Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study

BACKGROUND

Eating disorders affect an increasing number of people. Social networks provide information that can help.

OBJECTIVE

We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain.

METHODS

We collected tweets related to eating disorders, for 3 consecutive months. After preprocessing, a subset of 2000 tweets was labeled: (1) messages written by people suffering from eating disorders or not, (2) messages promoting suffering from eating disorders or not, (3) informative messages or not, and (4) scientific or nonscientific messages. Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for each model.

RESULTS

A total of 1,058,957 tweets related to eating disorders were collected. were obtained in the 4 categorizations, with The bidirectional encoder representations from transformer-based models had the best score among the machine learning and deep learning techniques applied to the 4 categorization tasks (F1 scores 71.1%-86.4%).

CONCLUSIONS

Bidirectional encoder representations from transformer-based models have better performance, although their computational cost is significantly higher than those of traditional techniques, in classifying eating disorder-related tweets.

America's HIV Epidemic Analysis Dashboard: Protocol for a Data Resource to Support Ending the HIV Epidemic in the United States

Background

The Ending the HIV Epidemic (EHE) plan aims to end the HIV epidemic in the United States by 2030. Having timely and accessible data to assess progress toward EHE goals at the local level is a critical resource to achieve this goal.

Objective

The aim of this paper was to introduce America’s HIV Epidemic Analysis Dashboard (AHEAD), a data visualization tool that displays relevant data on the 6 HIV indicators provided by the Centers for Disease Control and Prevention. AHEAD can be used to monitor progress toward ending the HIV epidemic in local communities across the United States. Its objective is to make data available to stakeholders, which can be used to measure national and local progress toward 2025 and 2030 EHE goals and to help jurisdictions make local decisions that are grounded in high-quality data.

Methods

AHEAD displays data from public health data systems (eg, surveillance systems and census data), organized around the 6 EHE indicators (HIV incidence, knowledge of HIV status, HIV diagnoses, linkage to HIV medical care, viral HIV suppression, and preexposure prophylaxis coverage). Data are displayed for each of the EHE priority areas (48 counties in Washington, District of Columbia, and San Juan, Puerto Rico) which accounted for more than 50% of all US HIV diagnoses in 2016 and 2017 and 7 primarily southern states with high rates of HIV in rural communities. AHEAD also displays data for the 43 remaining states for which data are available. Data features prioritize interactive data visualization tools that allow users to compare indicator data stratified by sex at birth, race or ethnicity, age, and transmission category within a jurisdiction (when available) or compare data on EHE indicators between jurisdictions.

Results

AHEAD was launched on August 14, 2020. In the 11 months since its launch, the Dashboard has been visited 26,591 times by 17,600 unique users. About one-quarter of all users returned to the Dashboard at least once. On average, users engaged with 2.4 pages during their visit to the Dashboard, indicating that the average user goes beyond the informational landing page to engage with 1 or more pages of data and content. The most frequently visited content pages are the jurisdiction webpages.

Conclusions

The Ending the HIV Epidemic plan is described as a “whole of society” effort. Societal public health initiatives require objective indicators and require that all societal stakeholders have transparent access to indicator data at the level of the health jurisdictions responsible for meeting the goals of the plan. Data transparency empowers local stakeholders to track movement toward EHE goals, identify areas with needs for improvement, and make data-informed adjustments to deploy the expertise and resources required to locally tailor and implement strategies to end the HIV epidemic in their jurisdiction.

Unpacking the '15-Minute City' via 6G, IoT, and Digital Twins: Towards a New Narrative for Increasing Urban Efficiency, Resilience, and Sustainability

The ‘15-minute city’ concept is emerging as a potent urban regeneration model in post-pandemic cities, offering new vantage points on liveability and urban health. While the concept is primarily geared towards rethinking urban morphologies, it can be furthered via the adoption of Smart Cities network technologies to provide tailored pathways to respond to contextualised challenges through the advent of data mining and processing to better inform urban decision-making processes. We argue that the ‘15-minute city’ concept can value-add from Smart City network technologies in particular through Digital Twins, Internet of Things (IoT), and 6G. The data gathered by these technologies, and processed via Machine Learning techniques, can unveil new patterns to understand the characteristics of urban fabrics. Collectively, those dimensions, unpacked to support the ‘15-minute city’ concept, can provide new opportunities to redefine agendas to better respond to economic and societal needs as well as align more closely with environmental commitments, including the United Nations’ Sustainable Development Goal 11 and the New Urban Agenda. This perspective paper presents new sets of opportunities for cities arguing that these new connectivities should be explored now so that appropriate protocols can be devised and so that urban agendas can be recalibrated to prepare for upcoming technology advances, opening new pathways for urban regeneration and resilience crafting.

Digitizing health data for public health protection in the context of European and international coordination

BACKGROUND

The health sector has long been affected by programs, actions, plans to digitize data and care processes with a view to better protecting individual health, as well as public health, resulting in a slow and uneven development of different and often incompatible national services.

OBJECTIVE

This paper aims to explore the grounds behind the urgency of turning the digital priority into concrete actions, as acknowledged by political leaders in the Rome Declaration, by explaining the capacity of digital tools to enhance healthcare management and the current obstacles.

METHODS

It considers the progressive extension of the EU institutions' scope of action during the pandemic, the related supporting financial strategies launched and some examples of digital contact tracing systems.

RESULTS

It emerged that the pandemic highlighted the inadequacy of purely national policies and the advantages of leveraging the digital health data processing for governance, surveillance and response to cross-border and global threats.

CONCLUSIONS

Considering what emerged during the pandemic and the solemn commitment of the world's major political leaders, the solution to the still existing technical and organizational interoperability issues will no longer be postponed.

Digital Health Policy and Programs for Hospital Care in Vietnam: Scoping Review

Background

There are a host of emergent technologies with the potential to improve hospital care in low- and middle-income countries such as Vietnam. Wearable monitors and artificial intelligence–based decision support systems could be integrated with hospital-based digital health systems such as electronic health records (EHRs) to provide higher level care at a relatively low cost. However, the appropriate and sustainable application of these innovations in low- and middle-income countries requires an understanding of the local government’s requirements and regulations such as technology specifications, cybersecurity, data-sharing protocols, and interoperability.

Objective

This scoping review aims to explore the current state of digital health research and the policies that govern the adoption of digital health systems in Vietnamese hospitals.

Methods

We conducted a scoping review using a modification of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed and Web of Science were searched for academic publications, and Thư Viện Pháp Luật, a proprietary database of Vietnamese government documents, and the Vietnam Electronic Health Administration website were searched for government documents. Google Scholar and Google Search were used for snowballing searches. The sources were assessed against predefined eligibility criteria through title, abstract, and full-text screening. Relevant information from the included sources was charted and summarized. The review process was primarily undertaken by one researcher and reviewed by another researcher during each step.

Results

In total, 11 academic publications and 20 government documents were included in this review. Among the academic studies, 5 reported engineering solutions for information systems in hospitals, 2 assessed readiness for EHR implementation, 1 tested physicians’ performance before and after using clinical decision support software, 1 reported a national laboratory information management system, and 2 reviewed the health system’s capability to implement eHealth and artificial intelligence. Of the 20 government documents, 19 were promulgated from 2013 to 2020. These regulations and guidance cover a wide range of digital health domains, including hospital information management systems, general and interoperability standards, cybersecurity in health organizations, conditions for the provision of health information technology (HIT), electronic health insurance claims, laboratory information systems, HIT maturity, digital health strategies, electronic medical records, EHRs, and eHealth architectural frameworks.

Conclusions

Research about hospital-based digital health systems in Vietnam is very limited, particularly implementation studies. Government regulations and guidance for HIT in health care organizations have been released with increasing frequency since 2013, targeting a variety of information systems such as electronic medical records, EHRs, and laboratory information systems. In general, these policies were focused on the basic specifications and standards that digital health systems need to meet. More research is needed in the future to guide the implementation of digital health care systems in the Vietnam hospital setting.

