American Joint Committee on Cancer Eighth Edition Changes in Staging Criteria: Implications for Data Collection

The American Joint Committee on Cancer, in the eighth edition of its cancer staging manual, makes a number of changes to improve survival predictions in human malignancy. In this commentary, we examine the national collection of data for head and neck cancer. We outline and review the major changes made in head and neck cancer staging, identify the key data elements that will need to be collected with the new edition's criteria, and discuss the institutional adjustments currently being made to data collection under the new guidelines to improve the quality of data in our national cancer databases.

Detecting, reporting, and analysis of priority diseases for routine public health surveillance in Liberia

Public health officials depend on timely, complete, and accurate surveillance data for decision making. The quality of data generated from surveillance is highly dependent on external and internal factors which may either impede or enhance surveillance activities. One way of identifying challenges affecting the quality of data generated is to conduct a data quality audit. This case study, based on an audit conducted by residents of the Liberia Frontline Field Epidemiology Training Program, was designed to be a classroom simulation of a data quality audit in a health facility. It is suited to enforce theoretical lectures in surveillance data quality and auditing. The target group is public health trainees, who should be able to complete this exercise in approximately 2 hours and 30 minutes.

Bridging the Gap Between Experimental Data and Model Parameterization for Chikungunya Virus Transmission Predictions

Chikungunya virus (CHIKV) has experienced 2 major expansion events in the last decade. The most recently emerged sublineage (ECSA-V) was shown to have increased efficiency in a historically secondary vector, Aedes albopictus, leading to speculation that this was a major factor in expansion. Subsequently, a number of experimental studies focused on the vector competence of CHIKV, as well as transmission modeling efforts. Mathematical models have used these data to inform their own investigations, but some have incorrectly parameterized the extrinsic incubation period (EIP) of the mosquitoes, using vector competence data. Vector competence and EIP are part of the same process but are not often correctly reported together. Thus, the way these metrics are used for model parameterization can be problematic. We offer suggestions for bridging this gap for the purpose of standardization of reporting and to promote appropriate use of experimental data in modeling efforts.

Key data for outbreak evaluation: building on the Ebola experience

Following the detection of an infectious disease outbreak, rapid epidemiological assessment is critical for guiding an effective public health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained in the West African Ebola epidemic and prior emerging infectious disease outbreaks to set out a checklist of data needed to: (1) quantify severity and transmissibility; (2) characterize heterogeneities in transmission and their determinants; and (3) assess the effectiveness of different interventions. We differentiate data needs into individual-level data (e.g. a detailed list of reported cases), exposure data (e.g. identifying where/how cases may have been infected) and population-level data (e.g. size/demographics of the population(s) affected and when/where interventions were implemented). A remarkable amount of individual-level and exposure data was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However, gaps in population-level data (particularly around which interventions were applied when and where) posed challenges to the assessment of (3). Here we highlight recurrent data issues, give practical suggestions for addressing these issues and discuss priorities for improvements in data collection in future outbreaks.This article is part of the themed issue 'The 2013-2016 West African Ebola epidemic: data, decision-making and disease control'.

Phenomenology in Its Original Sense

In this article, I try to think through the question, "What distinguishes phenomenology in its original sense?" My intent is to focus on the project and methodology of phenomenology in a manner that is not overly technical and that may help others to further elaborate on or question the singular features that make phenomenology into a unique qualitative form of inquiry. I pay special attention to the notion of "lived" in the phenomenological term "lived experience" to demonstrate its critical role and significance for understanding phenomenological reflection, meaning, analysis, and insights. I also attend to the kind of experiential material that is needed to focus on a genuine phenomenological question that should guide any specific research project. Heidegger, van den Berg, and Marion provide some poignant exemplars of the use of narrative "examples" in phenomenological explorations of the phenomena of "boredom," "conversation," and "the meaningful look in eye-contact." Only what is given or what gives itself in lived experience (or conscious awareness) are proper phenomenological "data" or "givens," but these givens are not to be confused with data material that can be coded, sorted, abstracted, and accordingly analyzed in some "systematic" manner. The latter approach to experiential research may be appropriate and worthwhile for various types of qualitative inquiry but not for phenomenology in its original sense. Finally, I use the mythical figure of Kairos to show that the famous phenomenological couplet of the epoché-reduction aims for phenomenological insights that require experiential analysis and attentive (but serendipitous) methodical inquiry practices.

U.S. Transportation and Health Tool: Data for action

Transportation investments have the potential to improve health, but readily available data to guide transportation decisions that could promote health are limited. In October 2015, the U.S. Department of Transportation (USDOT) and the Centers for Disease Control and Prevention (CDC) released the Transportation and Health Tool (THT). The tool is a resource to help transportation professionals in states and metropolitan areas access data about transportation and health in their jurisdictions and stimulate discussions on how to improve public health through transportation planning and policy. To develop the tool, a multidisciplinary team identified 190 possible data indicators. Using input from expert panel workshops and criteria that addressed data availability, geographic scale, timeliness, feasibility, validity, and topic area, the team selected 14 transportation and health indicators that covered the four priority topic areas of safety, active transportation, air quality, and connectivity. The THT contains the raw values for each indicator and a standardized score to enable comparisons. Additionally, the THT contains 25 evidence-based strategies that can help practitioners in states and metropolitan areas take action to improve health outcomes.

Imagining tomorrow's university in an era of open science

As part of a recent workshop entitled "Imagining Tomorrow's University”, we were asked to visualize the future of universities as research becomes increasingly data- and computation-driven, and identify a set of principles characterizing pertinent opportunities and obstacles presented by this shift. In order to establish a holistic view, we take a multilevel approach and examine the impact of open science on individual scholars and how this impacts as well as on the university as a whole. At the university level, open science presents a double-edged sword: when well executed, open science can accelerate the rate of scientific inquiry across the institution and beyond; however, haphazard or half-hearted efforts are likely to squander valuable resources, diminish university productivity and prestige, and potentially do more harm than good. We present our perspective on the role of open science at the university.

