An AIHW framework for assessing data sources for population health monitoring: working paper

This paper outlines the Australian Institute of Health and Welfare\u27s (AIHW) assessment framework for determining the suitability of specific data sources for population health monitoring. AIHW\u27s Assessment Framework When identifying potential data sources for population health monitoring, it is important to ensure they are \u27fit-for-purpose\u27. The AIHW has developed a 3-step process to assess potential data sources for population health monitoring: Step 1 collects information about the data source Step 2 identifies the potential to inform key monitoring areas Step 3 assesses the quality of the data, using a modified version of the Australian Bureau of Statistics (ABS) Data Quality Framework (ABS 2009), to determine its \u27fitness-for-purpose\u27 by establishing its utility, strengths and limitations. The assessment framework has been designed for use by the AIHW and others with an interest in assessing new data sources for use in population health monitoring. With adaptation, it may also have wider applications in other sectors or subject areas. For an example of the application of the assessment framework, see the AIHW working paper Assessment of the Australian Rheumatology Association Database for national population health monitoring (AIHW 2014a)

A Lightweight and Flexible Mobile Agent Platform Tailored to Management Applications

Mobile Agents (MAs) represent a distributed computing technology that promises to address the scalability problems of centralized network management. A critical issue that will affect the wider adoption of MA paradigm in management applications is the development of MA Platforms (MAPs) expressly oriented to distributed management. However, most of available platforms impose considerable burden on network and system resources and also lack of essential functionality. In this paper, we discuss the design considerations and implementation details of a complete MAP research prototype that sufficiently addresses all the aforementioned issues. Our MAP has been implemented in Java and tailored for network and systems management applications.Comment: 7 pages, 5 figures; Proceedings of the 2006 Conference on Mobile Computing and Wireless Communications (MCWC'2006

Assessment of the Australian Rheumatology Association Database for national population health monitoring: working paper

This working paper uses the Australian Institute of Health and Welfare\u27s recently developed assessment framework to assess the suitability of the Australian Rheumatology Association Database as a potential new data source for population health monitoring of inflammatory arthritis. Summary A wide range of existing data sources could potentially be used to improve our understanding of arthritis in the Australian population. This working paper uses an assessment framework recently developed by the Australian Institute of Health and Welfare (AIHW) to assess the suitability of the Australian Rheumatology Association Database (ARAD) as a potential new data source for population health monitoring of inflammatory arthritis. More than 400,000 Australians have rheumatoid arthritis, the most common form of inflammatory arthritis. This auto-immune disease causes chronic inflammation, pain and swelling of the joints and can greatly reduce a person\u27s quality of life. The ARAD, managed by the Australian Rheumatology Association, is a national registry that collects health information from individuals with inflammatory arthritis. It was primarily established to monitor the benefits and safety of new treatments, particularly the biological disease-modifying anti-rheumatic drugs (bDMARDs). The AIHW\u27s assessment of the ARAD for the purpose of national population health monitoring is that: it has the potential to fill a range of identified data gaps in relation to key questions for monitoring arthritis, including treatment outcomes, disease progression, quality of life and economic impacts it has well established administrative and governance arrangements in place to ensure data quality and compliance with legislative requirements it has limited coverage which could potentially be improved by combining with, or linking to, other similar data sources on balance, it is a data source with the potential to provide useful information for population health monitoring of inflammatory arthritis, particularly if used in combination with, or linked to, other data sources

Identification of novel prognostic and predictive biomarkers in colorectal cancer

For patients with colorectal cancer, the risk for disease recurrence and death mainly depends on disease stage. Yet, patients with early stage colon cancer may still succumb to the disease. Therefore, to improve the management of patients with colorectal cancer, new biomarkers for risk stratification are needed that are independent of tumor stage. Here, we demonstrate that RBP7 is a strong prognostic biomarker in colon cancer that functionally contributes to the malignant phenotype of colon cancer cells. We quantified RBP7 expression in colon cancer tissue by digital image analysis, and high levels of RBP7 protein and mRNA expressions were associated with poor cancer specific survival. Additionally, GSEA analysis and cell migration and invasion assays demonstrated that RBP7 is functionally linked to invasion and epithelial-mesenchymal transition in colon cancer. Furthermore, we illustrate here an unbiased approach using publically available TCGA data to identify new biomarkers that may aid in colorectal cancer risk stratification beyond clinical staging. By this approach Annexin A9 was identified and validated as an independent prognostic predictor of poor outcomes and that was associated with distant metastasis in independent colon cancer case collections on the protein level. Collectively, these findings provide a rationale for considering RBP7 and Annexin A9 as promising independent predictors for prognosis. These may be useful for risk stratification in patients with colorectal cancer and aid in improving patient management

