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Cascini F, Pantovic A, Al-Ajlouni YA, Puleo V, De Maio L, Ricciardi W. Health data sharing attitudes towards primary and secondary use of data: a systematic review. EClinicalMedicine 2024; 71:102551. [PMID: 38533128 PMCID: PMC10963197 DOI: 10.1016/j.eclinm.2024.102551] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 03/28/2024] Open
Abstract
Background To receive the best care, people share their health data (HD) with their health practitioners (known as sharing HD for primary purposes). However, during the past two decades, sharing for other (i.e., secondary) purposes has become of great importance in numerous fields, including public health, personalized medicine, research, and development. We aimed to conduct the first comprehensive overview of all studies that investigated people's HD sharing attitudes-along with associated barriers/motivators and significant influencing factors-for all data types and across both primary and secondary uses. Methods We searched PubMed, MEDLINE, PsycINFO, Web of Science, EMBASE, and CINAHL for relevant studies published in English between database inception and February 28, 2023, using a predefined set of keywords. Studies were included, regardless of their design, if they reported outcomes related to attitudes towards sharing HD. We extracted key data from the included studies, including the type of HD involved and findings related to: HD sharing attitudes (either in general or depending on type of data/user); barriers/motivators/benefits/concerns of the study participants; and sociodemographic and other variables that could impact HD sharing behaviour. The qualitative synthesis was conducted by dividing the studies according to the data type (resulting in five subgroups) as well as the purpose the data sharing was focused on (primary, secondary or both). The Newcastle-Ottawa Scale (NOS) was used to assess the quality of non-randomised studies. This work was registered with PROSPERO, CRD42023413822. Findings Of 2109 studies identified through our search, 116 were included in the qualitative synthesis, yielding a total of 228,501 participants and various types of HD represented: person-generated HD (n = 17 studies and 10,771 participants), personal HD in general (n = 69 studies and 117,054 participants), Biobank data (n = 7 studies and 27,073 participants), genomic data (n = 13 studies and 54,716 participants), and miscellaneous data (n = 10 studies and 18,887 participants). The majority of studies had a moderate level of quality (83 [71.6%] of 116 studies), but varying levels of quality were observed across the included studies. Overall, studies suggest that sharing intentions for primary purposes were observed to be high regardless of data type, and it was higher than sharing intentions for secondary purposes. Sharing for secondary purposes yielded variable findings, where both the highest and the lowest intention rates were observed in the case of studies that explored sharing biobank data (98% and 10%, respectively). Several influencing factors on sharing intentions were identified, such as the type of data recipient, data, consent. Further, concerns related to data sharing that were found to be mutual for all data types included privacy, security, and data access/control, while the perceived benefits included those related to improvements in healthcare. Findings regarding attitudes towards sharing varied significantly across sociodemographic factors and depended on data type and type of use. In most cases, these findings were derived from single studies and therefore warrant confirmations from additional studies. Interpretation Sharing health data is a complex issue that is influenced by various factors (the type of health data, the intended use, the data recipient, among others) and these insights could be used to overcome barriers, address people's concerns, and focus on spreading awareness about the data sharing process and benefits. Funding None.