Addressing the Disconnect between the Estimated, Reported, and True Rabies Data: The Development of a Regional African Rabies Bulletin

It is evident that rabies continues to be a neglected tropical disease; however, a recent global drive aims to eliminate canine-mediated human rabies by 2030. Global efforts have been vested into creating and developing resources for countries to take ownership of and overcome the challenges that rabies poses. The disconnect between the numbers of rabies cases reported and the numbers estimated by prediction models is clear: the key to understanding the epidemiology and true burden of rabies lies within accurate and timely data; poor and discrepant data undermine its true burden and negate the advocacy efforts needed to curb this lethal disease. In an effort to address these challenges, the Pan-African Rabies Control Network is developing a regional rabies-specific disease surveillance bulletin based on the District Health Information System 2 platform-a web-based, open access health information platform. This bulletin provides a data repository from which specific key indicators, essential to any rabies intervention program, form the basis of data collection. The data are automatically analyzed, providing useful outputs for targeted intervention. Furthermore, in an effort to reduce reporting fatigue, the data submitted, under authority from the respective governments, can automatically be shared with approved international authorities. The implementation of a rabies-specific bulletin will facilitate targeted control efforts and provide measurements of success, while also acting as a basis for advocacy to raise the priority of this neglected disease.

Performance Data from the NIST Net-Zero Energy Residential Test Facility

Data were collected over two separate year-long test periods at the Net-Zero Energy Residential Test Facility, alaboratory designed to evaluate a variety of technologies and operational strategies that lead to energy efficient houses with comfortable and healthful indoor environments. In a net-zero energy building, all energy consumption over the course of a year is offset by on-site renewable energy production; this facility attempts to meet that goal through use of a photovoltaic array installed on the roof. Data are presented for one-year test periods over which the research team examined whether the facility would reach net-zero status. In both years, the house was operated in an all-electric configuration, with slight modifications made in the second year related to control schemes and equipment selection. A virtual family of four was simulated to carry out the operations that would typically occur in a home (e.g., appliance usage, lighting usage, hot water usage). Data are being released for the second year of operation at the time of publication of this document, with an expectation that data from the first year will be released at a later date.

Deployment of Analytics into the Healthcare Safety Net: Lessons Learned

BACKGROUND

As payment reforms shift healthcare reimbursement toward value-based payment programs, providers need the capability to work with data of greater complexity, scope and scale. This will in many instances necessitate a change in understanding of the value of data, and the types of data needed for analysis to support operations and clinical practice. It will also require the deployment of different infrastructure and analytic tools. Community health centers, which serve more than 25 million people and together form the nation's largest single source of primary care for medically underserved communities and populations, are expanding and will need to optimize their capacity to leverage data as new payer and organizational models emerge.

METHODS

To better understand existing capacity and help organizations plan for the strategic and expanded uses of data, a project was initiated that deployed contemporary, Hadoop-based, analytic technology into several multi-site community health centers (CHCs) and a primary care association (PCA) with an affiliated data warehouse supporting health centers across the state. An initial data quality exercise was carried out after deployment, in which a number of analytic queries were executed using both the existing electronic health record (EHR) applications and in parallel, the analytic stack. Each organization carried out the EHR analysis using the definitions typically applied for routine reporting. The analysis deploying the analytic stack was carried out using those common definitions established for the Uniform Data System (UDS) by the Health Resources and Service Administration.¹ In addition, interviews with health center leadership and staff were completed to understand the context for the findings.

RESULTS

The analysis uncovered many challenges and inconsistencies with respect to the definition of core terms (patient, encounter, etc.), data formatting, and missing, incorrect and unavailable data. At a population level, apparent underreporting of a number of diagnoses, specifically obesity and heart disease, was also evident in the results of the data quality exercise, for both the EHR-derived and stack analytic results.

CONCLUSION

Data awareness, that is, an appreciation of the importance of data integrity, data hygiene² and the potential uses of data, needs to be prioritized and developed by health centers and other healthcare organizations if analytics are to be used in an effective manner to support strategic objectives. While this analysis was conducted exclusively with community health center organizations, its conclusions and recommendations may be more broadly applicable.

Why articles are retracted: a retrospective cross-sectional study of retraction notices at BioMed Central

OBJECTIVES

To assess why articles are retracted from BioMed Central journals, whether retraction notices adhered to the Committee on Publication Ethics (COPE) guidelines, and are becoming more frequent as a proportion of published articles.

DESIGN/SETTING

Retrospective cross-sectional analysis of 134 retractions from January 2000 to December 2015.

RESULTS

134 retraction notices were published during this timeframe. Although they account for 0.07% of all articles published (190 514 excluding supplements, corrections, retractions and commissioned content), the rate of retraction is rising. COPE guidelines on retraction were adhered to in that an explicit reason for each retraction was given. However, some notices did not document who retracted the article (eight articles, 6%) and others were unclear whether the underlying cause was honest error or misconduct (15 articles, 11%). The largest proportion of notices was issued by the authors (47 articles, 35%). The majority of retractions were due to some form of misconduct (102 articles, 76%), that is, compromised peer review (44 articles, 33%), plagiarism (22 articles, 16%) and data falsification/fabrication (10 articles, 7%). Honest error accounted for 17 retractions (13%) of which 10 articles (7%) were published in error. The median number of days from publication to retraction was 337.5 days.

CONCLUSIONS

The most common reason to retract was compromised peer review. However, the majority of these cases date to March 2015 and appear to be the result of a systematic attempt to manipulate peer review across several publishers. Retractions due to plagiarism account for the second largest category and may be reduced by screening manuscripts before publication although this is not guaranteed. Retractions due to problems with the data may be reduced by appropriate data sharing and deposition before publication. Adopting a checklist (linked to COPE guidelines) and templates for various classes of retraction notices would increase transparency of retraction notices in future.

Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample

As part of a study into the molecular genetics of sexually dimorphic complex traits, we used high-throughput sequencing to obtain data on genomic variation in an outbred laboratory-adapted fruit fly ( Drosophila melanogaster) population. We successfully resequenced the whole genome of 220 hemiclonal females that were heterozygous for the same Berkeley reference line genome (BDGP6/dm6), and a unique haplotype from the outbred base population (LH _M). The use of a static and known genetic background enabled us to obtain sequences from whole-genome phased haplotypes. We used a BWA-Picard-GATK pipeline for mapping sequence reads to the dm6 reference genome assembly, at a median depth-of coverage of 31X, and have made the resulting data publicly-available in the NCBI Short Read Archive (Accession number SRP058502). We used Haplotype Caller to discover and genotype 1,726,931 small genomic variants (SNPs and indels, <200bp). Additionally we detected and genotyped 167 large structural variants (1-100Kb in size) using GenomeStrip/2.0. Sequence and genotype data are publicly-available at the corresponding NCBI databases: Short Read Archive, dbSNP and dbVar (BioProject PRJNA282591). We have also released the unfiltered genotype data, and the code and logs for data processing and summary statistics ( https://zenodo.org/communities/sussex_drosophila_sequencing/).