WormBase in 2022-data, processes, and tools for analyzing Caenorhabditis elegans

WormBase (www.wormbase.org) is the central repository for the genetics and genomics of the nematode Caenorhabditis elegans. We provide the research community with data and tools to facilitate the use of C. elegans and related nematodes as model organisms for studying human health, development, and many aspects of fundamental biology. Throughout our 22-year history, we have continued to evolve to reflect progress and innovation in the science and technologies involved in the study of C. elegans. We strive to incorporate new data types and richer data sets, and to provide integrated displays and services that avail the knowledge generated by the published nematode genetics literature. Here, we provide a broad overview of the current state of WormBase in terms of data type, curation workflows, analysis, and tools, including exciting new advances for analysis of single-cell data, text mining and visualization, and the new community collaboration forum. Concurrently, we continue the integration and harmonization of infrastructure, processes, and tools with the Alliance of Genome Resources, of which WormBase is a founding member.

Influencing policy and practice is work in progress

Focusing on policy impact without a functioning regional devolved government required Administrative Data Research Centre Northern Ireland (ADRC NI) researchers and staff to develop new ways of influencing policy and practice in Northern Ireland. The development of a co-production model with policymakers, service providers and third sector representatives has embedded pathways to impact within research programmes, to great success. This paper describes the efforts of the ADRC NI to influence policy and practice, in Northern Ireland in challenging political circumstances, outlining the context, approach, processes, strengths, challenges and lessons for the future.

A Dynamic Four-Step Data Security Model for Data in Cloud Computing Based on Cryptography and Steganography

Cloud computing is a rapidly expanding field. It allows users to access computer system resources as needed, particularly data storage and computational power, without managing them directly. This paper aims to create a data security model based on cryptography and steganography for data in cloud computing that seeks to reduce existing security and privacy concerns, such as data loss, data manipulation, and data theft. To identify the problem and determine its core cause, we studied various literature on existing cloud computing security models. This study utilizes design science research methodology. The design science research approach includes problem identification, requirements elicitation, artifact design and development, demonstration, and assessment. Design thinking and the Python programming language are used to build the artifact, and discussion about its working is represented using histograms, tables, and algorithms. This paper's output is a four-step data security model based on Rivest-Shamir-Adleman, Advanced Encryption Standard, and identity-based encryption algorithms alongside Least Significant Bit steganography. The four steps are data protection and security through encryption algorithms, steganography, data backup and recovery, and data sharing. This proposed approach ensures more cloud data redundancy, flexibility, efficiency, and security by protecting data confidentiality, privacy, and integrity from attackers.

Surgeon involvement in clinical coding to improve data accuracy and remuneration in a shoulder and elbow unit

BACKGROUND

Clinical coders are dependent on clear data regarding diagnoses and procedures to generate an accurate representation of clinical activity and ensure appropriate remuneration is received. The accuracy of this process may potentially be improved by collaboration with the surgical team.

METHODS

Between November 2017 and November 2019, 19 meetings took place between the Senior Clinical Fellow of our tertiary Shoulder & Elbow Unit and the coding validation lead of our Trust. At each meeting, the Clinical Fellow assessed the operative note of cases in which uncertainty existed as to the most suitable clinical codes to apply and selected the codes which most accurately represented the operative intervention performed.

RESULTS

Over a 24-month period, clinical coding was reviewed in 153 cases (range 3-14 per meeting, mean 8). Following review, the clinical coding was amended in 102 (67%) of these cases. A total of £115,160 additional income was generated as a result of this process (range £1677-£15,796 per meeting, mean £6061). Only 6 out of 28 (21%) cases initially coded as arthroscopic sub-acromial decompressions were correctly coded as such.

DISCUSSION

Surgeon input into clinical coding greatly improves data quality and increases remuneration received for operative interventions performed.

The Ethics of Algorithms in Healthcare

The amount of data available to healthcare practitioners is growing, and the rapid increase in available patient data is becoming a problem for healthcare practitioners, as they are often unable to fully survey and process the data relevant for the treatment or care of a patient. Consequently, there are currently several efforts to develop systems that can aid healthcare practitioners with reading and processing patient data and, in this way, provide them with a better foundation for decision-making about the treatment and care of patients. There are also efforts to develop algorithms that provide suggestions for such decisions. However, the development of these systems and algorithms raises several concerns related to the privacy of patients, the patient-practitioner relationship, and the autonomy of healthcare practitioners. The aim of this article is to provide a foundation for understanding the ethical challenges related to the development of a specific form of data-processing systems, namely clinical algorithms.

A Platform and Multisided Market for Translational, Software-Defined Medical Procedures in the Operating Room (OP 4.1): Proof-of-Concept Study

Background

Although digital and data-based technologies are widespread in various industries in the context of Industry 4.0, the use of smart connected devices in health care is still in its infancy. Innovative solutions for the medical environment are affected by difficult access to medical device data and high barriers to market entry because of proprietary systems.

Objective

In the proof-of-concept project OP 4.1, we show the business viability of connecting and augmenting medical devices and data through software add-ons by giving companies a technical and commercial platform for the development, implementation, distribution, and billing of innovative software solutions.

Methods

The creation of a central platform prototype requires the collaboration of several independent market contenders, including medical users, software developers, medical device manufacturers, and platform providers. A dedicated consortium of clinical and scientific partners as well as industry partners was set up.

Results

We demonstrate the successful development of the prototype of a user-centric, open, and extensible platform for the intelligent support of processes starting with the operating room. By connecting heterogeneous data sources and medical devices from different manufacturers and making them accessible for software developers and medical users, the cloud-based platform OP 4.1 enables the augmentation of medical devices and procedures through software-based solutions. The platform also allows for the demand-oriented billing of apps and medical devices, thus permitting software-based solutions to fast-track their economic development and become commercially successful.

Conclusions

The technology and business platform OP 4.1 creates a multisided market for the successful development, implementation, distribution, and billing of new software solutions in the operating room and in the health care sector in general. Consequently, software-based medical innovation can be translated into clinical routine quickly, efficiently, and cost-effectively, optimizing the treatment of patients through smartly assisted procedures.

Physical Layer Components Security Risks in Optical Fiber Infrastructures

Optical fiber communications are essential for all types of long- and short-distance transmissions. The aim of this paper is to analyze the previously presented security risks and, based on measurements, provide the risk level evaluation. The major risk is the possibility of inserting a splitter into the optical distribution network and capturing a portion of the entire spectrum, i.e., all channels in the optical fiber. Another significant security risk is crosstalk on multiplexers in networks with wavelength division multiplexing. The paper covers the macrobend attenuation evaluation of fiber and back-reflection measurements. Based on the measurements, risks were evaluated for both point-to-point and point-to-multipoint networks and, lastly, the paper covers crosstalk measurements of an optomechanical switch. Finally, all individual risks are evaluated according to the severity, and a proposal for risk minimization is provided.

Using Data Integration to Improve Health and Welfare Insights

The Australian Institute of Health and Welfare (AIHW) is a leader in the provision of high-quality health and welfare information. Its work program has built a strong evidence base for better decisions that deliver improved health and welfare outcomes. The evolution of the AIHW's data integration program has exemplified innovation in identifying and addressing key information gaps, as well as responsiveness to opportunities to develop and capture the data required to inform national priorities. The AIHW conducts data integration in partnership with data custodians and specialists in integration and analysis. A linkage project requiring the integration of Australian government data must be undertaken by an accredited integrating authority. The AIHW has met stringent criteria covering project governance, capability, and data management to gain this accreditation. In this capacity, the AIHW is trusted to integrate Australian government data for high-risk research projects. To date, the AIHW's integration projects have generated improved research outcomes that have identified vulnerable population groups, improved the understanding of health risk factors, and contributed to the development of targeted interventions. These projects have fostered new insights into dementia, disability, health service use, patient experiences of healthcare, and suicide. Upcoming projects aim to further the understanding of interrelationships between determinants of wellbeing.

