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Polesie S, Alsterholm M. A systematic review investigating the proportion of clinical images shared in prospective randomized controlled trials involving patients with atopic dermatitis and systemic pharmacotherapy. J DERMATOL TREAT 2024; 35:2338280. [PMID: 38569598 DOI: 10.1080/09546634.2024.2338280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 03/27/2024] [Indexed: 04/05/2024]
Abstract
For individuals with atopic dermatitis (AD), interpreting scientific papers that present clinical outcomes including the Eczema Area and Severity Index (EASI) and Investigators Global Assessment may be difficult. When compared to tabulated data and graphs, images from before and after treatment are often far more meaningful to these patients that ultimately will be candidates for the treatment. This systematic review focused on determining the frequency of clinical image sharing in AD research. Conducted in accordance with PRISMA guidelines, the review concentrated on randomized controlled trials that investigated predefined and available systemic treatments for AD. The search was performed in the MEDLINE database for studies published from the inception until 21 December 2023. The review included 60 studies, encompassing 17,799 randomized patients. Across these studies, 16 images representing 6 patients were shared in the manuscripts, leading to a sharing rate of 0.3‰. The almost missing inclusion of patient images in clinical trial publications hinders patient understanding. Adding images to scientific manuscripts could significantly improve patients' comprehension of potential treatment outcomes. This review highlights the need for authors, the pharmaceutical industry, study sponsors, and publishers to enhance and promote patient information through increased use of visual data.
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Affiliation(s)
- Sam Polesie
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Dermatology and Venereology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mikael Alsterholm
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Dermatology and Venereology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
- Dermatology and Venereology Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
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2
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DeFranco JF, Roberts J, Ferraiolo D, Compton DC. An infrastructure for secure data sharing: a clinical data implementation. JAMIA Open 2024; 7:ooae040. [PMID: 38751412 PMCID: PMC11095973 DOI: 10.1093/jamiaopen/ooae040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/02/2024] [Accepted: 04/23/2024] [Indexed: 05/18/2024] Open
Abstract
Objective To address database interoperability challenges to improve collaboration among disparate organizations. Materials and Methods We developed a lightweight system to allow broad but well-controlled data sharing while preserving local data protection policies. We used 2 NIST-developed technologies-Next-generation Database Access Control (NDAC) and the Data Block Matrix (DBM)-to create a proof-of-concept system called the Secure Federated Data Sharing System (SFDS). NDAC controls access to database resources down to the field level based on attributes assigned to users. The DBM manages and shares authoritative user-attribute assignments across a federation of organizations, implemented using a modified open-source permissioned blockchain, to manage and share authoritative user-attribute assignments across a federation of organizations. We used synthetic data to demonstrate a clinical research data-sharing use case using the SFDS. Results We demonstrated, through consent, the onboarding of previously unknown users into NDAC via assignments to their DBM-validated attributes, allowing those users policy-preserving access to local database resources. The SFDS main system components-NDAC and DBM-also showed excellent performance metrics. Discussion The SFDS provides a generic data-sharing infrastructure that effectively and securely achieves data-sharing objectives. It is completely transparent to the otherwise normal business operations of participating organizations. It requires no changes to database management systems or existing methods of authenticating and authorizing local user access to local resources. Conclusion This efficiency, flexibility of deployment, and granularity of control make this new infrastructure solution practical for meeting the data-sharing and protection objectives of the clinical research community.
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Affiliation(s)
- Joanna F DeFranco
- Computer and Information Science, Penn State University, Malvern, PA 19355, United States
| | - Joshua Roberts
- National Institute of Standards and Technology, Secure Systems and Applications Group, Gaithersburg, MD 20899, United States
| | - David Ferraiolo
- National Institute of Standards and Technology, Secure Systems and Applications Group, Gaithersburg, MD 20899, United States
| | - D Chris Compton
- National Institute of Standards and Technology, Secure Systems and Applications Group, Gaithersburg, MD 20899, United States
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3
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Trunnell M, Frankenberger C, Hota B, Hughes T, Martinov P, Ravichandran U, Shah NS, Grossman RL. The Pandemic Response Commons. JAMIA Open 2024; 7:ooae025. [PMID: 38617994 PMCID: PMC11009464 DOI: 10.1093/jamiaopen/ooae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/25/2023] [Accepted: 04/05/2024] [Indexed: 04/16/2024] Open
Abstract
Objectives A data commons is a software platform for managing, curating, analyzing, and sharing data with a community. The Pandemic Response Commons (PRC) is a data commons designed to provide a data platform for researchers studying an epidemic or pandemic. Methods The PRC was developed using the open source Gen3 data platform and is based upon consortium, data, and platform agreements developed by the not-for-profit Open Commons Consortium. A formal consortium of Chicagoland area organizations was formed to develop and operate the PRC. Results The consortium developed a general PRC and an instance of it for the Chicagoland region called the Chicagoland COVID-19 Commons. A Gen3 data platform was set up and operated with policies, procedures, and controls for a NIST SP 800-53 revision 4 Moderate system. A consensus data model for the commons was developed, and a variety of datasets were curated, harmonized and ingested, including statistical summary data about COVID cases, patient level clinical data, and SARS-CoV-2 viral variant data. Discussion and conclusions Given the various legal and data agreements required to operate a data commons, a PRC is designed to be in place and operating at a low level prior to the occurrence of an epidemic, with the activities increasing as required during an epidemic. A regional instance of a PRC can also be part of a broader data ecosystem or data mesh consisting of multiple regional commons supporting pandemic response through sharing regional data.
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Affiliation(s)
| | | | - Bala Hota
- Rush University Medical Center, Chicago, IL 60612, United States
| | - Troy Hughes
- Center for Translational Data Science, University of Chicago, Chicago, IL 60615, United States
| | | | | | - Nirav S Shah
- NorthShore University HealthSystem, Evanston, IL 60201, United States
| | - Robert L Grossman
- Center for Translational Data Science, University of Chicago, Chicago, IL 60615, United States
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4
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Hamilton DG, Everitt S, Page MJ, Fidler F. What do Australians affected by cancer think about oncology researchers sharing research data? A cross-sectional survey. Asia Pac J Clin Oncol 2024. [PMID: 38708950 DOI: 10.1111/ajco.14075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 04/15/2024] [Accepted: 04/23/2024] [Indexed: 05/07/2024]
Abstract
AIM Previous research has shown patients and the public in Australia generally support medical researchers in making de-identified research data available to other scientists. However, this research has focussed on certain types of data and recipients. We surveyed Australians affected by cancer to characterize their attitudes toward the sharing of research data with multiple third parties, including the public. METHODS A short, anonymous online survey of Australians with a previous diagnosis of cancer was advertised between October 27, 2022, and February 27, 2023. Quantitative responses were analyzed with descriptive statistics. Free-text responses were coded deductively and summarised using content analysis. RESULTS In total, 551 respondents contributed data to the survey. There was strong support for cancer researchers sharing non-human and de-identified human research data with clinicians (90% and 95%, respectively) and non-profit researchers (both 94%). However, fewer participants supported sharing data with for-profit researchers (both 64%) or publicly (both 61%). When asked if they would hypothetically consent to researchers at their treatment location using and sharing their de-identified data publicly, only half agreed. In contrast, after being shown a visual representation of the de-identified survey data, 80% of respondents supported sharing it publicly. CONCLUSION Australians affected by cancer support the sharing of research data, particularly with clinicians and non-profit researchers. Our results also imply that visualization of the data to be shared may enhance support for making it publicly available. These results should help alleviate any concerns about research participants' attitudes toward data sharing, as well as boost researchers' motivation for sharing.
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Affiliation(s)
- Daniel G Hamilton
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, Australia
- Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia
| | - Sarah Everitt
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Matthew J Page
- Methods in Evidence Synthesis Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Fiona Fidler
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, Australia
- School of History & Philosophy of Sciences, University of Melbourne, Melbourne, Australia
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5
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Palojoki S, Lehtonen L, Vuokko R. Semantic Interoperability of Electronic Health Records: Systematic Review of Alternative Approaches for Enhancing Patient Information Availability. JMIR Med Inform 2024; 12:e53535. [PMID: 38686541 PMCID: PMC11066539 DOI: 10.2196/53535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/21/2024] [Accepted: 02/24/2024] [Indexed: 05/02/2024] Open
Abstract
Background Semantic interoperability facilitates the exchange of and access to health data that are being documented in electronic health records (EHRs) with various semantic features. The main goals of semantic interoperability development entail patient data availability and use in diverse EHRs without a loss of meaning. Internationally, current initiatives aim to enhance semantic development of EHR data and, consequently, the availability of patient data. Interoperability between health information systems is among the core goals of the European Health Data Space regulation proposal and the World Health Organization's Global Strategy on Digital Health 2020-2025. Objective To achieve integrated health data ecosystems, stakeholders need to overcome challenges of implementing semantic interoperability elements. To research the available scientific evidence on semantic interoperability development, we defined the following research questions: What are the key elements of and approaches for building semantic interoperability integrated in EHRs? What kinds of goals are driving the development? and What kinds of clinical benefits are perceived following this development? Methods Our research questions focused on key aspects and approaches for semantic interoperability and on possible clinical and semantic benefits of these choices in the context of EHRs. Therefore, we performed a systematic literature review in PubMed by defining our study framework based on previous research. Results Our analysis consisted of 14 studies where data models, ontologies, terminologies, classifications, and standards were applied for building interoperability. All articles reported clinical benefits of the selected approach to enhancing semantic interoperability. We identified 3 main categories: increasing the availability of data for clinicians (n=6, 43%), increasing the quality of care (n=4, 29%), and enhancing clinical data use and reuse for varied purposes (n=4, 29%). Regarding semantic development goals, data harmonization and developing semantic interoperability between different EHRs was the largest category (n=8, 57%). Enhancing health data quality through standardization (n=5, 36%) and developing EHR-integrated tools based on interoperable data (n=1, 7%) were the other identified categories. The results were closely coupled with the need to build usable and computable data out of heterogeneous medical information that is accessible through various EHRs and databases (eg, registers). Conclusions When heading toward semantic harmonization of clinical data, more experiences and analyses are needed to assess how applicable the chosen solutions are for semantic interoperability of health care data. Instead of promoting a single approach, semantic interoperability should be assessed through several levels of semantic requirements A dual model or multimodel approach is possibly usable to address different semantic interoperability issues during development. The objectives of semantic interoperability are to be achieved in diffuse and disconnected clinical care environments. Therefore, approaches for enhancing clinical data availability should be well prepared, thought out, and justified to meet economically sustainable and long-term outcomes.
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Affiliation(s)
- Sari Palojoki
- Department of Steering of Healthcare and Social Welfare, Ministry of Social Affairs and Health, Helsinki, Finland
| | - Lasse Lehtonen
- Diagnostic Center, Helsinki University Hospital District, Helsinki, Finland
| | - Riikka Vuokko
- Department of Steering of Healthcare and Social Welfare, Ministry of Social Affairs and Health, Helsinki, Finland
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6
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Pilgram L, Meurers T, Malin B, Schaeffner E, Eckardt KU, Prasser F. The Costs of Anonymization: Case Study Using Clinical Data. J Med Internet Res 2024; 26:e49445. [PMID: 38657232 PMCID: PMC11079766 DOI: 10.2196/49445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 01/14/2024] [Accepted: 02/13/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND Sharing data from clinical studies can accelerate scientific progress, improve transparency, and increase the potential for innovation and collaboration. However, privacy concerns remain a barrier to data sharing. Certain concerns, such as reidentification risk, can be addressed through the application of anonymization algorithms, whereby data are altered so that it is no longer reasonably related to a person. Yet, such alterations have the potential to influence the data set's statistical properties, such that the privacy-utility trade-off must be considered. This has been studied in theory, but evidence based on real-world individual-level clinical data is rare, and anonymization has not broadly been adopted in clinical practice. OBJECTIVE The goal of this study is to contribute to a better understanding of anonymization in the real world by comprehensively evaluating the privacy-utility trade-off of differently anonymized data using data and scientific results from the German Chronic Kidney Disease (GCKD) study. METHODS The GCKD data set extracted for this study consists of 5217 records and 70 variables. A 2-step procedure was followed to determine which variables constituted reidentification risks. To capture a large portion of the risk-utility space, we decided on risk thresholds ranging from 0.02 to 1. The data were then transformed via generalization and suppression, and the anonymization process was varied using a generic and a use case-specific configuration. To assess the utility of the anonymized GCKD data, general-purpose metrics (ie, data granularity and entropy), as well as use case-specific metrics (ie, reproducibility), were applied. Reproducibility was assessed by measuring the overlap of the 95% CI lengths between anonymized and original results. RESULTS Reproducibility measured by 95% CI overlap was higher than utility obtained from general-purpose metrics. For example, granularity varied between 68.2% and 87.6%, and entropy varied between 25.5% and 46.2%, whereas the average 95% CI overlap was above 90% for all risk thresholds applied. A nonoverlapping 95% CI was detected in 6 estimates across all analyses, but the overwhelming majority of estimates exhibited an overlap over 50%. The use case-specific configuration outperformed the generic one in terms of actual utility (ie, reproducibility) at the same level of privacy. CONCLUSIONS Our results illustrate the challenges that anonymization faces when aiming to support multiple likely and possibly competing uses, while use case-specific anonymization can provide greater utility. This aspect should be taken into account when evaluating the associated costs of anonymized data and attempting to maintain sufficiently high levels of privacy for anonymized data. TRIAL REGISTRATION German Clinical Trials Register DRKS00003971; https://drks.de/search/en/trial/DRKS00003971. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1093/ndt/gfr456.
