Reflections on 40 years of Disasters, 1977-2017

This reflection, based on a keynote address to celebrate the fortieth anniversary of Disasters on 14 September 2017, begins by considering some of the prevailing assumptions that existed at the time of the journal's creation. Next is a summary of significant milestones during the past 40 years, covering major global trends, changing disaster impacts, and key developments in disaster risk management. Contrasting approaches in the first and fourth decade of the journal's history are then followed by examples of changes in terminology in the disaster field as an indication of shifting values. The paper goes on to explore the context of 2017, with observations of problem areas such as the loss of knowledge, failures to tackle the root causes of disasters, shortfalls in international assistance, and some negative attitudes. It closes by evaluating some current positive occurrences, including a reduction in casualties, sharing of experience, technological advances, and dramatic improvements in disaster warning.

Comparing Apples With Apples: The Importance of Clean Data

This interview provides a practical example of how a school district is working to get and use clean, accurate data. It is a practical example of how to apply the principles found in the article on data fidelity, which is part of the "data and school nursing" articles series being published in NASN School Nurse during the 2018-2019 school year.

Artificial Neural Networks for Prediction of Tuberculosis Disease

Background: The global burden of tuberculosis (TB) and antibiotic resistance is attracting the attention of researchers to develop some novel and rapid diagnostic tools. Although, the conventional methods like culture are considered as the gold standard, they are time consuming in diagnostic procedure, during which there are more chances in the transmission of disease. Further, the Xpert MTB/RIF assay offers a fast diagnostic facility within 2 h, but due to low sensitivity in some sample types may lead to more serious state of the disease. The role of computer technologies is now increasing in the diagnostic procedures. Here, in the current study we have applied the artificial neural network (ANN) that predicted the TB disease based on the TB suspect data. Methods: We developed an approach for prediction of TB, based on an ANN. The data was collected from the TB suspects, guardians or care takers along with samples, referred by TB units and health centers. All the samples were processed and cultured. Data was trained on 12,636 records of TB patients, collected during the years 2016 and 2017 from the provincial TB reference laboratory, Khyber Pakhtunkhwa, Pakistan. The training and test set of the suspect data were kept as 70 and 30%, respectively, followed by validation and normalization. The ANN takes the TB suspect's information such as gender, age, HIV-status, previous TB history, sample type, and signs and symptoms for TB prediction. Results: Based on TB patient data, ANN accurately predicted the Mycobacterium tuberculosis (MTB) positive or negative with an overall accuracy of >94%. Further, the accuracy of the test and validation were found to be >93%. This increased accuracy of ANN in the detection of TB suspected patients might be useful for early management of disease to adopt some control measures in further transmission and reduce the drug resistance burden. Conclusion: ANNs algorithms may play an effective role in the early diagnosis of TB disease that might be applied as a supportive tool. Modern computer technologies should be trained in diagnostics for rapid disease management. Delays in TB diagnosis and initiation treatment may allow the emergence of new cases by transmission, causing high drug resistance in countries with a high TB burden.

Merging Data Diversity of Clinical Medical Records to Improve Effectiveness

Medicine is a knowledge area continuously experiencing changes. Every day, discoveries and procedures are tested with the goal of providing improved service and quality of life to patients. With the evolution of computer science, multiple areas experienced an increase in productivity with the implementation of new technical solutions. Medicine is no exception. Providing healthcare services in the future will involve the storage and manipulation of large volumes of data (big data) from medical records, requiring the integration of different data sources, for a multitude of purposes, such as prediction, prevention, personalization, participation, and becoming digital. Data integration and data sharing will be essential to achieve these goals. Our work focuses on the development of a framework process for the integration of data from different sources to increase its usability potential. We integrated data from an internal hospital database, external data, and also structured data resulting from natural language processing (NPL) applied to electronic medical records. An extract-transform and load (ETL) process was used to merge different data sources into a single one, allowing more effective use of these data and, eventually, contributing to more efficient use of the available resources.

Breaking the Glass Cage: The Power of Data, Courage, and Voice

A glass ceiling or cage is the notion that certain groups-mostly women or minorities-are held back, often due to their own beliefs. Yet there are limiting beliefs and actions that also put school nursing in a glass cage. This article explores five actions school nurses must take in order to break the glass cage holding school nursing back as a profession.

Adjusting the focus: A public health ethics approach to data research

This paper contends that a research ethics approach to the regulation of health data research is unhelpful in the era of population-level research and big data because it results in a primary focus on consent (meta-, broad, dynamic and/or specific consent). Two recent guidelines - the 2016 WMA Declaration of Taipei on ethical considerations regarding health databases and biobanks and the revised CIOMS International ethical guidelines for health-related research involving humans - both focus on the growing reliance on health data for research. But as research ethics documents, they remain (to varying degrees) focused on consent and individual control of data use. Many current and future uses of health data make individual consent impractical, if not impossible. Many of the risks of secondary data use apply to communities and stakeholders rather than individual data subjects. Shifting from a research ethics perspective to a public health lens brings a different set of issues into view: how are the benefits and burdens of data use distributed, how can data research empower communities, who has legitimate decision-making capacity? I propose that a public health ethics framework - based on public benefit, proportionality, equity, trust and accountability - provides more appropriate tools for assessing the ethical uses of health data. The main advantage of a public health approach for data research is that it is more likely to foster debate about power, justice and equity and to highlight the complexity of deciding when data use is in the public interest.