Labelling Assessment of Greek "Quality Label" Prepacked Cheeses as the Basis for a Branded Food Composition Database

A labelling assessment study of Greek prepacked “quality label” cheeses was conducted with a view to provide an overview of the whole category. In total, 158 prepacked products belonging to 19 “quality label” cheeses were identified in the Greek market. Among them, Feta had the highest share followed by Kasseri, Graviera Kritis, Kefalograviera and Ladotyri Mitilinis with 81, 16, 15, 11 and 9 products found in the market, respectively. For the rest of the 14 cheeses, the share was limited, ranging from 1 to 4. All labelling indications, nutritional information, claims and other labelling data were recorded and analysed in relation to their compliance against European food law requirements. The results of the analysis showed that for only 6 of the 19 cheeses, all products fully complied with EU labelling legislation. Among the 14 mandatory labelling requirements, the lowest overall compliance was observed for allergens declaration (65%). The analysis of the nutritional data showed a remarkable variability between cheeses and products. Differences in the nutritional characteristics were more pronounced among soft, semi-hard, hard and whey cheese. The above data were entered into an archival database. Application of global harmonisation and standardisation guidelines and tools lead to the initialisation of a branded food composition database (BFCD), conceptualising a specialised database for “quality label” foods.

Submission of structural biology data for review purposes

The editors discuss the submission of structural biology data.

What Is Wrong with Monetary Sanctions? Directions for Policy, Practice, and Research

Monetary sanctions are an integral and increasingly debated feature of the American criminal legal system. Emerging research, including that featured in this volume, offers important insight into the law governing monetary sanctions, how they are levied, and how their imposition affects inequality. Monetary sanctions are assessed for a wide range of contacts with the criminal legal system ranging from felony convictions to alleged traffic violations with important variability in law and practice across states. These differences allow for the identification of features of law, policy, and practice that differentially shape access to justice and equality before the law. Common practices undermine individuals' rights and fuel inequality in the effects of unpaid monetary sanctions. These observations lead us to offer a number of specific recommendations to improve the administration of justice, mitigate some of the most harmful effects of monetary sanctions, and advance future research.

Comparing Descriptive Statistics for Retrospective Studies From One-per-Minute and One-per-Second Data

BACKGROUND

Large amounts of data are collected in neonatal intensive care units, which could be used for research. It is unclear whether these data, usually sampled at a lower frequency, are sufficient for retrospective studies. We investigated what to expect when using one-per-minute data for descriptive statistics.

METHODS

One-per-second inspiratory oxygen and saturation were processed to one-per-minute data and compared, on average, standard deviation, target range time, hypoxia, days of supplemental oxygen, and missing signal.

RESULTS

Outcomes calculated from data recordings (one-per-minute = 92, one-per-second = 92) showed very little to no difference. Sub analyses of recordings under 100 and 200 h showed no difference.

CONCLUSION

In our study, descriptive statistics of one-per-minute data were comparable to one-per-second and could be used for retrospective analyses. Comparable routinely collected one-per-minute data could be used to develop algorithms or find associations, retrospectively.

The algorithmic regulation of security: An infrastructural perspective

This article contributes to debates on algorithmic regulation by focusing on the domain of security. It develops an infrastructural perspective, by analyzing how algorithmic regulation is enacted through the custom-built transatlantic data infrastructures of the EU-U.S. Passenger Name Records and Terrorism Financing Tracking Program programs. Concerning regulation through algorithms, this approach analyzes how specific, commercial data are rendered transferable and meaningful in a security context. Concerning the regulation of algorithms, an infrastructural perspective examines how public values like privacy and accountability are built into international data infrastructures. The creation of data infrastructures affects existing modes of governance and fosters novel power relations among public and private actors. We highlight emergent modes of standard setting, thus enriching Yeung's (2018) taxonomy, and question the practical effects of operationalizing public values through infrastructural choices. Ultimately, the article offers a critical reading of algorithmic security, and how it materially, legally, and politically supports specific ways of doing security.

Adherence to r-hGH Therapy in Pediatric Growth Hormone Deficiency: Current Perspectives on How Patient-Generated Data Will Transform r-hGH Treatment Towards Integrated Care

Pediatric growth hormone (GH) deficiency is a licensed indication for replacement therapy with recombinant human growth hormone (r-hGH). Treatment, consisting of daily subcutaneous injections, extends from the time of diagnosis until cessation of linear growth at completion of puberty. Suboptimal adherence to r-hGH therapy is common and has been well documented to substantially impair the growth response and achievement of the optimal goal which is attainment of adult height within the genetic target range. The causes of poor adherence are complex and include disease-, patient-, doctor-, and treatment-related factors. Interventions for suboptimal adherence are important for a long-term successful outcome and can include both face-to-face and digital strategies. Face-to-face interventions include behavioral change approaches such as motivational interviewing and non-judgmental assessment. Medical and nursing staff require training in these techniques. Digital solutions are rapidly advancing as evidenced by the electronic digital auto-injector device, easypod® (Merck Healthcare KGaA, Darmstadt, Germany), which uses the web-based easypod® connect platform allowing adherence data to be transmitted electronically to healthcare professionals (HCPs), who can then access GH treatment history, enhancing clinical decisions. Over the past 10 years, the multi-national Easypod® Connect Observational Study has reported high levels of adherence (>85%) from up to 40 countries. The easypod® connect system can be supported by a smartphone app, growlink™, which facilitates the interactions between the patients, their care team, and patient support services. HCPs are empowered by new digital techniques, however, the human-digital partnership remains essential for optimal growth management. The pediatric patient on r-hGH therapy will benefit from these innovations to enhance adherence and optimize long-term response.

How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks

As the rapid growth of mobile users and Internet-of-Everything devices will continue in the upcoming decade, more and more network capacity will be needed to accommodate such a constant increase in data volumes (DVs). To satisfy such a vast DV increase, the implementation of the fifth-generation (5G) and future sixth-generation (6G) mobile networks will be based on heterogeneous networks (HetNets) composed of macro base stations (BSs) dedicated to ensuring basic signal coverage and capacity, and small BSs dedicated to satisfying capacity for increased DVs at locations of traffic hotspots. An approach that can accommodate constantly increasing DVs is based on adding additional capacity in the network through the deployment of new BSs as DV increases. Such an approach represents an implementation challenge to mobile network operators (MNOs), which is reflected in the increased power consumption of the radio access part of the mobile network and degradation of network energy efficiency (EE). In this study, the impact of the expected increase of DVs through the 2020s on the EE of the 5G radio access network (RAN) was analyzed by using standardized data and coverage EE metrics. An analysis was performed for five different macro and small 5G BS implementation and operation scenarios and for rural, urban, dense-urban and indoor-hotspot device density classes (areas). The results of analyses reveal a strong influence of increasing DV trends on standardized data and coverage EE metrics of 5G HetNets. For every device density class characterized with increased DVs, we here elaborate on the process of achieving the best and worse combination of data and coverage EE metrics for each of the analyzed 5G BSs deployment and operation approaches. This elaboration is further extended on the analyses of the impact of 5G RAN instant power consumption and 5G RAN yearly energy consumption on values of standardized EE metrics. The presented analyses can serve as a reference in the selection of the most appropriate 5G BS deployment and operation approach, which will simultaneously ensure the transfer of permanently increasing DVs in a specific device density class and the highest possible levels of data and coverage EE metrics.

Austria's Digital Vaccination Registry: Stakeholder Views and Implications for Governance

In this study, we explore the recent setup of a digital vaccination record in Austria. Working from a social-scientific perspective, we find that the introduction of the electronic vaccination pass was substantially accelerated by the COVID-19 pandemic. Our interviews with key stakeholders (n = 16) indicated that three main factors drove this acceleration. The pandemic (1) sidelined historical conflicts regarding data ownership and invoked a shared sense of the value of data, (2) accentuated the need for enhanced administrative efficiency in an institutionally fragmented system, and (3) helped invoke the national vaccination registry as an indispensable infrastructure for public health governance with the potential to innovate its healthcare system in the long term.

