Domesticating data: Traveling and value-making in the data economy

Data are versatile objects that can travel across contexts. While data's travels have been widely discussed, little attention has been paid to the sites from where and to which data flow. Drawing upon ethnographic fieldwork in two connected data-intensive laboratories and the concept of domestication, we explore what it takes to bring data 'home' into the laboratory. As data come and dwell in the home, they are made to follow rituals, and as a result, data are reshaped and form ties with the laboratory and its practitioners. We identify four main ways of domesticating data. First, through storytelling about the data's origins, data practitioners draw the boundaries of their laboratory. Second, through standardization, staff transform samples into digital data that can travel well while ruling what data can be let into the home. Third, through formatting, data practitioners become familiar with their data and at the same time imprint the data, thus making them belong to their home. Finally, through cultivation, staff turn data into a resource for knowledge production. Through the lens of domestication, we see the data economy as a collection of homes connected by flows, and it is because data are tamed and attached to homes that they become valuable knowledge tools. Such domestication practices also have broad implications for staff, who in the process of 'homing' data, come to belong to the laboratory. To conclude, we reflect on what these domestication processes-which silence unusual behaviours in the data-mean for the knowledge produced in data-intensive research.

Enhancing multi-site autism research through the development of a collaborative data platform

Data repositories, particularly those storing data on vulnerable populations, increasingly need to carefully consider not only what data is being collected, but how it will be used. As such, the Autism Intervention Research Network on Physical Health (AIR-P) has created the Infrastructure for Collaborative Research (ICR) to establish standards on data collection practices in Autism repositories. The ICR will strive to encourage inter-site collaboration, amplify autistic voices, and widen accessibility to data. The ICR is staged as a three-tiered framework consisting of (1) a request for proposals system, (2) a REDCap-based data repository, and (3) public data dashboards to display aggregate de-identified data. Coupled with a review process including autistic and non-autistic researchers, this framework aims to propel the implementation of equitable autism research, enhance standardization within and between studies, and boost transparency and dissemination of findings. In addition, the inclusion of a contact registry that study participants can opt into creates the base for a robust participant pool. As such, researchers can leverage the platform to identify, reach, and distribute electronic materials to a greater proportion of potential participants who likely fall within their eligibility criteria. By incorporating practices that promote effective communication between researchers and participants, the ICR can facilitate research that is both considerate of and a benefit to autistic people.

The Cooperation Between Nurses and a New Digital Colleague "AI-Driven Lifestyle Monitoring" in Long-Term Care for Older Adults: Viewpoint

Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial intelligence (AI), have particularly strong potential to support nurses in their work. However, their use also introduces ambiguities. An example of such a technology is AI-driven lifestyle monitoring in long-term care for older adults, based on data collected from ambient sensors in an older adult's home. Designing and implementing this technology in such an intimate setting requires collaboration with nurses experienced in long-term and older adult care. This viewpoint paper emphasizes the need to incorporate nurses and the nursing perspective into every stage of designing, using, and implementing AI-driven lifestyle monitoring in long-term care settings. It is argued that the technology will not replace nurses, but rather act as a new digital colleague, complementing the humane qualities of nurses and seamlessly integrating into nursing workflows. Several advantages of such a collaboration between nurses and technology are highlighted, as are potential risks such as decreased patient empowerment, depersonalization, lack of transparency, and loss of human contact. Finally, practical suggestions are offered to move forward with integrating the digital colleague.

Data analytics in the football industry: a survey investigating operational frameworks and practices in professional clubs and national federations from around the world

The use of data and analytics in professional football organisations has grown steadily over the last decade. Nevertheless, how and whether these advances in sports analytics address the needs of professional football remain unexplored. Practitioners from national federations qualified for the FIFA World Cup Qatar 2022™ and professional football clubs from an international community of practitioners took part in a survey exploring the characteristics of their data analytics infrastructure, their role, and their value for elaborating player monitoring and positional data. Respondents from 29 national federations and 32 professional clubs completed the survey, with response rates of 90.6% and 77.1%, respectively. Summary information highlighted the underemployment of staff with expertise in applied data analytics across organisations. Perceptions regarding analytical capabilities and data governance framework were heterogenous, particularly in the case of national federations. Only a third of national federation respondents (~30%) perceived information on positional data from international sports data analytics providers to be sufficiently clear. The general resourcing limitations, the overall lack of expertise in data analytics methods, and the absence of operational taxonomies for reference performance metrics pose constraints to meaningful knowledge translations from raw data in professional football organisations.

A century of statistical Ecology

As data and computing power have surged in recent decades, statistical modeling has become an important tool for understanding ecological patterns and processes. Statistical modeling in ecology faces two major challenges. First, ecological data may not conform to traditional methods, and second, professional ecologists often do not receive extensive statistical training. In response to these challenges, the journal Ecology has published many innovative statistical ecology papers that introduced novel modeling methods and provided accessible guides to statistical best practices. In this paper, we reflect on Ecology's history and its role in the emergence of the subdiscipline of statistical ecology, which we define as the study of ecological systems using mathematical equations, probability, and empirical data. We showcase 36 influential statistical ecology papers that have been published in Ecology over the last century and, in so doing, comment on the evolution of the field. As data and computing power continue to increase, we anticipate continued growth in statistical ecology to tackle complex analyses and an expanding role for Ecology to publish innovative and influential papers, advancing the discipline and guiding practicing ecologists.

Association of the Type of Public Pension With Mental Health Among South Korean Older Adults: Longitudinal Observational Study

BACKGROUND

As income and health are closely related, retirement is considered undesirable for health. Many studies have shown the association between pension and health, but no research has considered the association between contribution-based public pensions or their types and health.

OBJECTIVE

This study investigates the association between the type of contributory public pension and depressive symptoms among older adults.

METHODS

We analyzed the data of 4541 older adults who participated in the South Korea Welfare Panel Study (2014-2020). Depressive symptoms were measured using the 11-item Center for Epidemiologic Studies Depression scale. Public pensions in South Korea are classified into specific corporate pensions and national pensions. For subgroup analyses, pensioners were categorized according to the amount of pension received and the proportion of public pension over gross income. Analyses using generalized estimating equations were conducted for longitudinal data.

RESULTS

Individuals receiving public pension, regardless of the pension type, demonstrated significantly decreased depressive symptoms (national pension: β=-.734; P<.001; specific corporate pension: β=-.775; P=.02). For both pension types, the higher the amount of benefits, the lower were the depression scores. However, this association was absent for those who received the smaller amount among the specific corporate pensioners. In low-income households, the decrease in the depressive symptoms based on the amount of public pension benefits was greater (fourth quartile of national pension: β=-1.472; P<.001; second and third quartiles of specific corporate pension: β=-3.646; P<.001).

CONCLUSIONS

Our study shows that contributory public pension is significantly associated with lower depressive symptoms, and this association is prominent in low-income households. Thus, contributory public pensions may be good income sources for improving the mental health of older adults after retirement.

