Can Social Marketing Segmentation Initiatives Be Used to Increase Household Electricity Conservation?

Australia is one of the highest per capita emitters of carbon dioxide in the world, therefore improving energy conservation efforts is important to Australia's economic, social, and environmental well-being. In such an environment, long-term policy changes to energy conservation are likely to be contentious, complex, and costly. In this context the use of a complementary social marketing segmentation framework to guide the intervention process could provide additional leverage for policy makers to facilitate short-term improvements, which are aligned with their longer-term goals. This article focuses on the application of market segmentation techniques as potential motivators to electricity conservation efforts in the existing context of residential users of electricity in New South Wales (NSW), Australia. It investigates the impact of a range of hypothetical consequence incentive packages (i.e., financial reward and feedback frequency) on attitudes toward energy conservation using a cross-sectional survey of 188 respondents from metropolitan NSW residents. It is proposed that incentive packages for energy conservation campaigns will be most effective when targeted specifically to each of the environmental attitudes and beliefs segments.     

High Performance Work Systems and Employee Experience of Work in the Service Sector: The Case of Aged Care

In spite of the growing body of research on high performance work systems (HPWS), there is little evidence on their application in the service sector. It is commonly argued, however, that occupational segmentation in services is a barrier to HPWS. Analysis of data from aged‐care workers indicates that: HPWS have positive outcomes for workers; highly skilled nurses are no more likely than lowly skilled personal care workers to be subject to HPWS; and in some cases, HPWS are associated with more positive outcomes for low‐skilled than high‐skilled workers. These findings suggest that HPWS may well be widely applicable in service settings.     

Insights into the intrinsic and extrinsic challenges for implementing technology education: case studies of Queensland teachers

This study, embedded within the Researching School Change in Technology Education (RSCTE) project in Queensland, Australia, aimed to gain insights into the intrinsic and extrinsic challenges experienced by teachers during the implementation of technology education within primary school settings. The official publication and launch of the Technology years 1–10 syllabus and associated curriculum materials by the Queensland Studies Authority during 2003 saw the first formal Technology curriculum for primary schools in Queensland. The Queensland Government announced that all Queensland schools were to aim for full implementation of this new Key Learning Area (KLA) by 2007. This presented a challenge for Queensland teachers as they began to understand this new KLA and subsequently, were required to implement technology education for the first time. Education Queensland released a number of different strategies that were designed to assist this implementation, including research partnerships with universities. Thus, the RSCTE project, a partnership project between Education Queensland and Griffith University included implementation research within schools. Through the identification of insights into intrinsic and extrinsic challenges, this study, while recognising the limitations of transferability beyond the case studies presented, provides suggestions to assist the implementation of technology education.     

Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm

Abstract

Aims: As many cases of atrial fibrillation (AF) are asymptomatic, patients often remain undiagnosed until complications (e.g. stroke) manifest. Risk-prediction algorithms may help to efficiently identify people with undiagnosed AF. However, the cost-effectiveness of targeted screening remains uncertain. This study aimed to assess the cost-effectiveness of targeted screening, informed by a machine learning (ML) risk prediction algorithm, to identify patients with AF.

Methods: Cost-effectiveness analyses were undertaken utilizing a hybrid screening decision tree and Markov disease progression model. Costs and outcomes associated with the detection of AF compared traditional systematic and opportunistic AF screening strategies to targeted screening informed by a ML risk prediction algorithm. Model analyses were based on adults ≥50?years and adopted the UK NHS perspective.

Results: Targeted screening using the ML risk prediction algorithm required fewer patients to be screened (61 per 1,000 patients, compared to 534 and 687 patients in the systematic and opportunistic strategies) and detected more AF cases (11 per 1,000 patients, compared to 6 and 8?AF cases in the systematic and opportunistic screening strategies). The targeted approach demonstrated cost-effectiveness under base case settings (cost per QALY gained of £4,847 and £5,544 against systematic and opportunistic screening respectively). The targeted screening strategy was predicted to provide an additional 3.40 and 2.05 QALYs per 1,000 patients screened versus systematic and opportunistic strategies. The targeted screening strategy remained cost-effective in all scenarios evaluated.

Limitations: The analysis relied on assumptions that include the extended period of patient life span and the lack of consideration for treatment discontinuations/switching, as well as the assumption that the ML risk-prediction algorithm will identify asymptomatic AF.

Conclusions: Targeted screening using a ML risk prediction algorithm has the potential to enhance the clinical and cost-effectiveness of AF screening, improving health outcomes through efficient use of limited healthcare resources.     

The burden of illness of hepatorenal syndrome (HRS) in the United States: a retrospective analysis of electronic health records

Objectives: Hepatorenal Syndrome (HRS) is characterized by renal failure in patients with advanced chronic liver disease (CLD) and is the leading cause of hospitalizations in CLD. This study examines the clinical and economic burden, outcomes, and unmet need of HRS treatment in US hospitals.

Method: A retrospective cohort study was conducted based on a large electronic health records database (Cerner HealthFacts) with records for hospitalized HRS patients from January 2009–June 2015. Demographics, clinical characteristics, treatment patterns, and economic outcomes were analyzed. Prognostic indicators of cirrhosis, kidney injury, end-stage liver disease, and acute-on-chronic liver failure were used to determine mortality risk.

Results: A total of 2,542 patients hospitalized with HRS were identified (average age = 57.9 years, 61.8% males, 74.2% Caucasian), with an average total hospital charge of $91,504 per patient and a mean length of stay (LOS) of 30.5?days. The mortality rate was 36.9% with 8.9% of patients discharged to hospice. Of all patients, 1,660 patients had acute kidney injury, 859 with Stage 3 disease, and 26.7% had dialysis. The 30-day readmission rate was 33.1%, 41% of which were unplanned. Nearly one-third of study patients had commercial insurance (30.2%), followed by Medicare (29.9%); hospital charges varied by LOS, receipt of dialysis, and discharge status. Regression analysis demonstrated that HRS costs are associated with LOS, dialysis, and hospital mortality.

Conclusion: HRS is associated with poor outcomes and high hospital costs. Analysis of HRS cost drivers demonstrated an unmet need for additional treatment options to improve outcomes in this patient population.