A Review of Options for Health and Disability Support Purchasing in New Zealand

The introduction of an internal market into New Zealand's publicly financed health system based on a purchaser-provider separation was highly controversial. From four Regional Health Authorities, the purchasing side of the internal market was subsequently reconfigured into a single national purchasing agency, the Health Funding Authority, in 1997. This Working Paper reviews the original rationale for the separation of purchase from provision, discusses the recent experience of the separation in New Zealand and reviews options for the possible evolution of the purchasing function. The options reviewed include vertical integration (ie, the abolition of the purchaser-provider split). It is argued that there are benefits associated with the separation of purchaser and provider, and that, on balance, it was a good thing, although its application to all services was probably inadvisable. However, no one model of purchaser organisation can fulfil all the requirements described by proponents of system change. Health services' purchasers are inescapably in a weak position vis-à-vis their providers. Whichever model of purchasing/planning services is implemented, purchasers will need to develop a new set of relationships with primary care providers, especially general practitioners, since primary care services are important for the functioning of the remainder of the health system. There are currently few incentives on primary care providers to consider the wider implications of their decisions for the rest of the sector and the delivery of primary care is imperfectly coordinated with other services. This paper is a background paper and does not provide policy advice, nor does it propose any particular course of action. The Treasury has chosen to publish it (or make it available) in order to encourage peer comment with a view to ensuring that it is of good quality. The views expressed in the paper is/are those of the author(s) and do not necessarily reflect the views of the New Zealand Treasury. The Treasury takes no responsibility for any errors or omissions in, or for the correctness of, the information contained in this paper.

Bayesian anomaly detection methods for social networks

Learning the network structure of a large graph is computationally demanding, and dynamically monitoring the network over time for any changes in structure threatens to be more challenging still. This paper presents a two-stage method for anomaly detection in dynamic graphs: the first stage uses simple, conjugate Bayesian models for discrete time counting processes to track the pairwise links of all nodes in the graph to assess normality of behavior; the second stage applies standard network inference tools on a greatly reduced subset of potentially anomalous nodes. The utility of the method is demonstrated on simulated and real data sets.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS329 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Intelligent Agents for Disaster Management

ALADDIN [1] is a multi-disciplinary project that is developing novel techniques, architectures, and mechanisms for multi-agent systems in uncertain and dynamic environments. The application focus of the project is disaster management. Research within a number of themes is being pursued and this is considering different aspects of the interaction between autonomous agents and the decentralised system architectures that support those interactions. The aim of the research is to contribute to building more robust multi-agent systems for future applications in disaster management and other similar domains

Finding Groups in Gene Expression Data

The vast potential of the genomic insight offered by microarray technologies has led to their widespread use since they were introduced a decade ago. Application areas include gene function discovery, disease diagnosis, and inferring regulatory networks. Microarray experiments enable large-scale, high-throughput investigations of gene activity and have thus provided the data analyst with a distinctive, high-dimensional field of study. Many questions in this field relate to finding subgroups of data profiles which are very similar. A popular type of exploratory tool for finding subgroups is cluster analysis, and many different flavors of algorithms have been used and indeed tailored for microarray data. Cluster analysis, however, implies a partitioning of the entire data set, and this does not always match the objective. Sometimes pattern discovery or bump hunting tools are more appropriate. This paper reviews these various tools for finding interesting subgroups

Dynamic recruitment of microRNAs to their mRNA targets in the regenerating liver.

BACKGROUND: Validation of physiologic miRNA targets has been met with significant challenges. We employed HITS-CLIP to identify which miRNAs participate in liver regeneration, and to identify their target mRNAs. RESULTS: miRNA recruitment to the RISC is highly dynamic, changing more than five-fold for several miRNAs. miRNA recruitment to the RISC did not correlate with changes in overall miRNA expression for these dynamically recruited miRNAs, emphasizing the necessity to determine miRNA recruitment to the RISC in order to fully assess the impact of miRNA regulation. We incorporated RNA-seq quantification of total mRNA to identify expression-weighted Ago footprints, and developed a microRNA regulatory element (MRE) prediction algorithm that represents a greater than 20-fold refinement over computational methods alone. These high confidence MREs were used to generate candidate \u27competing endogenous RNA\u27 (ceRNA) networks. CONCLUSION: HITS-CLIP analysis provide novel insights into global miRNA:mRNA relationships in the regenerating liver

Correction to: Explaining firms’ earnings announcement stock returns using FactSet and I/B/E/S data feeds

A Correction to this paper has been published: https://doi.org/10.1007/s11142-021-09616-

Explaining firms’ earnings announcement stock returns using FactSet and I/B/E/S data feeds

Since 2001, the number of financial statement line items forecasted by analysts and managers that I/B/E/S and FactSet capture in their data feeds has soared. Using this new data, we find that 13 item surprises—11 income statement-based and 2 cash flow statement-based analyst and management guidance surprises—reliably explain firms’ signed earnings announcement returns. No balance sheet or expense surprises are significant. The most important surprises are (i) one-quarter-ahead sales guidance surprise, (ii) analyst sales surprise, (iii) annual Street earnings guidance surprise, and (iv) analyst Street earnings surprise. We also find that the adjusted R2s of our multivariate regressions are three times higher than the adjusted R2s of univariate Street earnings surprise regressions, and that the four most important surprises account for approximately half of this increase in explanatory power

Enhancing wind erosion monitoring and assessment for U.S. rangelands

Wind erosion is a major resource concern for rangeland managers because it can impact soil health, ecosystem structure and function, hydrologic processes, agricultural production, and air quality. Despite its significance, little is known about which landscapes are eroding, by how much, and when. The National Wind Erosion Research Network was established in 2014 to develop tools for monitoring and assessing wind erosion and dust emissions across the United States. The Network, currently consisting of 13 sites, creates opportunities to enhance existing rangeland soil, vegetation, and air quality monitoring programs. Decision-support tools developed by the Network will improve the prediction and management of wind erosion across rangeland ecosystems. © 2017 The Author(s)The Rangelands archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information