On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data

Using information-theoretic quantities in practical applications with continuous data is often hindered by the fact that probability density functions need to be estimated in higher dimensions, which can become unreliable or even computationally unfeasible. To make these useful quantities more accessible, alternative approaches such as binned frequencies using histograms and k-nearest neighbors (k-NN) have been proposed. However, a systematic comparison of the applicability of these methods has been lacking. We wish to fill this gap by comparing kernel-density-based estimation (KDE) with these two alternatives in carefully designed synthetic test cases. Specifically, we wish to estimate the information-theoretic quantities: entropy, Kullback-Leibler divergence, and mutual information, from sample data. As a reference, the results are compared to closed-form solutions or numerical integrals. We generate samples from distributions of various shapes in dimensions ranging from one to ten. We evaluate the estimators' performance as a function of sample size, distribution characteristics, and chosen hyperparameters. We further compare the required computation time and specific implementation challenges. Notably, k-NN estimation tends to outperform other methods, considering algorithmic implementation, computational efficiency, and estimation accuracy, especially with sufficient data. This study provides valuable insights into the strengths and limitations of the different estimation methods for information-theoretic quantities. It also highlights the significance of considering the characteristics of the data, as well as the targeted information-theoretic quantity when selecting an appropriate estimation technique. These findings will assist scientists and practitioners in choosing the most suitable method, considering their specific application and available data. We have collected the compared estimation methods in a ready-to-use open-source Python 3 toolbox and, thereby, hope to promote the use of information-theoretic quantities by researchers and practitioners to evaluate the information in data and models in various disciplines.

Machine learning-based clinical decision support using laboratory data

Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments.

Preferences on Governance Models for Mental Health Data: Qualitative Study With Young People

BACKGROUND

Improving access to mental health data to accelerate research and improve mental health outcomes is a potentially achievable goal given the substantial data that can now be collected from mobile devices. Smartphones can provide a useful mechanism for collecting mental health data from young people, especially as their use is relatively ubiquitous in high-resource settings such as the United Kingdom and they have a high capacity to collect active and passive data. This raises the interesting opportunity to establish a large bank of mental health data from young people that could be accessed by researchers worldwide, but it is important to clarify how to ensure that this is done in an appropriate manner aligned with the values of young people.

OBJECTIVE

In this study, we discussed the preferences of young people in the United Kingdom regarding the governance, sharing, and use of their mental health data with the establishment of a global data bank in mind. We aimed to determine whether young people want and feel safe to share their mental health data; if so, with whom; and their preferences in doing so.

METHODS

Young people (N=46) were provided with 2 modules of educational material about data governance models and background in scientific research. We then conducted 2-hour web-based group sessions using a deliberative democracy methodology to reach a consensus where possible. Findings were analyzed using the framework method.

RESULTS

Young people were generally enthusiastic about contributing data to mental health research. They believed that broader availability of mental health data could be used to discover what improves or worsens mental health and develop new services to support young people. However, this enthusiasm came with many concerns and caveats, including distributed control of access to ensure appropriate use, distributed power, and data management that included diverse representation and sufficient ethical training for applicants and data managers.

CONCLUSIONS

Although it is feasible to use smartphones to collect mental health data from young people in the United Kingdom, it is essential to carefully consider the parameters of such a data bank. Addressing and embedding young people's preferences, including the need for robust procedures regarding how their data are managed, stored, and accessed, will set a solid foundation for establishing any global data bank.

Supervised machine learning for the prediction of post-operative clinical outcomes of hip and knee replacements: a review

Prediction models are being increasingly used in the medical field to identify risk factors and possible outcomes. Some of these are presently being used to develop guidelines for improving clinical practice. The application of machine learning (ML), comprising a powerful set of computational tools for analysing data, has been clearly expanding in the role of predictive modelling. This paper reviews the latest developments of supervised ML techniques that have been used to analyse data related to post-operative total hip and knee replacements. The aim was to review the most recent findings of relevant published studies by outlining the methodologies employed (most-widely used supervised ML techniques), data sources, domains, limitations of predictive analytics and the quality of predictions.

Translating the COVID-19 epidemiological situation into policies and measures: the Belgian experience

The COVID-19 pandemic led to sustained surveillance efforts, which made unprecedented volumes and types of data available. In Belgium, these data were used to conduct a targeted and regular assessment of the epidemiological situation. In addition, management tools were developed, incorporating key indicators and thresholds, to define risk levels and offer guidance to policy makers. Categorizing risk into various levels provided a stable framework to monitor the COVID-19 epidemiological situation and allowed for clear communication to authorities. Although translating risk levels into specific public health measures has remained challenging, this experience was foundational for future evaluation of the situation for respiratory infections in general, which, in Belgium, is now based on a management tool combining different data sources.

Discussion paper: implications for the further development of the successfully in emergency medicine implemented AUD2IT-algorithm

The AUD2IT-algorithm is a tool to structure the data, which is collected during an emergency treatment. The goal is on the one hand to structure the documentation of the data and on the other hand to give a standardised data structure for the report during handover of an emergency patient. AUD2IT-algorithm was developed to provide residents a documentation aid, which helps to structure the medical reports without getting lost in unimportant details or forgetting important information. The sequence of anamnesis, clinical examination, considering a differential diagnosis, technical diagnostics, interpretation and therapy is rather an academic classification than a description of the real workflow. In a real setting, most of these steps take place simultaneously. Therefore, the application of the AUD2IT-algorithm should also be carried out according to the real processes. A big advantage of the AUD2IT-algorithm is that it can be used as a structure for the entire treatment process and also is entirely usable as a handover protocol within this process to make sure, that the existing state of knowledge is ensured at each point of a team-timeout. PR-E-(AUD2IT)-algorithm makes it possible to document a treatment process that, in principle, does not have to be limited to the field of emergency medicine. Also, in the outpatient treatment the PR-E-(AUD2IT)-algorithm could be used and further developed. One example could be the preparation and allocation of needed resources at the general practitioner. The algorithm is a standardised tool that can be used by healthcare professionals of any level of training. It gives the user a sense of security in their daily work.

Pima County COVID-19 vaccine solutions dashboard project: lessons learned

Recent improvements in the accessibility of mapping tools and an increased recognition of the importance of leveraging data to inform public health operations has led to enthusiasm among public health departments to rapidly evolve their ability to analyze and apply data to programs. As the COVID-19 pandemic made evident, many health department data systems have been neglected for decades and data literacy among staff low. Significant federal dollars have been allocated to local health departments to modernize health systems. This case study recounts the effort to equip the Pima County Health Department with a highly sophisticated "COVID-19 Vaccines Solutions Dashboard" in 2021-2022, quantifying community vulnerability in the midst of the COVID-19 pandemic and shares key successes and challenges in process and outcomes that can guide other such dashboard initiatives. The experience informed the development of Pima' County Health Department's Data & Informatics Team as well as efforts to cultivate a more robust data culture throughout the department. Many health departments around the United States are in a similar position, and these lessons learned are widely applicable.

Ophthalmology Optical Coherence Tomography Databases for Artificial Intelligence Algorithm: A Review

BACKGROUND

Imaging plays a pivotal role in eye assessment. With the introduction of advanced machine learning and artificial intelligence (AI), the focus has shifted to imaging datasets in ophthalmology. While disparities and health inequalities hidden within data are well-documented, the ophthalmology field faces specific challenges to the creation and maintenance of datasets. Optical Coherence Tomography (OCT) is useful for the diagnosis and monitoring of retinal pathologies, making it valuable for AI applications. This review aims to identify and compare the landscape of publicly available optical coherence tomography databases for AI applications.