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Affiliation(s)
- Lisa Pilgram
- Junior Digital Clinician Scientist Program, Biomedical Innovation Academy, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Thierry Meurers
- Medical Informatics Group, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Bradley Malin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Elke Schaeffner
- Institute of Public Health, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Department of Nephrology and Hypertension, Universitätsklinikum Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Fabian Prasser
- Medical Informatics Group, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
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Lee JS, Tyler ARB, Veinot TC, Yakel E. Now Is the Time to Strengthen Government-Academic Data Infrastructures to Jump-Start Future Public Health Crisis Response. JMIR Public Health Surveill 2024; 10:e51880. [PMID: 38656780 PMCID: PMC11079773 DOI: 10.2196/51880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/24/2024] [Accepted: 03/05/2024] [Indexed: 04/26/2024] Open
Abstract
During public health crises, the significance of rapid data sharing cannot be overstated. In attempts to accelerate COVID-19 pandemic responses, discussions within society and scholarly research have focused on data sharing among health care providers, across government departments at different levels, and on an international scale. A lesser-addressed yet equally important approach to sharing data during the COVID-19 pandemic and other crises involves cross-sector collaboration between government entities and academic researchers. Specifically, this refers to dedicated projects in which a government entity shares public health data with an academic research team for data analysis to receive data insights to inform policy. In this viewpoint, we identify and outline documented data sharing challenges in the context of COVID-19 and other public health crises, as well as broader crisis scenarios encompassing natural disasters and humanitarian emergencies. We then argue that government-academic data collaborations have the potential to alleviate these challenges, which should place them at the forefront of future research attention. In particular, for researchers, data collaborations with government entities should be considered part of the social infrastructure that bolsters their research efforts toward public health crisis response. Looking ahead, we propose a shift from ad hoc, intermittent collaborations to cultivating robust and enduring partnerships. Thus, we need to move beyond viewing government-academic data interactions as 1-time sharing events. Additionally, given the scarcity of scholarly exploration in this domain, we advocate for further investigation into the real-world practices and experiences related to sharing data from government sources with researchers during public health crises.
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Affiliation(s)
- Jian-Sin Lee
- School of Information, University of Michigan, Ann Arbor, MI, United States
| | | | - Tiffany Christine Veinot
- School of Information, University of Michigan, Ann Arbor, MI, United States
- Department of Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Department of Learning Health Sciences, School of Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Elizabeth Yakel
- School of Information, University of Michigan, Ann Arbor, MI, United States
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Abu Attieh H, Neves DT, Guedes M, Mirandola M, Dellacasa C, Rossi E, Prasser F. A Scalable Pseudonymization Tool for Rapid Deployment in Large Biomedical Research Networks: Development and Evaluation Study. JMIR Med Inform 2024; 12:e49646. [PMID: 38654577 PMCID: PMC11063579 DOI: 10.2196/49646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 10/03/2023] [Accepted: 03/07/2024] [Indexed: 04/26/2024] Open
Abstract
Background The SARS-CoV-2 pandemic has demonstrated once again that rapid collaborative research is essential for the future of biomedicine. Large research networks are needed to collect, share, and reuse data and biosamples to generate collaborative evidence. However, setting up such networks is often complex and time-consuming, as common tools and policies are needed to ensure interoperability and the required flows of data and samples, especially for handling personal data and the associated data protection issues. In biomedical research, pseudonymization detaches directly identifying details from biomedical data and biosamples and connects them using secure identifiers, the so-called pseudonyms. This protects privacy by design but allows the necessary linkage and reidentification. Objective Although pseudonymization is used in almost every biomedical study, there are currently no pseudonymization tools that can be rapidly deployed across many institutions. Moreover, using centralized services is often not possible, for example, when data are reused and consent for this type of data processing is lacking. We present the ORCHESTRA Pseudonymization Tool (OPT), developed under the umbrella of the ORCHESTRA consortium, which faced exactly these challenges when it came to rapidly establishing a large-scale research network in the context of the rapid pandemic response in Europe. Methods To overcome challenges caused by the heterogeneity of IT infrastructures across institutions, the OPT was developed based on programmable runtime environments available at practically every institution: office suites. The software is highly configurable and provides many features, from subject and biosample registration to record linkage and the printing of machine-readable codes for labeling biosample tubes. Special care has been taken to ensure that the algorithms implemented are efficient so that the OPT can be used to pseudonymize large data sets, which we demonstrate through a comprehensive evaluation. Results The OPT is available for Microsoft Office and LibreOffice, so it can be deployed on Windows, Linux, and MacOS. It provides multiuser support and is configurable to meet the needs of different types of research projects. Within the ORCHESTRA research network, the OPT has been successfully deployed at 13 institutions in 11 countries in Europe and beyond. As of June 2023, the software manages data about more than 30,000 subjects and 15,000 biosamples. Over 10,000 labels have been printed. The results of our experimental evaluation show that the OPT offers practical response times for all major functionalities, pseudonymizing 100,000 subjects in 10 seconds using Microsoft Excel and in 54 seconds using LibreOffice. Conclusions Innovative solutions are needed to make the process of establishing large research networks more efficient. The OPT, which leverages the runtime environment of common office suites, can be used to rapidly deploy pseudonymization and biosample management capabilities across research networks. The tool is highly configurable and available as open-source software.
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Affiliation(s)
- Hammam Abu Attieh
- Medical Informatics Group, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Diogo Telmo Neves
- Medical Informatics Group, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Mariana Guedes
- Infection and Antimicrobial Resistance Control and Prevention Unit, Centro Hospitalar Universitário São João, Porto, Portugal
- Infectious Diseases and Microbiology Division, Hospital Universitario Virgen Macarena, Sevilla, Spain
- Department of Medicine, University of Sevilla/Instituto de Biomedicina de Sevilla (IBiS)/Consejo Superior de Investigaciones Científicas (CSIC), Sevilla, Spain
| | - Massimo Mirandola
- Infectious Diseases Division, Diagnostic and Public Health Department, University of Verona, Verona, Italy
| | - Chiara Dellacasa
- High Performance Computing (HPC) Department, CINECA - Consorzio Interuniversitario, Bologna, Italy
| | - Elisa Rossi
- High Performance Computing (HPC) Department, CINECA - Consorzio Interuniversitario, Bologna, Italy
| | - Fabian Prasser
- Medical Informatics Group, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
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9
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Karimian Sichani E, Smith A, El Emam K, Mosquera L. Creating High-Quality Synthetic Health Data: Framework for Model Development and Validation. JMIR Form Res 2024; 8:e53241. [PMID: 38648097 PMCID: PMC11034549 DOI: 10.2196/53241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 01/09/2024] [Accepted: 03/01/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Electronic health records are a valuable source of patient information that must be properly deidentified before being shared with researchers. This process requires expertise and time. In addition, synthetic data have considerably reduced the restrictions on the use and sharing of real data, allowing researchers to access it more rapidly with far fewer privacy constraints. Therefore, there has been a growing interest in establishing a method to generate synthetic data that protects patients' privacy while properly reflecting the data. OBJECTIVE This study aims to develop and validate a model that generates valuable synthetic longitudinal health data while protecting the privacy of the patients whose data are collected. METHODS We investigated the best model for generating synthetic health data, with a focus on longitudinal observations. We developed a generative model that relies on the generalized canonical polyadic (GCP) tensor decomposition. This model also involves sampling from a latent factor matrix of GCP decomposition, which contains patient factors, using sequential decision trees, copula, and Hamiltonian Monte Carlo methods. We applied the proposed model to samples from the MIMIC-III (version 1.4) data set. Numerous analyses and experiments were conducted with different data structures and scenarios. We assessed the similarity between our synthetic data and the real data by conducting utility assessments. These assessments evaluate the structure and general patterns present in the data, such as dependency structure, descriptive statistics, and marginal distributions. Regarding privacy disclosure, our model preserves privacy by preventing the direct sharing of patient information and eliminating the one-to-one link between the observed and model tensor records. This was achieved by simulating and modeling a latent factor matrix of GCP decomposition associated with patients. RESULTS The findings show that our model is a promising method for generating synthetic longitudinal health data that is similar enough to real data. It can preserve the utility and privacy of the original data while also handling various data structures and scenarios. In certain experiments, all simulation methods used in the model produced the same high level of performance. Our model is also capable of addressing the challenge of sampling patients from electronic health records. This means that we can simulate a variety of patients in the synthetic data set, which may differ in number from the patients in the original data. CONCLUSIONS We have presented a generative model for producing synthetic longitudinal health data. The model is formulated by applying the GCP tensor decomposition. We have provided 3 approaches for the synthesis and simulation of a latent factor matrix following the process of factorization. In brief, we have reduced the challenge of synthesizing massive longitudinal health data to synthesizing a nonlongitudinal and significantly smaller data set.
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Affiliation(s)
| | - Aaron Smith
- Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON, Canada
| | - Khaled El Emam
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
- Replica Analytics Ltd, Ottawa, ON, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Lucy Mosquera
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
- Replica Analytics Ltd, Ottawa, ON, Canada
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10
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Klein A, Kosinski L, Loupy A, Frey E, Stegall M, Helanterä I, Newell K, Meier-Kriesche HU, Mannon RB, Fitzsimmons WE. Comparing the prognostic performance of iBOX and biopsy-proven acute rejection for long-term kidney graft survival. Am J Transplant 2024:S1600-6135(24)00275-2. [PMID: 38642711 DOI: 10.1016/j.ajt.2024.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/20/2024] [Accepted: 04/08/2024] [Indexed: 04/22/2024]
Abstract
Biopsy-proven acute rejection (BPAR) occurs in approximately 10% of kidney transplant recipients in the first year, making superiority trials unfeasible. iBOX, a quantitative composite of estimated glomerular filtration rate, proteinuria, antihuman leukocyte antigen donor-specific antibody, and + full/- abbreviated kidney histopathology, is a new proposed surrogate endpoint. BPAR's prognostic ability was compared with iBOX in a pooled cohort of 1534 kidney transplant recipients from 4 data sets, including 2 prospective randomized controlled trials. Discrimination analyses showed mean c-statistic differences between both iBOX compared with BPAR of 0.25 (95% confidence interval: 0.17-0.32) for full iBOX and 0.24 (95% confidence interval: 0.16-0.32) for abbreviated iBOX, indicating statistically significantly higher c-statistic values for the iBOX prognosis of death-censored graft survival. Mean (± standard error) c-statistics were 0.81 ± 0.03 for full iBOX, 0.80 ± 0.03 for abbreviated iBOX, and 0.57 ± 0.03 for BPAR. In calibration analyses, predicted graft loss events from both iBOX models were not significantly different from those observed. However, for BPAR, the predicted events were significantly (P < .01) different (observed: 64; predicted: 70; full iBOX: 76; abbreviated iBOX: 173 BPAR). IBOX at 1-year posttransplant is superior to BPAR in the first year posttransplant in graft loss prognostic performance, providing valuable additional information and facilitating the demonstration of superiority of novel immunosuppressive regimens.
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Affiliation(s)
| | | | - Alexandre Loupy
- Université de Paris, Cité, Institut national de la santé et de la recherche médicale, U970, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France
| | - Eric Frey
- Critical Path Institute, Tucson, Arizona, USA
| | - Mark Stegall
- Department of Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Ilkka Helanterä
- Helsinki University Hospital, Department of Transplantation and Liver Surgery, and University of Helsinki, Finland
| | - Kenneth Newell
- Division of Transplantation, Department of Surgery, Emory University School of Medicine, Atlanta, Georgia, USA
| | | | - Roslyn B Mannon
- Department of Medicine, Division of Nephrology, University of Nebraska Medical Center, Omaha, Nebraska, USA
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11
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Chen H, Pang J, Zhao Y, Giddens S, Ficek J, Valente MJ, Cao B, Daley E. A data-driven approach to choosing privacy parameters for clinical trial data sharing under differential privacy. J Am Med Inform Assoc 2024; 31:1135-1143. [PMID: 38457282 PMCID: PMC11031247 DOI: 10.1093/jamia/ocae038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/27/2024] [Accepted: 02/16/2024] [Indexed: 03/10/2024] Open
Abstract
OBJECTIVES Clinical trial data sharing is crucial for promoting transparency and collaborative efforts in medical research. Differential privacy (DP) is a formal statistical technique for anonymizing shared data that balances privacy of individual records and accuracy of replicated results through a "privacy budget" parameter, ε. DP is considered the state of the art in privacy-protected data publication and is underutilized in clinical trial data sharing. This study is focused on identifying ε values for the sharing of clinical trial data. MATERIALS AND METHODS We analyzed 2 clinical trial datasets with privacy budget ε ranging from 0.01 to 10. Smaller values of ε entail adding greater amounts of random noise, with better privacy as a result. Comparison of rates, odds ratios, means, and mean differences between the original clinical trial datasets and the empirical distribution of the DP estimator was performed. RESULTS The DP rate closely approximated the original rate of 6.5% when ε > 1. The DP odds ratio closely aligned with the original odds ratio of 0.689 when ε ≥ 3. The DP mean closely approximated the original mean of 164.64 when ε ≥ 1. As ε increased to 5, both the minimum and maximum DP means converged toward the original mean. DISCUSSION There is no consensus on how to choose the privacy budget ε. The definition of DP does not specify the required level of privacy, and there is no established formula for determining ε. CONCLUSION Our findings suggest that the application of DP holds promise in the context of sharing clinical trial data.