Attacks on healthcare in conflict: generating attention in the modern information landscape

Attacks on healthcare in conflict (AHCC) is receiving increased attention as a pressing global concern from humanitarian organisations, the media, policymakers, academics, and the public. This study examines the extent to which this increased attention leads to mainstream reporting and understanding of AHCC that is representative of reality by investigating the ways that information and knowledge about the issue is produced. Based on a rapid review of documentation on AHCC since 2011, this is one of the first studies to assess the rigour, validity, and representativeness of mainstream data on AHCC. Findings indicate that information and knowledge about AHCC is molded by a particular representation of the issue created by international media and humanitarian organisations seeking to capture public and political attention. This dominant understanding is not representative of the complex reality of AHCC, but is instead driven by organisational objectives and advocacy agendas. These findings hold important implications for the above-mentioned stakeholders by exploring the complexity and impacts of documenting AHCC. Most notably, the research investigates the relationship between information and understanding about the issue and the action that it encourages.

The practice of female genital mutilation across the world: Data availability and approaches to measurement

While Female Genital Mutilation (FGM) has been in existence for centuries, the rigorous and systematic documentation of the extent of the practice is a recent undertaking. This paper discusses data availability related to the practice of FGM and reviews the methods used to generate prevalence estimates. The aim is to illustrate strengths and limitations of the available data. The review is organised around two main categories of countries: FGM countries of origin, where representative prevalence data exist, and countries of migration for women and girls who have undergone FGM, for which representative prevalence data are lacking. This second category also includes countries across the world where FGM is only found among small autochthonous populations.

Crosstalk analysis of dysregulated pathways in preeclampsia

A crosstalk between multiple biological pathways has been proposed in biological processes. However, the existence and degree of this phenomenon in patients with preeclampsia (PE) have not been strictly investigated. Thus, this study explored an dysregulated pathway set (DPS) for PE based on pathway crosstalk network (PCN) related analysis. In the present study, four steps were performed in the inference of DPS: acquiring data of gene expression, pathway and protein-protein interaction (PPI) construction; building a PCN through integrating the information in these datasets and Pearson's correlation coefficient (PCC). A principal component analysis (PCA) approach was used to compute the activity of every pathway for selecting seed pathway of PCN. DPS was evaluated by measuring of an area under the receiver operating characteristics curve (AUC) and seed pathway from PCN. Consequently, a total of 420 pathways and 6,032 crosstalks were mapped to the PCN, in which RIG-I/MDA5-mediated induction of IFN-α/β pathways was identified as the seed pathway that had the greatest changes in activity scores across PE patients and normal controls. DPS was composed of 15 dysregulated pathways and 46 crosstalks, in which CLEC7A (Dectin-1) signaling possessed the highest degree of 12, which indicated it exerted an important role in the DPS. Our results revealed crosstalk between pathways and the DPS crucial for PE pathogenesis, which aid in excavating potential biomarkers of PE therapy and unveil the underlying pathological mechanism of this disease.

Quantitative Data From Rating Scales: An Epistemological and Methodological Enquiry

Rating scales are popular methods for generating quantitative data directly by persons rather than automated technologies. But scholars increasingly challenge their foundations. This article contributes epistemological and methodological analyses of the processes involved in person-generated quantification. They are crucial for measurement because data analyses can reveal information about study phenomena only if relevant properties were encoded systematically in the data. The Transdisciplinary Philosophy-of-Science Paradigm for Research on Individuals (TPS-Paradigm) is applied to explore psychological and social-science concepts of measurement and quantification, including representational measurement theory, psychometric theories and their precursors in psychophysics. These are compared to theories from metrology specifying object-dependence of measurement processes and subject-independence of outcomes as key criteria, which allow tracing data to the instances measured and the ways they were quantified. Separate histories notwithstanding, the article's basic premise is that general principles of scientific measurement and quantification should apply to all sciences. It elaborates principles by which these metrological criteria can be implemented also in psychology and social sciences, while considering their research objects' peculiarities. Application of these principles is illustrated by quantifications of individual-specific behaviors ('personality'). The demands rating methods impose on data-generating persons are deconstructed and compared with the demands involved in other quantitative methods (e.g., ethological observations). These analyses highlight problematic requirements for raters. Rating methods sufficiently specify neither the empirical study phenomena nor the symbolic systems used as data nor rules of assignment between them. Instead, pronounced individual differences in raters' interpretation and use of items and scales indicate considerable subjectivity in data generation. Together with recoding scale categories into numbers, this introduces a twofold break in the traceability of rating data, compromising interpretability of findings. These insights question common reliability and validity concepts for ratings and provide novel explanations for replicability problems. Specifically, rating methods standardize only data formats but not the actual data generation. Measurement requires data generation processes to be adapted to the study phenomena's properties and the measurement-executing persons' abilities and interpretations, rather than to numerical outcome formats facilitating statistical analyses. Researchers must finally investigate how people actually generate ratings to specify the representational systems underlying rating data.

Catching the Data Vision

As part of the "data and school-nursing" articles series, we will include an accompanying interview with practicing school nurses regarding the same topic and how it applies to the real life of school nurses. Our hope is that the practical application found in the interviews will provide readers with an increased understanding that will assist them on their own data journey.

[Design of Experiment Platform for Noninvasive Ventilator Based on LabVIEW]

OBJECTIVE

The study presents an experiment platform to perform ventilation and develop the control strategy for ventilator.

METHODS

The software designed by LabVEIW displays and saves data in ventilation support through connecting with data acquisition device and signal processing circuit, which is improved by control strategy in order to achieve the more scientific ventilation.

RESULTS

The experiment platform realizes the ventilation support and is close to the data outputted from ventilator.

CONCLUSIONS

The platform provides values for ventilation control strategy in development phase.

Data, Year-End Reports, and Perseverance

This is the companion interview to the "data and school nursing" articles series, sharing practical examples of how frontline school nurses use data. This interview is with Janis Hogan who shares her data in a year-end report and uses her data to advocate for programs.