METHODS

We conducted a literature review on OCT and AI articles with publicly accessible datasets, using PubMed, Scopus, and Web of Science databases. The review retrieved 183 articles, and after full-text analysis, 50 articles were included. From the included articles were identified 8 publicly available OCT datasets, focusing on patient demographics and clinical details for thorough assessment and comparison.

RESULTS

The resulting datasets encompass 154,313 images collected from Spectralis, Cirrus HD, Topcon 3D, and Bioptigen devices. These datasets included normal exams, age-related macular degeneration, and diabetic maculopathy, among others. Comprehensive demographic information is available in one dataset and the USA is the most represented population.

DISCUSSION

Current publicly available OCT databases for AI applications exhibit limitations, stemming from their non-representative nature and the lack of comprehensive demographic information. Limited datasets hamper research and equitable AI development. To promote equitable AI algorithmic development in ophthalmology, there is a need for the creation and dissemination of more representative datasets.

Outcomes from a proof-of-concept specialist lymphoedema clinic in the community

The Health and Safety Execultive lymphoedema model of care was published in 2018 highlighting the lack of dedicated lymphoedema services in Ireland. This led to the introduction of a proof-of-concept primary care specialist lymphoedema clinic. The clinic was responsible for all patients from their county. A comprehensive dataset was gathered which included the patient's history for 1 year prior to their presentation at clinic and then 6 monthly. A quality of life tool (LymQoL) and a patient satisfaction survey were completed. Completed 1-year data showed a significant reduction in GP and public health nurse visits as well as a reduction in the occurrence of cellulitis and associated hospital admissions. All areas of quality of life were improved and patient satisfaction was either excellent (89%) or very good (11%). The 1-year findings strongly support the roll-out of specialist clinics to all regional health areas.

Unclearing the air: Data's unexpected limitations for environmental advocacy

What makes one dataset powerful for civic advocacy, and another fall flat? Drawing from a citizen science project on environmental health, I argue that there is an underacknowledged quality of datasets-their topology-that shapes the social, cultural, and political possibilities they can sustain or subvert. Data topologies are formal qualities of a dataset that connect data collectors' intentions with the types of calculations that can and cannot be performed. This configures how numerical arguments are made, and the sociotechnical imaginaries those arguments sustain or subvert. The citizen science project's data topology made any easy notion of shared exposure to pollutants, or singular health effects, unravel. The data appeared to tell a story of atypicality at scale, where each person suffers differently from different exposure. Lacking a central tendency, or pockets of tendency disproportionately carried by different subgroups, it became it harder, not easier, for citizen scientists to use data in regulatory contexts, where dominant sociotechnical imaginaries conceive of difference in epidemiological and toxicological terms.

Workshop summary: building an NHANES for the future

The American Society for Nutrition's (ASN) Committee on Advocacy and Science Policy (CASP) organized a workshop, "Building a National Health and Nutrition Examination Survey (NHANES) for the Future," held during NUTRITION 2023, which took place in Boston, MA in July 2023. CASP had already identified an urgent need for increased support and modernization to ensure that a secure future for NHANES is achievable. The survey faces challenges associated with data collection, stagnant funding, and a need for more granular data for subpopulations and groups at risk. The workshop provided an overview of NHANES, including the nutrition component, and the many other uses for the survey's data, which extend beyond nutrition. Speakers highlighted NHANES's current and emerging challenges, as well as possible solutions to address these challenges, especially with regard to response rates of underrepresented groups, linkage of survey data to other resources, incorporation of new survey methodologies, and emerging data needs. The workshop also included a "Town Hall" component to gather additional feedback on NHANES' challenges and proposed solutions from audience members. The workshop provided many possible action items that ASN will explore and use to inform effective continued advocacy in support of NHANES and to find possible opportunities for ASN and others to partner with the Centers for Disease Control and Prevention National Center for Health Statistics to strengthen this vital survey and maintain its robust and relevant data moving forward.

Stakeholder engagement does not guarantee impact: A co-productionist perspective on model-based drought research

Stakeholder engagement has become a watchword for environmental scientists to assert the societal relevance of their projects to funding agencies. In water research based on computer simulation modelling, stakeholder engagement has attracted interest as a means to overcome low uptake of new tools for water management. An increasingly accepted view is that more and better stakeholder involvement in research projects will lead to increased adoption of the modelling tools created by scientists in water management. However, we cast doubt on this view by drawing attention to how the freedom of stakeholder organizations to adopt new scientific modelling tools in their regular practices is circumscribed by the societal context. We use a modified concept of co-production in an analysis of a case of scientific research on drought in the UK to show how relationships between actors in the drought governance space influence the uptake of scientific modelling tools. The analysis suggests an explanation of why stakeholder engagement with one scientific project led to one output (data) getting adopted by stakeholders while another output (modelling tools) attracted no discernible interest. Our main objective is to improve the understanding of the limitations to stakeholder engagement as a means of increasing societal uptake of scientific research outputs.

Limitations of Sexual Orientation and Gender Identity Information as Reported in the National Violent Death Reporting System

The National Violent Death Reporting System (NVDRS) is a Centers for Disease Control and Prevention (CDC) restricted-access database detailing precipitating circumstances to U.S. violent deaths. In 2013 and 2015, the CDC added codes denoting sexual orientation and gender identity (SOGI) and sex of partner. In the past decade, researchers have leveraged NVDRS data to document SOGI-related patterns and characteristics of violent death including suicide. Yet, there are substantial limitations to NVDRS SOGI information that should be considered in responsible reporting by researchers and informed assessment by reviewers. In this perspective, we summarize some of these challenges and offer recommendations for using NVDRS SOGI data responsibly.

'I know you like the back of my hand': biometric practices of humanitarian organisations in international aid

Humanitarian organisations are increasingly utilising biometric data. However, we know little about the extent and scope of this practice, as its benefits and risks have attracted all the attention so far. This paper explores the biometric practices of the United Nations Refugee Agency, the United Nations World Food Programme, the International Committee of the Red Cross, Médecins Sans Frontières, and World Vision International. The study analysed relevant documents published over the past two decades and 17 semi-structured interviews with humanitarian workers conducted between June 2021 and June 2022. The findings reveal that humanitarian organisations use diverse types and functions of biometric data for different services, collaborate with many actors, and employ various data protection measures. Ultimately, challenging the straightforward generalisations about the use of such data, the paper argues that variational applications of biometrics in the humanitarian context require case-by-case analysis, as each instance will likely produce a different outcome.

APS calculator: a data-driven tool for setting outcome-based analytical performance specifications for measurement uncertainty using specific clinical requirements and population data

OBJECTIVES

According to ISO 15189:2022, analytical performance specifications (APS) should relate to intended clinical use and impact on patient care. Therefore, we aimed to develop a web application for laboratory professionals to calculate APS based on a simulation of the impact of measurement uncertainty (MU) on the outcome using the chosen decision limits, agreement thresholds, and data of the population of interest.