Why a long-term perspective is beneficial for demographers

Although many contemporary demographers pay attention to historical demography, there is often a surprising lack of appreciation of the demographic circumstances and systems of the past, suggesting an implicit assumption that they are not relevant to the present or that the methods, data, and questions addressed by historical and contemporary demographers are different. This paper provides an overview of historical demography as published in Population Studies and how this has developed over time. Drawing on this, I demonstrate that historical and contemporary demography use similar data sources and identical methods, and they often address comparable questions. I argue that an appreciation of demographic patterns and processes is beneficial for all demographers, even those who work on the most recent time periods, and that better integration of historical and contemporary demography would be beneficial to both. The paper also considers three challenges for historical demography as it moves forward.

Moving towards the centre or the exit? Migration in population studies and in Population Studies 1996-2021

This paper examines the position of migration in population studies, focusing on the period 1996-2021. It considers the reasons why migration remains problematic for demographers, but also how approaches to migration have changed over the last 25 years. While it has arguably become more important to both demography and population studies because of the transition to low fertility and mortality, migration has metamorphosed into a complex field in its own right, almost independently from changes in demography. Both internal and international migration form the subject of this examination and four main themes are pursued: data and measurement; theories and approaches; migration and development; and migration and political demography. The papers published in the journal Population Studies are used to provide a mirror through which to view these changes over the last 25 years. This paper concludes by looking at likely future directions in migration studies, demography, and population studies.

Research data infrastructure for high-throughput experimental materials science

The High-Throughput Experimental Materials Database (HTEM-DB, htem.nrel.gov) is a repository of inorganic thin-film materials data collected during combinatorial experiments at the National Renewable Energy Laboratory (NREL). This data asset is enabled by NREL's Research Data Infrastructure (RDI), a set of custom data tools that collect, process, and store experimental data and metadata. Here, we describe the experimental data flow from the RDI to the HTEM-DB to illustrate the strategies and best practices currently used for materials data at NREL. Integration of the data tools with experimental instruments establishes a data communication pipeline between experimental researchers and data scientists. This work motivates the creation of similar workflows at other institutions to aggregate valuable data and increase their usefulness for future machine learning studies. In turn, such data-driven studies can greatly accelerate the pace of discovery and design in the materials science domain.

•

Automated curation of experimental materials data

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Integration of data tools into the experimental laboratory

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Simple, effective, and flexible data archival system

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Collection of metadata for enhanced total data value

For machine learning to make significant contributions to a scientific domain, algorithms must ingest and learn from high-quality, large-volume datasets. The Research Data Infrastructure (RDI) that feeds the High-Throughput Experimental Materials Database (HTEM-DB, htem.nrel.gov) provides such a dataset from existing experimental data streams at the National Renewable Energy Laboratory (NREL). The described methods for curating experimental data can be applied to other materials research laboratory settings, paving the way for increased application of machine learning to materials science. In turn, the resulting new materials and new knowledge will benefit the society by advancing new technologies in energy, fuels, computing, security, and other important areas.

Without IPv6, there is no digital transformation for healthcare

BACKGROUND

Originally, digital healthcare was created to support underserved and rural patients gain access to health services. Phones, devices, and computers need IP (Internet Protocol) addresses to connect to the Internet.

OBJECTIVE

This paper aims to study the close relationship between the Internet and the transformation of healthcare services.

METHODS

The current protocol in use is the Internet protocol version 4 (IPv4), whose number of Internet addresses has been globally exhausted. The Internet Protocol version 6 (IPv6) with 47 octillion unique addresses for every person on the planet has become the only option for sustainable growth and innovation. However, most of the worldwide industry is still in IPv4. In the era of Artificial Intelligence, Internet of Things, Fifth Generation of Cellular Technology (5G), and Blockchain, there will be a massive need of IP addresses. For 2025, experts predict over 1.5 billion new IPv6 users which will continue to grow exponentially.

RESULTS

Nations need to tackle the increasing industry requirements for IPv6 and telehealth adoption to benefit from the full IPv6 connectivity which is the key strategic advantage for the healthcare industry.

CONCLUSION

The strategic potential that telehealth brings to the healthcare industry is widely appreciated. However, what are the implications of its expansion around the world? How can we prioritize the poorest and most vulnerable in society without new technologies?

Investigating the Use of Digital Health Technology to Monitor COVID-19 and Its Effects: Protocol for an Observational Study (Covid Collab Study)

BACKGROUND

The ubiquity of mobile phones and increasing use of wearable fitness trackers offer a wide-ranging window into people's health and well-being. There are clear advantages in using remote monitoring technologies to gain an insight into health, particularly under the shadow of the COVID-19 pandemic.

OBJECTIVE

Covid Collab is a crowdsourced study that was set up to investigate the feasibility of identifying, monitoring, and understanding the stratification of SARS-CoV-2 infection and recovery through remote monitoring technologies. Additionally, we will assess the impacts of the COVID-19 pandemic and associated social measures on people's behavior, physical health, and mental well-being.

METHODS

Participants will remotely enroll in the study through the Mass Science app to donate historic and prospective mobile phone data, fitness tracking wearable data, and regular COVID-19-related and mental health-related survey data. The data collection period will cover a continuous period (ie, both before and after any reported infections), so that comparisons to a participant's own baseline can be made. We plan to carry out analyses in several areas, which will cover symptomatology; risk factors; the machine learning-based classification of illness; and trajectories of recovery, mental well-being, and activity.

RESULTS

As of June 2021, there are over 17,000 participants-largely from the United Kingdom-and enrollment is ongoing.

CONCLUSIONS

This paper introduces a crowdsourced study that will include remotely enrolled participants to record mobile health data throughout the COVID-19 pandemic. The data collected may help researchers investigate a variety of areas, including COVID-19 progression; mental well-being during the pandemic; and the adherence of remote, digitally enrolled participants.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/32587.

Teachers' Perspectives on the Acceptability and Feasibility of Wearable Technology to Inform School-Based Physical Activity Practices

Background: Many children are not engaging in sufficient physical activity and there are substantial between-children physical activity inequalities. In addition to their primary role as educators, teachers are often regarded as being well-placed to make vital contributions to inclusive visions of physical activity promotion. With the dramatic increase in popularity of wearable technologies for physical activity promotion in recent years, there is a need to better understand teachers' perspectives about using such devices, and the data they produce, to support physical activity promotion in schools. Method: Semi-structured interviews were conducted with 26 UK-based primary school teachers, exploring their responses to children's physical activity data and their views about using wearable technologies during the school day. Interview discussions were facilitated by an elicitation technique whereby participants were presented with graphs illustrating children's in-school physical activity obtained from secondary wearable technology data. Interview transcripts were thematically analyzed. Results: Most teachers spoke positively about the use of wearable technologies specifically designed for school use, highlighting potential benefits and considerations. Many teachers were able to understand and critically interpret data showing unequal physical activity patterns both within-and between-schools. Being presented with the data prompted teachers to provide explanations about observable patterns, emotional reactions-particularly about inequalities-and express motivations to change the current situations in schools. Conclusion: These findings suggest that primary school teachers in the UK are open to integrating wearable technology for measuring children's physical activity into their practices and can interpret the data produced by such devices. Visual representations of physical activity elicited strong responses and thus could be used when working with teachers as an effective trigger to inform school practices and policies seeking to address in-school physical inactivity and inequalities.

Submission of structural biology data for review purposes

The editors discuss the submission of structural biology data.