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Affiliation(s)
- Fidelia Cascini
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L. go Francesco Vito 1, Rome, 00168, Italy
- Directorate General for the Digitisation of the Health Information System and Statistics, Ministry of Health, Italy
| | - Ana Pantovic
- Faculty of Biology, University of Belgrade, Belgrade, Serbia
| | | | - Valeria Puleo
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L. go Francesco Vito 1, Rome, 00168, Italy
| | - Lucia De Maio
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L. go Francesco Vito 1, Rome, 00168, Italy
| | - Walter Ricciardi
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L. go Francesco Vito 1, Rome, 00168, Italy
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Rius C, Liu Y, Sixto-Costoya A, Valderrama-Zurián JC, Lucas-Dominguez R. State of open science in cancer research. Clin Transl Oncol 2024:10.1007/s12094-024-03468-7. [PMID: 38635076 DOI: 10.1007/s12094-024-03468-7] [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: 02/02/2024] [Accepted: 03/15/2024] [Indexed: 04/19/2024]
Abstract
PURPOSE This study has been focused on assessing the Open Science scenario of cancer research during the period 2011-2021, in terms of the derived scientific publications and raw data dissemination. METHODS A cancer search equation was executed in the Science Citation Index-Expanded, collecting the papers signed by at least one Spanish institution. The same search strategy was performed in the Data Citation Index to describe dataset diffusion. RESULTS 50,822 papers were recovered, 71% of which belong to first and second quartile journals. 59% of the articles were published in Open Access (OA) journals. The Open Access model and international collaboration positively conditioned the number of citations received. Among the most productive journals stood out Plos One, Cancers, and Clinical and Translational Oncology. 2693 genomics, proteomics and metabolomics datasets were retrieved, being Gene Expression Omnibus the favoured repository. CONCLUSIONS There has been an increase in oncology publications in Open Access. Most were published in first quartile journals and received higher citations than non-Open Access articles, as well as when oncological investigation was performed between international research teams, being relevant in the context of Open Science. Genetic repositories have been the preferred for sharing oncology datasets. Further investigation of research and data sharing in oncology is needed, supported by stronger Open Science policies, to achieve better data sharing practices among three scientific main pillars: researchers, publishers, and scientific organizations.
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Affiliation(s)
- Cristina Rius
- UISYS Group, Department of History of Science and Information Science, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain
- Unit associated with the Interuniversity Institute for Advanced Research on the Evaluation of Science and the University (INAECU) UC3M-UAM, Madrid, Spain
- Spanish National Center for Cardiovascular Research (CNIC), Madrid, Spain
- CIBERCV, Madrid, Spain
| | - Yiming Liu
- UISYS Group, Department of History of Science and Information Science, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain
- Unit associated with the Interuniversity Institute for Advanced Research on the Evaluation of Science and the University (INAECU) UC3M-UAM, Madrid, Spain
| | - Andrea Sixto-Costoya
- UISYS Group, Department of History of Science and Information Science, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain
- Unit associated with the Interuniversity Institute for Advanced Research on the Evaluation of Science and the University (INAECU) UC3M-UAM, Madrid, Spain
- Department of Social Work and Social Services, Faculty of Social Sciences, Universitat de València, Valencia, Spain
| | - Juan Carlos Valderrama-Zurián
- UISYS Group, Department of History of Science and Information Science, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain
- Unit associated with the Interuniversity Institute for Advanced Research on the Evaluation of Science and the University (INAECU) UC3M-UAM, Madrid, Spain
| | - Rut Lucas-Dominguez
- UISYS Group, Department of History of Science and Information Science, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain.
- Unit associated with the Interuniversity Institute for Advanced Research on the Evaluation of Science and the University (INAECU) UC3M-UAM, Madrid, Spain.
- CIBERONC, Valencia, Spain.
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Wen F, Wang Z, Qu L, Huang H, Hu X. Enhancing secure multi-group data sharing through integration of IPFS and hyperledger fabric. PeerJ Comput Sci 2024; 10:e1962. [PMID: 38660153 PMCID: PMC11041925 DOI: 10.7717/peerj-cs.1962] [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: 08/09/2023] [Accepted: 03/05/2024] [Indexed: 04/26/2024]
Abstract
Data sharing is increasingly important across various industries. However, issues such as data integrity verification during sharing, encryption key leakage, and difficulty sharing data between different user groups have been identified. To address these challenges, this study proposes a multi-group data sharing network model based on Consortium Blockchain and IPFS for P2P sharing. This model uses a dynamic key encryption algorithm to provide secure data sharing, avoiding the problems associated with existing data transmission techniques such as key cracking or data leakage due to low security and reliability. Additionally, the model establishes an IPFS network for users within the group, allowing for the generation of data probes to verify data integrity, and the use of the Fabric network to record log information and probe data related to data operations and encryption. Data owners retain full control over access to their data to ensure privacy and security. The experimental results show that the system proposed in this study has wide applicability.