METHODS

We developed the "APS Calculator" allowing users to upload and select data of concern, specify decision limits and agreement thresholds, and conduct simulations to determine APS for MU. The simulation involved categorizing original measurand concentrations, generating measured (simulated) results by introducing different degrees of MU, and recategorizing measured concentrations based on clinical decision limits and acceptable clinical misclassification rates. The agreements between original and simulated result categories were assessed, and values that met or exceeded user-specified agreement thresholds that set goals for the between-category agreement were considered acceptable. The application generates contour plots of agreement rates and corresponding MU values. We tested the application using National Health and Nutrition Examination Survey data, with decision limits from relevant guidelines.

RESULTS

We determined APS for MU of six measurands (blood total hemoglobin, plasma fasting glucose, serum total and high-density lipoprotein cholesterol, triglycerides, and total folate) to demonstrate the potential of the application to generate APS.

CONCLUSIONS

The developed data-driven web application offers a flexible tool for laboratory professionals to calculate APS for MU using their chosen decision limits and agreement thresholds, and the data of the population of interest.

Chest Wall Motion Model of Cardiac Activity for Radar-Based Vital-Sign-Detection System

An increasing number of studies on non-contact vital sign detection using radar are now beginning to turn to data-driven neural network approaches rather than traditional signal-processing methods. However, there are few radar datasets available for deep learning due to the difficulty of acquiring and labeling the data, which require specialized equipment and physician collaboration. This paper presents a new model of heartbeat-induced chest wall motion (CWM) with the goal of generating a large amount of simulation data to support deep learning methods. An in-depth analysis of published CWM data collected by the VICON Infrared (IR) motion capture system and continuous wave (CW) radar system during respiratory hold was used to summarize the motion characteristics of each stage within a cardiac cycle. In combination with the physiological properties of the heartbeat, appropriate mathematical functions were selected to describe these movement properties. The model produced simulation data that closely matched the measured data as evaluated by dynamic time warping (DTW) and the root-mean-squared error (RMSE). By adjusting the model parameters, the heartbeat signals of different individuals were simulated. This will accelerate the application of data-driven deep learning methods in radar-based non-contact vital sign detection research and further advance the field.

Evaluation of the Gonadotoxicity of Cancer Therapies to Improve Counseling of Patients About Fertility and Fertility Preservation Measures: Protocol for a Retrospective Systematic Data Analysis and a Prospective Cohort Study

BACKGROUND

Cytotoxic treatments such as chemo- and radiotherapy and immune therapies are required in cancer diseases. These therapies have the potential to cure patients but may also have an impact on gonadal function and, therefore, on fertility. Consequently, fertility preservation treatments such as freezing of gametes and gonadal tissue might be required. However, as detailed data about the necessity to perform fertility preservation treatment are very limited, this study was designed to fill this data gap.

OBJECTIVE

Primary objective of this study is to analyze the impact of cancer therapies and chemotherapies on the ovarian reserve and sperm quality. Secondary objectives are to analyze the (1) impact of cancer therapies and chemotherapies on other fertility parameters and (2) probability of undergoing fertility preservation treatments in relation to specific cancer diseases and treatment protocols and the probability to use the frozen gametes and gonadal tissue to achieve pregnancies.

METHODS

First, previously published studies on the gonadotoxicity of chemo- and radiotherapies among patients with cancer will be systematically analyzed. Second, a prospective cohort study set up by approximately 70 centers in Germany, Switzerland, and Austria will collect the following data: ovarian function by analyzing anti-Müllerian hormone (AMH) concentrations and testicular function by analyzing sperm parameters and total testosterone immediately before and around 1 year after gonadotoxic therapies (short-term fertility). A follow-up of these fertility parameters, including history of conceptions, will be performed 5 and 10 years after gonadotoxic therapies (long-term fertility). Additionally, the proportion of patients undergoing fertility-preserving procedures, their satisfaction with these procedures, and the amount of gametes and gonadal tissue and the children achieved by using the frozen material will be analyzed. Third, the data will be merged to create the internet-based data platform FertiTOX. The platform will be structured in accordance with the ICD (International Classification of Diseases) classification of cancer diseases and will be easily be accessible using a specific App.

RESULTS

Several funding bodies have funded this study. Ten systematic reviews are in progress and the first one has been accepted for publication. All Swiss and many German and Austrian ethics committees have provided their approval for the prospective cohort study. The study registry has been set up, and a study website has been created. In total, 50 infertility centers have already been prepared for data collection, which started on December 1, 2023.

CONCLUSIONS

The study can be expected to bridge the data gap regarding the gonadotoxicity of cancer therapies to better counsel patients about their infertility risk and their need to undergo fertility preservation procedures. Initial data are expected to be uploaded on the FertiTOX platform in 2026.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05885048; https://clinicaltrials.gov/study/NCT05885048.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/51145.

Forcing a square into a circle: why South Africa's draft revised material transfer agreement is not fit for purpose

The South African National Health Research Ethics Council (NHREC) recently released a final draft revision of the standard material transfer agreement (MTA) that was promulgated into law in 2018. This new draft MTA raises pertinent questions about the NHREC's mandate, the way in which the draft MTA deals with data and with human biological material, and its avoidance of the concept of ownership. After South Africa's data protection legislation, the Protection of Personal Information Act (POPIA), became operational in mid 2021, the legal landscape changed and it is doubtful that the NHREC has a residual mandate to govern personal information in health research. Furthermore, data is dealt with in a superficial, throw-away fashion in the draft MTA. The position with human biological material is not substantially better, as the draft MTA fails to recognise that human biological material can contain pathogens, which has important legal and ethical ramifications that are not sufficiently addressed. A central problem with the draft MTA is its use of the term 'steward', and avoidance of the legal concept of 'ownership'. This is not only misaligned with the South African legal framework, but also fails to consider the ethical case for recognising ownership. Finally, a call to embrace decolonial thinking in health research underscores the importance of recognising ownership in order to foster the growth of the local bio-economy. Key recommendations to reshape the draft MTA include: Making use of the eventual revised MTA optional, and allowing it to evolve with input from scientific and legal communities; regulating the transfer of associated data in a separate data transfer agreement that can be incorporated by reference in the MTA; enhancing guidance on liability and risk management in respect of human biological material that contains pathogens; and, finally, adopting a decolonial approach in health research governance, which requires recognising the ownership rights of South African research institutions.

Data Visualization Support for Tumor Boards and Clinical Oncology: Protocol for a Scoping Review

BACKGROUND

Complex and expanding data sets in clinical oncology applications require flexible and interactive visualization of patient data to provide the maximum amount of information to physicians and other medical practitioners. Interdisciplinary tumor conferences in particular profit from customized tools to integrate, link, and visualize relevant data from all professions involved.

OBJECTIVE

The scoping review proposed in this protocol aims to identify and present currently available data visualization tools for tumor boards and related areas. The objective of the review will be to provide not only an overview of digital tools currently used in tumor board settings, but also the data included, the respective visualization solutions, and their integration into hospital processes.

