-based treatment (57%) and lowest among young adults in residential treatment (35%). Polysubstance use was negatively associated with treatment completion among adolescents [adjusted odds ratio (adjOR) = 0.71, P < 0.001] and adults (adjOR = 0.70, P < 0.001) in community-based treatment, and adolescents in residential treatment (adjOR = 0.62, P = 0.006), as was housing insecurity (adolescents in community treatment, adjOR = 0.61, P = 0.001; adults in community treatment, adjOR = 0.77, P = 0.002; adolescents in residential treatment, adjOR = 0.42, P = 0.005). Attending youth-specific services was associated with higher treatment completion rates among adults in community-based (adjOR = 1.81, P < 0.001) and residential treatment (adjOR = 1.72, P < 0.001

Introduction: Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Despite therapeutic advances, systemic chemotherapy remains a standard of care for the majority of advanced-stage diseases. Sleep disturbance is an unrecognized and commonly unaddressed problem in lung cancer, possibly potentiated by chemotherapy. A detailed understanding may assist in the development of optimal interventions to improve quality of life. The aims were to study sleep prior to/without treatment and to investigate the impact of chemotherapy on sleep in patients with lung cancer. Methods : In compliance with PRISMA guidelines, we performed a literature search of 3558 articles between 1946 and 2020. Articles that investigated the impact of chemotherapy compared with no treatment (baseline), on sleep disturbance were included. Articles that reported sleep at baseline, without treatment, were also included for comparative analysis. The main outcome measure was the insomnia subscale score from the Quality of Life Questionnaire for Cancer Patients (QLQ-C30) at baseline, during and after chemotherapy. The authenticity of the results was measured using sensitivity analysis. Results : A total of 14 studies were included in the meta-analysis. Sleep disturbance prior to treatment in lung cancer patients, as measured on the QLQ-C30 Insomnia measure (0-100; higher worse), was 33.73 (CI: 32.52 - 34.95). During chemotherapy, measured during cycles 3 and 4 (weeks 9-12), the QLQ-C30 Insomnia score was 21.96 (CI: 21.96 - 24.60) in lung cancer patients. The mean difference was (CI). There was significant heterogeneity chi2= , I2= 'Final Analysis will be presented at the meeting" Conclusion(s): Lung cancer patients who received chemotherapy reported lower levels of sleep disturbance than those who did not receive treatment. It remains unclear the exact mechanisms responsible for these findings but it is evident the need for longer prospective randomized control studies to examine the re

Developments in time-lapse electrical resistivity tomography (ERT) technology are transforming our ability to monitor the subsurface due to purpose-built monitoring instruments, advances in automation and modeling, and the resulting improvements in spatial and temporal resolution. We describe the development of a novel ERT-based remote monitoring system called PRIME that integrates new low-power measurement instrumentation with data delivery, automated data processing and image generation, and web-based information delivery. Due to the sensitivity of ERT to hydrologic processes in the near surface, we focus on the application of PRIME for moisture-driven landslide monitoring. Case examples are considered of landslides in engineered and natural slopes, including those impacting geotechnical assets in rail and highways, where slope hydrology is seen to be controlled by lithology, vegetation, fissuring, and drainage structures. We conclude by taking a forward look at emerging developments in ERT monitoring relating to hardware, software and modeling, and applications.

BACKGROUND: Lung cancer management is characterised by a high disease burden, poor survival and substantial variation in management and outcomes. Service redesign provides opportunities for quality improvement (QI) and this improvement may be leveraged across multiple sites using QI collaboration. AIM: This initiative targeted Quality Improvement (QI) in lung cancer management, engaging a QI collaborative using service redesign methodologies in five Victorian hospitals. QI targets included timeliness from referral and diagnosis to treatment, multi-disciplinary meeting (MDM) presentation and supportive care screening. Redesign strategies targeted process sustainability through enhanced team capability. METHODS: This study engaged a prospective quality improvement cohort design targeting newly diagnosed tissue confirmed lung cancer with 6-month pre-intervention period and 6-month redesign implementation period, between September 2016 and August 2017, evaluated using Interrupted Time Series Analysis. Hospital sites included three regional and two metropolitan hospitals in Victoria. QI redesign targeted time intervals from referral to first specialist appointment (FSA), referral to diagnosis, diagnosis to first treatment (any intent), MDM documented in medical records and Supportive Care Screening Tool documented in medical records. RESULTS: There was a marked reduction in referral to FSA interval across all sites, with median (interquartile range) falling from 6 (0-15) to 4 (1-10) days, and proportion seen by a specialist within 14 days increased from 74.3% to 84.2%. The interval between diagnosis and treatment was not substantively changed in the 6-month implementation period. The proportion of subjects with documented presentation to the MDM increased from 61% to 67%. The proportion for which Supportive Care Screening documentation remained low at 26.3% post-intervention. CONCLUSIONS: Data-driven redesign initiatives enable identification and analysis of clinical pra

Background: Contracting non-government services to provide alcohol and other drug (AOD) treatment requires the specification of performance measures to ensure accountability for public funds. There is currently no standardised approach by funders to the measurement of performance of AOD treatment in Australia. Funding to non-government organisations (NGOs) is complex, with NGOs managing funding agreements from a range of sources that contain a large number of differing performance measures. This study aimed to assess performance measures used in contracts for NGO AOD treatment providers and how they align with best practice. Methods: Performance measures contained in funding agreements for treatment providers were collected from a diverse sample of both funders (n=8) and treatment providers (n=20) resulting in more than 1,100 measures. The list of measures was synthesized to a finite, non-duplicative list (n=537). Measures were assessed by three raters against 11 criteria (for example, measurable and timely) documented as best practice elements of performance measures (Council of Australian Governments, 2011a). Measures were also coded into different measurement types (such as outcome and process). Results: None of the 537 unique performance measures used in funding agreements for treatment providers fully met the criteria for best practice in performance measurement. Whilst the literature and government policy is being directed towards outcomes-based funding and reporting, only 7.6% of measures were classified as outcome measures. The majority of measures were classified at output (41.3%) and process (23.6%) measures. Conclusion: Current measures in contracts applied by funders to treatment services do not adhere to best practice. The development and implementation of new performance measures is required to inform AOD policy and accountability of public funds. Further, identifying more robust performance measures has the potential to lead to reduced reporting burde