Collaboration between Government and Research Community to Respond to COVID-19: Israel’s Case

Triggered by the COVID-19 crisis, Israel’s Ministry of Health (MoH) held a virtual datathon based on deidentified governmental data. Organized by a multidisciplinary committee, Israel’s research community was invited to offer insights to help solve COVID-19 policy challenges. The Datathon was designed to develop operationalizable data-driven models to address COVID-19 health policy challenges. Specific relevant challenges were defined and diverse, reliable, up-to-date, deidentified governmental datasets were extracted and tested. Secure remote-access research environments were established. Registration was open to all citizens. Around a third of the applicants were accepted, and they were teamed to balance areas of expertise and represent all sectors of the community. Anonymous surveys for participants and mentors were distributed to assess usefulness and points for improvement and retention for future datathons. The Datathon included 18 multidisciplinary teams, mentored by 20 data scientists, 6 epidemiologists, 5 presentation mentors, and 12 judges. The insights developed by the three winning teams are currently considered by the MoH as potential data science methods relevant for national policies. Based on participants’ feedback, the process for future data-driven regulatory responses for health crises was improved. Participants expressed increased trust in the MoH and readiness to work with the government on these or future projects.

Analysis of a Web-Based Dashboard to Support the Use of National Audit Data in Quality Improvement: Realist Evaluation

Background

Dashboards can support data-driven quality improvements in health care. They visualize data in ways intended to ease cognitive load and support data comprehension, but how they are best integrated into working practices needs further investigation.

Objective

This paper reports the findings of a realist evaluation of a web-based quality dashboard (QualDash) developed to support the use of national audit data in quality improvement.

Methods

QualDash was co-designed with data users and installed in 8 clinical services (3 pediatric intensive care units and 5 cardiology services) across 5 health care organizations (sites A-E) in England between July and December 2019. Champions were identified to support adoption. Data to evaluate QualDash were collected between July 2019 and August 2021 and consisted of 148.5 hours of observations including hospital wards and clinical governance meetings, log files that captured the extent of use of QualDash over 12 months, and a questionnaire designed to assess the dashboard’s perceived usefulness and ease of use. Guided by the principles of realist evaluation, data were analyzed to understand how, why, and in what circumstances QualDash supported the use of national audit data in quality improvement.

Results

The observations revealed that variation across sites in the amount and type of resources available to support data use, alongside staff interactions with QualDash, shaped its use and impact. Sites resourced with skilled audit support staff and established reporting systems (sites A and C) continued to use existing processes to report data. A number of constraints influenced use of QualDash in these sites including that some dashboard metrics were not configured in line with user expectations and staff were not fully aware how QualDash could be used to facilitate their work. In less well-resourced services, QualDash automated parts of their reporting process, streamlining the work of audit support staff (site B), and, in some cases, highlighted issues with data completeness that the service worked to address (site E). Questionnaire responses received from 23 participants indicated that QualDash was perceived as useful and easy to use despite its variable use in practice.

Conclusions

Web-based dashboards have the potential to support data-driven improvement, providing access to visualizations that can help users address key questions about care quality. Findings from this study point to ways in which dashboard design might be improved to optimize use and impact in different contexts; this includes using data meaningful to stakeholders in the co-design process and actively engaging staff knowledgeable about current data use and routines in the scrutiny of the dashboard metrics and functions. In addition, consideration should be given to the processes of data collection and upload that underpin the quality of the data visualized and consequently its potential to stimulate quality improvement.

International Registered Report Identifier (IRRID)

RR2-10.1136/bmjopen-2019-033208

Differences in Secure Messaging, Self-management, and Glycemic Control Between Rural and Urban Patients: Secondary Data Analysis

BACKGROUND

Rural patients with diabetes have difficulty accessing care and are at higher risk for poor diabetes management. Sustained use of patient portal features such as secure messaging (SM) can provide accessible support for diabetes self-management.

OBJECTIVE

This study explored whether rural patients' self-management and glycemic control was associated with the use of SM.

METHODS

This secondary, cross-sectional, mixed methods analysis of 448 veterans with diabetes used stratified random sampling to recruit a diverse sample from the United States (rural vs urban and good vs poor glycemic control). Administrative, clinical, survey, and interview data were used to determine patients' rurality, use of SM, diabetes self-management behaviors, and glycemic control. Moderated mediation analyses assessed these relationships.

RESULTS

The sample was 51% (n=229) rural and 49% (n=219) urban. Mean participant age was 66.4 years (SD 7.7 years). More frequent SM use was associated with better diabetes self-management (P=.007), which was associated with better glycemic control (P<.001). Among rural patients, SM use was indirectly associated with better glycemic control through improved diabetes self-management (95% CI 0.004-0.927). These effects were not observed among urban veterans with diabetes (95% CI -1.039 to 0.056). Rural patients were significantly more likely than urban patients to have diabetes-related content in their secure messages (P=.01).

CONCLUSIONS

More frequent SM use is associated with engaging in diabetes self-management, which, in turn, is associated with better diabetes control. Among rural patients with diabetes, SM use is indirectly associated with better diabetes control. Frequent patient-team communication through SM about diabetes-related content may help rural patients with diabetes self-management, resulting in better glycemic control.

The critical need for advanced training of electronic records use: implications for clinical practice, education, and the advancement of athletic training

CONTEXT

The effective use of electronic records (ie, electronic health/medical records) is essential to professional initiatives and the overall advancement of the athletic training profession. However, evidence suggests comprehensive patient care documentation and wide-spread use of electronic records is still limited in athletic training. The lack of formal training and education for clinicians and students are often cited as primary barriers to electronic records use. Other healthcare disciplines have used academic electronic health records (AEHR) systems to address these barriers with promising results.

OBJECTIVES

To identify common challenges associated with the effective use of electronic records in clinical practice, discuss how an AEHR can address these challenges and encourage more effective use of electronic records, and describe strategies for deploying AEHRs within the athletic training profession.

DESCRIPTION

The AEHR is an electronic records system specifically designed for educational use to support simulation learning among all types of learners (eg, practicing clinicians, students). Mimicking the form and function of an EHR, the AEHR offers various educational tasks including patient care documentation projects, critical reviews of standardized patient cases, and assessments of patient care data for quality improvement efforts. Clinical and Research Advantages: Recent evidence suggests the use of an AEHR can improve knowledge and enhance skills. Specifically, AEHR use has been associated with enhanced attitudes toward EHR technology, enhanced informatics competencies, and improved documentation skills. Also, the use of an AEHR has been associated with improved critical thinking and decision-making skills. AEHRs appear to be valuable tools for health professions education and athletic training stands to benefit from AEHR use to better train and upskill clinicians and students alike for clinical practice. Although the implementation of an AEHR will require much time and large-scale coordinated efforts, it will be a worthy investment to address current challenges and advance the athletic training profession.

Online Newspaper Reports on Ambulance Accidents in Austria, Germany, and Switzerland: Retrospective Cross-sectional Review

Background

Ambulance accidents are an unfortunate indirect result of ambulance emergency calls, which create hazardous environments for personnel, patients, and bystanders. However, in European German-speaking countries, factors contributing to ambulance accidents have not been optimally researched and analyzed.

Objective

The objective of this study was to extract, analyze, and compare data from online newspaper articles on ambulance accidents for Austria, Germany, and Switzerland. We hope to highlight future strategies to offset the deficit in research data and official registers for prevention of ambulance and emergency vehicle accidents.

Methods

Ambulance accident data were collected from Austrian, German, and Swiss free web-based daily newspapers, as listed in Wikipedia, for the period between January 2014 and January 2019. All included newspapers were searched for articles reporting ambulance accidents using German terms representing “ambulance” and “ambulance accident.” Characteristics of the accidents were compiled and analyzed. Only ground ambulance accidents were covered.