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Affiliation(s)
- Feng Wen
- School of Information Science and Engineering, Shenyang Ligong University, Shenyang, Liaoning, China
| | - Zhuo Wang
- School of Information Science and Engineering, Shenyang Ligong University, Shenyang, Liaoning, China
| | - Leda Qu
- School of Information Science and Engineering, Shenyang Ligong University, Shenyang, Liaoning, China
| | - Haixin Huang
- School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, Liaoning, China
| | - Xiaojie Hu
- School of Information Science and Engineering, Shenyang Ligong University, Shenyang, Liaoning, China
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Thorogood A. Population Neuroscience: Strategies to Promote Data Sharing While Protecting Privacy. Curr Top Behav Neurosci 2024. [PMID: 38509403 DOI: 10.1007/7854_2024_467] [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] [Indexed: 03/22/2024]
Abstract
Population neuroscience aims to advance our understanding of how genetic and environmental factors influence brain development and brain health over the life span, by integrating genomics, epidemiology, and neuroscience at population scale. This big data approach depends on data sharing strategies at both the micro- and macro-level, as well as attention to effective data management and protection of participant privacy. At the micro-level, researchers participate in international consortia that support collaboration, standards, and data sharing. They also seek to link together cohort studies, administrative health databases, and measures of the physical, built, and social environment in creative ways. Large-scale, longitudinal, and multi-modal cohorts are being designed to support explorations of genetic and environmental impacts on the brain. At a macro-level, funding agency policies now require data across health research domains to be managed according to the FAIR (findable, accessible, interoperable, and re-useable) Data principles and made available to the research community in a timely manner to support reproducibility and re-use. Data repositories provide technical infrastructure for storing, accessing, and increasingly also analyzing rich population-level data. Federated and cloud-based approaches are being leveraged to improve the security, remote accessibility, and performance of repositories. Finally, legal frameworks are being developed to facilitate secure health data access, integration, and analysis, providing new opportunities for the field.
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Ohmann C, Panagiotopoulou M, Canham S, Felder G, Verde PE. An assessment of the informative value of data sharing statements in clinical trial registries. BMC Med Res Methodol 2024; 24:61. [PMID: 38461273 PMCID: PMC10924983 DOI: 10.1186/s12874-024-02168-8] [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: 04/18/2023] [Accepted: 02/02/2024] [Indexed: 03/11/2024] Open
Abstract
BACKGROUND The provision of data sharing statements (DSS) for clinical trials has been made mandatory by different stakeholders. DSS are a device to clarify whether there is intention to share individual participant data (IPD). What is missing is a detailed assessment of whether DSS are providing clear and understandable information about the conditions for data sharing of IPD for secondary use. METHODS A random sample of 200 COVID-19 clinical trials with explicit DSS was drawn from the ECRIN clinical research metadata repository. The DSS were assessed and classified, by two experienced experts and one assessor with less experience in data sharing (DS), into different categories (unclear, no sharing, no plans, yes but vague, yes on request, yes with specified storage location, yes but with complex conditions). RESULTS Between the two experts the agreement was moderate to substantial (kappa=0.62, 95% CI [0.55, 0.70]). Agreement considerably decreased when these experts were compared with a third person who was less experienced and trained in data sharing ("assessor") (kappa=0.33, 95% CI [0.25, 0.41]; 0.35, 95% CI [0.27, 0.43]). Between the two experts and under supervision of an independent moderator, a consensus was achieved for those cases, where both experts had disagreed, and the result was used as "gold standard" for further analysis. At least some degree of willingness of DS (data sharing) was expressed in 63.5% (127/200) cases. Of these cases, around one quarter (31/127) were vague statements of support for data sharing but without useful detail. In around half of the cases (60/127) it was stated that IPD could be obtained by request. Only in in slightly more than 10% of the cases (15/127) it was stated that the IPD would be transferred to a specific data repository. In the remaining cases (21/127), a more complex regime was described or referenced, which could not be allocated to one of the three previous groups. As a result of the consensus meetings, the classification system was updated. CONCLUSION The study showed that the current DSS that imply possible data sharing are often not easy to interpret, even by relatively experienced staff. Machine based interpretation, which would be necessary for any practical application, is currently not possible. Machine learning and / or natural language processing techniques might improve machine actionability, but would represent a very substantial investment of research effort. The cheaper and easier option would be for data providers, data requestors, funders and platforms to adopt a clearer, more structured and more standardised approach to specifying, providing and collecting DSS. TRIAL REGISTRATION The protocol for the study was pre-registered on ZENODO ( https://zenodo.org/record/7064624#.Y4DIAHbMJD8 ).