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Affiliation(s)
- Henian Chen
- Study Design and Data Analysis, College of Public Health, University of South Florida, Tampa, FL 33612, United States
| | - Jinyong Pang
- Study Design and Data Analysis, College of Public Health, University of South Florida, Tampa, FL 33612, United States
| | - Yayi Zhao
- Study Design and Data Analysis, College of Public Health, University of South Florida, Tampa, FL 33612, United States
| | - Spencer Giddens
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Joseph Ficek
- Oncology Statistics, GlaxoSmithKline, Collegeville, PA 19426, United States
| | - Matthew J Valente
- Study Design and Data Analysis, College of Public Health, University of South Florida, Tampa, FL 33612, United States
| | - Biwei Cao
- Study Design and Data Analysis, College of Public Health, University of South Florida, Tampa, FL 33612, United States
| | - Ellen Daley
- The Lawton and Rhea Chiles Center for Children and Families, College of Public Health, University of South Florida, Tampa, FL 33612, United States
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12
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Durazzo A, D’Andrea T, Gabrielli P, Pilla N, Aguzzi A, Lucarini M, Sagratini G. Development of a Database of LanguaL TM and FoodEx2 Codes of 50 Ready-to-Eat Products. Nutrients 2024; 16:1151. [PMID: 38674842 PMCID: PMC11054341 DOI: 10.3390/nu16081151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024] Open
Abstract
Ready-to-eat (RTE) and ready-to-heat (RTH) dishes are food items that help save time, physical energy, and mental effort in all food-related activities. Convenience of use, variability of supply, and adaptability to different consumption occasions have led to an increase of acceptance among consumers through the years. Specialized databases can help in this context, where food composition databases can provide information and data to create sustainable nutritional models by reducing the now growing number of chronic diseases. This paper aims at developing a database of LanguaLTM and FoodEx2 codes of 50 food preparations and ready-to-eat dishes designed for consumption outside the home. LanguaLTM, as well as FoodEx2, are classification and description systems for indexing, in the sense of a systematic description, of foods based on a hierarchical model (parent-child relationship), thus facilitating the international exchange of data on food composition, consumption, assessing chronic and/or acute exposure to a certain agent, and not least the assessment of nutrient intake. The database, here presented, consists of the codes of fifty ready-to-eat products present on the market in Italy, obtained by using the two mostly commonly used and widely recognized coding systems: LanguaLTM and FoodEx2. This database represents a tool and a guideline for other compilers and users to apply coding systems to ready-to-eat products. Moreover, it can be represented a resource for several applications, such as nutritional cards, nutritional facts, food labels, or booklet and brochures for promotion of food products, to be used at health and food nutrition interface, useful for consumers, dieticians, and food producers.
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Affiliation(s)
- Alessandra Durazzo
- CREA-Research Centre for Food and Nutrition, Via Ardeatina 546, 00178 Rome, Italy
| | - Tommaso D’Andrea
- Chemistry Interdisciplinary Project (ChIP), School of Pharmacy, University of Camerino, Via Madonna delle Carceri, 62032 Camerino, Italy
| | - Paolo Gabrielli
- CREA-Research Centre for Food and Nutrition, Via Ardeatina 546, 00178 Rome, Italy
| | - Niccolò Pilla
- Università di Torino, Via Verdi, 8, 10124 Turin, Italy
- Università Campus Biomedico di Roma, Via Álvaro del Portillo, 21, 00128 Rome, Italy
| | - Altero Aguzzi
- CREA-Research Centre for Food and Nutrition, Via Ardeatina 546, 00178 Rome, Italy
| | - Massimo Lucarini
- CREA-Research Centre for Food and Nutrition, Via Ardeatina 546, 00178 Rome, Italy
| | - Gianni Sagratini
- Chemistry Interdisciplinary Project (ChIP), School of Pharmacy, University of Camerino, Via Madonna delle Carceri, 62032 Camerino, Italy
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13
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Norris ML, Obeid N, El-Emam K. Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders. Int J Eat Disord 2024. [PMID: 38597344 DOI: 10.1002/eat.24215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 03/22/2024] [Accepted: 03/22/2024] [Indexed: 04/11/2024]
Abstract
OBJECTIVE To provide a brief overview of artificial intelligence (AI) application within the field of eating disorders (EDs) and propose focused solutions for research. METHOD An overview and summary of AI application pertinent to EDs with focus on AI's ability to address issues relating to data sharing and pooling (and associated privacy concerns), data augmentation, as well as bias within datasets is provided. RESULTS In addition to clinical applications, AI can utilize useful tools to help combat commonly encountered challenges in ED research, including issues relating to low prevalence of specific subpopulations of patients, small overall sample sizes, and bias within datasets. DISCUSSION There is tremendous potential to embed and utilize various facets of artificial intelligence (AI) to help improve our understanding of EDs and further evaluate and investigate questions that ultimately seek to improve outcomes. Beyond the technology, issues relating to regulation of AI, establishing ethical guidelines for its application, and the trust of providers and patients are all needed for ultimate adoption and acceptance into ED practice. PUBLIC SIGNIFICANCE Artificial intelligence (AI) offers a promise of significant potential within the realm of eating disorders (EDs) and encompasses a broad set of techniques that offer utility in various facets of ED research and by extension delivery of clinical care. Beyond the technology, issues relating to regulation, establishing ethical guidelines for application, and the trust of providers and patients are needed for the ultimate adoption and acceptance of AI into ED practice.
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Affiliation(s)
- Mark L Norris
- Department of Pediatrics, Children's Hospital of Eastern Ontario (CHEO), University of Ottawa, Ottawa, Ontario, Canada
- CHEO Research Institute, Ottawa, Ontario, Canada
| | - Nicole Obeid
- CHEO Research Institute, Ottawa, Ontario, Canada
- Department of Psychiatry, University of Ottawa, Ottawa, Ontario, Canada
| | - Khaled El-Emam
- CHEO Research Institute, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
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14
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Jamshidi-Naeini Y, Golzarri-Arroyo L, Thapa DK, Brown AW, Kpormegbey DE, Allison DB. Ambiguity in Statistical Analysis Methods and Nonconformity With Prespecified Commitment to Data Sharing in a Cluster Randomized Controlled Trial. J Med Internet Res 2024; 26:e54090. [PMID: 38568721 PMCID: PMC11024742 DOI: 10.2196/54090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 12/23/2023] [Accepted: 02/22/2024] [Indexed: 04/05/2024] Open
Affiliation(s)
- Yasaman Jamshidi-Naeini
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States
| | - Lilian Golzarri-Arroyo
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States
| | - Deependra K Thapa
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States
| | - Andrew W Brown
- Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Arkansas Children's Research Institute, Little Rock, AR, United States
| | - Daniel E Kpormegbey
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States
| | - David B Allison
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States
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15
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Lawson J, Rahimzadeh V, Baek J, Dove ES. Achieving Procedural Parity in Managing Access to Genomic and Related Health Data: A Global Survey of Data Access Committee Members. Biopreserv Biobank 2024; 22:123-129. [PMID: 37192473 DOI: 10.1089/bio.2022.0205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023] Open
Abstract
Data access committees (DACs) are critical players in the data sharing ecosystem. DACs review requests for access to data held in one or more repositories and where specific constraints determine how the data may be used and by whom. Our team surveyed DAC members affiliated with genomic data repositories worldwide to understand standard processes and procedures, operational metrics, bottlenecks, and efficiencies, as well as their perspectives on possible improvements to quality review. We found that DAC operations and systemic issues were common across repositories globally. In general, DAC members endeavored to achieve an appropriate balance of review efficiency, quality, and compliance. Our results suggest a similarly proportionate path forward that helps DACs pursue mutual improvements to efficiency and compliance without sacrificing review quality.
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Affiliation(s)
- Jonathan Lawson
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Vasiliki Rahimzadeh
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, Texas, USA
| | - Jinyoung Baek
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Edward S Dove
- School of Law, University of Edinburgh, Edinburgh, United Kingdom
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16
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Arias JJ, Tyler AM, Beskow LM, Carillo MC, Dickinson S, Goldman J, Majumder MA, Mello MM, Snyder HM, Yokoyama JS. Data stewardship in FTLD research: Investigator and research participant views. Alzheimers Dement 2024; 20:2886-2893. [PMID: 38456576 PMCID: PMC11032535 DOI: 10.1002/alz.13719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/26/2023] [Accepted: 01/04/2024] [Indexed: 03/09/2024]
Abstract
INTRODUCTION Federal policies and guidelines have expanded the return of individual results to participants and expectations for data sharing between investigators and through repositories. Here, we report investigators' and study participants' views and experiences with data stewardship practices within frontotemporal lobal degeneration (FTLD) research, which reveal unique ethical challenges. METHODS Semi-structured interviews with (1) investigators conducting FTLD research that includes genetic data collection and/or analysis and (2) participants enrolled in a single site longitudinal FTLD study. RESULTS Analysis of the interviews identified three meta themes: perspectives on data sharing, experiences with enrollment and participation, and data management and security as mechanisms for participant protections. DISCUSSION This study identified a set of preliminary gaps and needs regarding data stewardship within FTLD research. The results offer initial insights on ethical challenges to data stewardship aimed at informing future guidelines and policies.
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Affiliation(s)
- Jalayne J. Arias
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of Health Policy & Behavioral SciencesSchool of Public HealthGeorgia State UniversityAtlantaGeorgiaUSA
| | - Ana M. Tyler
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Laura M. Beskow
- Center for Biomedical Ethics and SocietyVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Maria C. Carillo
- Division of Medical & Scientific RelationsAlzheimer's AssociationChicagoIllinoisUSA
| | - Susan Dickinson
- The Association for Frontotemporal DegenerationKing of PrussiaPennsylvaniaUSA
| | - Jill Goldman
- Neurological InstituteColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Mary A. Majumder
- Center for Medical Ethics and Health PolicyBaylor College of MedicineHoustonTexasUSA
| | - Michelle M. Mello
- Stanford Law School and Department of MedicineStanford UniversityPalo AltoCaliforniaUSA
| | - Heather M. Snyder
- Division of Medical & Scientific RelationsAlzheimer's AssociationChicagoIllinoisUSA
| | - Jennifer S. Yokoyama
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
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Ibrahim ME, Adarmouch L, Elgamri A, Abd ElHafeez S, Mohammed Z, Abdelgawad F, Elsebaie EH, Abdelhafiz AS, Gamel E, El Rhazi K, Abdelnaby A, Ahram M, Silverman H. Researchers' Perspectives Regarding Ethical Issues of Biobank Research in the Arab Region. Biopreserv Biobank 2024; 22:98-109. [PMID: 36951637 PMCID: PMC11044858 DOI: 10.1089/bio.2022.0112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023] Open
Abstract
Background: The recent expansion of genomic biobank research in the Arab region in the Middle East North Africa has raised complex ethical and regulatory issues. However, there is a lack of studies regarding the views of Arab researchers involved in such research. We aimed to assess the perceptions and attitudes of Arab researchers regarding these issues in biobank research. Methods: We developed a questionnaire to assess the perceptions and attitudes regarding genetic research of researchers from Egypt, Sudan, Morocco, and Jordan. The questionnaire requested demographic data, perceptions, and attitudes regarding the collection, storage, and use of biospecimens and data, the use of broad consent, data security, data sharing, and community engagement. We used multiple linear regressions to identify predictors of perceptions and attitudes. Results: We recruited 383 researchers. Researchers favored equally the use of broad and tiered consent (44.1% and 39.1%, respectively). Most respondents agreed with the importance of confidentiality protections to ensure data security (91.8%). However, lower percentages were seen regarding the importance of community engagement (64.5%), data sharing with national colleagues and international partners (60.9% and 41.1%, respectively), and biospecimen sharing with national colleagues and international partners (59.9% and 36.2%, respectively). Investigators were evenly split on whether the return of individual research results should depend on the availability or not of a medical intervention that can be offered to address the genetic anomaly (47.5% and 46.4%, respectively). Predictors of attitudes toward biospecimen research included serving on Research Ethics Committees, prior research ethics training, and affiliation with nonacademic institutions. Conclusions: We recommend further exploratory research with researchers regarding the importance of community engagement and to address their concerns about data sharing, with researchers within and outside their countries.