Epidemiological Data Challenges: Planning for a More Robust Future Through Data Standards

Accessible epidemiological data are of great value for emergency preparedness and response, understanding disease progression through a population, and building statistical and mechanistic disease models that enable forecasting. The status quo, however, renders acquiring and using such data difficult in practice. In many cases, a primary way of obtaining epidemiological data is through the internet, but the methods by which the data are presented to the public often differ drastically among institutions. As a result, there is a strong need for better data sharing practices. This paper identifies, in detail and with examples, the three key challenges one encounters when attempting to acquire and use epidemiological data: (1) interfaces, (2) data formatting, and (3) reporting. These challenges are used to provide suggestions and guidance for improvement as these systems evolve in the future. If these suggested data and interface recommendations were adhered to, epidemiological and public health analysis, modeling, and informatics work would be significantly streamlined, which can in turn yield better public health decision-making capabilities.

Power of the Past, Celebrate the Present, Force of the Future, Part 3: Interview With NASN Leaders

For Part 3 of the NASN School Nurse series exploring NASN's past, present, and future, Lindsey Minchella interviews NASN President, Nina Fekaris and President-elect, Laurie Combe to discuss their school nursing perspective and philosophies. These three school nursing leaders discuss what is responsible for their long tenure as school nurses, changes witnessed in student health over the years, how NASN makes a difference for students and school nurses, the future of school nursing, and the power of NASN members' voices to improve student health and academic outcomes.

Digital Diabetes Congress 2018

Digital health is capturing the attention of the healthcare community. This paradigm whereby healthcare meets the internet uses sensors that communicate wirelessly along with software residing on smartphones to deliver data, information, treatment recommendations, and in some cases control over an effector device. As artificial intelligence becomes more widely used, this approach to creating individualized treatment plans will increase the opportunities for patients, even if they are in remote settings, to communicate with and learn from healthcare professionals. Simple design is needed to promote use of these tools, especially for the purpose of increased adherence to treatment. Widespread adoption by the healthcare industry will require better outcomes data, which will most likely be in the form of safety and effectiveness results from robust randomized controlled trials, as well as evidence of privacy and security. Such data will be needed to convince investors to direct resources into and regulators to clear new digital health tools. Diabetes Technology Society and William Sansum Diabetes Center launched the Digital Diabetes Congress in 2017 because of great interest in determining the potential benefits, metrics of success, and appropriate components of mobile applications for diabetes. The second annual meeting in this series took place on May 22-23, 2018 in San Francisco. This report contains summaries of the meeting's 4 plenary lectures and 10 sessions. This meeting report presents a summary of how 55 panelists, speakers, and moderators, who are leaders in healthcare technology, see the current and future landscape of digital health tools applied to diabetes.

Volume outlier benchmark proposal for Australian Mohs surgery

Peer-derived Australian Mohs data were used to define volume outliers and a benchmark for quality improvement. Higher volume practice was linked to less complex anatomical locations for surgery. Reflection on individual practice compared to peers may reduce the number of volume outliers in Mohs surgery.

The Role of Data in Model Building and Prediction: A Survey Through Examples

The goal of Science is to understand phenomena and systems in order to predict their development and gain control over them. In the scientific process of knowledge elaboration, a crucial role is played by models which, in the language of quantitative sciences, mean abstract mathematical or algorithmical representations. This short review discusses a few key examples from Physics, taken from dynamical systems theory, biophysics, and statistical mechanics, representing three paradigmatic procedures to build models and predictions from available data. In the case of dynamical systems we show how predictions can be obtained in a virtually model-free framework using the methods of analogues, and we briefly discuss other approaches based on machine learning methods. In cases where the complexity of systems is challenging, like in biophysics, we stress the necessity to include part of the empirical knowledge in the models to gain the minimal amount of realism. Finally, we consider many body systems where many (temporal or spatial) scales are at play—and show how to derive from data a dimensional reduction in terms of a Langevin dynamics for their slow components.

Using primary care data for health research in England - an overview

In contrast to secondary care, where handwritten records remain widespread, electronic patient records have long been a key feature of UK general practice. By 1996, 96% of general practices were computerised and now almost every primary care consultation in the UK is recorded on a computerised clinical system. Consequently, we now have a vast repository of patient health data that spans decades, which could be used to address a range of important research questions. Unfortunately, accessing primary care data for health researchers can be a burdensome, confusing and time-consuming process. Understanding the way in which primary care data are recorded and 'coded' is not intuitive to those unfamiliar with general practice. The requirements of information governance mean that some data, or data presented in particular ways, are not available at all. This review provides a practical overview of the types of data recorded in primary care, the bodies responsible for them and how they can be accessed.

Using Data as an Instrument for Change

This is the companion interview to the "data and school nursing" articles series, sharing practical examples of how frontline school nurses use data. This interview is with Claire Molner who not only shares how she used data for her practice but also addresses how she shares the importance of data with school nursing colleagues.

Selecting best-suited "patient-related outcomes" in older people admitted to an acute geriatric or emergency frailty unit and applying quality improvement research to improve patient care

The population is aging worldwide, and hospitals are admitting a higher proportion of acutely unwell older people. Population-specific factors such as multimorbidity and frailty in older people compounded by deficient expertise contribute to longer lengths of stay, higher readmission rates, and increased rates of institutionalization. A wide range of acute geriatric care models are currently providing acute care to frail older people and these have been shown to provide a cost-effective high-quality service. In this review, population-specific factors, service models, and a wide range of patient-related outcomes of "at risk" older people admitted to an acute geriatric care unit are explored. In addition, we also discuss data measurements and a quality improvement methodology to improve the delivery of care based on the patient outcome data. We hope, in addition to ensuring effectiveness and sustainability of our current services, this may also enhance academic research. Regular monitoring and evaluation of patient-related clinical outcomes not only improve the patient care and reduce the caregiver burden but also help in implementing quality initiatives to develop existing services.