METHODS

The planned scoping review process is based on the Arksey and O'Malley scoping study framework. The following electronic databases will be searched for articles published in English: PubMed, Web of Knowledge, and SCOPUS. Eligible articles will first undergo a deduplication step, followed by the screening of titles and abstracts. Second, a full-text screening will be used to reach the final decision about article selection. At least 2 reviewers will independently screen titles, abstracts, and full-text reports. Conflicting inclusion decisions will be resolved by a third reviewer. The remaining literature will be analyzed using a data extraction template proposed in this protocol. The template includes a variety of meta information as well as specific questions aiming to answer the research question: "What are the key features of data visualization solutions used in molecular and organ tumor boards, and how are these elements integrated and used within the clinical setting?" The findings will be compiled, charted, and presented as specified in the scoping study framework. Data for included tools may be supplemented with additional manual literature searches. The entire review process will be documented in alignment with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart.

RESULTS

The results of this scoping review will be reported per the expanded PRISMA-ScR guidelines. A preliminary search using PubMed, Web of Knowledge, and Scopus resulted in 1320 articles after deduplication that will be included in the further review process. We expect the results to be published during the second quarter of 2024.

CONCLUSIONS

Visualization is a key process in leveraging a data set's potentially available information and enabling its use in an interdisciplinary setting. The scoping review described in this protocol aims to present the status quo of visualization solutions for tumor board and clinical oncology applications and their integration into hospital processes.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/53627.

Data Submission: HOW the Power of One Creates the Power of Many

This article is the third and final article in a series exploring the WHAT, WHY, and HOW of data collection and data utilization. The final step, the HOW of data submission, provides discussion and guidance in contributing your data to the collective voice, including submitting data from the school, district, state, and national levels. Submitting individual school nursing data enriches the bigger story and increases the awareness and meaningfulness of school health data, the role of the school nurse as an integral member of the school community, and the connections of student health and academic outcomes. This article will also explore how to submit your school health data and the opportunities to submit it to district, state, or national levels, including to the National School Health Data Set: Every Student Counts! (ESC!).

Local Validation of a National Orthopaedic Registry

BACKGROUND/OBJECTIVE

Registries are limited by the quality of the data they collect. We aimed to measure the data entry error rate at a regional orthopaedic unit in a national arthroplasty registry and to assess a proposed intervention of restricting data entry to senior trainees.

METHODS AND MATERIALS

A total of 200 primary and revision arthroplasty cases (119 hips, 81 knees) were randomly selected from a single year, 2020. The Irish National Orthopaedic Registry was examined for the grade of the trainee that populated the form and the accuracy of 24 parameters by comparison with data recorded elsewhere in the patient record.

RESULTS

The mean number of errors per form was 2.17 (95% confidence interval (CI): 1.95-2.39), giving an overall error rate of 9% (95% CI: 8%-10.0%). Eighty-seven percent of forms examined contained inaccuracies, ranging from one to nine errors (4%-38%). Some parameters were more prone to errors, ranging from 1% to 28%. There was no evidence of total errors varying by trainee grade (analysis of variance (ANOVA) p-value: 0.34).

CONCLUSIONS

Error rates were in line with the literature. Results did not support restricting data entry to senior trainees.

A pathway to strengthening open science: comments on the draft South African Ethics in Health Research Guidelines

The recently released draft South African Ethics in Health Research Guidelines: Principles, Processes and Structures (Draft Guidelines) by the National Health Research Ethics Council recognize open data and provide guiding principles for this in the context of health research in South Africa. While its inclusion is a positive development, there is room for improvement. Although the Draft Guidelines leverage the Draft National Policy on Data and Cloud, it lacks incorporation of other relevant government policies, notably the Draft National Open Science Policy, and fails to sufficiently detail the principles of open science and open access. This limited scope and lack of comprehensive definition and detailed guidance present challenges for researchers in conducting ethical and responsible health research in South Africa. It constrains the Draft Guidelines from fully aligning with national imperatives and from fostering African-centric approaches. To address these issues, it is recommended that the Draft Guidelines integrate broader policies and principles, enhance clarity through comprehensive definitions, provide detailed guidance on open access, and promote African-centric approaches. Implementing these solutions will strengthen the Draft Guidelines, aligning them with national visions of open science, and thereby harnessing the full potential of South Africa's diverse scientific community in advancing health research.

Using Longitudinal Surveillance of Unemployment Claims During Public Health Emergencies to Provide Timely and Granular Data on the Social Determinants of Health

OBJECTIVE

Employment is a well-documented social determinant of physical and mental health and can be used to determine who is disproportionately affected by public health emergencies. We examined trends in unemployment overall and by gender, by race or ethnic group, and by their interaction for 2 public health emergencies (the COVID-19 pandemic and the 2020 California wildfires).

METHODS

We obtained summary data files on the number of initial unemployment insurance (IUI) claims made in all 58 California counties from January 2018 through December 2021. We fit fixed-effects Poisson regression models to county data on weekly IUI claims cross-classified by gender and race or ethnic group. We used models to evaluate the overall effect of COVID-19, whether this effect changed over time increasing under compounding emergencies, and whether the overall and compounding effects of COVID-19 differed by gender and race or ethnic group.

RESULTS

During the COVID-19 pandemic, weekly IUI claims rates increased to as much as 10 times their prepandemic level. The increase in IUI claims for COVID-19 weeks, compared with weeks from the same month in the 2 years prior, was greater for women than for men of all race or ethnic groups, except for Black women. The higher rates of IUI claims for most women during COVID-19 entailed a reversal of prepandemic gender differences in claims that persisted through 2021.

CONCLUSION

Public health officials should consider using IUI claims for surveillance of social determinants of health, particularly in the context of emergencies, which we show can have a persisting effect on the social patterning of social determinants. Future research is needed to forecast these affects and inform public health and policy mitigation and prevention strategies.

Validation and verification framework and data integration of biosensors and in vitro diagnostic devices: a position statement of the IFCC Committee on Mobile Health and Bioengineering in Laboratory Medicine (C-MBHLM) and the IFCC Scientific Division

Advances in technology have transformed healthcare and laboratory medicine. Biosensors have emerged as a promising technology in healthcare, providing a way to monitor human physiological parameters in a continuous, real-time, and non-intrusive manner and offering value and benefits in a wide range of applications. This position statement aims to present the current situation around biosensors, their perspectives and importantly the need to set the framework for their validation and safe use. The development of a qualification framework for biosensors should be conceptually adopted and extended to cover digitally measured biomarkers from biosensors for advancing healthcare and achieving more individualized patient management and better patient outcome.

A summary of the U.S. Marine Recruit Assessment Program (RAP) procedures and survey from 2003 to 2021

The Recruit Assessment Program (RAP) is a cross-sectional, baseline survey of U.S. Marine recruits administered at Marine Corps Recruit Depot, San Diego. This report presents RAP study procedures and survey content that was administered to 229,015 participants between 2003 and 2021. Self-reported data were collected on recruit demographics, physical and mental health, adverse life experiences, lifestyle and risky behaviors, and substance use. In 2013, the survey was updated to remove questions with other linkable and reliable sources and those with low completion rates and low relevance to Marine health research; the removal of these items allowed for the addition of instrument measures for major depression, post-traumatic stress disorder, anger, and resilience with no significant change to overall survey length. Average completion rates are approximately 95%. Multiple studies have shown the utility of RAP data collected thus far as a robust data repository of pre-service health and behavioral measures.