A public database of macromolecular diffraction experiments

The low reproducibility of published experimental results in many scientific disciplines has recently garnered negative attention in scientific journals and the general media. Public transparency, including the availability of `raw' experimental data, will help to address growing concerns regarding scientific integrity. Macromolecular X-ray crystallography has led the way in requiring the public dissemination of atomic coordinates and a wealth of experimental data, making the field one of the most reproducible in the biological sciences. However, there remains no mandate for public disclosure of the original diffraction data. The Integrated Resource for Reproducibility in Macromolecular Crystallography (IRRMC) has been developed to archive raw data from diffraction experiments and, equally importantly, to provide related metadata. Currently, the database of our resource contains data from 2920 macromolecular diffraction experiments (5767 data sets), accounting for around 3% of all depositions in the Protein Data Bank (PDB), with their corresponding partially curated metadata. IRRMC utilizes distributed storage implemented using a federated architecture of many independent storage servers, which provides both scalability and sustainability. The resource, which is accessible via the web portal at http://www.proteindiffraction.org, can be searched using various criteria. All data are available for unrestricted access and download. The resource serves as a proof of concept and demonstrates the feasibility of archiving raw diffraction data and associated metadata from X-ray crystallographic studies of biological macromolecules. The goal is to expand this resource and include data sets that failed to yield X-ray structures in order to facilitate collaborative efforts that will improve protein structure-determination methods and to ensure the availability of `orphan' data left behind for various reasons by individual investigators and/or extinct structural genomics projects.

Effect-Size Measures as Descriptors of Assay Quality in High-Content Screening: A Brief Review of Some Available Methodologies

The field of high-content screening (HCS) typically uses measures of screen quality conceived for fairly straightforward high-throughput screening (HTS) scenarios. However, in contrast to HTS, image-based HCS systems rely on multidimensional readouts reporting biological responses associated with complex cellular phenotypes. Not only is the dimensionality in which the screens operate higher, but also the scale of the individual features describing the quantified phenotypic changes is often smaller than what is seen in one-dimensional HTS platforms. Therefore, the use of HTS-type quality measures to characterize HCS screens may severely underestimate the detection power of the assays used, and it may mislead the screening scientists regarding the necessary screen optimization. Also, traditional HTS-based measures are typically reported without any estimation of precision, which makes them unsuitable for computation of confidence intervals or for meta-analysis. This review summarizes the well-established statistical techniques for reporting effect sizes and argues that this broadly accepted methodology could be seamlessly integrated into HCS quality reporting, supplanting or even replacing measures such as Z' or S_w (assay window).

Review of meningitis surveillance data, upper West Region, Ghana 2009-2013

Introduction

The Upper West region of Ghana is within the meningitis belt. Analysis of long term surveillance data is necessary for understanding changes in the disease occurrence. We analyzed five years of surveillance data to describe by person, place and time and to determine trends in meningitis.

Methods

Meningitis surveillance data from Ghana Health Service in the Upper West Region, from 2009 to 2013 were reviewed. Data was obtained from District-Health Information Management System and line list from the Disease Control Unit. Population figures (denominators) and rainfall data were also analyzed.

Results

Within the period 980 cases of meningitis were reported in the region, 507(52%) females and 473(48%) males. The mean age of cases was 20.1years and standard deviation 18.8 years with, 77.6 %( 761/980) cases occurring in persons aged under 30 years. Children under five years were 19.3% (190/980). Attack rates ranged from 6.1/100,000 population in the Daffiama-bussei-Issa-district to 47.5/100,000 in Jirapa. Overall case fatality rate of meningitis was 12.2% with 14deaths/100,000 population. Bacterial agents were isolated from 35% (245/702) of CSF. Majority were Streptococcus pneumonia 48.2 % ( 122/258), and N. meningitides Y/W 135 40.3% (102/258). Meningitis was found to be seasonal with peaks in the dry season.

Conclusion

Meningitis in the region is seasonal, and showed a decreasing trend. Jirapa, Lawra, Nadowli and Wa West districts had the highest burden. Control effort of the disease should focus on vaccination against streptococcus pneumonia and N. meningitis W135 especially within crowded settlements such as boarding schools.

A fully featured COMBINE archive of a simulation study on syncytial mitotic cycles in Drosophila embryos

COMBINE archives are standardised containers for data files related to a simulation study in computational biology. This manuscript describes a fully featured archive of a previously published simulation study, including (i) the original publication, (ii) the model, (iii) the analyses, and (iv) metadata describing the files and their origin. With the archived data at hand, it is possible to reproduce the results of the original work. The archive can be used for both, educational and research purposes. Anyone may reuse, extend and update the archive to make it a valuable resource for the scientific community.

An Assessment of Data Availability, Quality, and Use in Malaria Program Decision Making in Nigeria

Abstract-In 2014, Nigeria shifted its malaria policy and strategy from control to elimination. Studies show that data-driven decision making is essential to achieving elimination. It is therefore important that policy makers have access to and use good quality and relevant data to inform program decisions. This article presents findings from an assessment of availability, quality, and use of malaria data in three states in Nigeria, namely, Akwa-Ibom, Cross River, and Niger, as part of a larger study on how organizational structure affects outcomes of malaria programs. A literature search to determine the availability and range of malaria data in Nigeria was conducted, followed by 65 key informant interviews to understand how malaria data are used in the study states. It was observed that the District Health Information System (DHIS) was the major source of data used in managing programs; however, the range of malaria indicators in the DHIS is limited, lacking indicators such as active case detection and entomological data, which are important for surveillance and decision making toward malaria elimination. On data quality, routine data from the DHIS were reviewed using the national protocol for data quality assessment. Data quality was found to be suboptimal, with quality scores ranging from 54% to 64% compared to the national target of 80%. DHIS data were reportedly used most often for performance and/or supply chain management. Overall, the study demonstrates gaps in data availability and quality and highlights the need for more data sources and improved quality data to inform decision making toward malaria elimination in Nigeria.

