Data sharing: A Long COVID perspective, challenges, and road map for the future

The article focuses on the need for standardized data collection, interpretation, and reporting in the management of Long COVID. Topics include the lack of a consistent definition for Long COVID, the benefits of adopting a data-driven framework for Long COVID management, and the challenges and strategies of data sharing in Long COVID research.

Blockchain-based Health Data Sharing for Continuous Disease Surveillance in Smart Environments

The Covid-19 pandemic ushered in multiple paradigms of personal health data sharing with particular emphasis on Person-to-Institution sharing and Institution-to-Institution sharing. While the data aggregated by technology companies and health authorities was instrumental in the development of vaccines and ultimately flattening the curve of infection rates, egregious abuses of privacy occurred. In many instances acceptable guarantees of appropriate utility for the data were not made available. Personal health data sharing for the containment of infections with privacy limitations present a classic case of collaboration among mutually distrustful entities. In this regard the blockchain network and attendant protocols for data integrity, transaction transmission and provenance can prove useful. Thus, in this paper we present a blockchain-based method for disease surveillance in a smart environment where smart contracts are deployed to monitor public locations instead of individuals. The data aggregated is analysed and tagged with a lifetime commensurate with the time for infection. Once the data utility period has elapsed the monitored data are removed from the active surveillance pool and the entities involved can be notified. Such a method of continual surveillance protects privacy by shifting the emphasis from individuals to locations. Experimental data suggests this method is efficient and can be implemented on top of existing disease surveillance strategies for later pandemics.

The Importance of Data Sharing in Managing Public Health Crises

The COVID-19 crisis has emphasized the importance of the free flow of information and data-driven applications in the management of public health crises. This chapter examines the potential benefits, concerns, and solutions related to sustainable and secure access to public health data. We study some of the data-driven solutions in action worldwide and present them as replicable use cases. We also examine why a large volume of data from public and private sources never reaches the desks of decision-makers and suggest technical and policy solutions to eliminate these sources of ‘data friction'. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer 2022.

No Transfer Without Validation: A Data Sharing Framework Use Case.

Availability and accessibility are important preconditions for using real-world patient data across organizations. To facilitate and enable the analysis of data collected at a large number of independent healthcare providers, syntactic- and semantic uniformity need to be achieved and verified. With this paper, we present a data transfer process implemented using the Data Sharing Framework to ensure only valid and pseudonymized data is transferred to a central research repository and feedback on success or failure is provided. Our implementation is used within the CODEX project of the German Network University Medicine to validate COVID-19 datasets at patient enrolling organizations and securely transfer them as FHIR resources to a central repository.

Blockchain-Based Patient-to-Patient Health Data Sharing

The Covid-19 pandemic catalyzed many exciting forms of health data sharing. Aside from the institution-to-institution health data sharing among cooperating institutions for research and discovery of insights in healthcare, individual-to-many and individual-to-individual health data sharing also came to the fore. However, the security risks involved here are substantial since health data disclosures can lead to privacy and security breaches or complications. In this research, we present a scheme to enable individuals to share details of medical experiences with other individuals or interested groups. Our system provides the sharing entities with anonymity and thus, facilitates rapid dissemination of empirical insights during public health emergencies like Covid-19.

A Collabortive Model for Lithic Shape Digitization in Museum Settings

The COVID-19 pandemic halted scientific research across the world, revealing the vulnerabilities of field-based disciplines to disruption. To ensure resilience in the face of future emergencies, archaeology needs to be more sustainable with international collaboration at the forefront. This article presents a collaborative data collection model for documenting lithics using digital photography and physical measurements taken in-situ by local collaborators. Data capture protocols to optimise standardisation are outlined, and guidelines are provided for data curation, storage and sharing. Adopting collaborative research strategies can have long-term advantages beyond the COVID-19 pandemic, by encouraging knowledge-sharing between international collaborators, decreasing emissions associated with archaeological research, and improving accessibility for those who are not able to travel for access to international samples. This article proposes that archaeology should use the COVID-19 pandemic as a catalyst for change through encouraging deeper collaborations and the development of remote models of science as a complement to in-person research.