Identification of novel prognostic and predictive biomarkers in colorectal cancer

For patients with colorectal cancer, the risk for disease recurrence and death mainly depends on disease stage. Yet, patients with early stage colon cancer may still succumb to the disease. Therefore, to improve the management of patients with colorectal cancer, new biomarkers for risk stratification are needed that are independent of tumor stage. Here, we demonstrate that RBP7 is a strong prognostic biomarker in colon cancer that functionally contributes to the malignant phenotype of colon cancer cells. We quantified RBP7 expression in colon cancer tissue by digital image analysis, and high levels of RBP7 protein and mRNA expressions were associated with poor cancer specific survival. Additionally, GSEA analysis and cell migration and invasion assays demonstrated that RBP7 is functionally linked to invasion and epithelial-mesenchymal transition in colon cancer. Furthermore, we illustrate here an unbiased approach using publically available TCGA data to identify new biomarkers that may aid in colorectal cancer risk stratification beyond clinical staging. By this approach Annexin A9 was identified and validated as an independent prognostic predictor of poor outcomes and that was associated with distant metastasis in independent colon cancer case collections on the protein level. Collectively, these findings provide a rationale for considering RBP7 and Annexin A9 as promising independent predictors for prognosis. These may be useful for risk stratification in patients with colorectal cancer and aid in improving patient management

A composite immune signature parallels disease progression across T1D subjects

At diagnosis, most people with type 1 diabetes (T1D) produce measurable levels of endogenous insulin, but the rate at which insulin secretion declines is heterogeneous. To explain this heterogeneity, we sought to identify a composite signature predictive of insulin secretion, using a collaborative assay evaluation and analysis pipeline that incorporated multiple cellular and serum measures reflecting beta cell health and immune system activity. The ability to predict decline in insulin secretion would be useful for patient stratification for clinical trial enrollment or therapeutic selection. Analytes from 12 qualified assays were measured in shared samples from subjects newly diagnosed with T1D. We developed a computational tool to identify a composite panel associated with decline in insulin secretion over 2 years after diagnosis. The tool employs multiple filtering steps to reduce data dimensionality, incorporates error-estimation techniques including cross-validation and sensitivity analysis, and is flexible to assay type, clinical outcome and disease setting. Using this novel analytical tool, we identified a panel of immune markers that, in combination, are highly associated with loss of insulin secretion. The methods used here represent a novel process for identifying combined immune signatures that predict outcomes relevant for complex and heterogeneous diseases like T1D

Steganography and Data Loss Prevention: An overlooked risk?

Steganography is the art or science of hiding information into a carrier in such a way that the hidden data could not be detected at first sight. Steganography techniques have broadened their scope of action, from hiding information into picture media, to audio steganography and to the field of network steganography. All these methods entail a potential threat to the information security policies of any business; having into the data leakage threats its likely focus. In this scenario, business corporations cannot remain blind to these types of threats and should consider adequate policies and prevention techniques to avoid these risks. We have analyzed in this article the potential dangers that an organization could face in the light of these types of steganography techniques along with a review of current commercial software vendors to analyze their offers and mishaps on Data Leakage Prevention regarding steganography risks

Public Geospatial Data in Wisconsin: Information Access, Data Sharing, and the University

This research explores public geospatial data sharing in Wisconsin. The research is informed by literature on GIS and Society, Participatory GIS, Spatial Data Infrastructure, Information Justice, The Digital Divide, and Library and Information Science. Original research consists of a survey and follow up interview to public land information professionals in Wisconsin gauging their interest in a UW System-wide geographic information portal for distributing public spatial data to UW System users. The research finds that social and institutional rather than technical factors are major drivers of data-sharing activities in Wisconsin. However, technical aspects of geographic information are changing quickly with a move to more hosted services in the cloud. This research explores how this shift influences data-sharing, academic library GIS services, and university level education. While social and institutional influences are critical, GIS professionals, students, and educators must be ready for the cloud