Results

In Germany, a total of 597 ambulance accidents were recorded, corresponding to 0.719 (95% CI 0.663-0.779) per 100,000 inhabitants; 453 of these accidents left 1170 people injured, corresponding to 1.409 (95% CI 1.330-1.492) per 100,000 inhabitants, and 28 of these accidents caused 31 fatalities, corresponding to 0.037 (95% CI 0.025-0.053) per 100,000 inhabitants. In Austria, a total of 62 ambulance accidents were recorded, corresponding to 0.698 (95% CI 0.535-0.894) per 100,000 inhabitants; 47 of these accidents left 115 people injured, corresponding to 1.294 (95% CI 1.068-1.553) per 100,000 inhabitants, and 6 of these accidents caused 7 fatalities, corresponding to 0.079 (95% CI 0.032-0.162) per 100,000 inhabitants. In Switzerland, a total of 25 ambulance accidents were recorded, corresponding to 0.293 (95% CI 0.189-0.432) per 100,000 inhabitants; 11 of these accidents left 18 people injured, corresponding to 0.211(95% CI 0.113-0.308) per 100,000 inhabitants. There were no fatalities. In each of the three countries, the majority of the accidents involved another car (77%-81%). In Germany and Switzerland, most accidents occurred at an intersection. In Germany, Austria, and Switzerland, 38.7%, 26%, and 4%, respectively, of ambulance accidents occurred at intersections for which the ambulance had a red light (P<.001). In all three countries, most of the casualties were staff and not uncommonly a third party. Most accidents took place on weekdays and during the daytime. Ambulance accidents were evenly distributed across the four seasons. The direction of travel was reported in 28%-37% of the accidents and the patient was in the ambulance approximately 50% of the time in all countries. The cause of the ambulance accidents was reported to be the ambulance itself in 125 (48.1% of accidents where the cause was reported), 22 (42%), and 8 (40%) accidents in Germany, Austria, and Switzerland, respectively (P=.02), and another vehicle in 118 (45.4%), 29 (56%), and 9 (45%) accidents, respectively (P<.001). A total of 292 accidents occurred while blue lights and sirens were used, which caused 3 deaths and 577 injuries.

Conclusions

This study draws attention to much needed auxiliary sources of data that may allow for creation of a contemporary registry of all ambulance accidents in Austria, Germany, and Switzerland. To improve risk management and set European standards, it should be mandatory to collect standardized goal-directed and representative information using various sources (including the wide range presented by the press and social media), which should then be made available for audit, analysis, and research.

GA4GH Passport standard for digital identity and access permissions

The Global Alliance for Genomics and Health (GA4GH) supports international standards that enable a federated data sharing model for the research community while respecting data security, ethical and regulatory frameworks, and data authorization and access processes for sensitive data. The GA4GH Passport standard (Passport) defines a machine-readable digital identity that conveys roles and data access permissions (called "visas") for individual users. Visas are issued by data stewards, including data access committees (DACs) working with public databases, the entities responsible for the quality, integrity, and access arrangements for the datasets in the management of human biomedical data. Passports streamline management of data access rights across data systems by using visas that present a data user's digital identity and permissions across organizations, tools, environments, and services. We describe real-world implementations of the GA4GH Passport standard in use cases from ELIXIR Europe, National Institutes of Health, and the Autism Sharing Initiative. These implementations demonstrate that the Passport standard has provided transparent mechanisms for establishing permissions and authorizing data access across platforms.

Privacy-Preserving Anonymity for Periodical Releases of Spontaneous Adverse Drug Event Reporting Data: Algorithm Development and Validation

Background

Spontaneous reporting systems (SRSs) have been increasingly established to collect adverse drug events for fostering adverse drug reaction (ADR) detection and analysis research. SRS data contain personal information, and so their publication requires data anonymization to prevent the disclosure of individuals’ privacy. We have previously proposed a privacy model called MS(k, θ*)-bounding and the associated MS-Anonymization algorithm to fulfill the anonymization of SRS data. In the real world, the SRS data usually are released periodically (eg, FDA Adverse Event Reporting System [FAERS]) to accommodate newly collected adverse drug events. Different anonymized releases of SRS data available to the attacker may thwart our single-release-focus method, that is, MS(k, θ*)-bounding.

Objective

We investigate the privacy threat caused by periodical releases of SRS data and propose anonymization methods to prevent the disclosure of personal privacy information while maintaining the utility of published data.

Methods

We identify potential attacks on periodical releases of SRS data, namely, BFL-attacks, mainly caused by follow-up cases. We present a new privacy model called PPMS(k, θ*)-bounding, and propose the associated PPMS-Anonymization algorithm and 2 improvements: PPMS+-Anonymization and PPMS++-Anonymization. Empirical evaluations were performed using 32 selected FAERS quarter data sets from 2004Q1 to 2011Q4. The performance of the proposed versions of PPMS-Anonymization was inspected against MS-Anonymization from some aspects, including data distortion, measured by normalized information loss; privacy risk of anonymized data, measured by dangerous identity ratio and dangerous sensitivity ratio; and data utility, measured by the bias of signal counting and strength (proportional reporting ratio).

Results

The best version of PPMS-Anonymization, PPMS++-Anonymization, achieves nearly the same quality as MS-Anonymization in both privacy protection and data utility. Overall, PPMS++-Anonymization ensures zero privacy risk on record and attribute linkage, and exhibits 51%-78% and 59%-82% improvements on information loss over PPMS+-Anonymization and PPMS-Anonymization, respectively, and significantly reduces the bias of ADR signal.

Conclusions

The proposed PPMS(k, θ*)-bounding model and PPMS-Anonymization algorithm are effective in anonymizing SRS data sets in the periodical data publishing scenario, preventing the series of releases from disclosing personal sensitive information caused by BFL-attacks while maintaining the data utility for ADR signal detection.

Sex differences in neck strength and head impact kinematics in university rugby union players

Globally, over three million women participate in rugby union, yet injury prevention and training strategies are predominantly based on androcentric data. These strategies may have limited generalisability to females, given the cervical spine is more susceptible to whiplash and less adept at resisting inertial loading. A total of 53 university rugby union players (25 female, 28 male, 20.7 ± 1.8 years) had their isometric neck strength measured. Bespoke instrumented mouthguards were used to record the magnitude of head impact events in six female and seven male competitive matches. Mean female maximal isometric neck strength was 47% lower than male. Independent samples Mann-Whitney U tests showed no significant differences for peak linear head acceleration (female: median 11.7 g, IQR 7.9 g; male: median 12.5 g, IQR 7.0 g p=.23) or peak rotational head acceleration (female: median 800.2 rad·s^-2, IQR 677.7 rad·s^-2; male: median 849.4 rad·s^-2, IQR 479.8 rad·s^-2; p=.76), despite the mean male body mass being 24% greater than female. Coded video analysis revealed substantial differences in head-impact mechanisms; uncontrolled whiplash dominated >50% of all recorded female impact events and <0.5% in males. Direct head-to-ground impacts comprised 26.1% of female and 9.7% of male impacts, with whiplash occurring in 78.0% and 0.5%, respectively. Overall, the data provided in this study do not support the generalisation of male-derived training and injury-prevention data to female rugby athletes. These results suggest a considerable research effort is required to identify specific weakness of female rugby players and derive appropriate training, injury prevention and return to play protocols.

Use of the Washington Group Questions in Non-Government Programming

The Washington Group questions (WGQ) on functioning have been widely promoted as the go-to tool for disability data collection. Designed for use by government, the WGQ have been adopted by non-government organizations (NGOs) for use in programming. However, little is known about how the WGQs are being used by NGOs or how use may be contributing to disability inclusion. Method: This paper describes exploratory research on the use of the WGQ in NGO programming. An online survey provided an overview of adoption followed by semi-structured interviews from a purposive sample to explore data collection, analysis, and use. Results: Thematic analysis showed limited inclusion outcomes directly attributable to use of the WGQ, adoption driven by individual champions rather than systematically across organizations, and challenges in data collection resulting in a wide range of prevalence rates. What information the WGQ can realistically contribute to programs was also overestimated. However, the process of using the WGQ was raising awareness on disability inclusion within program teams and communities. Conclusion: Acknowledging differences in emerging use by NGOs beyond the WGQ’s intended purpose, alongside promoting a flexible and staged approach to adoption and use in programming, may improve utility and disability inclusion outcomes over time.

Implementation of the World Health Organization Global Antimicrobial Resistance Surveillance System in Uganda, 2015-2020: Mixed-Methods Study Using National Surveillance Data

Background

Antimicrobial resistance (AMR) is an emerging public health crisis in Uganda. The World Health Organization (WHO) Global Action Plan recommends that countries should develop and implement National Action Plans for AMR. We describe the establishment of the national AMR program in Uganda and present the early microbial sensitivity results from the program.