Evaluating Mobile Survey Tools (MSTs) for Field-Level Monitoring and Data Collection: Development of a Novel Evaluation Framework, and Application to MSTs for Rural Water and Sanitation Monitoring

Information and communications technologies (ICTs) such as mobile survey tools (MSTs) can facilitate field-level data collection to drive improvements in national and international development programs. MSTs allow users to gather and transmit field data in real time, standardize data storage and management, automate routine analyses, and visualize data. Dozens of diverse MST options are available, and users may struggle to select suitable options. We developed a systematic MST Evaluation Framework (EF), based on International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) software quality modeling standards, to objectively assess MSTs and assist program implementers in identifying suitable MST options. The EF is applicable to MSTs for a broad variety of applications. We also conducted an MST user survey to elucidate needs and priorities of current MST users. Finally, the EF was used to assess seven MSTs currently used for water and sanitation monitoring, as a validation exercise. The results suggest that the EF is a promising method for evaluating MSTs.

Mobile health: the power of wearables, sensors, and apps to transform clinical trials

Mobile technology has become a ubiquitous part of everyday life, and the practical utility of mobile devices for improving human health is only now being realized. Wireless medical sensors, or mobile biosensors, are one such technology that is allowing the accumulation of real-time biometric data that may hold valuable clues for treating even some of the most devastating human diseases. From wearable gadgets to sophisticated implantable medical devices, the information retrieved from mobile technology has the potential to revolutionize how clinical research is conducted and how disease therapies are delivered in the coming years. Encompassing the fields of science and engineering, analytics, health care, business, and government, this report explores the promise that wearable biosensors, along with integrated mobile apps, hold for improving the quality of patient care and clinical outcomes. The discussion focuses on groundbreaking device innovation, data optimization and validation, commercial platform integration, clinical implementation and regulation, and the broad societal implications of using mobile health technologies.

Multivariate Chemical Image Fusion of Vibrational Spectroscopic Imaging Modalities

Chemical image fusion refers to the combination of chemical images from different modalities for improved characterisation of a sample. Challenges associated with existing approaches include: difficulties with imaging the same sample area or having identical pixels across microscopic modalities, lack of prior knowledge of sample composition and lack of knowledge regarding correlation between modalities for a given sample. In addition, the multivariate structure of chemical images is often overlooked when fusion is carried out. We address these challenges by proposing a framework for multivariate chemical image fusion of vibrational spectroscopic imaging modalities, demonstrating the approach for image registration, fusion and resolution enhancement of chemical images obtained with IR and Raman microscopy.

Categorization in Mechanically Ventilated Pediatric Subjects: A Proposed Method to Improve Quality

BACKGROUND

Thousands of children require mechanical ventilation each year. Although mechanical ventilation is lifesaving, it is also associated with adverse events if not properly managed. The systematic implementation of evidence-based practice through the use of guidelines and protocols has been shown to mitigate risk, yet variation in care remains prevalent. Advances in health-care technology provided the ability to stream data about mechanical ventilation and therapeutic response. Through these advances, a computer system was developed to enable the coupling of physiologic and ventilation data for real-time interpretation. Our aim was to assess the feasibility and utility of a newly developed patient categorization and scoring system to objectively measure compliance with standards of care.

METHODS

We retrospectively categorized the ventilation and oxygenation statuses of subjects within our pediatric ICU utilizing 15 rules-based algorithms. Targets were predetermined based on generally accepted practices. All patient categories were calculated and presented as a percent score (0-100%) of acceptable ventilation, acceptable oxygenation, barotrauma-free, and volutrauma-free states.

RESULTS

Two hundred twenty-two subjects were identified and analyzed encompassing 1,578 d of mechanical ventilation. Median age was 3 y, median ideal body weight was 14.7 kg, and 63% were male. The median acceptable ventilation score was 84.6%, and the median acceptable oxygenation score was 70.1% (100% being maximally acceptable). Potential for ventilator-induced lung injury was broken into 2 components: barotrauma and volutrauma. There was very little potential for barotrauma, with a median barotrauma-free state of 100%. Median potential for a volutrauma-free state was 56.1%.

CONCLUSIONS

We demonstrate the first patient categorization system utilizing a coordinated data-banking system and analytics to determine patient status and a surveillance of mechanical ventilation quality. Further research is needed to determine whether interventions such as visual display of variance from goal and patient categorization summaries can improve outcomes. (ClinicalTrials.gov registration NCT02184208.).

The Changed World of Work

The majority of workers in the global south, and increasing numbers in the north, work in the informal economy. Most are de facto or de jure excluded from services and regulation of occupational health and safety. Significant recent improvements in classification of status of informal employment have enabled improved labor force data; it has not been matched by progress with data collection on work-related risks, hazards, and health outcomes for informal workers. This special edition describes some of the risks and hazards, but focuses on strategies and interventions-such as taking occupational health and safety services to markets where informal workers operate, legal reforms, and designing appropriate equipment. The diversity of occupations and work places (many in public space) mean that new stakeholders such as local municipalities, informal workers associations and unions, as well as health professionals, need to be considered when striving for a more inclusive occupational health and safety.

Confessions of PCICU Leaders: Tales From the Past, Lessons for the Future

BACKGROUND

The pediatric cardiac intensive care environment is challenging and unpredictable due to the heterogeneous patient population. Leadership within this complex environment is critical for optimal outcomes.

METHODS

The 10th International Meeting of the Pediatric Cardiac Intensive Care Society provided a forum for leaders to share their own practice and experience that concluded with take-home messages regarding quality, safety, clinical effectiveness, stewardship, and leadership.

RESULTS

Presentations defined vital aspects for successful outcomes and highlighted ongoing challenges.

CONCLUSIONS

Accomplishing exceptional outcomes requires a blend of clinical expertise, leadership, communication skills with briefing and debriefing, meaningful use of data, and transparency among peers and toward patients and their families.