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Affiliation(s)
- Maha E. Ibrahim
- Department of Physical Medicine, Rheumatology and Rehabilitation, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | - Latifa Adarmouch
- Department of Community Medicine, Public Health and Epidemiology, Faculty of Medicine, Cadi Ayyad University, Marrakesh, Morocco
| | - Alya Elgamri
- Department of Orthodontics, Pediatric Dentistry and Preventive Dentistry, Faculty of Dentistry, University of Khartoum, Khartoum, Sudan
| | - Samar Abd ElHafeez
- Epidemiology Department, High Institute of Public Health, Alexandria University, Alexandria, Egypt
| | - Zeinab Mohammed
- Department of Public Health and Community Medicine, Faculty of Medicine, Beni-Suef University, Beni-Suef, Egypt
| | - Fatma Abdelgawad
- Pediatric Dentistry and Dental Public Health Department, Faculty of Dentistry, Cairo University, Cairo, Egypt
| | - Eman H. Elsebaie
- Department of Public Health, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Ahmed Samir Abdelhafiz
- Department of Clinical Pathology, National Cancer Institute, Cairo University, Cairo, Egypt
| | - Ehsan Gamel
- Department of Oral Rehabilitation, Faculty of Dentistry, University of Khartoum, Khartoum, Sudan
| | - Karima El Rhazi
- Department of Epidemiology and Public Health, Faculty of Medicine, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Asmaa Abdelnaby
- Department of Public Health, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Mamoun Ahram
- Department of Physiology and Biochemistry, School of Medicine, The University of Jordan, Amman, Jordan
| | - Henry Silverman
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
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18
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Rigby RC, Ferdinand AO, Kum HC, Schmit C. Data Sharing in a Decentralized Public Health System: Lessons From COVID-19 Syndromic Surveillance. JMIR Public Health Surveill 2024; 10:e52587. [PMID: 38546731 PMCID: PMC11009847 DOI: 10.2196/52587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 01/17/2024] [Accepted: 01/20/2024] [Indexed: 04/14/2024] Open
Abstract
The COVID-19 pandemic revealed that data sharing challenges persist across public health information systems. We examine the specific challenges in sharing syndromic surveillance data between state, local, and federal partners. These challenges are complicated by US federalism, which decentralizes public health response and creates friction between different government units. The current policies restrict federal access to state and local syndromic surveillance data without each jurisdiction's consent. These policies frustrate legitimate federal governmental interests and are contrary to ethical guidelines for public health data sharing. Nevertheless, state and local public health agencies must continue to play a central role as there are important risks in interpreting syndromic surveillance data without understanding local contexts. Policies establishing a collaborative framework will be needed to support data sharing between federal, state, and local partners. A collaborative framework would be enhanced by a governance group with robust state and local involvement and policy guardrails to ensure the use of data is appropriate. These policy and relational challenges must be addressed to actualize a truly national public health information system.
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Affiliation(s)
- Ryan C Rigby
- Program in Health, Law, and Policy, Department of Health Policy and Management, Texas A&M University School of Public Health, College Station, TX, United States
| | - Alva O Ferdinand
- Department of Health Policy and Management, Texas A&M University School of Public Health, College Station, TX, United States
| | - Hye-Chung Kum
- Population Informatics Lab, Department of Health Policy and Management, Texas A&M University School of Public Health, College Station, TX, United States
| | - Cason Schmit
- Program in Health, Law, and Policy, Department of Health Policy and Management, Texas A&M University School of Public Health, College Station, TX, United States
- Population Informatics Lab, Department of Health Policy and Management, Texas A&M University School of Public Health, College Station, TX, United States
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Baek J, Lawson J, Rahimzadeh V. Investigating the Roles and Responsibilities of Institutional Signing Officials After Data Sharing Policy Reform for Federally Funded Research in the United States: National Survey. JMIR Form Res 2024; 8:e49822. [PMID: 38506894 PMCID: PMC10993121 DOI: 10.2196/49822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 01/04/2024] [Accepted: 01/07/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND New federal policies along with rapid growth in data generation, storage, and analysis tools are together driving scientific data sharing in the United States. At the same, triangulating human research data from diverse sources can also create situations where data are used for future research in ways that individuals and communities may consider objectionable. Institutional gatekeepers, namely, signing officials (SOs), are therefore at the helm of compliant management and sharing of human data for research. Of those with data governance responsibilities, SOs most often serve as signatories for investigators who deposit, access, and share research data between institutions. Although SOs play important leadership roles in compliant data sharing, we know surprisingly little about their scope of work, roles, and oversight responsibilities. OBJECTIVE The purpose of this study was to describe existing institutional policies and practices of US SOs who manage human genomic data access, as well as how these may change in the wake of new Data Management and Sharing requirements for National Institutes of Health-funded research in the United States. METHODS We administered an anonymous survey to institutional SOs recruited from biomedical research institutions across the United States. Survey items probed where data generated from extramurally funded research are deposited, how researchers outside the institution access these data, and what happens to these data after extramural funding ends. RESULTS In total, 56 institutional SOs participated in the survey. We found that SOs frequently approve duplicate data deposits and impose stricter access controls when data use limitations are unclear or unspecified. In addition, 21% (n=12) of SOs knew where data from federally funded projects are deposited after project funding sunsets. As a consequence, most investigators deposit their scientific data into "a National Institutes of Health-funded repository" to meet the Data Management and Sharing requirements but also within the "institution's own repository" or a third-party repository. CONCLUSIONS Our findings inform 5 policy recommendations and best practices for US SOs to improve coordination and develop comprehensive and consistent data governance policies that balance the need for scientific progress with effective human data protections.
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Affiliation(s)
| | | | - Vasiliki Rahimzadeh
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
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20
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Tecuatl C, Ljungquist B, Ascoli GA. Accelerating the continuous community sharing of digital neuromorphology data. bioRxiv 2024:2024.03.15.585306. [PMID: 38562736 PMCID: PMC10983892 DOI: 10.1101/2024.03.15.585306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The tree-like morphology of neurons and glia is a key cellular determinant of circuit connectivity and metabolic function in the nervous system of essentially all animals. To elucidate the contribution of specific cell types to both physiological and pathological brain states, it is important to access detailed neuroanatomy data for quantitative analysis and computational modeling. NeuroMorpho.Org is the largest online collection of freely available digital neural reconstructions and related metadata and is continuously updated with new uploads. Earlier in the project, we released multiple datasets together yearly, but this process caused an average delay of several months in making the data public. Moreover, in the past 5 years, >80% of invited authors agreed to share their data with the community via NeuroMorpho.Org, up from <20% in the first 5 years of the project. In the same period, the average number of reconstructions per publication increased 600%, creating the need for automatic processing to release more reconstructions in less time. The progressive automation of our pipeline enabled the transition to agile releases of individual datasets as soon as they are ready. The overall time from data identification to public sharing decreased by 63.7%; 78% of the datasets are now released in less than 3 months with an average workflow duration below 40 days. Furthermore, the mean processing time per reconstruction dropped from 3 hours to 2 minutes. With these continuous improvements, NeuroMorpho.Org strives to forge a positive culture of open data. Most importantly, the new, original research enabled through reuse of datasets across the world has a multiplicative effect on science discovery, benefiting both authors and users.
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Affiliation(s)
- Carolina Tecuatl
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Bengt Ljungquist
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Giorgio A. Ascoli
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
- Interdisciplinary Program in Neuroscience; College of Science; George Mason University, Fairfax, VA, USA
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21
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Cunha-Oliveira T, Ioannidis JPA, Oliveira PJ. Best Practices for Data Management and Sharing in Experimental Biomedical Research. Physiol Rev 2024. [PMID: 38451234 DOI: 10.1152/physrev.00043.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 02/29/2024] [Indexed: 03/08/2024] Open
Abstract
Effective data management is crucial for scientific integrity and reproducibility, a cornerstone of scientific progress. Well-organized and well-documented data enable validation and building upon results. Data management encompasses activities including organization, documentation, storage, sharing, and preservation. Robust data management establishes credibility, fostering trust within the scientific community and benefiting researchers' careers. In experimental biomedicine, comprehensive data management is vital due to the typically intricate protocols, extensive metadata, and large datasets. Low-throughput experiments, in particular, require careful management to address variations and errors in protocols and raw data quality. Transparent and accountable research practices rely on accurate documentation of procedures, data collection, and analysis methods. Proper data management ensures long-term preservation and accessibility of valuable datasets. Well-managed data can be revisited, contributing to cumulative knowledge and potential new discoveries. Publicly funded research has an added responsibility for transparency, resource allocation, and avoiding redundancy. Meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources. This review provides critical insights into raw and processed data, metadata, high-throughput versus low-throughput datasets, a common language for documentation, experimental and reporting guidelines, efficient data management systems, sharing practices, and relevant repositories. We systematically present available resources and optimal practices for wide use by experimental biomedical researchers.
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Affiliation(s)
| | - John P A Ioannidis
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, United States
| | - Paulo J Oliveira
- Center for Neuroscience and Cell Biology, University of Coimbra, Cantanhede, Portugal
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22
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Baines R, Stevens S, Austin D, Anil K, Bradwell H, Cooper L, Maramba ID, Chatterjee A, Leigh S. Patient and Public Willingness to Share Personal Health Data for Third-Party or Secondary Uses: Systematic Review. J Med Internet Res 2024; 26:e50421. [PMID: 38441944 PMCID: PMC10951832 DOI: 10.2196/50421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 12/01/2023] [Accepted: 12/18/2023] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND International advances in information communication, eHealth, and other digital health technologies have led to significant expansions in the collection and analysis of personal health data. However, following a series of high-profile data sharing scandals and the emergence of COVID-19, critical exploration of public willingness to share personal health data remains limited, particularly for third-party or secondary uses. OBJECTIVE This systematic review aims to explore factors that affect public willingness to share personal health data for third-party or secondary uses. METHODS A systematic search of 6 databases (MEDLINE, Embase, PsycINFO, CINAHL, Scopus, and SocINDEX) was conducted with review findings analyzed using inductive-thematic analysis and synthesized using a narrative approach. RESULTS Of the 13,949 papers identified, 135 were included. Factors most commonly identified as a barrier to data sharing from a public perspective included data privacy, security, and management concerns. Other factors found to influence willingness to share personal health data included the type of data being collected (ie, perceived sensitivity); the type of user requesting their data to be shared, including their perceived motivation, profit prioritization, and ability to directly impact patient care; trust in the data user, as well as in associated processes, often established through individual choice and control over what data are shared with whom, when, and for how long, supported by appropriate models of dynamic consent; the presence of a feedback loop; and clearly articulated benefits or issue relevance including valued incentivization and compensation at both an individual and collective or societal level. CONCLUSIONS There is general, yet conditional public support for sharing personal health data for third-party or secondary use. Clarity, transparency, and individual control over who has access to what data, when, and for how long are widely regarded as essential prerequisites for public data sharing support. Individual levels of control and choice need to operate within the auspices of assured data privacy and security processes, underpinned by dynamic and responsive models of consent that prioritize individual or collective benefits over and above commercial gain. Failure to understand, design, and refine data sharing approaches in response to changeable patient preferences will only jeopardize the tangible benefits of data sharing practices being fully realized.
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Affiliation(s)
- Rebecca Baines
- Centre for Health Technology, University of Plymouth, Plymouth, United Kingdom
| | - Sebastian Stevens
- Centre for Health Technology, University of Plymouth, Plymouth, United Kingdom
- Prometheus Health Technologies Ltd, Newquay, United Kingdom
| | - Daniela Austin
- Centre for Health Technology, University of Plymouth, Plymouth, United Kingdom
| | | | - Hannah Bradwell
- Centre for Health Technology, University of Plymouth, Plymouth, United Kingdom
| | - Leonie Cooper
- Centre for Health Technology, University of Plymouth, Plymouth, United Kingdom
| | | | - Arunangsu Chatterjee
- Centre for Health Technology, University of Plymouth, Plymouth, United Kingdom
- School of Medicine, University of Leeds, Leeds, United Kingdom
| | - Simon Leigh
- Prometheus Health Technologies Ltd, Newquay, United Kingdom
- Warwick Medical School, University of Warwick, Conventry, United Kingdom
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van Swieten MMH, Haselgrove C. Editorial: Navigating the landscape of FAIR data sharing and reuse: repositories, standards, and resources. Front Neuroinform 2024; 18:1387758. [PMID: 38495843 PMCID: PMC10943951 DOI: 10.3389/fninf.2024.1387758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 02/20/2024] [Indexed: 03/19/2024] Open
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24
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Shome M, MacKenzie TMG, Subbareddy SR, Snyder MP. The Importance, Challenges, and Possible Solutions for Sharing Proteomics Data While Safeguarding Individuals' Privacy. Mol Cell Proteomics 2024; 23:100731. [PMID: 38331191 PMCID: PMC10915627 DOI: 10.1016/j.mcpro.2024.100731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 01/28/2024] [Accepted: 02/05/2024] [Indexed: 02/10/2024] Open
Abstract
Proteomics data sharing has profound benefits at the individual level as well as at the community level. While data sharing has increased over the years, mostly due to journal and funding agency requirements, the reluctance of researchers with regard to data sharing is evident as many shares only the bare minimum dataset required to publish an article. In many cases, proper metadata is missing, essentially making the dataset useless. This behavior can be explained by a lack of incentives, insufficient awareness, or a lack of clarity surrounding ethical issues. Through adequate training at research institutes, researchers can realize the benefits associated with data sharing and can accelerate the norm of data sharing for the field of proteomics, as has been the standard in genomics for decades. In this article, we have put together various repository options available for proteomics data. We have also added pros and cons of those repositories to facilitate researchers in selecting the repository most suitable for their data submission. It is also important to note that a few types of proteomics data have the potential to re-identify an individual in certain scenarios. In such cases, extra caution should be taken to remove any personal identifiers before sharing on public repositories. Data sets that will be useless without personal identifiers need to be shared in a controlled access repository so that only authorized researchers can access the data and personal identifiers are kept safe.