Improved Socio-Emotional and Behavioral Functioning in Students with Autism Following School-Based Smartglasses Intervention: Multi-Stage Feasibility and Controlled Efficacy Study

Background: Students with Autism Spectrum Disorder (ASD) commonly demonstrate prominent social communication deficits, symptoms of attention-deficit/hyperactivity disorder, and chronic irritability. These challenges hinder academic progress and frequently persist despite educational, behavioral, and medical interventions. An assistive smartglasses technology may aid these individuals, especially if the technology is efficacious in ecologically-valid school settings. This study explored the feasibility and efficacy of Empowered Brain, a computerized smartglasses intervention designed as a socio-emotional behavioral aid for students with ASD. Methods: This two-part six-week study involved four school children with ASD from a public elementary school. The study incorporated an initial three-week feasibility stage followed by a three-week controlled longitudinal efficacy stage. Both stages involved the use of a twice-daily socio-emotional intervention with the smartglasses. Educators completed pre-intervention and post-intervention Aberrant Behavioral Checklist (ABC) ratings at the start of the feasibility stage, and weekly during the efficacy stage. Primary outcome measures were improvements in the ABC subscales of irritability, hyperactivity, and social withdrawal. Results: Students in both feasibility and efficacy stages demonstrated improvements (decreases) in irritability, hyperactivity, and social withdrawal compared to a baseline period and control periods, respectively. Participants in the controlled efficacy stage demonstrated decreased ABC subscale scores of 90% for irritability, 41.6% for hyperactivity, and 45.6% for social withdrawal. An intervention exposure-response improvement in irritability and hyperactivity was found during the efficacy stage. Educators rated the technology as superior or vastly superior compared to other assistive technologies. Conclusion: A substantial number of school children with ASD demonstrate chronic and impairing cognitive and behavioral challenges. This study provides evidence that Empowered Brain, a smartglasses-based socio-emotional aid for autism, is both feasible and efficacious in improving symptoms of social withdrawal, irritability, and hyperactivity in students with autism. The improvement is demonstrated as part of a longitudinal school-based intervention. Further studies involving larger samples and incorporation of randomized controlled trial methodology are underway to further elucidate the impact of this technology.

Analysing health data sources to inform chronic disease management decisions of health insurers: A mixed methods study

Background and Objective: Both health care providers and payers recognize the need to improve chronic disease care. Chronic disease management relies on high-quality health information for people with, and at risk of developing, chronic diseases. This article focuses on the health insurance sector and investigates ways that payment claims data and other data sources can provide useful information to support chronic disease management interventions. Methods and Results: In this mixed methods study, we first examine methods of selecting target populations from insurance claims data for common chronic conditions-diabetes, cardiovascular disease, and mental health disorders. The analysis of claims data reveals data quality issues and indicates that other data sources should be considered to provide additional information. We undertake a qualitative review of factors influencing the development of information systems for chronic disease management that use multiple data sources. Conclusions: Claims data should be supplemented with other data to inform chronic disease management. The article proposes a conceptual framework with four domains that need to be considered when developing chronic disease information systems using multiple data sources-information requirements, data sources, data collection, and information systems integration. There are policy and organizational factors that influence framework implementation.

Introducing NASN's New Data Initiative: National School Health Data Set: Every Student Counts! Make This YOUR Year of Data

The National Association of School Nurses (NASN) is launching a new data initiative: National School Health Data Set: Every Student Counts! This article describes the vision of the initiative, as well as what school nurses can do to advance a data-driven school health culture. This is the first article in a data and school nursing series for the 2018-2019 school year. For more information on NASN's initiative and to learn how school nurses can join the data revolution, go to http://nasn.org/everystudentcounts.

Evaluating clinical stop-smoking services globally: towards a minimum data set

BACKGROUND AND AIMS

Behavioural and pharmacological support for smoking cessation improves the chances of success and represents a highly cost-effective way of preventing chronic disease and premature death. There is a large number of clinical stop-smoking services throughout the world. These could be connected into a global network to provide data to assess what treatment components are most effective, for what populations and in what settings. To enable this, a minimum data set (MDS) is required to standardize the data captured from smoking cessation services globally.

METHODS

We describe some of the key steps involved in developing a global MDS for smoking cessation services and methodologies to be considered for their implementation, including approaches for reaching consensus on data items to include in a MDS and for its robust validation. We use informal approximations of these methods to produce an example global MDS for smoking cessation. Our aim with this is to stimulate further discussion around the development of a global MDS for smoking cessation services.

RESULTS

Our example MDS comprises three sections. The first is a set of data items characterizing treatments offered by a service. The second is a small core set of data items describing clients' characteristics, engagement with the service and outcomes. The third is an extended set of client data items to be captured in addition to the core data items wherever resources permit.

CONCLUSIONS

There would be benefit in establishing a minimum data set (MDS) to standardize data captured for smoking cessation services globally. Once implemented, a formal MDS could provide a basis for meaningful evaluations of different smoking cessation treatments in different populations in a variety of settings across many countries.

Comfort assessment of running footwear: Does assessment type affect inter-session reliability?