Biostatistics Series Module 1: Basics of Biostatistics

Although application of statistical methods to biomedical research began only some 150 years ago, statistics is now an integral part of medical research. A knowledge of statistics is also becoming mandatory to understand most medical literature. Data constitute the raw material for statistical work. They are records of measurement or observations or simply counts. A variable refers to a particular character on which a set of data are recorded. Data are thus the values of a variable. It is important to understand the different types of data and their mutual interconversion. Biostatistics begins with descriptive statistics that implies summarizing a collection of data from a sample or population. Categorical data are described in terms of percentages or proportions. With numerical data, individual observations within a sample or population tend to cluster about a central location, with more extreme observations being less frequent. The extent to which observations cluster is summarized by measures of central tendency while the spread can be described by measures of dispersion. The confidence interval (CI) is an increasingly important measure of precision. When we observe samples, there is no way of assessing true population parameters. We can, however, obtain a standard error and use it to define a range in which the true population value is likely to lie with a certain acceptable level of uncertainty. This range is the CI while its two terminal values are the confidence limits. Conventionally, the 95% CI is used. Patterns in data sets or data distributions are important, albeit not so obvious, component of descriptive statistics. The most common distribution is the normal distribution which is depicted as the well-known symmetrical bell-shaped Gaussian curve. Familiarity with other distributions such as the binomial and Poisson distributions is also helpful. Various graphs and plots have been devised to summarize data and trends visually. Some plots, such as the box-and-whiskers plot and the stem-and-leaf plot are used less often but provide useful summaries in select situations.

How blockchain-timestamped protocols could improve the trustworthiness of medical science

Trust in scientific research is diminished by evidence that data are being manipulated. Outcome switching, data dredging and selective publication are some of the problems that undermine the integrity of published research. Here we report a proof-of-concept study  using a 'blockchain' as a low cost, independently verifiable method that could be widely and readily used to audit and confirm the reliability of scientific studies.

How blockchain-timestamped protocols could improve the trustworthiness of medical science

Trust in scientific research is diminished by evidence that data are being manipulated. Outcome switching, data dredging and selective publication are some of the problems that undermine the integrity of published research. Methods for using blockchain to provide proof of pre-specified endpoints in clinical trial protocols were first reported by Carlisle. We wished to empirically test such an approach using a clinical trial protocol where outcome switching has previously been reported. Here we confirm the use of blockchain as a low cost, independently verifiable method to audit and confirm the reliability of scientific studies.

How blockchain-timestamped protocols could improve the trustworthiness of medical science

Trust in scientific research is diminished by evidence that data are being manipulated. Outcome switching, data dredging and selective publication are some of the problems that undermine the integrity of published research. Methods for using blockchain to provide proof of pre-specified endpoints in clinical trial protocols were first reported by Carlisle. We wished to empirically test such an approach using a clinical trial protocol where outcome switching has previously been reported. Here we confirm the use of blockchain as a low cost, independently verifiable method to audit and confirm the reliability of scientific studies.

Assisted reproductive technology in Europe, 2011: results generated from European registers by ESHRE

STUDY QUESTION

The 15th European IVF-monitoring (EIM) report presents the results of treatments involving assisted reproductive technology (ART) initiated in Europe during 2011: are there any changes in the trends compared with previous years?

SUMMARY ANSWER

Despite some fluctuations in the number of countries reporting data, while the overall number of ART cycles has continued to increase year by year, the pregnancy rates in 2011 decreased slightly to those reported in 2010, and the number of transfers with multiple embryos (3+) and the multiple delivery rates declined.

WHAT IS KNOWN ALREADY

Since 1997, ART data in Europe have been collected and reported in 14 manuscripts, published in Human Reproduction.

STUDY DESIGN, SIZE, DURATION

Retrospective data collection of European ART data by the EIM Consortium for the European Society of Human Reproduction and Embryology (ESHRE); cycles started between 1 January and 31 December 2011 are collected on a yearly basis. The data are collected by National Registers, when existing, or on a voluntary basis by personal information.

PARTICIPANTS/MATERIALS SETTING, METHODS

From 33 countries (+2 compared with 2010), 1064 clinics reported 609 973 treatment cycles including: IVF 138 592, ICSI 298 918, frozen embryo replacement (FER) 129 693, egg donation (ED) 30 198, in vitro maturation 511, preimplantation genetic diagnosis/screening 6824 and frozen oocyte replacements 5237. European data on intrauterine insemination (IUI) using husband/partner's semen (IUI-H) and donor semen (IUI-D) were reported from 861 IUI laboratories in 24 countries. A total of 174 390 IUI-H and 41 151 IUI-D cycles were included.

MAIN RESULTS AND THE ROLE OF CHANCE

In 17 countries where all clinics reported to the ART register, a total of 361 972 ART cycles were performed in a population of 285 million inhabitants, corresponding to 1269 cycles per million inhabitants. For all IVF cycles, the clinical pregnancy rates per aspiration and per transfer were stable with 29.1 and 33.2%, respectively, and for ICSI, the corresponding rates also were stable with 27.9 and 31.8%, respectively. In FER cycles, the pregnancy rate per thawing increased to 21.3% if compared with previous years. In ED cycles, the pregnancy rate per fresh transfer decreased to 45.8% (47.4% in 2010) and increased to 33.6% (33.3% in 2010) per thawed transfer. The delivery rate after IUI-H decreased to 8.3 (8.9 in 2010), and to 12.2% (13.8% in 2010) after IUI-D. In IVF and ICSI cycles, 1, 2, 3 and 4+ embryos were transferred in 27.5, 56.7, 14.5 and 1.3% of cycles, respectively. The proportions of singleton, twin and triplet deliveries after IVF and ICSI (added together) were 80.8, 18.6 and 0.6%, respectively, resulting in a total multiple delivery rate of 19.2% compared with 20.6% in 2010, 20.2% in 2009, 21.7% in 2008, 22.3% in 2007 and 20.8% in 2006. In FER cycles, the multiple delivery rate was 13.2% (12.8% twins and 0.4% triplets). Twin and triplet delivery rates associated with IUI cycles were 9.7/0.6% and 7.3/0.3%, following IUI-H and IUI-D treatment, respectively.

LIMITATIONS, REASONS FOR CAUTION

The method of reporting varies among countries, and registers from a number of countries have been unable to provide some of the relevant data such as initiated cycles and deliveries. As long as data are incomplete and generated through different methods of collection, results should be interpreted with caution.

