Chronic recurrent ankle sprain: A randomized clinical trial studying the impact of two treatment protocols

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Approaches to the evaluation of outbreak detection methods

BACKGROUND: An increasing number of methods are being developed for the early detection of infectious disease outbreaks which could be naturally occurring or as a result of bioterrorism; however, no standardised framework for examining the usefulness of various outbreak detection methods exists. To promote comparability between studies, it is essential that standardised methods are developed for the evaluation of outbreak detection methods. METHODS: This analysis aims to review approaches used to evaluate outbreak detection methods and provide a conceptual framework upon which recommendations for standardised evaluation methods can be based. We reviewed the recently published literature for reports which evaluated methods for the detection of infectious disease outbreaks in public health surveillance data. Evaluation methods identified in the recent literature were categorised according to the presence of common features to provide a conceptual basis within which to understand current approaches to evaluation. RESULTS: There was considerable variation in the approaches used for the evaluation of methods for the detection of outbreaks in public health surveillance data, and appeared to be no single approach of choice. Four main approaches were used to evaluate performance, and these were labelled the Descriptive, Derived, Epidemiological and Simulation approaches. Based on the approaches identified, we propose a basic framework for evaluation and recommend the use of multiple approaches to evaluation to enable a comprehensive and contextualised description of outbreak detection performance. CONCLUSION: The varied nature of performance evaluation demonstrated in this review supports the need for further development of evaluation methods to improve comparability between studies. Our findings indicate that no single approach can fulfil all evaluation requirements. We propose that the cornerstone approaches to evaluation identified provide key contributions to support internal and external validity and comparability of study findings, and suggest these be incorporated into future recommendations for performance assessment

An integrated clinical program and crowdsourcing strategy for genomic sequencing and Mendelian disease gene discovery.

Despite major progress in defining the genetic basis of Mendelian disorders, the molecular etiology of many cases remains unknown. Patients with these undiagnosed disorders often have complex presentations and require treatment by multiple health care specialists. Here, we describe an integrated clinical diagnostic and research program using whole-exome and whole-genome sequencing (WES/WGS) for Mendelian disease gene discovery. This program employs specific case ascertainment parameters, a WES/WGS computational analysis pipeline that is optimized for Mendelian disease gene discovery with variant callers tuned to specific inheritance modes, an interdisciplinary crowdsourcing strategy for genomic sequence analysis, matchmaking for additional cases, and integration of the findings regarding gene causality with the clinical management plan. The interdisciplinary gene discovery team includes clinical, computational, and experimental biomedical specialists who interact to identify the genetic etiology of the disease, and when so warranted, to devise improved or novel treatments for affected patients. This program effectively integrates the clinical and research missions of an academic medical center and affords both diagnostic and therapeutic options for patients suffering from genetic disease. It may therefore be germane to other academic medical institutions engaged in implementing genomic medicine programs

Using GIS to create synthetic disease outbreaks

BACKGROUND: The ability to detect disease outbreaks in their early stages is a key component of efficient disease control and prevention. With the increased availability of electronic health-care data and spatio-temporal analysis techniques, there is great potential to develop algorithms to enable more effective disease surveillance. However, to ensure that the algorithms are effective they need to be evaluated. The objective of this research was to develop a transparent user-friendly method to simulate spatial-temporal disease outbreak data for outbreak detection algorithm evaluation. A state-transition model which simulates disease outbreaks in daily time steps using specified disease-specific parameters was developed to model the spread of infectious diseases transmitted by person-to-person contact. The software was developed using the MapBasic programming language for the MapInfo Professional geographic information system environment. RESULTS: The simulation model developed is a generalised and flexible model which utilises the underlying distribution of the population and incorporates patterns of disease spread that can be customised to represent a range of infectious diseases and geographic locations. This model provides a means to explore the ability of outbreak detection algorithms to detect a variety of events across a large number of stochastic replications where the influence of uncertainty can be controlled. The software also allows historical data which is free from known outbreaks to be combined with simulated outbreak data to produce files for algorithm performance assessment. CONCLUSION: This simulation model provides a flexible method to generate data which may be useful for the evaluation and comparison of outbreak detection algorithm performance

The perceived impact of location privacy: A web-based survey of public health perspectives and requirements in the UK and Canada