Blockchain-based Health Data Sharing for Continuous Disease Surveillance in Smart Environments

The Covid-19 pandemic ushered in multiple paradigms of personal health data sharing with particular emphasis on Person-to-Institution sharing and Institution-toInstitution sharing. While the data aggregated by technology companies and health authorities was instrumental in the development of vaccines and ultimately flattening the curve of infection rates, egregious abuses of privacy occurred. In many instances acceptable guarantees of appropriate utility for the data were not made available. Personal health data sharing for the containment of infections with privacy limitations present a classic case of collaboration among mutually distrustful entities. In this regard the blockchain network and attendant protocols for data integrity, transaction transmission and provenance can prove useful. Thus, in this paper we present a blockchain-based method for disease surveillance in a smart environment where smart contracts are deployed to monitor public locations instead of individuals. The data aggregated is analysed and tagged with a lifetime commensurate with the time for infection. Once the data utility period has elapsed the monitored data are removed from the active surveillance pool and the entities involved can be notified. Such a method of continual surveillance protects privacy by shifting the emphasis from individuals to locations. Experimental data suggests this method is efficient and can be implemented on top of existing disease surveillance strategies for later pandemics. © 2023 IEEE.

Telehealth Utilization among Low Income Population during COVID-19: An Analysis of COVID-19 Research Database

The COVID-19 Research Database is a public data platform. This platform is a result of private and public partnerships across industries to facilitate data sharing and promote public health research. We analyzed its linked database and examined claims of 2,850,831 unique persons to investigate the influence of demographic, socio-economic, and behavioral factors on telehealth utilization in the low-income population. Our results suggest that patients who had higher education, income, and full-time employment were more likely to use telehealth. Patients who had unhealthy behaviors such as smoking were less likely to use telehealth. Our findings suggest that interventions to bolster education, employment, and healthy behaviors should be considered to promote the use of telehealth services. © 2023 IEEE Computer Society. All rights reserved.

Application of big data analytics in the COVID-19 pandemic: Selected problems

The coronavirus disease 2019 (COVID-19) pandemic enabled many governments around the globe to test and apply big data-based tracing technologies and various big data-driven tools to curb and monitor the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. The regular procedures of data and privacy protection were partially sacrificed to fight the pandemic. For the public health safety, all incidents of COVID-19 are considered of being hazardous, uncertain, and sudden. If a government can continuously and efficiently collect big data from various sources and apply suitable and efficient analytical methods, it might instantly respond to the public health threats by executing optimal decisions to slow the spread of the pandemic and for a fast return to normality. A specific framework is presented as a multidimensional recommendation for the efficient utilization of big data analytical technologies to control and prevent pandemics and epidemics. The recommendations and challenges with regard to employing big data for combatting COVID-19 are being discussed along with the background information. © 2023 Elsevier Inc. All rights reserved.

Trust and Willingness to Share Health Related Information: A quantitative study of Danish students' use of mHealth applications

mHealth technology has the potential to transform healthcare and realize the goal of precision medicine through systematic data collection and use. Meanwhile, mHealth applications developed during COVID-19 have had limited effect, as people have been reluctant to adopt them due to a lack of trust and willingness to share data. The aim of this empirical study is to provide insights into young people's use, trust, and willingness to share data through mHealth apps as future users of healthcare services. A survey comprising 484 Danish students was conducted. It focuses on mHealth app use, willingness to share data, and trust. The findings show that the trustworthiness of the technology and data requesting organization is important for establishing trust in mHealth apps. These insights indicate how young people can be motivated to trust mHealth apps, which can be used to develop future apps and exploit the untapped potential of the collected data. © 2023 IEEE Computer Society. All rights reserved.

Exploring patient perspectives on the secondary use of their personal health information: an interview study.

BACKGROUND: The increased digitalisation of health records has resulted in increased opportunities for the secondary use of health information for advancing healthcare. Understanding how patients want their health information used is vital to ensure health services use it in an appropriate and patient-informed manner. The aim of this study was to explore patient perceptions of the use of their health information beyond their immediate care. METHODS: Semi-structured in-depth interviews were conducted with current users of health services in Aotearoa New Zealand. Different scenarios formed the basis of the discussions in the interviews covering different types of information use (current practice, artificial intelligence and machine learning, clinical calculators, research, registries, and public health surveillance). Transcripts were analysed using thematic analysis. RESULTS: Twelve interviews were conducted with individual's representative of key ethnicity groups and rural/urban populations, and at the time of recruitment, had been accessing a diverse range of health services. Participants ranged from high users of health care (e.g., weekly dialysis) through to low users (e.g., one-off presentation to the emergency department). Four interrelated overarching themes were identified from the transcripts describing the main issues for participants: helping others, sharing of data is important, trust, and respect. CONCLUSIONS: People currently engaging with health services are supportive of their health information being used to help others, advance science, and contribute to the greater good but their support is conditional. People need to be able to trust the health service to protect, care for, and respect their health information and ensure no harm comes from its use. This study has identified key considerations for services and researchers to reflect on when using patient health information for secondary purposes to ensure they use it in a patient-informed way. TRIAL REGISTRATION: NA.