Assessing comfort of running footwear reliably is challenging. The purpose of this study was to compare the intra-rater reliability between different assessment types, to calculate intra-individual reliability scores and to evaluate the effect of rater selection based on individual reliability scores on group level reliability. Three assessment types: ranking, Visual Analogue Scale (VAS), and Likert Scale (LS) were provided twice in six separate sessions among 30 participants, who assessed comfort of five shoes after treadmill running. Spearman's rho provided an evaluation of inter-session relative reliability and typical error as a measure of absolute reliability for each assessment type. Ranking (r = 0.70, 95% confidence interval [CI] 0.61-0.78) yielded the highest relative reliability for overall comfort, followed by VAS (r = 0.67, 95% CI 0.56-0.75) and LS (r = 0.63, 95% CI 0.52-0.72), with large-scale overlaps of CIs between assessment types. The same order of assessment types was found for the percentage of reliable raters (r ≥ 0.7) with 60% in ranking scale, 47% in VAS and 37% in LS. Forming subgroups corresponding to the intra-individual reliability substantially increased group level reliabilities. Based on measures of relative reliability, an extreme reduction in resolution as provided by the ranking from pairwise comparisons seems to be a valuable tool in footwear comfort assessments if assessment time is of minor importance. No preference can be provided for the two investigated rating scales. Besides the assessment type, a selection of the best raters in additional reliability checks seems to be a prerequisite for further comfort-related studies.

Building the capacity of public health programmes to become data rich, information rich and action rich

Good quality, timely data are the cornerstone of health systems, but in many countries these data are not used for evidence-informed decision making and/or for improving public health. The SORT IT (Structured Operational Research and Training Initiative) model has, over 8 years, trained health workers in low- and middle-income countries to use data to answer important public health questions by taking research projects through to completion and publication in national or international journals. The D2P (data to policy) training initiative is relatively new, and it teaches health workers how to apply 'decision analysis' and develop policy briefs for policy makers: this includes description of a problem and the available evidence, quantitative comparisons of policy options that take into account predicted health and economic impacts, and political and feasibility assessments. Policies adopted from evidence-based information generated through the SORT IT and D2P approaches can be evaluated to assess their impact, and the cycle repeated to identify and resolve new public health problems. Ministries of Health could benefit from this twin-training approach to make themselves 'data rich, information rich and action rich', and thereby use routinely collected data in a synergistic manner to improve public health policy making and health care delivery.

Data-driven quantification of the effect of wind on athletics performance

So far, the relationship between wind and athletics performance has been studied mainly for 100 m sprint, based on simulation of biomechanical models, requiring several assumptions. In this study, this relationship is quantified empirically for all five horizontal jump and sprint events where wind is measured, with freely available competition results. After systematic scraping several elite and sub-elite results sites, the obtained results (n = 150,169) were filtered and matched to athletes. A quadratic mixed effects model with athlete and season as random effects was applied to express the influence of wind velocity on performance in each event. Whether this effect differs with performance level was investigated by applying the model on subgroups based on performance level. In the fitted quadratic model, the linear coefficients were significant (p < .001) for all events; the quadratic coefficients were significant for all events (p < .001) except long jump (p = .138). A 2.0 m s^-1 tail wind provides an average advantage of 0.125, 0.140 and 0.146-s for the 100, 200 and 100/110 m hurdles, respectively, and an advantage of 0.058 and 0.102 m for long jump and triple jump, respectively. Performance level had a significant effect on the wind influence only for 100 m (p < .001). Amateur athletes (∼13 s) benefit 69% more from a 2.0 m s^-1 tail wind than elite athletes (∼10 s). Practical formulas are presented for each event. These can easily be used correct results for wind speed, allowing better talent scouting and championship selection. This study demonstrates the efficacy of answering scientific questions empirically, through freely available data.

Computational modelling for decision-making: where, why, what, who and how

In order to deal with an increasingly complex world, we need ever more sophisticated computational models that can help us make decisions wisely and understand the potential consequences of choices. But creating a model requires far more than just raw data and technical skills: it requires a close collaboration between model commissioners, developers, users and reviewers. Good modelling requires its users and commissioners to understand more about the whole process, including the different kinds of purpose a model can have and the different technical bases. This paper offers a guide to the process of commissioning, developing and deploying models across a wide range of domains from public policy to science and engineering. It provides two checklists to help potential modellers, commissioners and users ensure they have considered the most significant factors that will determine success. We conclude there is a need to reinforce modelling as a discipline, so that misconstruction is less likely; to increase understanding of modelling in all domains, so that the misuse of models is reduced; and to bring commissioners closer to modelling, so that the results are more useful.

[The Problems of Analysis of Medical Statistics Data]

The article presents the results of usability of medical statistics in complex automated analysis. It is demonstrated that processing of data of large volume is necessary both for stage of analysis and procedures of its preliminary processing. The article summarizes and classifies problems limiting quality of complex analysis of data of medical statistics. The algorithm is proposed including sequentially applied procedures supporting correct preparation of indices for further analysis. The algorithm was applied to 1.5 million of records of medical statistics collected in the Medial Informational Analytical Center of the Health Care Department of the Primorskiy Krai in 2004-2014.

Nuanced Reporting of Fistulas in Laryngectomy Studies

Pharyngocutaneous fistula is an important complication of laryngectomy and can vary significantly in severity. Many authors have advocated for the use of vascularized flaps (eg, pectoralis major) to reduce the risk of fistula. Prevention of small, self-limited fistulas may not be worth the morbidity of a vascularized flap in some cases. More nuanced analysis of fistula outcomes, stratified by severity, may enable better surgeon-patient decision making regarding the use of vascularized flaps in laryngectomy.

Strengthening Routine Data Systems to Track the HIV Epidemic and Guide the Response in Sub-Saharan Africa

The global HIV response has entered a new phase with the recommendation of treating all persons living with HIV with antiretroviral therapy, and with the goals of reducing new infections and AIDS-related deaths to fewer than 500,000 by 2020. This new phase has intensive data requirements that will need to utilize routine data collected through service delivery platforms to monitor progress toward these goals. With a focus on sub-Saharan African, we present the following priorities to improve the demand, supply, and use of routine HIV data: (1) strengthening patient-level HIV data systems that support continuity of clinical care and document sentinel events; (2) leveraging data from HIV testing programs; (3) using targeting data collection in communities and among clients; and (4) building capacity and promoting a culture of HIV data quality assessment and use. When fully leveraged, routine data can efficiently provide timely information at a local level to inform action, as well as provide information at scale with wide geographic coverage to strengthen estimation efforts.