Objective

The aim of this study is to describe a national surveillance program that was developed to perform the systematic and continuous collection, analysis, and interpretation of AMR data.

Methods

A systematic qualitative description of the process and progress made in the establishment of the national AMR program is provided, detailing the progress made from 2015 to 2020. This is followed by a report of the findings of the isolates that were collected from AMR surveillance sites. Identification and antimicrobial susceptibility testing (AST) of the bacterial isolates were performed using standard methods at both the surveillance sites and the reference laboratory.

Results

Remarkable progress has been achieved in the establishment of the national AMR program, which is guided by the WHO Global Laboratory AMR Surveillance System (GLASS) in Uganda. A functional national coordinating center for AMR has been established with a supporting designated reference laboratory. WHONET software for AMR data management has been installed in the surveillance sites and laboratory staff trained on data quality assurance. Uganda has progressively submitted data to the WHO GLASS reporting system. Of the 19,216 isolates from WHO GLASS priority specimens collected from October 2015 to June 2020, 22.95% (n=4411) had community-acquired infections, 9.46% (n=1818) had hospital-acquired infections, and 68.57% (n=12,987) had infections of unknown origin. The highest proportion of the specimens was blood (12,398/19,216, 64.52%), followed by urine (5278/19,216, 27.47%) and stool (1266/19,216, 6.59%), whereas the lowest proportion was urogenital swabs (274/19,216, 1.4%). The mean age was 19.1 (SD 19.8 years), whereas the median age was 13 years (IQR 28). Approximately 49.13% (9440/19,216) of the participants were female and 50.51% (9706/19,216) were male. Participants with community-acquired infections were older (mean age 28, SD 18.6 years; median age 26, IQR 20.5 years) than those with hospital-acquired infections (mean age 17.3, SD 20.9 years; median age 8, IQR 26 years). All gram-negative (Escherichia coli, Klebsiella pneumoniae, and Neisseria gonorrhoeae) and gram-positive (Staphylococcus aureus and Enterococcus sp) bacteria with AST showed resistance to each of the tested antibiotics.

Conclusions

Uganda is the first African country to implement a structured national AMR surveillance program in alignment with the WHO GLASS. The reported AST data indicate very high resistance to the recommended and prescribed antibiotics for treatment of infections. More effort is required regarding quality assurance of laboratory testing methodologies to ensure optimal adherence to WHO GLASS–recommended pathogen-antimicrobial combinations. The current AMR data will inform the development of treatment algorithms and clinical guidelines.

Health Information Systems in the COVID-19 Pandemic: A Short Survey of Experiences and Lessons Learned From the European Region

Introduction: The COVID-19 crisis provides an opportunity to reflect on what worked during the pandemic, what could have been done differently, and what innovations should become part of an enhanced health information system in the future. Methods: An online qualitative survey was designed and administered online in November 2020 to all the 37 Member States that are part of the WHO European Health Information Initiative and the WHO Central Asian Republics Information Network. Results: Nineteen countries responded to the survey (Austria, Belgium, Croatia, Czech Republic, Finland, Greece, Iceland, Ireland, Israel, Italy, Kazakhstan, Latvia, Lithuania, Romania, Russian Federation, Sweden, Turkey, United Kingdom, and Uzbekistan). The COVID-19 pandemic required health information systems (HIS) to rapidly adapt to identify, collect, store, manage, and transmit accurate and timely COVID-19 related data. HIS stakeholders have been put to the test, and valuable experience has been gained. Despite critical gaps such as under-resourced public health services, obsolete health information technologies, and lack of interoperability, most countries believed that their information systems had worked reasonably well in addressing the needs arising during the COVID-19 pandemic. Conclusion: Strong enabling environments and advanced and digitized health information systems are vital to controlling epidemics. Sustainable finance and government support are required for the continued implementation and enhancement of HIS. It is important to promote digital solutions beyond the COVID-19 pandemic. Now is the time to discuss potential solutions to obtain timely, accurate, and reliable health information and steer policy-making while protecting privacy rights and meeting the highest ethical standards.

An Overview of Sensors, Design and Healthcare Challenges in Smart Homes: Future Design Questions

The ageing population increases the demand for customized home care. As a result, sensing technologies are finding their way into the home environment. However, challenges associated with how users interact with sensors and data are not well-researched, particularly from a design perspective. This review explores the literature on important research projects around sensors, design and smart healthcare in smart homes, and highlights challenges for design research. A PRISMA protocol-based screening procedure is adopted to identify relevant articles (n = 180) on the subject of sensors, design and smart healthcare. The exploration and analysis of papers are performed using hierarchical charts, force-directed layouts and 'bedraggled daisy' Venn diagrams. The results show that much work has been carried out in developing sensors for smart home care. Less attention is focused on addressing challenges posed by sensors in homes, such as data accessibility, privacy, comfort, security and accuracy, and how design research might solve these challenges. This review raises key design research questions, particularly in working with sensors in smart home environments.

ASA classification and surgical severity grading used to identify a high-risk population, a multicenter prospective cohort study in Swedish tertiary hospitals

BACKGROUND

Identification of surgical populations at high risk for negative outcomes is needed for clinical and research purposes. We hypothesized that combining two classification systems, ASA (American Society of Anesthesiology physical status) and surgical severity, we could identify a high-risk population before surgery. We aimed to describe postoperative outcomes in a population selected by these two classifications system.

METHODS

Data were collected in a Swedish multicentre, time-interrupted prospective, consecutive cohort study. Eligibility criteria were age ≥18 years, ASA ≥3, elective or emergent, major to Xmajor/complex (Specialist Procedure Codes used in United Kingdom), gastrointestinal, urogenital or orthopaedic procedures. Postoperative morbidity was identified by the Postoperative Morbidity Survey on postoperative days 3 ± 1, 7 ± 1, 10 + 5 and graded for severity by the Clavien-Dindo system. Mortality was assessed at 30, 180 and 360 days.

RESULTS

Postoperative morbidity was 78/48/47 per cent on postoperative days 3/7/10. Majority of morbidities (67.5 per cent) were graded as >1 by Clavien-Dindo. Any type of postoperative morbidity graded >1 was associated with increased risk for death up to one year. The mortality was 5.7 per cent (61/1063) at 30 days, 13.3 per cent (142/1063) at 6 months and 19.1 per cent (160/1063) at 12 months.

CONCLUSION

Severity classification as major to Xmajor/complex and ASA ≥3 could be used to identify a high-risk surgical population concerning postoperative morbidity and mortality before surgery. Combining the two systems future electronic data extraction is possible of a high-risk population in tertiary hospitals.

Indigenous and Tribal Peoples Data Governance in Health Research: A Systematic Review

There is increasing potential to improve the research and reporting on the health and wellbeing of Indigenous and Tribal peoples through the collection and (re)use of population-level data. As the data economy grows and the value of data increases, the optimization of data pertaining to Indigenous peoples requires governance that defines who makes decisions on behalf of whom and how these data can and should be used. An international a priori PROSPERO (#CRD42020170033) systematic review was undertaken to examine the health research literature to (1) identify, describe, and synthesize definitions and principles; (2) identify and describe data governance frameworks; and (3) identify, describe, and synthesize processes, policies and practices used in Indigenous Data Governance (ID-GOV). Sixty-eight articles were included in the review that found five components that require consideration in the governance of health research data pertaining to Indigenous people. This included (1) Indigenous governance; (2) institutional ethics; (3) socio-political dynamics; (4) data management and data stewardship; and (5) overarching influences. This review provides the first systematic international review of ID-GOV that could potentially be used in a range of governance strategies moving forward in health research.

Columbia Open Health Data for COVID-19 Research: Database Analysis

BACKGROUND

COVID-19 has threatened the health of tens of millions of people all over the world. Massive research efforts have been made in response to the COVID-19 pandemic. Utilization of clinical data can accelerate these research efforts to combat the pandemic since important characteristics of the patients are often found by examining the clinical data. Publicly accessible clinical data on COVID-19, however, remain limited despite the immediate need.