A New Chapter for Medical Image Generation: The Stable Diffusion Method

Data collecting and sharing have been widely accepted and adopted to improve the performance of deep learning models in almost every field. Nevertheless, in the medical field, sharing the data of patients can raise several critical issues, such as privacy and security or even legal issues. Synthetic medical images have been proposed to overcome such challenges;these synthetic images are generated by learning the distribution of realistic medical images but completely different from them so that they can be shared and used across different medical institutions. Currently, the diffusion model (DM) has gained lots of attention due to its potential to generate realistic and high-resolution images, particularly outperforming generative adversarial networks (GANs) in many applications. The DM defines state of the art for various computer vision tasks such as image inpainting, class-conditional image synthesis, and others. However, the diffusion model is time and power consumption due to its large size. Therefore, this paper proposes a lightweight DM to synthesize the medical image;we use computer tomography (CT) scans for SARS-CoV-2 (Covid-19) as the training dataset. Then we do extensive simulations to show the performance of the proposed diffusion model in medical image generation, and then we explain the key component of the model. © 2023 IEEE.

A data sharing method for remote medical system based on federated distillation learning and consortium blockchain

With the development of Medical Internet of Things (MIoT) technology and the global COVID-19 pandemic, hospitals gain access to patients' health data from remote wearable medical equipment. Federated learning (FL) addresses the difficulty of sharing data in remote medical systems. However, some key issues and challenges persist, such as heterogeneous health data stored in hospitals, which leads to high communication cost and low model accuracy. There are many approaches of federated distillation (FD) methods used to solve these problems, but FD is very vulnerable to poisoning attacks and requires a centralised server for aggregation, which is prone to single-node failure. To tackle this issue, we combine FD and blockchain to solve data sharing in remote medical system called FedRMD. FedRMD use reputation incentive to defend against poisoning attacks and store reputation values and soft labels of FD in Hyperledger Fabric. Experimenting on COVID-19 radiography and COVID-Chestxray datasets shows our method can reduce communication cost, and the performance is higher than FedAvg, FedDF, and FedGen. In addition, the reputation incentive can reduce the impact of poisoning attacks. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

FedTD: Efficiently Share Telemedicine Data with Federated Distillation Learning

With the Internet of Things and medical technology development, patients use wearable telemedicine devices to transmit health data to hospitals. The need for data sharing for public health has become more urgent under the COVID-19 pandemic. Previously, security protection technology was difficult to solve the increasing security risks and challenges of telemedicine. To address the above hindrances, Federated learning (FL) solves the difficulty for companies and institutions to share user data securely. The global server iterative aggregates the model parameters from the local server instead of uploading the user's data directly to the cloud server. We propose a new model of federated distillation learning called FedTD, which allows the different models between local hospital servers and global servers. Unlike traditional federated learning, we combine the knowledge distillation method to solve the non-Independent Identically Distribution (non-IID) problem of patient medical data. It provides a security solution for sharing patients' medical information among hospitals. We tested our approach on the COVID-19 Radiography and COVID-Chestxray datasets to improve the model performance and reduce communication costs. Extensive experiments show that our FedTD significantly outperforms the state-of-the-art. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The pandemic-induced personal data explosion

Background: Government and private responses to the COVID-19 pandemic resulted in the generation and dissemination of personal data not previously available in the public sphere. Focus of the Article: This "Notes from the Field" paper reflects on the implications of this surge of new data for the study and practice of social marketing. The paper examines how this phenomenon impacts on the following aspects of social marketing: (1) Setting of explicit social goals;(2) citizen orientation and focus;(3) value proposition delivery via the social marketing intervention mix;(4) theory-, insight-, data-, and evidence-informed audience segmentation;(5) competition/barrier and asset analysis;and (6) critical thinking, reflexivity, and being ethical. Research Question: How are the government and private responses to the pandemic shaping the generation and use of personal data, and what are the implications of this eruption of data for the social marketing scholarly community? Approach: The paper highlights how the pandemic resulted in significant changes in behavior of government and citizens alike, and how these changes, in turn, spurred the generation and dissemination of new personal data. Subsequently, we draw on the Core Social Marketing Concepts framework to explore how the aforementioned data explosion impacts on the six dimensions of this central framework. Importance to the Social Marketing Field: The COVID-19 pandemic is more than a temporary public health event. Therefore, it is important to consider the lasting consequences that may stem from the pandemic-induced personal data explosion, for both consumers and social marketing scholars and practitioners. Methods: This paper comments on a topical matter, and discusses its implications for the social marketing community. Results: We find that the data explosion creates conflicting social marketing goals, and that inequalities in access to digital technology are increasingly impacting what voices are heard, and which concerns are prioritized. Moreover, new innovations may be enabled or needed, leading to the improvement of firms' ability to create value for individual citizens;the creation of new datasets-particularly among demographics that previously had a limited digital footprint-enhances the ability to segment markets and target social marketing activities. Furthermore, the pandemic-induced data explosion may lead to the identification of additional barriers to positive social behaviors that have emerged, diminished, or even disappeared during the pandemic;but researchers need to critically examine the consequences of the government and private behaviors at the macro, meso, and micro levels. Recommendations for Research or Practice: We propose a research agenda for the social marketing community, consisting of 21 research questions. Limitations: Our analysis focuses on the behavior of government and citizens in North America and Western Europe. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Equitable data sharing in epidemics and pandemics.