Data Resources for Conducting Health Services and Policy Research

Rich federal data resources provide essential data inputs for monitoring the health and health care of the US population and are essential for conducting health services policy research. The six household surveys we document in this article cover a broad array of health topics, including health insurance coverage (American Community Survey, Current Population Survey), health conditions and behaviors (National Health Interview Survey, Behavioral Risk Factor Surveillance System), health care utilization and spending (Medical Expenditure Panel Survey), and longitudinal data on public program participation (SIPP). New federal activities are linking federal surveys with administrative data to reduce duplication and response burden. In the private sector, vendors are aggregating data from medical records and claims to enhance our understanding of treatment, quality, and outcomes of medical care. Federal agencies must continue to innovate to meet the continuous challenges of scarce resources, pressures for more granular data, and new multimode data collection methodologies.

Ethical and Legal Considerations in Biometric Data Usage-Bulgarian Perspective

Ethical and legal considerations with regards to biometric data usage are directly related to the right to protection of personal data, which is part of the rights protected under the European Convention of human rights. Specific protection is required to the process and use of sensitive data which reveals certain personal characteristic and is related to the health status of individuals. Biometric data and information on individual upon which people could be identified based on specifics and distinguishing signs. Bulgaria, as a country progressing in terms of integration of digital technologies and as a European Union member state has adopted international and universal legal instruments related on the procession and use of digital data and data protection. On legislative and ethical grounds, it has been established the particular importance of not violating human rights and individual freedoms when processing and using personal data. It has been noted that the processing of special categories of personal data may be necessary for reasons of public interest in the field of public health and that is why under such circumstances it has been permitted the procession to be carried on without the consent of the data subject. Lack of transparency and lawfulness of the processing of personal data could lead to physical, tangible, or intangible damages where processing could lead to discrimination, identity theft, or identity fraud as a result of which may be significant adverse economic or social consequences. Increasingly, widespread use of biometrics in the implementation of medical activities requires the application of a new approach in terms of awareness regarding existing risks to the rights, ethics, and freedoms of all of us, as a user of medical service.

Identification of publicly available data sources to inform the conduct of Health Technology Assessment in India

Background: Health technology assessment (HTA) provides a globally-accepted and structured approach to synthesising evidence for cost and clinical effectiveness alongside ethical and equity considerations to inform evidence-based priorities. India is one of the most recent countries to formally commit to institutionalising HTA as an integral component of the heath resource allocation decision-making process. The effective conduct of HTA depends on the availability of reliable data.   Methods: We draw from our experience of collecting, synthesizing, and analysing health-related datasets in India and internationally, to highlight the complex requirements for undertaking HTA, and explore the availability of such data in India. We first outlined each of the core data components required for the conduct of HTA, and their availability in India, drawing attention to where data can be accessed, and different ways in which researchers can overcome the challenges of missing or low quality data. Results: We grouped data into the following categories: clinical efficacy; cost; epidemiology; quality of life; service use/consumption; and equity. We identified numerous large local data sources containing epidemiological information. There was a marked absence of other locally-collected data necessary for informing HTA, particularly data relating to cost, service use, and quality of life. Conclusions: The introduction of HTA into the health policy space in India provides an opportunity to comprehensively assess the availability and quality of health data capture across the country. While epidemiological information is routinely collected across India, other data inputs necessary for HTA are not readily available. This poses a significant bottleneck to the efficient generation and deployment of HTA into the health decision space. Overcoming these data gaps by strengthening the routine collection of comprehensive and verifiable health data will have important implications not only for embedding economic analyses into the priority setting process, but for strengthening the health system as a whole.

Identification of publicly available data sources to inform the conduct of Health Technology Assessment in India

Background: Health technology assessment (HTA) provides a globally-accepted and structured approach to synthesising evidence for cost and clinical effectiveness alongside ethical and equity considerations to inform evidence-based priorities. India is one of the most recent countries to formally commit to institutionalising HTA as an integral component of the heath resource allocation decision-making process. The effective conduct of HTA depends on the availability of reliable data.   Methods: We draw from our experience of collecting, synthesizing, and analysing health-related datasets in India and internationally, to highlight the complex requirements for undertaking HTA, and explore the availability of such data in India. We first outlined each of the core data components required for the conduct of HTA, and their availability in India, drawing attention to where data can be accessed, and different ways in which researchers can overcome the challenges of missing or low quality data. Results: We grouped data into the following categories: clinical efficacy; cost; epidemiology; quality of life; service use/consumption; and equity. We identified numerous large local data sources containing epidemiological information. There was a marked absence of other locally-collected data necessary for informing HTA, particularly data relating to cost, service use, and quality of life. Conclusions: The introduction of HTA into the health policy space in India provides an opportunity to comprehensively assess the availability and quality of health data capture across the country. While epidemiological information is routinely collected across India, other data inputs necessary for HTA are not readily available. This poses a significant bottleneck to the efficient generation and deployment of HTA into the health decision space. Overcoming these data gaps by strengthening the routine collection of comprehensive and verifiable health data will have important implications not only for embedding economic analyses into the priority setting process, but for strengthening the health system as a whole.

Know Your Epidemic, Strengthen Your Response: Developing a New HIV Surveillance Architecture to Guide HIV Resource Allocation and Target Decisions

To guide HIV prevention and treatment activities up to 2020, we need to generate and make better use of high quality HIV surveillance data. To highlight our surveillance needs, a special collection of papers in JMIR Public Health and Surveillance has been released under the title “Improving Global and National Responses to the HIV Epidemic Through High Quality HIV Surveillance Data.” We provide a summary of these papers and highlight methods for developing a new HIV surveillance architecture.