WIDER IMPLICATIONS OF THE FINDINGS

The 15th ESHRE report on ART shows a continuing expansion of the number of treatment cycles in Europe, with more than 600 000 cycles reported in 2011. Since 2006, the proportion of IVF to ICSI cycles has reached a plateau after a small decrease in 2009. Pregnancy and delivery rates after IVF remained relatively stable compared with 2010 and 2009. The pregnancy rate per aspiration in ICSI cycles declined for the first time by 0.9%. The multiple delivery rate is lower than ever before.

STUDY FUNDING/COMPETING INTERESTS

The study had no external funding; all costs are covered by ESHRE. There are no competing interests.

Atmospheres of progress in a data-based school

In this article, I seek to extend the geographies of education, youth and young people by offering an account of the significant shifts taking place in contemporary English state education around the production and use of data. I present material from pupils, for whom the changes are putatively made, whose voices are absent in existing educational and sociological literature on data in schools. I do this through an exploration of one specific feature of school datascapes: the use of data to create and maintain a sense of 'progress'. This is not progress solely as developmental fact, logic, ideology or discourse but as felt. This article draws attention to profound changes to cultures of education that are evinced in relation to contemporary proliferations of data, contributes to theorisations of affective atmospheres in geography and how they come to be known (as a question of both experience and method), and it advances a novel theorisation of progress 'after the affective turn'.

Data Lakes and Data Visualization: An Innovative Approach to Address the Challenges of Access to Health Care in Mississippi

Background: There are a variety of challenges to developing strategies to improve access to health care, but access to data is critical for effective evidence-based decision-making. Many agencies and organizations throughout Mississippi have been collecting quality health data for many years. However, those data have historically resided in data silos and have not been readily shared. A strategy was developed to build and coordinate infrastructure, capacity, tools, and resources to facilitate health workforce and population health planning throughout the state. Objective: Realizing data as the foundation upon which to build, the primary objective was to develop the capacity to collect, store, maintain, visualize, and analyze data from a variety of disparate sources -- with the ultimate goal of improving access to health care. Specific aims were to: 1) build a centralized data repository and scalable informatics platform, 2) develop a data management solution for this platform and then, 3) derive value from this platform by facilitating data visualization and analysis.

Methods: A managed data lake was designed and constructed for health data from disparate sources throughout the state of Mississippi. A data management application was developed to log and track all data sources, maps and geographies, and data marts. With this informatics platform as a foundation, a variety of tools are used to visualize and analyze data. To illustrate, a web mapping application was developed to examine the health workforce geographically and attractive data visualizations and dynamic dashboards were created to facilitate health planning and research.

Results: Samples of data visualizations that aim to inform health planners and policymakers are presented. Many agencies and organizations throughout the state benefit from this platform.

Conclusion: The overarching goal is that by providing timely, reliable information to stakeholders, Mississippians in general will experience improved access to quality care.

A Digital Ecosystem of Diabetes Data and Technology: Services, Systems, and Tools Enabled by Wearables, Sensors, and Apps

The management of type 1 diabetes (T1D) ideally involves regimented measurement of various health signals; constant interpretation of diverse kinds of data; and consistent cohesion between patients, caregivers, and health care professionals (HCPs). In the context of myriad factors that influence blood glucose dynamics for each individual patient (eg, medication, activity, diet, stress, sleep quality, hormones, environment), such coordination of self-management and clinical care is a great challenge, amplified by the routine unavailability of many types of data thought to be useful in diabetes decision-making. While much remains to be understood about the physiology of diabetes and blood glucose dynamics at the level of the individual, recent and emerging medical and consumer technologies are helping the diabetes community to take great strides toward truly personalized, real-time, data-driven management of this chronic disease. This review describes "connected" technologies--such as smartphone apps, and wearable devices and sensors--which comprise part of a new digital ecosystem of data-driven tools that can link patients and their care teams for precision management of diabetes. These connected technologies are rich sources of physiologic, behavioral, and contextual data that can be integrated and analyzed in "the cloud" for research into personal models of glycemic dynamics, and employed in a multitude of applications for mobile health (mHealth) and telemedicine in diabetes care.

[The Roots of Idiographic Paleontology: Karl Alfred von Zittel's Methodology and Conception of the Fossil Record]

This paper examines Karl Alfred von Zittel’s practice in order to uncover the roots of so-called idiographic paleontology.The great American paleontologist Stephen Jay Gould (1941–2002) defined the discipline of idiographic paleontology as illustration and description of the morphological features of extinct species. However, this approach does not investigate macroevolutionary patterns and processes. On the contrary, the paleobiological revolution of the 1970s implemented an epistemic methodology that illustrates macrovelutionary patterns and laws by combining idiographic data with a nomothetic form of explanation. This article elucidates the features of the idiographic data as well as the acquired knowledge coupled with this approach. First of all, Heinrich G. Bronn’s (1800–1862) statistical method is analyzed. Zittel’s practice arose as a reaction against the approximate conclusions reached by Bronn’s quantitative approach. Second, the details of Zittel’s methodology are described in order to bring out its peculiarities.The microscope played a pivotal role in creating and forming Zittel’s morphological data. This analysis sheds new light on the reasons behind the so-called ideographic paleontology, thus revising Gould’s historical reconstruction, as well as on the notion of paleontological data. However, even though Zittel aimed at reaching precise and stable conclusions,his data cannot be used for elucidating evolutionary mechanisms: they are scientific in a purely descriptive sense, but completely useless for biological investigations. Finally, this paper examines how Zittel’s methodology affects the contemporary paleobiological enterprise and thereby reflects upon the notion of natural history.

[Eleven thesis on the archive of scientific research, for a new patrimonial and scientific policy]

Abstracting the main content of a recent report on the bad state of the archives of scientific research, this paper puts forward eleven thesis likely to feed, in this time of numeric transition to a new documentary regime and to a new patrimonial policy. The recent numeric conditions impose to set new archival pratices, more proactive, anticipative and prospective. Archives of scientific research must be thought in a double memorial and scientific dimension, and not only as a patrimonial or historical one.