BACKGROUND: Rapid data sharing can maximize the utility of data. In epidemics and pandemics like Zika, Ebola, and COVID-19, the case for such practices seems especially urgent and warranted. Yet rapidly sharing data widely has previously generated significant concerns related to equity. The continued lack of understanding and guidance on equitable data sharing raises the following questions: Should data sharing in epidemics and pandemics primarily advance utility, or should it advance equity as well? If so, what norms comprise equitable data sharing in epidemics and pandemics? Do these norms address the equity-related concerns raised by researchers, data providers, and other stakeholders? What tensions must be balanced between equity and other values? METHODS: To explore these questions, we undertook a systematic scoping review of the literature on data sharing in epidemics and pandemics and thematically analyzed identified literature for its discussion of ethical values, norms, concerns, and tensions, with a particular (but not exclusive) emphasis on equity. We wanted to both understand how equity in data sharing is being conceptualized and draw out other important values and norms for data sharing in epidemics and pandemics. RESULTS: We found that values of utility, equity, solidarity, and reciprocity were described, and we report their associated norms, including researcher recognition; rapid, real-time sharing; capacity development; and fair benefits to data generators, data providers, and source countries. The value of utility and its associated norms were discussed substantially more than others. Tensions between utility norms (e.g., rapid, real-time sharing) and equity norms (e.g., researcher recognition, equitable access) were raised. CONCLUSIONS: This study found support for equity being advanced by data sharing in epidemics and pandemics. However, norms for equitable data sharing in epidemics and pandemics require further development, particularly in relation to power sharing and participatory approaches prioritizing inclusion. Addressing structural inequities in the wider global health landscape is also needed to achieve equitable data sharing in epidemics and pandemics.

COVID-19 trials were not more likely to report intent to share individual data than non-COVID-19 trials in ClinicalTrials.gov.

OBJECTIVES: To compare intent to share individual participant data (IPD) between COVID-19 and non-COVID-19 trials registered at ClinicalTrials.gov between 01/09/2020, and 01/03/2021. We also evaluated factors independently associated with intent to share IPD and whether intent to share IPD has improved as compared with the prepandemic period. METHODS: We searched ClinicalTrials.gov for all interventional phase 3 studies registered between 01/09/2020, and 01/03/2021. Then, we identified COVID-19 trials and selected a random sample of non-COVID-19 trials with a ratio 2:1. We compared the intent to share IPD between these trials and with 292 trials registered between 01/12/2019, and 01/03/2020 (prepandemic period). RESULTS: We included 148 COVID-19 trials and 296 non-COVID-19 trials. Intent to share IPD did not significantly differ between COVID-19 and non-COVID-19 trials (22.3% vs. 27.0%, P = 0.3). Intent to share IPD was independently associated with industry-sponsorship (odds ratio [OR] = 2.92; 95% confidence interval [CI]: 1.65-5.27) and location in the United States (OR = 2.93; 95% CI: 1.64-5.41) or the European Union (OR = 2.06; 95% CI: 1.03-4.19). The intent to share IPD has not significantly improved compared with the prepandemic period (P = 0.16). CONCLUSION: Data-sharing intent at registration does not seem better for COVID-19 trials.

A preregistered vignette experiment on determinants of health data sharing behavior Willingness to donate sensor data, medical records, and biomarkers.

The COVID-19 pandemic has spotlighted the importance of high-quality data for empirical health research and evidence-based political decision-making. To leverage the full potential of these data, a better understanding of the determinants and conditions under which people are willing to share their health data is critical. Building on the privacy theory of contextual integrity, the privacy calculus, and previous findings regarding different data types and recipients, we argue that established social norms shape the acceptance of novel practices of data collection and use. To investigate the willingness to share health data, we conducted a preregistered vignette experiment. The scenarios experimentally varied the vignette dimensions by data type, recipient, and research purpose. While some findings contradict our hypotheses, the results indicate that all three dimensions affected respondents' data sharing decisions. Additional analyses suggest that institutional and social trust, privacy concerns, technical affinity, altruism, age, and device ownership influence the willingness to share health data.