A Digitally Enabled Combined Lifestyle Intervention for Weight Loss: Pilot Study in a Dutch General Population Cohort

BACKGROUND

Overweight and obesity rates among the general population of the Netherlands keep increasing. Combined lifestyle interventions (CLIs) focused on physical activity, nutrition, sleep, and stress management can be effective in reducing weight and improving health behaviors. Currently available CLIs for weight loss (CLI-WLs) in the Netherlands consist of face-to-face and community-based sessions, which face scalability challenges. A digitally enabled CLI-WL with digital and human components may provide a solution for this challenge; however, the feasibility of such an intervention has not yet been assessed in the Netherlands.

OBJECTIVE

The aim of this study was two-fold: (1) to determine how weight and other secondary cardiometabolic outcomes (lipids and blood pressure) change over time in a Dutch population with overweight or obesity and cardiometabolic risk participating in a pilot digitally enabled CLI-WL and (2) to collect feedback from participants to guide the further development of future iterations of the intervention.

METHODS

Participants followed a 16-week digitally enabled lifestyle coaching program rooted in the Fogg Behavior Model, focused on nutrition, physical activity, and other health behaviors, from January 2020 to December 2021. Participants could access the digital app to register and track health behaviors, weight, and anthropometrics data at any time. We retrospectively analyzed changes in weight, blood pressure, and lipids for remeasured users. Surveys and semistructured interviews were conducted to assess critical positive and improvement points reported by participants and health care professionals.

RESULTS

Of the 420 participants evaluated at baseline, 53 participated in the pilot. Of these, 37 (70%) were classified as overweight and 16 (30%) had obesity. Mean weight loss of 4.2% occurred at a median of 10 months postintervention. The subpopulation with obesity (n=16) showed a 5.6% weight loss on average. Total cholesterol decreased by 10.2% and low-density lipoprotein cholesterol decreased by 12.9% on average. Systolic and diastolic blood pressure decreased by 3.5% and 7.5%, respectively. Participants identified the possibility of setting clear action plans to work toward and the multiple weekly touch points with coaches as two of the most positive and distinctive components of the digitally enabled intervention. Surveys and interviews demonstrated that the digital implementation of a CLI-WL is feasible and well-received by both participants and health care professionals.

CONCLUSIONS

Albeit preliminary, these findings suggest that a behavioral lifestyle program with a digital component can achieve greater weight loss than reported for currently available offline CLI-WLs. Thus, a digitally enabled CLI-WL is feasible and may be a scalable alternative to offline CLI-WL programs. Evidence from future studies in a Dutch population may help elucidate the mechanisms behind the effectiveness of a digitally enabled CLI-WL.

Early Results of an Initiative to Assess Exposure to Firearm Violence in Ambulatory Care: Descriptive Analysis of Electronic Health Record Data

BACKGROUND

 Current research on firearm violence is largely limited to patients who received care in emergency departments or inpatient acute care settings or who died. This is because standardized disease classification codes for firearm injury only represent bodily trauma. As a result, research on pathways and health impacts of firearm violence is largely limited to people who experienced acute bodily trauma and does not include the estimated millions of individuals who were exposed to firearm violence but did not sustain acute injury. Assessing and collecting data on exposure to firearm violence in ambulatory care settings can expand research and more fully frame the public health issue.

OBJECTIVE

The aim of the study is to evaluate the demographic and clinical characteristics of patients who self-reported exposure to firearm violence during a behavioral health visit.

METHODS

This study assessed early data from an initiative implemented in 2022 across a national network of ambulatory behavioral health centers to support trauma-informed care by integrating structured data fields on trauma exposure into an electronic health record behavioral health patient assessment form (SmartForm), as such variables are generally not included in standard outpatient medical records. We calculated descriptive statistics on clinic characteristics, patient demographics, and select clinical conditions among clinics that chose to implement the SmartForm and among patients who reported an exposure to firearm violence. Data on patient counts are limited to positive reports of exposure to firearm violence, and the representativeness of firearm exposure among all patients could not be calculated due to unknown variability in the implementation of the SmartForm.

RESULTS

There were 323 of 629 (51%) clinics that implemented the SmartForm and reported at least 1 patient exposed to firearm violence. In the first 11 months of implementation, 3165 patients reported a recent or past exposure to firearm violence across the 323 clinics. Among patients reporting exposure, 52.7% (n=1669) were male, 38.8% (n=1229) were Black, 45.7% (n=1445) had posttraumatic stress disorder, 37.5% (n=1186) had a substance abuse disorder (other than nicotine), and 11.7% (n=371) had hypertension.

CONCLUSIONS

Current research on firearm violence using standardized data is limited to acute care settings and death data. Early results from an initiative across a large network of behavioral health clinics demonstrate that a high number of clinics chose to implement the SmartForm, resulting in thousands of patients reporting exposure to firearm violence. This study demonstrates that collecting standardized data on firearm violence exposure in ambulatory care settings is feasible. This study further demonstrates that resultant data from ambulatory settings can be used for meaningful analysis in describing populations affected by firearm violence. The results of this study hold promise for further collection of structured data on exposure to firearm violence in ambulatory settings.

Assessing the role of social determinants of health in health disparities: The need for data on work

BACKGROUND

Work is a key social determinant of health. Without the collection of work-related information in public health data systems, the role of social determinants in creating and reinforcing health disparities cannot be fully assessed.

METHODS

The Centers for Disease Control and Prevention (CDC) maintains or supports a number of public health surveillance and health monitoring systems, including surveys, case-based disease and exposure systems, vital status records, and administrative data systems. We evaluated a convenience sample of these systems for inclusion of information in three work-related domains: employment status, industry and occupation, and working conditions.

RESULTS

While 12 of 39 data systems were identified as collecting work-related data, this information was often minimal (e.g., only employment status), restricted to a subset of respondents, or only gathered periodically. Information on working conditions was particularly sparse.

CONCLUSION

Historically, the limited and inconsistent collection of work-related information in public health data systems has hindered understanding of the role work plays in health disparities. Current CDC data modernization efforts present opportunities to enhance the identification and mitigation of health disparities by prioritizing inclusion of an expanded set of work-related data elements.

The Utility of Administrative Data in Understanding the COVID-19 Pandemic's Impact on Child Maltreatment: Learning From the Scotland Experience

The COVID-19 pandemic and associated public health 'stay at home' restrictions have intensified familial risk factors. Children would appear to be at increased risk of harm and abuse, yet administrative data from the early months of the pandemic showed falling cases of child maltreatment. Using weekly administrative data from Scotland, UK that span the first 17 months of the pandemic, this article found that child maltreatment activity levels fluctuated as 'stay at home' restrictions changed. During lockdown periods, the number of children subject to Inter-agency Referral Discussion fell but a higher number of children were placed on the Child Protection Register. When restrictions were eased, the number of Inter-agency Referral Discussions increased but the number of children placed on the Child Protection Register fell. To explain the fluctuations, the article asserts that the pandemic's impact on services' ability to engage directly with children and families has been critical, but the limitations of administrative data in providing an accurate measure of child maltreatment levels also need to be recognised. The article advocates that analysis of administrative data is best done in tandem with wider quantitative and qualitative sources in order to understand the impact of crisis events on children and families.