Virtual Environments for Visualizing Structural Health Monitoring Sensor Networks, Data, and Metadata

Visualization of sensor networks, data, and metadata is becoming one of the most pivotal aspects of the structural health monitoring (SHM) process. Without the ability to communicate efficiently and effectively between disparate groups working on a project, an SHM system can be underused, misunderstood, or even abandoned. For this reason, this work seeks to evaluate visualization techniques in the field, identify flaws in current practices, and devise a new method for visualizing and accessing SHM data and metadata in 3D. More precisely, the work presented here reflects a method and digital workflow for integrating SHM sensor networks, data, and metadata into a virtual reality environment by combining spherical imaging and informational modeling. Both intuitive and interactive, this method fosters communication on a project enabling diverse practitioners of SHM to efficiently consult and use the sensor networks, data, and metadata. The method is presented through its implementation on a case study, Streicker Bridge at Princeton University campus. To illustrate the efficiency of the new method, the time and data file size were compared to other potential methods used for visualizing and accessing SHM sensor networks, data, and metadata in 3D. Additionally, feedback from civil engineering students familiar with SHM is used for validation. Recommendations on how different groups working together on an SHM project can create SHM virtual environment and convey data to proper audiences, are also included.

Identifying MAIS 3+ injury severity collisions in UK police collision records

OBJECTIVE

This study represents the first stage of a project to identify serious injury, at the level of Maximum Abbreviated Injury Scale (MAIS) 3 + (excluding fatal collisions) from within the police collision data. The resulting data will then be used to identify the vehicle drivers concerned and in later studies these will be culpability scored and profiled to allow targeting of interventions.

METHOD

UK police collision data known as STATS19 for the county of Cambridgeshire were linked using Stata with Trauma Audit and Research Network (TARN) hospital trauma patient data for the same geographical area for the period April 2012 to March 2017. Linking was 2-stage: A deterministic process followed by a probabilistic process.

RESULTS

The linked records represent an individual trauma patient from TARN data linked to an individual trauma casualty from STATS19 data. Full collision data for the incident resulting in the trauma casualty were extracted. The resulting subset of collisions has the MAIS 3+ injury criteria applied. From the 10,498 recorded collisions, the deterministic linking process was successful in linking 257 MAIS 3+ trauma patients to collision injury subjects from 232 separate collisions with the probabilistic process linking a further 22 MAIS 3+ subjects from 21 collision events. The combined collision data for the 253 collisions involved 434 motor vehicle drivers.

CONCLUSIONS

We produced viable results from the available data to identify MAIS 3+ collisions from the overall collision data.

Creation of Data Repositories to Advance Nursing Science

Data repositories are a strategy in line with precision medicine and big data initiatives, and are an efficient way to maximize data utility and form collaborative research relationships. Nurse researchers are uniquely positioned to make a valuable contribution using this strategy. The purpose of this article is to present a review of the benefits and challenges associated with developing data repositories, and to describe the process we used to develop and maintain a data repository in HIV research. Systematic planning, data collection, synthesis, and data sharing have enabled us to conduct robust cross-sectional and longitudinal analyses with more than 200 people living with HIV. Our repository building has also led to collaboration and training, both in and out of our organization. We present a pragmatic and affordable way that nurse scientists can build and maintain a data repository, helping us continue to make to our understanding of health phenomena.

Fundamentals and Catalytic Innovation: The Statistical and Data Management Center of the Antibacterial Resistance Leadership Group

The Statistical and Data Management Center (SDMC) provides the Antibacterial Resistance Leadership Group (ARLG) with statistical and data management expertise to advance the ARLG research agenda. The SDMC is active at all stages of a study, including design; data collection and monitoring; data analyses and archival; and publication of study results. The SDMC enhances the scientific integrity of ARLG studies through the development and implementation of innovative and practical statistical methodologies and by educating research colleagues regarding the application of clinical trial fundamentals. This article summarizes the challenges and roles, as well as the innovative contributions in the design, monitoring, and analyses of clinical trials and diagnostic studies, of the ARLG SDMC.

The Truth is in the Data - Differences in the Same Measure Based on Different Sources among HVHC Members Using ICU Length of Stay as an Example

Introduction:

Intensive Care Unit (ICU) length of stay is a strong indicator of severity of illness and cost in the care of sepsis patients. In this case study, we examine the difference between an electronic health record (EHR) based submissions with Centers for Medicare and Medicaid Services (CMS) payment data.

Methods:

Member submitted EHR data contained 26,733 unique patient’s records. The CMS data contained demographics, diagnosis, and revenue codes. After linking EHR data to CMS data, we found a discrepancy in ICU days from CMS claims vs. EHR data. Our hypothesis was that removing intermediate ICU LOS would result in a closer match from CMS claims with EHR data. We suspected the use of Intermediate ICU stays in our CMS ICU definition contaminated our ICU LOS data. This resulted in a review of the sepsis specification, further investigation of the data, and follow up conversations with the Member organizations.

Results:

Agreement between EHR and CMS data improved from 73 percent to 86 percent once the Intermediate ICU time had been removed.

Discussion and Conclusions:

The inclusion of Intermediate ICU in the analysis of severely ill sepsis patients from CMS data diluted the importance of using an ICU LOS for estimating the severity of illness and the cost to the healthcare system. We must ensure that clinical definitions are consistent between data sources that were built for different purposes. Additionally, we learned that engaging with clinicians, analysts, and clinical coders early in the process is required to fully understand the complexities from different sources.