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Affiliation(s)
- Mahasish Shome
- Department of Genetics, Stanford University, Palo Alto, California, USA
| | - Tim M G MacKenzie
- Department of Genetics, Stanford University, Palo Alto, California, USA
| | | | - Michael P Snyder
- Department of Genetics, Stanford University, Palo Alto, California, USA.
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Blackburn DC, Boyer DM, Gray JA, Winchester J, Bates JM, Baumgart SL, Braker E, Coldren D, Conway KW, Rabosky AD, de la Sancha N, Dillman CB, Dunnum JL, Early CM, Frable BW, Gage MW, Hanken J, Maisano JA, Marks BD, Maslenikov KP, McCormack JE, Nagesan RS, Pandelis GG, Prestridge HL, Rabosky DL, Randall ZS, Robbins MB, Scheinberg LA, Spencer CL, Summers AP, Tapanila L, Thompson CW, Tornabene L, Watkins-Colwell GJ, Welton LJ, Stanley EL. Increasing the impact of vertebrate scientific collections through 3D imaging: The openVertebrate (oVert) Thematic Collections Network. Bioscience 2024; 74:169-186. [PMID: 38560620 PMCID: PMC10977868 DOI: 10.1093/biosci/biad120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/08/2023] [Indexed: 04/04/2024] Open
Abstract
The impact of preserved museum specimens is transforming and increasing by three-dimensional (3D) imaging that creates high-fidelity online digital specimens. Through examples from the openVertebrate (oVert) Thematic Collections Network, we describe how we created a digitization community dedicated to the shared vision of making 3D data of specimens available and the impact of these data on a broad audience of scientists, students, teachers, artists, and more. High-fidelity digital 3D models allow people from multiple communities to simultaneously access and use scientific specimens. Based on our multiyear, multi-institution project, we identify significant technological and social hurdles that remain for fully realizing the potential impact of digital 3D specimens.
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Affiliation(s)
- David C Blackburn
- Florida Museum of Natural History (FLMNH), University of Florida, Gainesville, Florida, United States
- Blackburn served as the lead principal investigator for the oVert Thematic Collections Network
| | - Doug M Boyer
- Duke University, Durham, North Carolina, United States
| | - Jaimi A Gray
- Florida Museum of Natural History (FLMNH), University of Florida, Gainesville, Florida, United States
- Blackburn served as the lead principal investigator for the oVert Thematic Collections Network
| | | | - John M Bates
- Field Museum of Natural History, Chicago, Illinois, United States
| | - Stephanie L Baumgart
- University of Chicago and University of Florida, Gainesville, Florida, United States
| | - Emily Braker
- University of Colorado, Boulder, Colorado, United States
| | - Daryl Coldren
- Field Museum of Natural History, Chicago, Illinois, United States
| | - Kevin W Conway
- Texas A&M University, College Station, Texas, United States
| | | | - Noé de la Sancha
- Chicago State University DePaul University, Chicago, Illinois, United States
| | | | - Jonathan L Dunnum
- Museum of Southwestern Biology, University of New Mexico, Albuquerque, New Mexico, United States
| | - Catherine M Early
- FLMNH Science Museum of Minnesota, St. Paul, Minnesota, United States
| | - Benjamin W Frable
- Scripps Institute of Oceanography, University of California, San Diego, San Diego, California, United States
| | - Matt W Gage
- Harvard University, Cambridge, Massachusetts, United States
| | - James Hanken
- Harvard University, Cambridge, Massachusetts, United States
| | | | - Ben D Marks
- Field Museum of Natural History, Chicago, Illinois, United States
| | | | | | | | | | | | | | - Zachary S Randall
- Florida Museum of Natural History (FLMNH), University of Florida, Gainesville, Florida, United States
- Blackburn served as the lead principal investigator for the oVert Thematic Collections Network
| | | | | | - Carol L Spencer
- University of California, Berkeley, in Berkeley, California, United States
| | - Adam P Summers
- University of Washington, Seattle, Washington, United States
| | - Leif Tapanila
- Idaho State University, Pocatello, Idaho, United States
| | | | - Luke Tornabene
- University of Washington, Seattle, Washington, United States
| | | | - Luke J Welton
- University of Kansas, Lawrence, Kansas, United States
| | | | - Edward L Stanley
- Florida Museum of Natural History (FLMNH), University of Florida, Gainesville, Florida, United States
- Blackburn served as the lead principal investigator for the oVert Thematic Collections Network
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Lloyd D, House JS, Akhtari FS, Schmitt CP, Fargo DC, Scholl EH, Phillips J, Choksi S, Shah R, Hall JE, Motsinger-Reif AA. Interactive data sharing for multiple questionnaire-based exposome-wide association studies and exposome correlations in the Personalized Environment and Genes Study. Exposome 2024; 4:osae003. [PMID: 38425336 PMCID: PMC10899804 DOI: 10.1093/exposome/osae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/20/2023] [Accepted: 01/01/2024] [Indexed: 03/02/2024]
Abstract
The correlations among individual exposures in the exposome, which refers to all exposures an individual encounters throughout life, are important for understanding the landscape of how exposures co-occur, and how this impacts health and disease. Exposome-wide association studies (ExWAS), which are analogous to genome-wide association studies (GWAS), are increasingly being used to elucidate links between the exposome and disease. Despite increased interest in the exposome, tools and publications that characterize exposure correlations and their relationships with human disease are limited, and there is a lack of data and results sharing in resources like the GWAS catalog. To address these gaps, we developed the PEGS Explorer web application to explore exposure correlations in data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS) that were rigorously calculated to account for differing data types and previously published results from ExWAS. Through globe visualizations, PEGS Explorer allows users to explore correlations between exposures found to be associated with complex diseases. The exposome data used for analysis includes not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The web application addresses the lack of accessible data and results sharing, a major challenge in the field, and enables users to put results in context, generate hypotheses, and, importantly, replicate findings in other cohorts. PEGS Explorer will be updated with additional results as they become available, ensuring it is an up-to-date resource in exposome science.
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Affiliation(s)
- Dillon Lloyd
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - John S House
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Farida S Akhtari
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Charles P Schmitt
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - David C Fargo
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| | | | | | | | | | - Janet E Hall
- Clinical Research Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Alison A Motsinger-Reif
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
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Charles WM, van der Waal MB, Flach J, Bisschop A, van der Waal RX, Es-Sbai H, McLeod CJ. Blockchain-Based Dynamic Consent and its Applications for Patient-Centric Research and Health Information Sharing: Protocol for an Integrative Review. JMIR Res Protoc 2024; 13:e50339. [PMID: 38315514 PMCID: PMC10877491 DOI: 10.2196/50339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 12/01/2023] [Accepted: 12/07/2023] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND Blockchain has been proposed as a critical technology to facilitate more patient-centric research and health information sharing. For instance, it can be applied to coordinate and document dynamic informed consent, a procedure that allows individuals to continuously review and renew their consent to the collection, use, or sharing of their private health information. Such has been suggested to facilitate ethical, compliant longitudinal research, and patient engagement. However, blockchain-based dynamic consent is a relatively new concept, and it is not yet clear how well the suggested implementations will work in practice. Efforts to critically evaluate implementations in health research contexts are limited. OBJECTIVE The objective of this protocol is to guide the identification and critical appraisal of implementations of blockchain-based dynamic consent in health research contexts, thereby facilitating the development of best practices for future research, innovation, and implementation. METHODS The protocol describes methods for an integrative review to allow evaluation of a broad range of quantitative and qualitative research designs. The PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) framework guided the review's structure and nature of reporting findings. We developed search strategies and syntax with the help of an academic librarian. Multiple databases were selected to identify pertinent academic literature (CINAHL, Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science) and gray literature (Electronic Theses Online Service, ProQuest Dissertations and Theses, Open Access Theses and Dissertations, and Google Scholar) for a comprehensive picture of the field's progress. Eligibility criteria were defined based on PROSPERO (International Prospective Register of Systematic Reviews) requirements and a criteria framework for technology readiness. A total of 2 reviewers will independently review and extract data, while a third reviewer will adjudicate discrepancies. Quality appraisal of articles and discussed implementations will proceed based on the validated Mixed Method Appraisal Tool, and themes will be identified through thematic data synthesis. RESULTS Literature searches were conducted, and after duplicates were removed, 492 articles were eligible for screening. Title and abstract screening allowed the removal of 312 articles, leaving 180 eligible articles for full-text review against inclusion criteria and confirming a sufficient body of literature for project feasibility. Results will synthesize the quality of evidence on blockchain-based dynamic consent for patient-centric research and health information sharing, covering effectiveness, efficiency, satisfaction, regulatory compliance, and methods of managing identity. CONCLUSIONS The review will provide a comprehensive picture of the progress of emerging blockchain-based dynamic consent technologies and the rigor with which implementations are approached. Resulting insights are expected to inform best practices for future research, innovation, and implementation to benefit patient-centric research and health information sharing. TRIAL REGISTRATION PROSPERO CRD42023396983; http://tinyurl.com/cn8a5x7t. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/50339.
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Affiliation(s)
- Wendy M Charles
- Health Administration Program, Business School, University of Colorado, Denver, Denver, CO, United States
- Healthcare Informatics Program, University of Denver, Denver, CO, United States
| | - Mark B van der Waal
- Triall, Maarssen, Netherlands
- Athena Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | | | | | | | | | - Christopher J McLeod
- Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL, United States
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Tong W, Yang L, Li Z, Jin X, Tan L. Enhancing Security and Flexibility in the Industrial Internet of Things: Blockchain-Based Data Sharing and Privacy Protection. Sensors (Basel) 2024; 24:1035. [PMID: 38339751 PMCID: PMC10857713 DOI: 10.3390/s24031035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 01/22/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
To address the complexities, inflexibility, and security concerns in traditional data sharing models of the Industrial Internet of Things (IIoT), we propose a blockchain-based data sharing and privacy protection (BBDSPP) scheme for IIoT. Initially, we characterize and assign values to attributes, and employ a weighted threshold secret sharing scheme to refine the data sharing approach. This enables flexible combinations of permissions, ensuring the adaptability of data sharing. Subsequently, based on non-interactive zero-knowledge proof technology, we design a lightweight identity proof protocol using attribute values. This protocol pre-verifies the identity of data accessors, ensuring that only legitimate terminal members can access data within the system, while also protecting the privacy of the members. Finally, we utilize the InterPlanetary File System (IPFS) to store encrypted shared resources, effectively addressing the issue of low storage efficiency in traditional blockchain systems. Theoretical analysis and testing of the computational overhead of our scheme demonstrate that, while ensuring performance, our scheme has the smallest total computational load compared to the other five schemes. Experimental results indicate that our scheme effectively addresses the shortcomings of existing solutions in areas such as identity authentication, privacy protection, and flexible combination of permissions, demonstrating a good performance and strong feasibility.
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Affiliation(s)
- Weiming Tong
- Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China;
| | - Luyao Yang
- School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China; (L.Y.); (Z.L.); (X.J.)
| | - Zhongwei Li
- School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China; (L.Y.); (Z.L.); (X.J.)
| | - Xianji Jin
- School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China; (L.Y.); (Z.L.); (X.J.)
| | - Liguo Tan
- Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China;
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Jagu S, Mardis ER, Wedekind MF, Widemann BC, Kingery RK, Gonzalez SL, Shern JF, Reaman GH. Childhood cancer data initiative: Status report. Pediatr Blood Cancer 2024; 71:e30745. [PMID: 37889049 DOI: 10.1002/pbc.30745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/10/2023] [Accepted: 10/15/2023] [Indexed: 10/28/2023]
Abstract
In March 2023, over 800 researchers, clinicians, patients, survivors, and advocates from the pediatric oncology community met to discuss the progress of the National Cancer Institute's Childhood Cancer Data Initiative. We present here the status of the initiative's efforts in building its data ecosystem and updates on key programs, especially the Molecular Characterization Initiative and the planned Coordinated National Initiative for Rare Cancers in Children and Young Adults. These activities aim to improve access to childhood cancer data, foster collaborations, facilitate integrative data analysis, and expand access to molecular characterization, ultimately leading to the development of innovative therapeutic approaches.