The Relationship Between Access to Natural Environmental Amenities and Obesity

Background Various aspects of the environment are correlated with obesity. Most of the previous work in this area centers on the built environment. We sought to better understand the association of the natural environment with obesity. Methods We used the Natural Amenities Scale to characterize the attractiveness of 2,545 US counties based on access to open water, varied topography, and mild climate. We obtained the height, weight, age, sex, and address of adults from three different sources. The Departments of Motor Vehicles from seven US states provided over 38 million records. A web survey contributed 3,012 from 48 states and the District of Columbia. A clinical study of adults with diabetes from four states provided 974 more for a total of 38,159,046 analyzable records. We used logistic regression to model the association of obesity with natural amenities while controlling for age, sex, year of data collection, and various socioeconomic characteristics of the county. Results Natural amenities were inversely associated with obesity in all three populations. Over 20% of residents of low amenity areas were obese, but less than 10% of those living with the best natural amenities were obese. Conclusions The natural environment may affect health. Residing in areas with access to open water and a variety of topographic features as well as cool, dry summers and warm, sunny winters is associated with lower rates of obesity.

Acquisition, Analysis, and Sharing of Data in 2015 and Beyond: A Survey of the Landscape: A Conference Report From the American Heart Association Data Summit 2015

BACKGROUND

A 1.5-day interactive forum was convened to discuss critical issues in the acquisition, analysis, and sharing of data in the field of cardiovascular and stroke science. The discussion will serve as the foundation for the American Heart Association's (AHA's) near-term and future strategies in the Big Data area. The concepts evolving from this forum may also inform other fields of medicine and science.

METHODS AND RESULTS

A total of 47 participants representing stakeholders from 7 domains (patients, basic scientists, clinical investigators, population researchers, clinicians and healthcare system administrators, industry, and regulatory authorities) participated in the conference. Presentation topics included updates on data as viewed from conventional medical and nonmedical sources, building and using Big Data repositories, articulation of the goals of data sharing, and principles of responsible data sharing. Facilitated breakout sessions were conducted to examine what each of the 7 stakeholder domains wants from Big Data under ideal circumstances and the possible roles that the AHA might play in meeting their needs. Important areas that are high priorities for further study regarding Big Data include a description of the methodology of how to acquire and analyze findings, validation of the veracity of discoveries from such research, and integration into investigative and clinical care aspects of future cardiovascular and stroke medicine. Potential roles that the AHA might consider include facilitating a standards discussion (eg, tools, methodology, and appropriate data use), providing education (eg, healthcare providers, patients, investigators), and helping build an interoperable digital ecosystem in cardiovascular and stroke science.

CONCLUSION

There was a consensus across stakeholder domains that Big Data holds great promise for revolutionizing the way cardiovascular and stroke research is conducted and clinical care is delivered; however, there is a clear need for the creation of a vision of how to use it to achieve the desired goals. Potential roles for the AHA center around facilitating a discussion of standards, providing education, and helping establish a cardiovascular digital ecosystem. This ecosystem should be interoperable and needs to interface with the rapidly growing digital object environment of the modern-day healthcare system.

Big biomedical data as the key resource for discovery science

Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including imaging, biometrics, and clinical data. The Big Data for Discovery Science Center is taking an "-ome to home" approach to discover linkages between these disparate data sources by mining existing databases of proteomic and genomic data, brain images, and clinical assessments. In support of this work, the authors developed new technological capabilities that make it easy for researchers to manage, aggregate, manipulate, integrate, and model large amounts of distributed data. Guided by biological domain expertise, the Center's computational resources and software will reveal relationships and patterns, aiding researchers in identifying biomarkers for the most confounding conditions and diseases, such as Parkinson's and Alzheimer's.

Analysis of crystallization data in the Protein Data Bank

The Protein Data Bank (PDB) is the largest available repository of solved protein structures and contains a wealth of information on successful crystallization. Many centres have used their own experimental data to draw conclusions about proteins and the conditions in which they crystallize. Here, data from the PDB were used to reanalyse some of these results. The most successful crystallization reagents were identified, the link between solution pH and the isoelectric point of the protein was investigated and the possibility of predicting whether a protein will crystallize was explored.

The development of an online database for interventions tested in transgenic mouse models of Alzheimer's disease

Despite many efforts by the research community, Alzheimer's disease (AD) is still an incurable neurodegenerative condition that affects an estimated 44 million individuals worldwide and this figure is expected to increase to 135 million by the year 2050. As the research community currently reflects on previous endeavours, it is essential that we maximize the use of existing knowledge to inform future trials in the field. This article describes the development of a systematically identified data set relating to over 300 interventions tested in over 10,000 animals. The data set includes cohort‐level information for six structural outcomes and six behavioural assessments. We encourage others to use this dataset to inform the design of future animal experiments modelling AD and to promote effective translation to human health.

A review of accessibility of administrative healthcare databases in the Asia-Pacific region

OBJECTIVE

We describe and compare the availability and accessibility of administrative healthcare databases (AHDB) in several Asia-Pacific countries: Australia, Japan, South Korea, Taiwan, Singapore, China, Thailand, and Malaysia.

METHODS

The study included hospital records, reimbursement databases, prescription databases, and data linkages. Databases were first identified through PubMed, Google Scholar, and the ISPOR database register. Database custodians were contacted. Six criteria were used to assess the databases and provided the basis for a tool to categorise databases into seven levels ranging from least accessible (Level 1) to most accessible (Level 7). We also categorised overall data accessibility for each country as high, medium, or low based on accessibility of databases as well as the number of academic articles published using the databases.

RESULTS

Fifty-four administrative databases were identified. Only a limited number of databases allowed access to raw data and were at Level 7 [Medical Data Vision EBM Provider, Japan Medical Data Centre (JMDC) Claims database and Nihon-Chouzai Pharmacy Claims database in Japan, and Medicare, Pharmaceutical Benefits Scheme (PBS), Centre for Health Record Linkage (CHeReL), HealthLinQ, Victorian Data Linkages (VDL), SA-NT DataLink in Australia]. At Levels 3-6 were several databases from Japan [Hamamatsu Medical University Database, Medi-Trend, Nihon University School of Medicine Clinical Data Warehouse (NUSM)], Australia [Western Australia Data Linkage (WADL)], Taiwan [National Health Insurance Research Database (NHIRD)], South Korea [Health Insurance Review and Assessment Service (HIRA)], and Malaysia [United Nations University (UNU)-Casemix]. Countries were categorised as having a high level of data accessibility (Australia, Taiwan, and Japan), medium level of accessibility (South Korea), or a low level of accessibility (Thailand, China, Malaysia, and Singapore). In some countries, data may be available but accessibility was restricted based on requirements by data custodians.