Statistics in the Operating Room: A Cardiovascular Surgeon's Guide to Numbers That Matter

Pediatric cardiac surgery demands meticulous technique, but optimal outcomes hinge on translating data into actionable insights. This editorial bridges the gap between scalpel and statistical jargon, empowering surgeons to decipher common tests. Descriptive statistics paint portraits of patient cohorts, while hypothesis testing discerns real differences from chance. Regression analysis unveils hidden relationships, predicting outcomes based on complex interplays of variables. Survival analysis tracks the delicate dance of time and survival, informing therapeutic strategies. By embracing statistical fluency, surgeons become architects of personalized care, tailoring interventions to mitigate risks and maximize the precious gift of a beating heart.

Characterising the performance measurement and management system in the primary health care systems of Malawi

BACKGROUND

 Performance Measurement and Management (PMM) systems are levers that support key management functions in health care systems. Just like many low- and middle-income countries (LMICs), Malawi strives to improve performance via evidence-based decision making and a suitable performance culture.

AIM

 This study sought to describe PMM practices at all levels of primary health care (PHC) in Malawi.

SETTING

 This study targeted three levels of PHC, namely the district health centres (DHCs), the zones, and the ministry headquarters.

METHODS

 This was a qualitative exploratory research study where decision-makers at each level of PHC were engaged using key-informant interviews (KII) and focus group discussions (FGDs).

RESULTS

 We found that there is a weak link among levels of PHC in supporting PMM practices leading to poor dissemination of priorities and goals. There is also failure to appropriately institute good PMM practices, and the use of performance information was found to be limited among decision-makers.

CONCLUSION

 Though PMM is acknowledged to be key in supporting health service delivery systems, Malawi's PHC system has not fully embarked on making this a priority. Some challenges include unsupportive culture and inadequate capacity for PMM.Contribution: This study contributes to the understanding of the PMM processes in Malawi and further highlights the salient challenges in the use of information for performance management. While the presence of policies on PMM is acknowledged, implementation studies that deal with challenges are urgent and imperative.

What Types of Data are Pharmacy Education Scholars Using in their Abstracts for Poster Presentations?

OBJECTIVE

The objective of this study was to describe the data being used to support poster presentations in pharmacy education scholarship.

METHODS

Research and education posters presented at the 2020 American Association of Colleges of Pharmacy Annual Meeting were unitized to isolate text to be coded, and two coders categorized the quantitative and qualitative data by type and source. Questionnaires, instruments, and exams were categorized as new (ie developed and used for this particular inquiry) vs. existing. Qualitative data types were categorized as interviews, focus groups, self-reflections, analysis of student work products (eg lab reports assessed for student understanding), comments (ie written or verbal comments), and other (eg course reports).

RESULTS

Two hundred and sixteen abstracts were included in the analysis, with 80 (37%) of abstracts relying on data derived from respondent's perceptions. Further, 143 abstracts (66%) used at least one new questionnaire, instrument, or exam. In 57% of the cases where multiple data sources were used, the study involved interprofessional education (eg multiple health professions learners) or pharmacy student-investigator combinations, and 28 abstracts (13%) did not use pharmacy students as a source. Less than 5% of all abstracts analyzed used traditional qualitative methods of interviews and focus groups.

CONCLUSION

This study can open conversations around how to improve the quality of pharmacy education research and the identification of areas within the scholarship of teaching and learning that may benefit from improvement.

Hyaluronidase for Dermal Filler Complications: Review of Applications and Dosage Recommendations

BACKGROUND

Hyaluronidase (Hyal) can reverse complications of hyaluronic acid (HA) fillers, which has contributed substantially to the popularity of such procedures. Still, there are differing opinions regarding Hyal treatment, including dosage recommendations in filler complication management.

OBJECTIVE

We aimed to address unanswered questions regarding Hyal treatment for HA filler complications, including timing and dosage, skin pretesting, properties of various Hyals and interactions with HA gels, and pitfalls of the treatment.

METHODS

PubMed and Google Scholar databases were searched from inception for articles on Hyal therapy for filler complications. Articles were evaluated regarding their contribution to the field. The extensive literature review includes international leaders' suggestions and expert panels' recommendations.

RESULTS

There are limited controlled data but increasing clinical experience with Hyal treatment. The currently used Hyals provide good results and have an acceptable safety profile. Nonemergent complications such as the Tyndall effect, noninflamed nodules, and allergic or hypersensitivity reactions should be treated with low or moderate Hyal doses. Hyal should be considered with prior or simultaneous oral antibiotic treatment in managing inflammatory nodules. Hyal may be tried for granulomas that have not responded to intralesional steroids. Emergent complications such as vascular occlusion and blindness require immediate, high-dose Hyal treatment. Regarding blindness, the injection technique, retrobulbar versus supraorbital, remains controversial. Ultrasound guidance can increase the efficacy of the above interventions.

CONCLUSIONS

Hyal is essential in aesthetic practice because it can safely treat most HA filler complications. Immediate Hyal treatment is required for emergent complications. Aesthetic practitioners should be versed in using Hyal and effective dosage protocols.

Assessing and Improving Data Integrity in Web-Based Surveys: Comparison of Fraud Detection Systems in a COVID-19 Study

BACKGROUND

Web-based surveys increase access to study participation and improve opportunities to reach diverse populations. However, web-based surveys are vulnerable to data quality threats, including fraudulent entries from automated bots and duplicative submissions. Widely used proprietary tools to identify fraud offer little transparency about the methods used, effectiveness, or representativeness of resulting data sets. Robust, reproducible, and context-specific methods of accurately detecting fraudulent responses are needed to ensure integrity and maximize the value of web-based survey research.

OBJECTIVE

This study aims to describe a multilayered fraud detection system implemented in a large web-based survey about COVID-19 attitudes, beliefs, and behaviors; examine the agreement between this fraud detection system and a proprietary fraud detection system; and compare the resulting study samples from each of the 2 fraud detection methods.

METHODS

The PhillyCEAL Common Survey is a cross-sectional web-based survey that remotely enrolled residents ages 13 years and older to assess how the COVID-19 pandemic impacted individuals, neighborhoods, and communities in Philadelphia, Pennsylvania. Two fraud detection methods are described and compared: (1) a multilayer fraud detection strategy developed by the research team that combined automated validation of response data and real-time verification of study entries by study personnel and (2) the proprietary fraud detection system used by the Qualtrics (Qualtrics) survey platform. Descriptive statistics were computed for the full sample and for responses classified as valid by 2 different fraud detection methods, and classification tables were created to assess agreement between the methods. The impact of fraud detection methods on the distribution of vaccine confidence by racial or ethnic group was assessed.

RESULTS

Of 7950 completed surveys, our multilayer fraud detection system identified 3228 (40.60%) cases as valid, while the Qualtrics fraud detection system identified 4389 (55.21%) cases as valid. The 2 methods showed only "fair" or "minimal" agreement in their classifications (κ=0.25; 95% CI 0.23-0.27). The choice of fraud detection method impacted the distribution of vaccine confidence by racial or ethnic group.