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Affiliation(s)
- Subhashini Jagu
- Office of Data Sharing, Center for Biomedical Informatics & Information Technology, National Cancer Institute, Bethesda, Maryland, USA
| | - Elaine R Mardis
- Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Mary F Wedekind
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Brigitte C Widemann
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | | | | | - Jack F Shern
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Gregory H Reaman
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, Maryland, USA
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Braunack‐Mayer AJ, Adams C, Nettel‐Aguirre A, Fabrianesi B, Carolan L, Beilby J, Flack F. Community views on the secondary use of general practice data: Findings from a mixed-methods study. Health Expect 2024; 27:e13984. [PMID: 38361335 PMCID: PMC10869884 DOI: 10.1111/hex.13984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 09/29/2023] [Accepted: 01/23/2024] [Indexed: 02/17/2024] Open
Abstract
INTRODUCTION General practice data, particularly when combined with hospital and other health service data through data linkage, are increasingly being used for quality assurance, evaluation, health service planning and research. In this study, we explored community views on sharing general practice data for secondary purposes, including research, to establish what concerns and conditions need to be addressed in the process of developing a social licence to support such use. METHODS We used a mixed-methods approach with focus groups (November-December 2021), followed by a cross-sectional survey (March-April 2022). RESULTS The participants in this study strongly supported sharing general practice data with the clinicians responsible for their care, and where there were direct benefits for individual patients. Over 90% of survey participants (N = 2604) were willing to share their general practice information to directly support their health care, that is, for the primary purpose of collection. There was less support for sharing data for secondary purposes such as research and health service planning (36% and 45% respectively in broad agreement) or for linking general practice data to data in the education, social services and criminal justice systems (30%-36%). A substantial minority of participants were unsure or could not see how benefits would arise from sharing data for secondary purposes. Participants were concerned about the potential for privacy breaches, discrimination and data misuse and they wanted greater transparency and an opportunity to consent to data release. CONCLUSION The findings of this study suggest that the public may be more concerned about sharing general practice data for secondary purposes than they are about sharing data collected in other settings. Sharing general practice data more broadly will require careful attention to patient and public concerns, including focusing on the factors that will sustain trust and legitimacy in general practice and GPs. PATIENT AND PUBLIC CONTRIBUTION Members of the public were participants in the study. Data produced from their participation generated study findings. CLINICAL TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Annette J. Braunack‐Mayer
- Australian Centre for Health Engagement, Evidence and Values (ACHEEV), School of Health and Society, Faculty of the Arts, Social Sciences and HumanitiesUniversity of WollongongWollongongNew South WalesAustralia
- Australia Health Services Research InstituteUniversity of WollongongWollongongNew South WalesAustralia
| | - Carolyn Adams
- Macquarie Law SchoolMacquarie UniversitySydneyNew South WalesAustralia
| | - Alberto Nettel‐Aguirre
- National Institute for Applied Statistics Research AustraliaUniversity of WollongongWollongongNew South WalesAustralia
| | - Belinda Fabrianesi
- Australian Centre for Health Engagement, Evidence and Values (ACHEEV), School of Health and Society, Faculty of the Arts, Social Sciences and HumanitiesUniversity of WollongongWollongongNew South WalesAustralia
| | - Lucy Carolan
- Australian Centre for Health Engagement, Evidence and Values (ACHEEV), School of Health and Society, Faculty of the Arts, Social Sciences and HumanitiesUniversity of WollongongWollongongNew South WalesAustralia
| | - Justin Beilby
- School of Health and SocietyUniversity of WollongongWollongongNew South WalesAustralia
| | - Felicity Flack
- Population Health Research NetworkUniversity of Western AustraliaPerthWestern AustraliaAustralia
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Greenfest‐Allen E, Valladares O, Kuksa PP, Gangadharan P, Lee W, Cifello J, Katanic Z, Kuzma AB, Wheeler N, Bush WS, Leung YY, Schellenberg G, Stoeckert CJ, Wang L. NIAGADS Alzheimer's GenomicsDB: A resource for exploring Alzheimer's disease genetic and genomic knowledge. Alzheimers Dement 2024; 20:1123-1136. [PMID: 37881831 PMCID: PMC10916966 DOI: 10.1002/alz.13509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/25/2023] [Accepted: 09/21/2023] [Indexed: 10/27/2023]
Abstract
INTRODUCTION The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site Alzheimer's Genomics Database (GenomicsDB) is a public knowledge base of Alzheimer's disease (AD) genetic datasets and genomic annotations. METHODS GenomicsDB uses a custom systems architecture to adopt and enforce rigorous standards that facilitate harmonization of AD-relevant genome-wide association study summary statistics datasets with functional annotations, including over 230 million annotated variants from the AD Sequencing Project. RESULTS GenomicsDB generates interactive reports compiled from the harmonized datasets and annotations. These reports contextualize AD-risk associations in a broader functional genomic setting and summarize them in the context of functionally annotated genes and variants. DISCUSSION Created to make AD-genetics knowledge more accessible to AD researchers, the GenomicsDB is designed to guide users unfamiliar with genetic data in not only exploring but also interpreting this ever-growing volume of data. Scalable and interoperable with other genomics resources using data technology standards, the GenomicsDB can serve as a central hub for research and data analysis on AD and related dementias. HIGHLIGHTS The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) offers to the public a unique, disease-centric collection of AD-relevant GWAS summary statistics datasets. Interpreting these data is challenging and requires significant bioinformatics expertise to standardize datasets and harmonize them with functional annotations on genome-wide scales. The NIAGADS Alzheimer's GenomicsDB helps overcome these challenges by providing a user-friendly public knowledge base for AD-relevant genetics that shares harmonized, annotated summary statistics datasets from the NIAGADS repository in an interpretable, easily searchable format.
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Affiliation(s)
- Emily Greenfest‐Allen
- Penn Neurodegeneration Genomics CenterPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Institute for Biomedical InformaticsPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Pathology and Laboratory MedicinePerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Otto Valladares
- Penn Neurodegeneration Genomics CenterPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Institute for Biomedical InformaticsPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Pathology and Laboratory MedicinePerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Pavel P. Kuksa
- Penn Neurodegeneration Genomics CenterPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Institute for Biomedical InformaticsPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Pathology and Laboratory MedicinePerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Prabhakaran Gangadharan
- Penn Neurodegeneration Genomics CenterPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Institute for Biomedical InformaticsPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Pathology and Laboratory MedicinePerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Wan‐Ping Lee
- Penn Neurodegeneration Genomics CenterPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Institute for Biomedical InformaticsPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Pathology and Laboratory MedicinePerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jeffrey Cifello
- Penn Neurodegeneration Genomics CenterPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Pathology and Laboratory MedicinePerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Zivadin Katanic
- Penn Neurodegeneration Genomics CenterPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Institute for Biomedical InformaticsPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Pathology and Laboratory MedicinePerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Amanda B. Kuzma
- Penn Neurodegeneration Genomics CenterPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Institute for Biomedical InformaticsPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Pathology and Laboratory MedicinePerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Nicholas Wheeler
- Cleveland Institute for Computational BiologyDepartment of Population and Quantitative Health SciencesCase Western Reserve UniversityClevelandOhioUSA
| | - William S. Bush
- Cleveland Institute for Computational BiologyDepartment of Population and Quantitative Health SciencesCase Western Reserve UniversityClevelandOhioUSA
| | - Yuk Yee Leung
- Penn Neurodegeneration Genomics CenterPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Institute for Biomedical InformaticsPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Pathology and Laboratory MedicinePerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Gerard Schellenberg
- Penn Neurodegeneration Genomics CenterPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Institute for Biomedical InformaticsPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Pathology and Laboratory MedicinePerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Christian J. Stoeckert
- Institute for Biomedical InformaticsPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of GeneticsPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Li‐San Wang
- Penn Neurodegeneration Genomics CenterPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Institute for Biomedical InformaticsPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Pathology and Laboratory MedicinePerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Hamilton DG, Page MJ, Everitt S, Fraser H, Fidler F. Cancer researchers' experiences with and perceptions of research data sharing: Results of a cross-sectional survey. Account Res 2024:1-28. [PMID: 38299475 DOI: 10.1080/08989621.2024.2308606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 01/18/2024] [Indexed: 02/02/2024]
Abstract
BACKGROUND Despite wide recognition of the benefits of sharing research data, public availability rates have not increased substantially in oncology or medicine more broadly over the last decade. METHODS We surveyed 285 cancer researchers to determine their prior experience with sharing data and views on known drivers and inhibitors. RESULTS We found that 45% of respondents had shared some data from their most recent empirical publication, with respondents who typically studied non-human research participants, or routinely worked with human genomic data, more likely to share than those who did not. A third of respondents added that they had previously shared data privately, with 74% indicating that doing so had also led to authorship opportunities or future collaborations for them. Journal and funder policies were reported to be the biggest general drivers toward sharing, whereas commercial interests, agreements with industrial sponsors and institutional policies were the biggest prohibitors. We show that researchers' decisions about whether to share data are also likely to be influenced by participants' desires. CONCLUSIONS Our survey suggests that increased promotion and support by research institutions, alongside greater championing of data sharing by journals and funders, may motivate more researchers in oncology to share their data.
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Affiliation(s)
- Daniel G Hamilton
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, Australia
- Melbourne Medical School, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, Australia
| | - Matthew J Page
- Methods in Evidence Synthesis Unit, School of Public Health & Preventive Medicine, Monash University, Melbourne, Australia
| | - Sarah Everitt
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Hannah Fraser
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, Australia
| | - Fiona Fidler
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, Australia
- School of History & Philosophy of Sciences, University of Melbourne, Melbourne, Australia
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Krefting D, Mutters NT, Pryss R, Sedlmayr M, Boeker M, Dieterich C, Koll C, Mueller M, Slagman A, Waltemath D, Wulf A, Zenker S. Herding Cats in Pandemic Times - Towards Technological and Organizational Convergence of Heterogeneous Solutions for Investigating and Mastering the Pandemic in University Medical Centers. Stud Health Technol Inform 2024; 310:1271-1275. [PMID: 38270019 DOI: 10.3233/shti231169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
To understand and handle the COVID-19 pandemic, digital tools and infrastructures were built in very short timeframes, resulting in stand-alone and non-interoperable solutions. To shape an interoperable, sustainable, and extensible ecosystem to advance biomedical research and healthcare during the pandemic and beyond, a short-term project called "Collaborative Data Exchange and Usage" (CODEX+) was initiated to integrate and connect multiple COVID-19 projects into a common organizational and technical framework. In this paper, we present the conceptual design, provide an overview of the results, and discuss the impact of such a project for the trade-off between innovation and sustainable infrastructures.
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Affiliation(s)
- Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Germany
| | - Nico T Mutters
- Institute for Hygiene and Public Health, Bonn University Hospital, Germany
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Germany
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, TU Dresden, Germany
| | - Martin Boeker
- Institute of Artificial Intelligence and Informatics in Medicine, Chair of Medical Informatics, Medical Center rechts der Isar, Technical University of Munich
| | | | - Carolin Koll
- Department I for Internal Medicine, University Hospital of Cologne, Germany
| | - Martina Mueller
- Department of Internal Medicine I, University Hospital Regensburg, Germany
| | - Anna Slagman
- Department of Emergency Medicine, Charité - Universitätsmedizin Berlin, Germany
| | - Dagmar Waltemath
- Medical Informatics Laboratory, University Medicine Greifswald, Germany
| | - Antje Wulf
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany
- Big Data in Medicine, Carl von Ossietzky University Oldenburg, Germany
| | - Sven Zenker
- Staff Unit for Medical & Scientific Technology Development & Coordination, Bonn University Hospital, Germany
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Martone ME. The past, present and future of neuroscience data sharing: a perspective on the state of practices and infrastructure for FAIR. Front Neuroinform 2024; 17:1276407. [PMID: 38250019 PMCID: PMC10796549 DOI: 10.3389/fninf.2023.1276407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 10/31/2023] [Indexed: 01/23/2024] Open
Abstract
Neuroscience has made significant strides over the past decade in moving from a largely closed science characterized by anemic data sharing, to a largely open science where the amount of publicly available neuroscience data has increased dramatically. While this increase is driven in significant part by large prospective data sharing studies, we are starting to see increased sharing in the long tail of neuroscience data, driven no doubt by journal requirements and funder mandates. Concomitant with this shift to open is the increasing support of the FAIR data principles by neuroscience practices and infrastructure. FAIR is particularly critical for neuroscience with its multiplicity of data types, scales and model systems and the infrastructure that serves them. As envisioned from the early days of neuroinformatics, neuroscience is currently served by a globally distributed ecosystem of neuroscience-centric data repositories, largely specialized around data types. To make neuroscience data findable, accessible, interoperable, and reusable requires the coordination across different stakeholders, including the researchers who produce the data, data repositories who make it available, the aggregators and indexers who field search engines across the data, and community organizations who help to coordinate efforts and develop the community standards critical to FAIR. The International Neuroinformatics Coordinating Facility has led efforts to move neuroscience toward FAIR, fielding several resources to help researchers and repositories achieve FAIR. In this perspective, I provide an overview of the components and practices required to achieve FAIR in neuroscience and provide thoughts on the past, present and future of FAIR infrastructure for neuroscience, from the laboratory to the search engine.
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Affiliation(s)
- Maryann E. Martone
- Department of Neurosciences, University of California, San Diego, CA, United States
- San Francisco Veterans Administration Hospital, San Francisco, CA, United States
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Morain SR, Bollinger J, Weinfurt K, Sugarman J. Stakeholder perspectives on data sharing from pragmatic clinical trials: Unanticipated challenges for meeting emerging requirements. Learn Health Syst 2024; 8:e10366. [PMID: 38249837 PMCID: PMC10797577 DOI: 10.1002/lrh2.10366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/24/2023] [Accepted: 03/29/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction Numerous arguments have been advanced for broadly sharing de-identified, participant-level clinical trial data. However, data sharing in pragmatic clinical trials (PCTs) presents ethical challenges. While prior scholarship has described aspects of PCTs that raise distinct considerations for data sharing, there have been no reports of the experiences of those at the leading edge of data-sharing efforts for PCTs, including how these particular challenges have been navigated. To address this gap, we conducted interviews with key stakeholders, with a focus on the ethical issues presented by sharing data from PCTs. Methods We recruited respondents using purposive sampling to reflect the range of stakeholder groups affected by efforts to expand PCT data sharing. Through semi-structured interviews, we explored respondents' experiences and perceptions about sharing de-identified, individual-level data from PCTs. An integrated approach was used to identify and describe key themes. Results We conducted 40 interviews between April and September 2022. Five overarching themes emerged through analysis: (1) challenges in sharing data collected under a waiver or alteration of consent; (2) conflicting views regarding PCT patient-subject preferences for data sharing; (3) identification of respect-promoting practices beyond consent; (4) concerns about elevated risks or burdens from sharing PCT data; and (5) diverse views about the likely benefits resulting from sharing PCT data. Conclusion Our data indicate unresolved tensions in how to fulfill the expectation to broadly share de-identified, individual-level data from PCTs, and suggest that those promulgating and implementing data-sharing policies must be sensitive to PCT-specific considerations. Future work could inform efforts to tailor data-sharing policy and practice to reflect the challenges presented by PCTs, including sharing experiences from trials that have successfully navigated these tensions.
