Rationing in healthcare

Rationing healthcare in some form is inevitable, even in wealthy countries, because resources are scarce and demand for healthcare is always likely to exceed supply. This means that decision-makers must make choices about which health programs and initiatives should receive public funding and which ones should not. These choices are often difficult to make, particularly in Australia, because: - 1 Make explicit rationing based on a national decision-making tool (such as Multi-criteria Decision Analysis) standard process in all jurisdictions. - 2 Develop nationally consistent methods for conducting economic evaluation in health so that good quality evidence on the relative efficiency of various programs and initiatives is generated. - 3 Generate more economic evaluation evidence to inform rationing decisions. - 4 Revise national health performance indicators so that they include true health system efficiency indicators, such as cost-effectiveness. - 5 Apply the Comprehensive Management Framework used to evaluate items on the Medicare Benefits Schedule (MBS) to the Pharmaceutical Benefits Scheme (PBS) and the Prosthesis List to accelerate disinvestment from low-value drugs and prostheses. - 6 Seek agreement among Commonwealth, state and territory governments to work together to undertake work similar to the National Institute for Health and Care Excellence in the United Kingdom and the Canadian Agency for Drugs and Technologies in Health.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Social networks and use of social supports of minority elders in East Harlem

Considerable empirical research substantiates the importance of social networks on health and well-being in later life. A study of ethnic minority elders living in two low income public housing buildings in East Harlem was undertaken to gain an understanding of the relationship between their health status and social networks. Findings demonstrate that elders with supportive housing had better psychological outcomes and used significantly more informal supports when in need. However, elders with serious health problems had poorer outcomes regardless of their level of social support. This study highlights the potential of supportive living environments to foster social integration and to optimise formal and informal networks.