OBJECTIVE

To provide shareable clinical data to catalyze COVID-19 research, we present Columbia Open Health Data for COVID-19 Research (COHD-COVID), a publicly accessible database providing clinical concept prevalence, clinical concept co-occurrence, and clinical symptom prevalence for hospitalized patients with COVID-19. COHD-COVID also provides data on hospitalized patients with influenza and general hospitalized patients as comparator cohorts.

METHODS

The data used in COHD-COVID were obtained from NewYork-Presbyterian/Columbia University Irving Medical Center's electronic health records database. Condition, drug, and procedure concepts were obtained from the visits of identified patients from the cohorts. Rare concepts were excluded, and the true concept counts were perturbed using Poisson randomization to protect patient privacy. Concept prevalence, concept prevalence ratio, concept co-occurrence, and symptom prevalence were calculated using the obtained concepts.

RESULTS

Concept prevalence and concept prevalence ratio analyses showed the clinical characteristics of the COVID-19 cohorts, confirming the well-known characteristics of COVID-19 (eg, acute lower respiratory tract infection and cough). The concepts related to the well-known characteristics of COVID-19 recorded high prevalence and high prevalence ratio in the COVID-19 cohort compared to the hospitalized influenza cohort and general hospitalized cohort. Concept co-occurrence analyses showed potential associations between specific concepts. In case of acute lower respiratory tract infection in the COVID-19 cohort, a high co-occurrence ratio was obtained with COVID-19-related concepts and commonly used drugs (eg, disease due to coronavirus and acetaminophen). Symptom prevalence analysis indicated symptom-level characteristics of the cohorts and confirmed that well-known symptoms of COVID-19 (eg, fever, cough, and dyspnea) showed higher prevalence than the hospitalized influenza cohort and the general hospitalized cohort.

CONCLUSIONS

We present COHD-COVID, a publicly accessible database providing useful clinical data for hospitalized patients with COVID-19, hospitalized patients with influenza, and general hospitalized patients. We expect COHD-COVID to provide researchers and clinicians quantitative measures of COVID-19-related clinical features to better understand and combat the pandemic.

The TRACTOR Project: TRACking and MoniToring Occupational Risks in Agriculture Using French Insurance Health Data (MSA)

OBJECTIVES

A vast data mining project called 'TRACking and moniToring Occupational Risks in agriculture' (TRACTOR) was initiated in 2017 to investigate work-related health events among the entire French agricultural workforce. The goal of this work is to present the TRACTOR project, the challenges faced during its implementation, to discuss its strengths and limitations and to address its potential impact for health surveillance.

METHODS

Three routinely collected administrative health databases from the National Health Insurance Fund for Agricultural Workers and Farmers (MSA) were made available for the TRACTOR project. Data management was required to properly clean and prepare the data before linking together all available databases.

RESULTS

After removing few missing and aberrant data (4.6% values), all available databases were fully linked together. The TRACTOR project is an exhaustive database of agricultural workforce (active and retired) from 2002 to 2016, with around 10.5 million individuals including seasonal workers and farm managers. From 2012 to 2016, a total of 6 906 290 individuals were recorded. Half of these individuals were active and 46% had at least one health event (e.g. declared chronic disease, reimbursed drug prescription) during this 5-year period.

CONCLUSIONS

The assembled MSA databases available in the TRACTOR project are regularly updated and represent a promising and unprecedent dataset for data mining analysis dedicated to the early identification of current and emerging work-related illnesses and hypothesis generation. As a result, this project could help building a prospective integrated health surveillance system for the benefit of agricultural workers.

Monitoring work and training load in military settings - what's in the toolbox?

Military personnel are required to complete physically demanding tasks when performing work and training, which may be quantified through the physical stress imposed (external load) or the resultant physiological strain (internal load). The aim of this narrative review is to provide an overview of the techniques used to monitor work and training load in military settings, summarise key findings, and discuss important practical, analytical, and conceptual considerations. Most investigations have focused upon measuring external and internal load in military training environments; however, limited data exist in operational settings. Accelerometry has been the primary tool used to estimate external load, with heart rate commonly used to quantify internal load. Supplemental to heart rate, psychophysiological and biochemical measures have also been investigated to elucidate aspects of internal load. Broadly, investigations have revealed that military training requires personnel to perform relatively large volumes of physical activity (e.g. averaging ∼15,000 steps·day^-1) of typically low-moderate intensity activity (<6 MET), although considerable temporal and inter-individual variability is observed from these gross mean estimates. There are limitations associated with these measures and, at best, estimates of external and internal load can only be inferred. These limitations are particularly pertinent for military tasks such as load carriage and manual material handling, which often involve complex activities performed individually or in teams, in a range of operational environments, with multiple layers of protection, over a protracted duration. Comprehensively quantifying external and internal loads during these functional activities poses substantial practical and analytical challenges.

Real-world data: come possono aiutare a migliorare la qualità dell’assistenza

The current COVID pandemic crisis made it even clearer that the solutions to several questions that public health must face require the access to good quality data. Several issues of the value and potential of health data and the current critical issues that hinder access are discussed in this paper. In particular, the paper (i) focuses on “real-world data” definition; (ii) proposes a review of the real-world data availability in our country; (iii) discusses its potential, with particular focus on the possibility of improving knowledge on the quality of care provided by the health system; (iv) emphasizes that the availability of data alone is not sufficient to increase our knowledge, underlining the need that innovative analysis methods (e.g., artificial intelligence techniques) must be framed in the paradigm of clinical research; and (v) addresses some ethical issues related to their use. The proposal is to realize an alliance between organizations interested in promoting research aimed at collecting scientifically solid evidence to support the clinical governance of public health.

Connected skiing: Validation of edge angle and radial force estimation as motion quality parameters during alpine skiing

Recent studies have developed wearable sensor systems to detect, classify and evaluate performance during alpine skiing. In order to enrich skiing data to provide motion quality feedback, edge angle (EA) and radial force (F_r) are parameters of interest. However, the estimation of these parameters via calibration-free wearable technologies has not been validated. The purpose of this study was to develop and validate a wearable method to estimate EA and F_r. Participants completed simulated skiing trials on an indoor skiing carpet. Two IMU's mounted to the ski boots estimated EA and F_r and compared to reference values measured with a 3D motion capture system. The performance of the wearable system was quantified by accuracy and precision. The overall accuracy and precision of the wearable system was 97.6 ± 12.4% and 15.5 ± 17.6% for EA, and 105.5 ± 5.7% and 29.8 ± 10.0%, respectively for F_r. The developed wearable system was accurate for the estimation of EA and F_r, but was highly variable with low precision for both metrics. Further research is needed to improve the precision of field-relevant skiing metrics during in-field studies using simple measurement setups that can easily be implemented by recreational and expert skiers alike.Highlights IMU's mounted on the boots are sufficient tools for accurate estimation of edge angle and radial force during both long and short style turns on a skiing simulator.As the estimation of edge angle and radial force are dependent on other estimated parameters (i.e. turn switch), the precision of these metrics is relatively low.The results of the current study apply only to simulated alpine skiing on a treadmill, and further work is required to prove the accuracy and precision of this system on snow.

Digital Mega-Studies as a New Research Paradigm: Governing the Health Research of the Future

'Digital Mega-Studies' are entirely or extensively digitised, longitudinal, population-scale initiatives, collecting, storing, and making available individual-level research data of different types and from multiple sources, shaped by technological developments and unforeseeable risks over time. The Australian 'Gen V' project exemplifies this new research paradigm. In 2019, we undertook a multidisciplinary, multi-stakeholder process to map Digital Mega-Studies' key characteristics, legal and governance challenges and likely solutions. We conducted large and small group processes within a one-day symposium and directed online synthesis and group prioritisation over subsequent weeks. We present our methods (including elicitation, affinity mapping and prioritisation processes) and findings, proposing six priority governance principles across three areas-data, participation, trust-to support future high-quality, large-scale digital research in health.