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Affiliation(s)
- Stephanie R. Morain
- Berman Insitute of BioethicsJohns Hopkins UniversityBaltimoreMarylandUSA
- Department of Health Policy & ManagementJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Juli Bollinger
- Berman Insitute of BioethicsJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Kevin Weinfurt
- Department of Population Health SciencesDuke University Medical CenterDurhamNorth CarolinaUSA
| | - Jeremy Sugarman
- Berman Insitute of BioethicsJohns Hopkins UniversityBaltimoreMarylandUSA
- Department of Health Policy & ManagementJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- Department of MedicineSchool of Medicine, Johns HopkinsBaltimoreMarylandUSA
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Nienaber McKay AG, Brand D, Botes M, Cengiz N, Swart M. The regulation of health data sharing in Africa: a comparative study. J Law Biosci 2024; 11:lsad035. [PMID: 38259628 PMCID: PMC10800019 DOI: 10.1093/jlb/lsad035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The sharing of health data is an essential component in the provision of healthcare, in medical research, and disease surveillance. Health data sharing is subject to regulatory frameworks that vary across jurisdictions. In Africa, numerous factors complicate the regulation of health data sharing, including technological, motivational, economic, and political barriers, as well as ethical and legal challenges. This comparative study examines the regulation of health data sharing in Africa by comparing and contrasting the legal and policy frameworks of five African countries. The study identifies gaps and inconsistencies in the current regulatory regimes and provides recommendations for improving the regulation of health data sharing in Africa.
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Affiliation(s)
- Annelize G Nienaber McKay
- Division of Law, School of Business, Law & Social Sciences, Abertay University, Dundee, Scotland, United Kingdom of Britain
- Department of Public Law, University of Pretoria, Pretoria, South Africa
| | - Dirk Brand
- School of Public Leadership, Stellenbosch University, Stellenbosch, South Africa
| | - Marietjie Botes
- Division of Medical Ethics and Law, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Nezerith Cengiz
- Division of Medical Ethics and Law, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Marno Swart
- Faculty of Law, University of Cambridge, Cambridge, United Kingdom of Britain
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Mu W, Kleter GA, Bouzembrak Y, Dupouy E, Frewer LJ, Radwan Al Natour FN, Marvin HJP. Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools. Compr Rev Food Sci Food Saf 2024; 23:e13296. [PMID: 38284601 DOI: 10.1111/1541-4337.13296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/25/2023] [Accepted: 12/15/2023] [Indexed: 01/30/2024]
Abstract
To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.
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Affiliation(s)
- Wenjuan Mu
- Wageningen Food Safety Research, Wageningen University and Research, Wageningen, The Netherlands
| | - Gijs A Kleter
- Wageningen Food Safety Research, Wageningen University and Research, Wageningen, The Netherlands
| | - Yamine Bouzembrak
- Information Technology, Wageningen University, Wageningen University and Research, Wageningen, The Netherlands
| | - Eleonora Dupouy
- Food and Agriculture Organization of the United Nations, Rome, Italy
| | - Lynn J Frewer
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK
| | | | - H J P Marvin
- Hayan Group B.V., Research department, Rhenen, The Netherlands
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Contaxis N. Hidden Ethical Challenges in Health Data Infrastructure. Hastings Cent Rep 2024; 54:15-19. [PMID: 38390677 DOI: 10.1002/hast.1564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Data infrastructure includes the bureaucratic, technical, and social mechanisms that assist in actions like data management, analysis, storage, and sharing. While issues like data sharing have been addressed in depth in bioethical literature, data infrastructure presents its own ethical considerations, apart from the actions (such as data sharing and data analysis) that it enables. This essay outlines some of these considerations-namely, the ethics of efficiency, the visibility of infrastructure, the power of standards, and the impact of new technologies-in order to invite the bioethics community to participate in conversations about infrastructure, as their expertise is both needed and welcomed.
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Abstract
This is the second in a series of articles looking at school health data collection from identification of data points to utilizing data to share your story and submit your data to contribute to the National School Health Data Set: Every Student Counts! This article focuses on using data to share your story. Data storytelling versus data visualization will be discussed as well as HOW schools nurses can utilize easy access programs to support this process. Building on the first article in the series, the school nurse will not only identify the WHY and WHAT related to data collection but also HOW to link school health data to educational data to increase the audience of the story and follow data sharing regulations.
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40
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Kidney International Editorial Board. On data sharing. Kidney Int 2024; 105:2-5. [PMID: 38182289 DOI: 10.1016/j.kint.2023.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 01/07/2024]
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41
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Forester S, Jennings-Dobbs E, Burton-Freeman B. Development of a Comprehensive Food Data Citation Standard: A Surprising Gap in the Nutrition Research Literature. Curr Dev Nutr 2024; 8:102048. [PMID: 38156342 PMCID: PMC10751823 DOI: 10.1016/j.cdnut.2023.102048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 11/06/2023] [Accepted: 11/20/2023] [Indexed: 12/30/2023] Open
Abstract
Currently, there is no standard for the citation of food composition data. This leads to the questions: how are food and nutrient data cited in research papers, and are they presented in a way that allows studies to be reproduced? To answer these questions, we performed a review of the literature and quantified the accuracy and completeness of data citations from publications (January to December 2020) in the top 5 nutrition journals as ranked by the Scimago Journal Rankings. We then performed a review of citation guidelines currently in place in other disciplines. Similar to the requirement of completing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist for systematic reviews, we have developed a comprehensive data citation checklist, the Comprehensive Food Data Citation (CFDC) checklist. The CFDC checklist was developed through a benchmarking assessment against established data citation standards. Its purpose is to establish a standardized, best-practice approach for reporting food composition data. The CFDC checklist has been designed to cater to both publishers and authors, ensuring consistency and accuracy in food composition data reporting. The CFDC checklist is also available as an interactive citation generator to facilitate the adoption of consistent and comprehensive citation of food composition data and is available at https://www.nutrientinstitute.org/cfdc. Despite general agreement that accurate data citation is paramount, this is the first citation standard specifically developed to capture food composition data. Because food composition data are the foundation of nutrition research, our proposed guidelines aim to provide the field with a much-needed foundation for acknowledging and sharing data in a way that fosters reproducibility.
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Affiliation(s)
- Shavawn Forester
- Nutrient Institute, a 501(c)(3) not-for-profit organization, Reno, NV, United States
| | - Emily Jennings-Dobbs
- Nutrient Institute, a 501(c)(3) not-for-profit organization, Reno, NV, United States
| | - Britt Burton-Freeman
- Department of Food Science and Nutrition, Illinois Institute of Technology, Chicago, IL, United States
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Chong CD, Ashina S, Kamins J, Scher A, Gentile CP, Finkel A, Oshinsky ML, Schwedt TJ. NINDS Common Data Elements for post-traumatic headache: A project from the American Headache Society Post-Traumatic Headache Special Interest Section. Headache 2024; 64:1-2. [PMID: 38009371 DOI: 10.1111/head.14653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 10/20/2023] [Indexed: 11/28/2023]
Affiliation(s)
- Catherine D Chong
- Department of Neurology and Biomedical Engineering, Mayo Clinic, Phoenix, Arizona, USA
| | - Sait Ashina
- Department of Neurology and Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Clinical Medicine, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Joshua Kamins
- Goldberg Migraine Program, Department of Neurology, UCLA, Los Angeles, California, USA
- Steve Tisch BrainSPORT Program, Department of Neurosurgery, UCLA, Los Angeles, California, USA
| | - Ann Scher
- Uniformed Services University, Bethesda, Maryland, USA
| | - Carlyn Patterson Gentile
- Departments of Neurology and Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Alan Finkel
- The Carolina Headache Foundation, Department of Psychiatry, Anesethia and Dentistry, University of North Carolina at Chapel Hill, Durham, North Carolina, USA
| | - Michael L Oshinsky
- National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA
| | - Todd J Schwedt
- Department of Neurology, Mayo Clinic, Phoenix, Arizona, USA
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Tufa AA, Gonfa G, Tesfa A, Getachew T, Bekele D, Dagnaw F, Djellouli N, Colbourn T, Marchant T, Lemma S. "We don't trust all data coming from all facilities": factors influencing the quality of care network data quality in Ethiopia. Glob Health Action 2023; 16:2279856. [PMID: 38018430 PMCID: PMC10795578 DOI: 10.1080/16549716.2023.2279856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 11/01/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND Good quality data are a key to quality health care. In 2017, WHO has launched the Quality of Care Network (QCN) to reduce maternal, newborn and stillbirth mortality via learning and sharing networks. Guided by the principle of equity and dignity, the network members agreed to implement the programme in 2017-2021. OBJECTIVE This paper seeks to explore how QCN has contributed to improving data quality and to identify factors influencing quality of data in Ethiopia. METHODS We conducted a qualitative study in selected QCN facilities in Ethiopia using key informant interview and observation methods. We interviewed 40 people at national, sub-national and facility levels. Non-participant observations were carried out in four purposively selected health facilities; we accessed monthly reports from 41 QCN learning facilities. A codebook was prepared following a deductive and inductive analytical approach, coded using Nvivo 12 and thematically analysed. RESULTS There was a general perception that QCN had improved health data documentation and use in the learning facilities, achieved through coaching, learning and building from pre-existing initiatives. QCN also enhanced the data elements available by introducing a broader set of quality indicators. However, the perception of poor data quality persisted. Factors negatively affecting data quality included a lack of integration of QCN data within routine health system activities, the perception that QCN was a pilot, plus a lack of inclusive engagement at different levels. Both individual and system capabilities needed to be strengthened. CONCLUSION There is evidence of QCN's contribution to improving data awareness. But a lack of inclusive engagement of actors, alignment and limited skill for data collection and analysis continued to affect data quality and use. In the absence of new resources, integration of new data activities within existing routine health information systems emerged as the most important potential action for positive change.
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Affiliation(s)
- Asebe Amenu Tufa
- Ethiopian Public Health Institute, HSRHRD, Addis Ababa, Ethiopia
| | - Geremew Gonfa
- Ethiopian Public Health Institute, HSRHRD, Addis Ababa, Ethiopia
| | - Anene Tesfa
- Ethiopian Public Health Institute, HSRHRD, Addis Ababa, Ethiopia
| | | | - Desalegn Bekele
- Ethiopian Ministry of Health, Quality and Clinical Service Directorate, Addis Ababa, Ethiopia
| | - Ftalew Dagnaw
- Ethiopian Ministry of Health, Quality and Clinical Service Directorate, Addis Ababa, Ethiopia
| | - Nehla Djellouli
- Institute for Global Health, University College London, London, UK
| | - Tim Colbourn
- Institute for Global Health, University College London, London, UK
| | - Tanya Marchant
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London, UK
| | - Seblewengel Lemma
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London, UK
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Meng G, Jan Ali M, Tse SM. Caregivers' Perceptions, Needs, and Data Sharing Concerns in mHealth Research on Pediatric Asthma: Cross-Sectional Survey Study. JMIR Pediatr Parent 2023; 6:e49521. [PMID: 38127911 PMCID: PMC10763990 DOI: 10.2196/49521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/11/2023] [Accepted: 10/12/2023] [Indexed: 12/23/2023] Open
Abstract
Background Pediatric asthma is the most common chronic respiratory disease of childhood. Caregivers often report lacking knowledge in several aspects of asthma management at home. Although the use of mobile health (mHealth) tools, such as mobile apps, could facilitate asthma self-management and, simultaneously, the collection of data for research, few studies have explored the features that caregivers would like to see in such a tool and their perceptions on data sharing. Objective This study evaluates caregivers' perceived knowledge gaps in asthma management; their perceptions of certain features and resources that should be included in a potential mobile app; and any concerns that they may have regarding data sharing for research, including privacy and security concerns. Methods In this cross-sectional study, we surveyed 200 caregivers of children (aged 1-13 y) with asthma who were followed at a pediatric tertiary care center in Montreal, Canada. Anonymous data were collected through the institutional web-based survey platform. We collected the participants' answers by using a 5-category Likert scale ("completely agree," "agree," "neither agree nor disagree," "disagree," and "completely disagree"), multiple-choice questions, and free-text questions on the abovementioned topics. Descriptive statistics were performed, and answers were compared between caregivers of preschool-aged children and caregivers of school-aged children. Results Participating children's mean age was 5.9 (SD 3.4) years, with 54% (108/200) aged ≤5 years and 46% (92/200) aged >6 years. Overall, caregivers reported having adequate knowledge about asthma and asthma self-management. Nonetheless, they identified several desirable features for a mobile app focused on asthma self-management. The most frequently identified features included receiving alerts about environmental triggers of asthma (153/199, 76.9%), having videos that demonstrate symptoms of asthma (133/199, 66.8%), and being able to log children's asthma action plans in the app (133/199, 66.8%). Interestingly, more caregivers of preschool-aged children preferred textual information when compared to caregivers of school-aged children (textual information for explaining asthma: P=.008; textual information for the symptoms of asthma: P=.005). Caregivers were generally highly in favor of sharing data collected through a mobile app for research. Conclusions Caregivers of children with asthma in our study identified several desirable educational and interactive features that they wanted to have in a mobile app for asthma self-management. These findings provide a foundation for designing and developing mHealth tools that are relevant to caregivers of children with asthma.