CONCLUSIONS

The selection of a fraud detection method can affect the study's sample composition. The findings of this study, while not conclusive, suggest that a multilayered approach to fraud detection that includes conservative use of automated fraud detection and integration of human review of entries tailored to the study's specific context and its participants may be warranted for future survey research.

An Epidemic Model with Infection Age and Vaccination Age Structure

A model of epidemic dynamics is developed that incorporates continuous variables for infection age and vaccination age. The model analyzes pre-symptomatic and symptomatic periods of an infected individual in terms of infection age. This property is shown to be of major importance in the severity of the epidemic, when the infectious period of an infected individual precedes the symptomatic period. The model also analyzes the efficacy of vaccination in terms of vaccination age. The immunity to infection of vaccinated individuals varies with vaccination age and is also of major significance in the severity of the epidemic. Application of the model to the 2003 SARS epidemic in Taiwan and the COVID-19 epidemic in New York provides insights into the dynamics of these diseases. It is shown that the SARS outbreak was effectively contained due to the complete overlap of infectious and symptomatic periods, allowing for the timely isolation of affected individuals. In contrast, the pre-symptomatic spread of COVID-19 in New York led to a rapid, uncontrolled epidemic. These findings underscore the critical importance of the pre-symptomatic infectious period and the vaccination strategies in influencing the dynamics of an epidemic.

Reflecting on Decades of Data: The Global Burden of Disease-Cochrane Project

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How registry data are used to inform activities for stroke care quality improvement across 55 countries: A cross-sectional survey of Registry of Stroke Care Quality (RES-Q) hospitals

BACKGROUND AND PURPOSE

The Registry of Stroke Care Quality (RES-Q) is a worldwide quality improvement data platform that captures performance and quality measures, enabling standardized comparisons of hospital care. The aim of this study was to determine if, and how, RES-Q data are used to influence stroke quality improvement and identify the support and educational needs of clinicians using RES-Q data to improve stroke care.

METHODS

A cross-sectional self-administered online survey was administered (October 2021-February 2022). Participants were RES-Q hospital local coordinators responsible for stroke data collection. Descriptive statistics are presented.

RESULTS

Surveys were sent to 1463 hospitals in 74 countries; responses were received from 358 hospitals in 55 countries (response rate 25%). RES-Q data were used "always" or "often" to: develop quality improvement initiatives (n = 213, 60%); track stroke care quality over time (n = 207, 58%); improve local practice (n = 191, 53%); and benchmark against evidence-based policies, procedures and/or guidelines to identify practice gaps (n = 179, 50%). Formal training in the use of RES-Q tools and data were the most frequent support needs identified by respondents (n = 165, 46%). Over half "strongly agreed" or "agreed" that to support clinical practice change, education is needed on: (i) using data to identify evidence-practice gaps (n = 259, 72%) and change clinical practice (n = 263, 74%), and (ii) quality improvement science and methods (n = 255, 71%).

CONCLUSION

RES-Q data are used for monitoring stroke care performance. However, to facilitate their optimal use, effective quality improvement methods are needed. Educating staff in quality improvement science may develop competency and improve use of data in practice.

APPLICATION OF QUANTILE DISCRETIZATION AND BAYESIAN NETWORK ANALYSIS TO PUBLICLY AVAILABLE CYSTIC FIBROSIS DATA SETS

The availability of multiple publicly-available datasets studying the same phenomenon has the promise of accelerating scientific discovery. Meta-analysis can address issues of reproducibility and often increase power. The promise of meta-analysis is especially germane to rarer diseases like cystic fibrosis (CF), which affects roughly 100,000 people worldwide. A recent search of the National Institute of Health's Gene Expression Omnibus revealed 1.3 million data sets related to cancer compared to about 2,000 related to CF. These studies are highly diverse, involving different tissues, animal models, treatments, and clinical covariates. In our search for gene expression studies of primary human airway epithelial cells, we identified three studies with compatible methodologies and sufficient metadata: GSE139078, Sala Study, and PRJEB9292. Even so, experimental designs were not identical, and we identified significant batch effects that would have complicated functional analysis. Here we present quantile discretization and Bayesian network construction using the Hill climb method as a powerful tool to overcome experimental differences and reveal biologically relevant responses to the CF genotype itself, exposure to virus, bacteria, and drugs used to treat CF. Functional patterns revealed by cluster Profiler included interferon signaling, interferon gamma signaling, interleukins 4 and 13 signaling, interleukin 6 signaling, interleukin 21 signaling, and inactivation of CSF3/G-CSF signaling pathways showing significant alterations. These pathways were consistently associated with higher gene expression in CF epithelial cells compared to non-CF cells, suggesting that targeting these pathways could improve clinical outcomes. The success of quantile discretization and Bayesian network analysis in the context of CF suggests that these approaches might be applicable to other contexts where exactly comparable data sets are hard to find.

The interoperability of crystallographic data and databases

Interoperability of crystallographic data with other disciplines is essential for the smooth and rapid progress of structure-based science in the computer age. Within crystallography and closely related subject areas, there is already a high level of conformance to the generally accepted FAIR principles (that data be findable, accessible, interoperable and reusable) through the adoption of common information exchange protocols by databases, publishers, instrument vendors, experimental facilities and software authors. Driven by the success within these domains, the IUCr has worked closely with CODATA (the Committee on Data of the International Science Council) to help develop the latter's commitment to cross-domain integration of discipline-specific data. The IUCr has, in particular, emphasized the need for standards relating to data quality and completeness as an adjunct to the FAIR data landscape. This can ensure definitive reusable data, which in turn can aid interoperability across domains. A microsymposium at the IUCr 2023 Congress provided an up-to-date survey of data interoperability within and outside of crystallography, expounded using a broad range of examples.

Learning from an equitable, data-informed response to COVID-19: Translating knowledge into future action and preparation

Introduction

The COVID-19 pandemic revealed numerous barriers to effectively managing public health crises, including difficulties in using publicly available, community-level data to create learning systems in support of local public health decision responses. Early in the COVID-19 pandemic, a group of health care partners began meeting to learn from their collective experiences. We identified key tools and processes for using data and learning system structures to drive equitable public health decision making throughout different phases of the pandemic.

Methods

In fall of 2021, the team developed an initial theory of change directed at achieving herd immunity for COVID-19. The theoretical drivers were explored qualitatively through a series of nine 45-min telephonic interviews conducted with 16 public health and community leaders across the United States. Interview responses were analyzed into key themes to inform potential future practices, tools, and systems. In addition to the interviews, partners in Dallas and Cincinnati reflected on their own COVID-19 experiences.

Results

Interview responses fell broadly into four themes that contribute to effective, community driven responses to COVID-19: real-time, accessible data that are mindful of the tension between community transparency and individual privacy; a continued fostering of public trust; adaptable infrastructures and systems; and creating cohesive community coalitions with shared alignment and goals. These themes and partner experiences helped us revise our preliminary theory of change around the importance of community collaboration and trust building and also helped refine the development of the Community Protection Dashboard tool.

Conclusions

There was broad agreement amongst public health and community leaders about the key elements of the data and learning systems required to manage public health responses to COVID-19. These findings may be informative for guiding the use of data and learning in the management of future public health crises or population health initiatives.