CONCLUSIONS

Compared with previous research, this study describes the landscape of databases in the selected countries with more granularity using an assessment tool developed for this purpose. A high number of databases were identified but most had restricted access, preventing their potential use to support research. We hope that this study helps to improve the understanding of the AHDB landscape, increase data sharing and database research in Asia-Pacific countries.

Repertoires: How to Transform a Project into a Research Community

How effectively communities of scientists come together and co-operate is crucial both to the quality of research outputs and to the extent to which such outputs integrate insights, data and methods from a variety of fields, laboratories and locations around the globe. This essay focuses on the ensemble of material and social conditions that makes it possible for a short-term collaboration, set up to accomplish a specific task, to give rise to relatively stable communities of researchers. We refer to these distinctive features as repertoires, and investigate their development and implementation across three examples of collaborative research in the life sciences. We conclude that whether a particular project ends up fostering the emergence of a resilient research community is partly determined by the degree of attention and care devoted by researchers to material and social elements beyond the specific research questions under consideration.

Data Rights and Responsibilities: A Human Rights Perspective on Data Sharing

A human-rights-based analysis can be a useful tool for the scientific community and policy makers as they develop codes of conduct, harmonized standards, and national policies for data sharing. The human rights framework provides a shared set of values and norms across borders, defines rights and responsibilities of various actors involved in data sharing, addresses the potential harms as well as the benefits of data sharing, and offers a framework for balancing competing values. The right to enjoy the benefits of scientific progress and its applications offers a particularly helpful lens through which to view data as both a tool of scientific inquiry to which access is vital and as a product of science from which everyone should benefit.

Data Standards in Tele-radiology

Data standards play an important role to provide interoperability among different system. As other applications of telemedicine, the tele-radiology needs these standards to work properly. In this article, we conducted a review to introduce some data standards about tele-radiology. By searching PUBMED and Google Scholar database, we find more relevant articles about data standards in tele-radiology. Three categories of standards identified, including data interchange, document and terminology standards. Data interchange standards, including those which facilitate the understanding of the format of a massage between systems, such as DICOM and HL7. Document standards, including those which facilitate the contents of a massage, such as DICOM SR and HL7 CDA. And terminology standards, including those which facilitate the understanding of concepts of the domain. Since, the harmonization between different standards are important to meet interoperability, so the more effort is needed to conduct harmonization between tele-radiology standards and other domain.

Problems Associated with the Microchip Data of Stray Dogs and Cats Entering RSPCA Queensland Shelters

A lack of published information documenting problems with the microchip data for the reclaiming of stray animals entering Australian shelters limits improvement of the current microchipping system. A retrospective study analysing admission data for stray, adult dogs (n = 7258) and cats (n = 6950) entering the Royal Society for the Prevention of Cruelty to Animals (RSPCA) Queensland between January 2012 and December 2013 was undertaken to determine the character and frequency of microchip data problems and their impact on outcome for the animal. Only 28% of dogs and 9% of cats were microchipped, and a substantial proportion (37%) had problems with their data, including being registered to a previous owner or organisation (47%), all phone numbers incorrect/disconnected (29%), and the microchip not registered (14%). A higher proportion of owners could be contacted when the microchip had no problems, compared to those with problems (dogs, 93% vs. 70%; cats, 75% vs. 41%). The proportion of animals reclaimed declined significantly between microchipped animals with no data problems, microchipped animals with data problems and non-microchipped animals-87%, 69%, and 37%, respectively, for dogs and 61%, 33%, and 5%, respectively, for cats. Strategies are needed to increase the accuracy of microchip data to facilitate the reclaiming of stray dogs and cats.

Cybersecurity for Connected Diabetes Devices

Diabetes devices are increasingly connected wirelessly to each other and to data-displaying reader devices. Threats to the accurate flow of information and commands may compromise the function of these devices and put their users at risk of health complications. Sound cybersecurity of connected diabetes devices is necessary to maintain confidentiality, integrity, and availability of the data and commands. Diabetes devices can be hacked by unauthorized agents and also by patients themselves to extract data that are not automatically provided by product software. Unauthorized access to connected diabetes devices has been simulated and could happen in reality. A cybersecurity standard designed specifically for connected diabetes devices will improve the safety of these products and increase confidence of users that the products will be secure.

Organizing for ontological change: The kernel of an AIDS research infrastructure

Is it possible to prepare and plan for emergent and changing objects of research? Members of the Multicenter AIDS Cohort Study have been investigating AIDS for over 30 years, and in that time, the disease has been repeatedly transformed. Over the years and across many changes, members have continued to study HIV disease while in the process regenerating an adaptable research organization. The key to sustaining this technoscientific flexibility has been what we call the kernel of a research infrastructure: ongoing efforts to maintain the availability of resources and services that may be brought to bear in the investigation of new objects. In the case of the Multicenter AIDS Cohort Study, these resources are as follows: specimens and data, calibrated instruments, heterogeneous experts, and participating cohorts of gay and bisexual men. We track three ontological transformations, examining how members prepared for and responded to changes: the discovery of a novel retroviral agent (HIV), the ability to test for that agent, and the transition of the disease from fatal to chronic through pharmaceutical intervention. Respectively, we call the work, 'technologies', and techniques of adapting to these changes, 'repurposing', 'elaborating', and 'extending the kernel'.

Using the Electronic Health Record in Nursing Research: Challenges and Opportunities

Changes in the patient record from the paper to the electronic health record format present challenges and opportunities for the nurse researcher. Current use of data from the electronic health record is in a state of flux. Novel data analytic techniques and massive data sets provide new opportunities for nursing science. Realization of a strong electronic data output future relies on meeting challenges of system use and operability, data presentation, and privacy. Nurse researchers need to rethink aspects of proposal development. Joining ongoing national efforts aimed at creating usable data output is encouraged as a means to affect system design. Working to address challenges and embrace opportunities will help grow the science in a way that answers important patient care questions.