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Affiliation(s)
- Glen Meng
- Faculty of Medicine, Université de Montréal, MontréalQC, Canada
| | - Maliha Jan Ali
- Faculty of Medicine, Université de Montréal, MontréalQC, Canada
| | - Sze Man Tse
- Faculty of Medicine, Université de Montréal, MontréalQC, Canada
- Division of Respiratory Medicine, Department of Pediatrics, Centre Hospitalier Universitaire Sainte-Justine, MontrealQC, Canada
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Nissen M, Perez CA, Jaeger KM, Bleher H, Flaucher M, Huebner H, Danzberger N, Titzmann A, Pontones CA, Fasching PA, Beckmann MW, Eskofier BM, Leutheuser H. Usability and Perception of a Wearable-Integrated Digital Maternity Record App in Germany: User Study. JMIR Pediatr Parent 2023; 6:e50765. [PMID: 38109377 PMCID: PMC10750977 DOI: 10.2196/50765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/20/2023] [Accepted: 10/02/2023] [Indexed: 12/20/2023] Open
Abstract
Background Although digital maternity records (DMRs) have been evaluated in the past, no previous work investigated usability or acceptance through an observational usability study. Objective The primary objective was to assess the usability and perception of a DMR smartphone app for pregnant women. The secondary objective was to assess personal preferences and habits related to online information searching, wearable data presentation and interpretation, at-home examination, and sharing data for research purposes during pregnancy. Methods A DMR smartphone app was developed. Key features such as wearable device integration, study functionalities (eg, questionnaires), and common pregnancy app functionalities (eg, mood tracker) were included. Women who had previously given birth were invited to participate. Participants completed 10 tasks while asked to think aloud. Sessions were conducted via Zoom. Video, audio, and the shared screen were recorded for analysis. Task completion times, task success, errors, and self-reported (free text) feedback were evaluated. Usability was measured through the System Usability Scale (SUS) and User Experience Questionnaire (UEQ). Semistructured interviews were conducted to explore the secondary objective. Results A total of 11 participants (mean age 34.6, SD 2.2 years) were included in the study. A mean SUS score of 79.09 (SD 18.38) was achieved. The app was rated "above average" in 4 of 6 UEQ categories. Sixteen unique features were requested. We found that 5 of 11 participants would only use wearables during pregnancy if requested to by their physician, while 10 of 11 stated they would share their data for research purposes. Conclusions Pregnant women rely on their medical caregivers for advice, including on the use of mobile and ubiquitous health technology. Clear benefits must be communicated if issuing wearable devices to pregnant women. Participants that experienced pregnancy complications in the past were overall more open toward the use of wearable devices in pregnancy. Pregnant women have different opinions regarding access to, interpretation of, and reactions to alerts based on wearable data. Future work should investigate personalized concepts covering these aspects.
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Affiliation(s)
- Michael Nissen
- Machine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Carlos A Perez
- Machine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Katharina M Jaeger
- Machine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Hannah Bleher
- Department of Social Ethics, University of Bonn, Bonn, Germany
| | - Madeleine Flaucher
- Machine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Hanna Huebner
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Nina Danzberger
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Adriana Titzmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Constanza A Pontones
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Bjoern M Eskofier
- Machine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Heike Leutheuser
- Machine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
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Rahimzadeh V, Jones KM, Majumder MA, Kahana MJ, Rutishauser U, Williams ZM, Cash SS, Paulk AC, Zheng J, Beauchamp MS, Collinger JL, Pouratian N, McGuire AL, Sheth SA. Benefits of sharing neurophysiology data from the BRAIN Initiative Research Opportunities in Humans Consortium. Neuron 2023; 111:3710-3715. [PMID: 37944519 PMCID: PMC10995938 DOI: 10.1016/j.neuron.2023.09.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 09/21/2023] [Accepted: 09/21/2023] [Indexed: 11/12/2023]
Abstract
Sharing human brain data can yield scientific benefits, but because of various disincentives, only a fraction of these data is currently shared. We profile three successful data-sharing experiences from the NIH BRAIN Initiative Research Opportunities in Humans (ROH) Consortium and demonstrate benefits to data producers and to users.
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Affiliation(s)
- Vasiliki Rahimzadeh
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kathryn Maxson Jones
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX 77030, USA; Department of History, Purdue University, West Lafayette, IN 47907, USA
| | - Mary A Majumder
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX 77030, USA
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Jie Zheng
- Department of Ophthalmology, Boston Children's Hospital, Boston, MA 02115, USA
| | - Michael S Beauchamp
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jennifer L Collinger
- Rehab Neural Engineering Labs, Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15219, USA
| | - Nader Pouratian
- Department of Neurological Surgery, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Amy L McGuire
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA.
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47
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Emanuele E, Minoretti P. Measuring the Impact of Data Sharing: From Author-Level Metrics to Quantification of Economic and Non-tangible Benefits. Cureus 2023; 15:e50308. [PMID: 38205488 PMCID: PMC10777335 DOI: 10.7759/cureus.50308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/10/2023] [Indexed: 01/12/2024] Open
Abstract
In early 2023, the National Institutes of Health (NIH) implemented its Data Management and Sharing (DMS) Policy, requiring researchers to share scientific data produced with NIH funding. The policy's objective is to amplify the benefits of public investment in research by promoting the dissemination and reusability of primary data. Given this backdrop, identifying a robust methodology to assess the impact of data sharing across diverse research domains is essential. In this review, we adopted two methodological paradigms, the bottom-up and top-down strategies, and employed content analysis to pinpoint established methodologies and innovative practices within this intricate field. Although numerous author-level metrics are available to gauge the impact of data sharing, their application is still limited. Non-traditional metrics, encompassing economic (e.g., cost savings) and intangible benefits, presently appear to hold more potential for evaluating the impact of primary data sharing. Finally, we address the primary obstacles encountered by open data policies and introduce an innovative "Shared model for shared data" framework to bolster data sharing practices and refine evaluation metrics.
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48
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Sánchez MC, Hernández Clemente JC, García López FJ. Public and Patients' Perspectives Towards Data and Sample Sharing for Research: An Overview of Empirical Findings. J Empir Res Hum Res Ethics 2023; 18:319-345. [PMID: 37936410 DOI: 10.1177/15562646231212644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
We aimed to review the attitudes and perspectives of the public and patients towards the sharing of data and biospecimens for research and to identify common dimensions, regardless of setting. Our review included systematic, scoping or thematic reviews of empirical studies retrieved from Medline (PubMed interface), Web of Science, Scopus, ProQuest and Cochrane Reviews. The main themes identified and synthesised across the 14 reviews were readiness and motivations; potential risks and safeguards; trust, transparency and accountability; autonomy and preferred type of consent; and factors influencing data and biospecimen sharing and consent. Sociodemographic factors and research and individual context remain relevant influencing factors in all settings, while preferences for types of consent are highly heterogeneous. Trusted environments and adapted consent options with participant engagement are relevant to improve research participation.
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49
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Golden E, Allen D, Amberg A, Anger LT, Baker E, Baran SW, Bringezu F, Clark M, Duchateau-Nguyen G, Escher SE, Giri V, Grevot A, Hartung T, Li D, Lotfi L, Muster W, Snyder K, Wange R, Steger-Hartmann T. Toward implementing virtual control groups in nonclinical safety studies. ALTEX 2023; 41:282-301. [PMID: 38043132 DOI: 10.14573/altex.2310041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 11/22/2023] [Indexed: 12/05/2023]
Abstract
Historical data from control groups in animal toxicity studies is currently mainly used for comparative purposes to assess validity and robustness of study results. Due to the highly controlled environment in which the studies are performed and the homogeneity of the animal collectives it has been proposed to use the historical data for building so-called virtual control groups, which could replace partly or entirely the concurrent control. This would constitute a substantial contribution to the reduction of animal use in safety studies. Before the concept can be implemented, the prerequisites regarding data collection, curation and statistical evaluation together with a validation strategy need to be identified to avoid any impairment of the study outcome and subsequent consequences for human risk assessment. To further assess and develop the concept of virtual control groups the transatlantic think tank for toxicology (t⁴) sponsored a workshop with stakeholders from the pharmaceutical and chemical industry, academia, FDA, pharmaceutical, contract research organizations (CROs), and non-governmental organizations in Washington, which took place in March 2023. This report summarizes the current efforts of a European initiative to share, collect and curate animal control data in a centralized database and the first approaches to identify optimal matching criteria between virtual controls and the treatment arms of a study as well as first reflections about strategies for a qualification procedure and potential pitfalls of the concept.
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Affiliation(s)
- Emily Golden
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | | | | | - Elizabeth Baker
- Physicians Committee for Responsible Medicine, Washington, DC, USA
| | | | - Frank Bringezu
- Merck Healthcare KGaA, Chemical & Preclinical Safety, Darmstadt, Germany
| | - Matthew Clark
- Charles River Laboratories, now KALOS Technologies, Philadelphia, PA, USA
| | - Guillemette Duchateau-Nguyen
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center, Basel, Switzerland
| | | | - Varun Giri
- BASF SE, Experimental Toxicology and Ecology, 67056 Ludwigshafen am Rhein, Germany
| | - Armelle Grevot
- Novartis Institute for Biomedical Research, Novartis AG, Basel, Switzerland
| | - Thomas Hartung
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- CAAT-Europe, University of Konstanz, Konstanz, Germany
| | - Dingzhou Li
- Pfizer, Global Biometrics and Data Management, Groton, CT, USA
| | | | - Wolfgang Muster
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center, Basel, Switzerland
| | - Kevin Snyder
- Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, USA
| | - Ronald Wange
- Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, USA
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50
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Sleiman PM, Qu HQ, Connolly JJ, Mentch F, Pereira A, Lotufo PA, Tollman S, Choudhury A, Ramsay M, Kato N, Ozaki K, Mitsumori R, Jeon JP, Hong CH, Son SJ, Roh HW, Lee DG, Mukadam N, Foote IF, Marshall CR, Butterworth A, Prins BP, Glessner JT, Hakonarson H. Trans-ethnic genomic informed risk assessment for Alzheimer's disease: An International Hundred K+ Cohorts Consortium study. Alzheimers Dement 2023; 19:5765-5772. [PMID: 37450379 PMCID: PMC10854406 DOI: 10.1002/alz.13378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 04/26/2023] [Accepted: 05/05/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND As a collaboration model between the International HundredK+ Cohorts Consortium (IHCC) and the Davos Alzheimer's Collaborative (DAC), our aim was to develop a trans-ethnic genomic informed risk assessment (GIRA) algorithm for Alzheimer's disease (AD). METHODS The GIRA model was created to include polygenic risk score calculated from the AD genome-wide association study loci, the apolipoprotein E haplotypes, and non-genetic covariates including age, sex, and the first three principal components of population substructure. RESULTS We validated the performance of the GIRA model in different populations. The proteomic study in the participant sites identified proteins related to female infertility and autoimmune thyroiditis and associated with the risk scores of AD. CONCLUSIONS As the initial effort by the IHCC to leverage existing large-scale datasets in a collaborative setting with DAC, we developed a trans-ethnic GIRA for AD with the potential of identifying individuals at high risk of developing AD for future clinical applications.
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Affiliation(s)
- Patrick M. Sleiman
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
| | - Hui-Qi Qu
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
| | - John J Connolly
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
| | - Frank Mentch
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
| | - Alexandre Pereira
- Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Centro de Pesquisas Clínicas e Epidemiológicas, Hospital Universitário, Universidade de São Paulo, São Paulo, Brazil
| | - Paulo A Lotufo
- Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Centro de Pesquisas Clínicas e Epidemiológicas, Hospital Universitário, Universidade de São Paulo, São Paulo, Brazil
| | - Stephen Tollman
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ananyo Choudhury
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Michele Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Norihiro Kato
- National Center for Global Health and Medicine, Tokyo, 1628655, Japan
| | - Kouichi Ozaki
- Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology (NCGG), Obu City, Aichi Prefecture, Japan
| | - Risa Mitsumori
- Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology (NCGG), Obu City, Aichi Prefecture, Japan
| | - Jae-Pil Jeon
- Korea Biobank Project, Korea National Institute of Health, Osong, Korea
| | - Chang Hyung Hong
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Korea
| | - Sang Joon Son
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Korea
| | - Hyun Woong Roh
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Korea
| | - Dong-gi Lee
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Korea
- Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Naaheed Mukadam
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, UK
| | - Isabelle F Foote
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, UK
- Genes & Health, Blizard Institute, Queen Mary University of London, UK
| | - Charles R Marshall
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, UK
- Genes & Health, Blizard Institute, Queen Mary University of London, UK
| | - Adam Butterworth
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Bram P Prins
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joseph T Glessner
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
| | - Hakon Hakonarson
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
- Division of Pulmonary Medicine, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
- Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
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