<p>Abstract</p> <p>Background</p> <p>The "place-consciousness" of public health professionals is on the rise as spatial analyses and Geographic Information Systems (GIS) are rapidly becoming key components of their toolbox. However, "place" is most useful at its most precise, granular scale – which increases identification risks, thereby clashing with privacy issues. This paper describes the views and requirements of public health professionals in Canada and the UK on privacy issues and spatial data, as collected through a web-based survey.</p> <p>Methods</p> <p>Perceptions on the impact of privacy were collected through a web-based survey administered between November 2006 and January 2007. The survey targeted government, non-government and academic GIS labs and research groups involved in public health, as well as public health units (Canada), ministries, and observatories (UK). Potential participants were invited to participate through personally addressed, standardised emails.</p> <p>Results</p> <p>Of 112 invitees in Canada and 75 in the UK, 66 and 28 participated in the survey, respectively. The completion proportion for Canada was 91%, and 86% for the UK. No response differences were observed between the two countries. Ninety three percent of participants indicated a requirement for personally identifiable data (PID) in their public health activities, including geographic information. Privacy was identified as an obstacle to public health practice by 71% of respondents. The overall self-rated median score for knowledge of privacy legislation and policies was 7 out of 10. Those who rated their knowledge of privacy as high (at the median or above) also rated it significantly more severe as an obstacle to research (<it>P </it>< 0.001). The most critical cause cited by participants in both countries was bureaucracy.</p> <p>Conclusion</p> <p>The clash between PID requirements – including granular geography – and limitations imposed by privacy and its associated bureaucracy require immediate attention and solutions, particularly given the increasing utilisation of GIS in public health. Solutions include harmonization of privacy legislation with public health requirements, bureaucratic simplification, increased multidisciplinary discourse, education, and development of toolsets, algorithms and guidelines for using and reporting on disaggregate data.</p

Inherited CHST11/MIR3922 deletion is associated with a novel recessive syndrome presenting with skeletal malformation and malignant lymphoproliferative disease

Glycosaminoglycans (GAGs) such as chondroitin are ubiquitous disaccharide carbohydrate chains that contribute to the formation and function of proteoglycans at the cell membrane and in the extracellular matrix. Although GAG-modifying enzymes are required for diverse cellular functions, the role of these proteins in human development and disease is less well understood. Here, we describe two sisters out of seven siblings affected by congenital limb malformation and malignant lymphoproliferative disease. Using Whole-Genome Sequencing (WGS), we identified in the proband deletion of a 55 kb region within chromosome 12q23 that encompasses part of CHST11 (encoding chondroitin-4-sulfotransferase 1) and an embedded microRNA (MIR3922). The deletion was homozygous in the proband but not in each of three unaffected siblings. Genotyping data from the 1000 Genomes Project suggest that deletions inclusive of both CHST11 and MIR3922 are rare events. Given that CHST11 deficiency causes severe chondrodysplasia in mice that is similar to human limb malformation, these results underscore the importance of chondroitin modification in normal skeletal development. Our findings also potentially reveal an unexpected role for CHST11 and/or MIR3922 as tumor suppressors whose disruption may contribute to malignant lymphoproliferative disease

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

BACKGROUND: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data was donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

Processes and factors involved in decisions regarding return of incidental genomic findings in research

Purpose: Studies have begun exploring whether researchers should return incidental findings in genomic studies, and if so, which findings should be returned; however, how researchers make these decisions—the processes and factors involved—has remained largely unexplored. Methods: We interviewed 28 genomics researchers in-depth about their experiences and views concerning the return of incidental findings. Results: Researchers often struggle with questions concerning which incidental findings to return and how to make those decisions. Multiple factors shape their views, including information about the gene variant (e.g., pathogenicity and disease characteristics), concerns about participants’ well-being and researcher responsibility, and input from external entities. Researchers weigh the evidence, yet they face conflicting pressures, with relevant data frequently being unavailable. Researchers vary in who they believe should decide: participants, principal investigators, institutional review boards, and/or professional organizations. Contextual factors can influence these decisions, including policies governing return of results by institutions and biobanks and the study design. Researchers vary in desires for: guidance from institutions and professional organizations, changes to current institutional processes, and community-wide genetics education. Conclusion: These data, the first to examine the processes by which researchers make decisions regarding the return of genetic incidental findings, highlight several complexities involved and have important implications for future genetics research, policy, and examinations of these issues

Association of Researcher Characteristics with Views on Return of Incidental Findings from Genomic Research

Whole exome/ genome sequencing (WES/WGS) is now commonly used in research and is increasingly used in clinical care to identify the genetic basis of rare and unknown diseases. The management of incidental findings (IFs) generated through these analyses is debated within the research community. To examine how views regarding genomic research IFs are associated with researcher characteristics and experiences, we surveyed genetic professionals and assessed the effect of professional background and experience on their opinions. Researchers who did not have clinical training, provide clinical care to research participants, or have prior experience returning research results were in general more inclined to offer return of IFs than their colleagues with these characteristics. Understanding this will be important to fully appreciate the impact that policies on return of genetic IFs could have on participants, researchers, and genomic research