Comparing Matchplay Characteristics and Physical Demands of Junior and Professional Tennis Athletes in the Era of Big Data

Differences in the competitive performance characteristics of junior and professional tennis players are not well understood. The present study provides a comprehensive comparative analysis of junior and professional matchplay. The study utilized multiple large-scale datasets covering match, point, and shot outcomes over multiple years of competition. Regression analysis was used to identify differences between junior and professional matchplay. Top professional men and women were found to play significantly more matches, sets, and games compared to junior players of an equivalent ranking. Professional players had a greater serve advantage, men winning 4 and women winning 2 additional percentage points on serve compared to juniors. Clutch ability in break point conversion was 6 to 8 percentage points greater for junior players. In general, shots were more powerful and more accurate at the professional level with the largest differences observed for male players on serve. Serving to the center of the court was more than two times more common for junior players on first serve. While male professionals performed 50% more total work in a Grand Slam match than juniors, junior girls performed 50% more work than professional women. Understanding how competitiveness, play demands, and the physical characteristics of shots differ between junior and professional tennis players can help set realistic expectations and developmentally appropriate training for transitioning players.

XRD and spectral dataset of the UV-A stable nanotubes of 3,5-bis(trifluoromethyl)benzylamine derivative of tyrosine

The data presented in this article are related to the research entitled of UV-A stable nanotubes. The nanotubes have been prepared from 3,5-bis(trifluoromethyl)benzylamine derivative of tyrosine (BTTP). XRD data reveals the size of the nanotubes. As-synthesized nanotubes (BTTPNTs) are characterized by UV–vis optical absorption studies [1] and photo physical degradation kinetics. The resulted dataset is made available to enable critical or extended analyzes of the BTTPNTs as an excellent light resistive materials.

Tamoxifen Pharmacovigilance: Implications for Safe Use in the Future

OBJECTIVE

To survey the status of current tamoxifen pharmacovigilance documentation reflecting tamoxifen use in an academic outpatient multispecialty practice in older adults. This data will help provide information to develop improved pharmacovigilance for a growing cohort of older adult users. The data will be utilized by an interdisciplinary team developing new methods of identifying factors for individualized pharmacovigilance in older adults.

DESIGN

Retrospective chart review to gather descriptive and quantitative data on tamoxifen pharmacovigilance.

SETTING

Multi-specialty clinic.

PATIENTS

Ninety-three patients 60 years of age and older.

MAIN OUTCOME MEASURES

Quantitative report of tamoxifen monitoring as well as descriptive analysis of individual cases.

RESULTS

We found 19 cases of serious adverse events possibly related to tamoxifen (thrombi, uterine malignancies). There were 15 cases with no documentation of pharmacovigilance. All cases had incomplete pharmacovigilance documented. There were two cases of hypercalcemia. There was one case of tamoxifen discontinuation resulting from muscle pain and with chronic muscle pain complaints while receiving tamoxifen. We observed a correlation in older age or high comorbidity burden patients and adverse events patients.

CONCLUSION

Some studies direct the important pharmacovigilance toward prevention of thrombi, uterine malignancies, and hypercalcemia; however, it is not easy to identify recommendations for frequency or focus of monitoring to prevent adverse events for individual older adults based on existing recommendations. The data collected and presented in this study serve to heighten awareness of tamoxifen pharmacovigilance and as a starting point for the application of machine learning techniques and modeling to identify high-risk patients and individualized pharmacovigilance recommendations.

Digital Diabetes Congress 2017

The purpose of developing mobile applications for diabetes is generally to: (1) provide enhanced access to timely information for patients, health care professionals, and researchers; (2) facilitate remote monitoring and diagnosis of patients, often based on information delivered by wearable devices; (3) provide decision support to assist patients in selecting treatment; or (4) deliver timely recommendations for treatment to increase adherence to prescribed therapy. There is a perception that mobile applications can provide meaningful clinical benefits, however, there is only sparse convincing evidence to support this belief at the present time. Compounding this problem is the short life span of digital software, such that if a traditional type of randomized controlled trial is conducted on a product, by the time the study has been designed, approved by an IRB, conducted, and analyzed, the product might have significantly changed to a next generation system. Because of great interest in establishing what are the potential benefits, metrics of success, and appropriate components of mobile applications for diabetes, Diabetes Technology Society and William Sansum Diabetes Center launched the Digital Diabetes Congress, March 7-8, 2017, in San Francisco. This report contains summaries of the meeting's 12 sessions. Each summary was written by the session's moderator who helped develop the session prior to the event and keep it on track during the event. This meeting report presents a summary of how 57 panelists, speakers, and moderators, who are leaders in digital health, see the current and future landscape of digital health tools applied to diabetes.

Data Security and Privacy in Apps for Dementia: An Analysis of Existing Privacy Policies

INTRODUCTION

Despite tremendous growth in the number of health applications (apps), little is known about how well these apps protect their users' health-related data. This gap in knowledge is of particular concern for apps targeting people with dementia, whose cognitive impairment puts them at increased risk of privacy breaches. In this article, we determine how many dementia apps have privacy policies and how well they protect user data.

METHODS

Our analysis included all iPhone apps that matched the search terms "medical + dementia" or "health & fitness + dementia" and collected user-generated content. We evaluated all available privacy policies for these apps based on criteria that systematically measure how individual user data is handled.

RESULTS

Seventy-two apps met the above search teams and collected user data. Of these, only 33 (46%) had an available privacy policy. Nineteen of the 33 with policies (58%) were specific to the app in question, and 25 (76%) specified how individual-user as opposed to aggregate data would be handled. Among these, there was a preponderance of missing information, the majority acknowledged collecting individual data for internal purposes, and most admitted to instances in which they would share user data with outside parties.

CONCLUSIONS

At present, the majority of health apps focused on dementia lack a privacy policy, and those that do exist lack clarity. Bolstering safeguards and improving communication about privacy protections will help facilitate consumer trust in apps, thereby enabling more widespread and meaningful use by people with dementia and those involved in their care.