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Affiliation(s)
- Christian Ohmann
- European Clinical Research Infrastructures Network (ECRIN), Kaiserswerther Strasse 70, 40477, Düsseldorf, Germany.
| | | | - Steve Canham
- European Clinical Research Infrastructure Network (ECRIN), 75014, Paris, France
| | - Gerd Felder
- European Clinical Research Infrastructure Network (ECRIN), 40764, Langenfeld, Germany
| | - Pablo Emilio Verde
- Coordination Centre for Clinical Trials, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Nordrhein-Westfalen, Germany
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Hopkins AM, Modi ND, Rockhold FW, Hoffmann T, Menz BD, Veroniki AA, McKinnon RA, Rowland A, Swain SM, Ross JS, Sorich MJ. Accessibility of clinical study reports supporting medicine approvals: a cross-sectional evaluation. J Clin Epidemiol 2024; 167:111263. [PMID: 38219810 DOI: 10.1016/j.jclinepi.2024.111263] [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: 09/02/2023] [Revised: 01/07/2024] [Accepted: 01/09/2024] [Indexed: 01/16/2024]
Abstract
OBJECTIVES Clinical study reports (CSRs) are highly detailed documents that play a pivotal role in medicine approval processes. Though not historically publicly available, in recent years, major entities including the European Medicines Agency (EMA), Health Canada, and the US Food and Drug Administration (FDA) have highlighted the importance of CSR accessibility. The primary objective herein was to determine the proportion of CSRs that support medicine approvals available for public download as well as the proportion eligible for independent researcher request via the study sponsor. STUDY DESIGN AND SETTING This cross-sectional study examined the accessibility of CSRs from industry-sponsored clinical trials whose results were reported in the FDA-authorized drug labels of the top 30 highest-revenue medicines of 2021. We determined (1) whether the CSRs were available for download from a public repository, and (2) whether the CSRs were eligible for request by independent researchers based on trial sponsors' data sharing policies. RESULTS There were 316 industry-sponsored clinical trials with results presented in the FDA-authorized drug labels of the 30 sampled medicines. Of these trials, CSRs were available for public download from 70 (22%), with 37 available at EMA and 40 at Health Canada repositories. While pharmaceutical company platforms offered no direct downloads of CSRs, sponsors confirmed that CSRs from 183 (58%) of the 316 clinical trials were eligible for independent researcher request via the submission of a research proposal. Overall, 218 (69%) of the sampled clinical trials had CSRs available for public download and/or were eligible for request from the trial sponsor. CONCLUSION CSRs were available from 69% of the clinical trials supporting regulatory approval of the 30 medicines sampled. However, only 22% of the CSRs were directly downloadable from regulatory agencies, the remaining required a formal application process to request access to the CSR from the study sponsor.
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Affiliation(s)
- Ashley M Hopkins
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia.
| | - Natansh D Modi
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Frank W Rockhold
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Tammy Hoffmann
- Institute for Evidence-Based Healthcare, Faculty of Health Sciences and Medicine, Bond University, Gold Coast, Queensland, Australia
| | - Bradley D Menz
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Areti-Angeliki Veroniki
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada; Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Ross A McKinnon
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Andrew Rowland
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Sandra M Swain
- Georgetown Lombardi Comprehensive Cancer Center, MedStar Health, Washington DC, USA
| | - Joseph S Ross
- Section of General Medicine, Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Michael J Sorich
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
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de Leeuw JR. DataPipe: Born-open data collection for online experiments. Behav Res Methods 2024; 56:2499-2506. [PMID: 37340239 DOI: 10.3758/s13428-023-02161-x] [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] [Accepted: 06/02/2023] [Indexed: 06/22/2023]
Abstract
DataPipe ( https://pipe.jspsych.org ) is a tool that allows researchers to save data from a behavioral experiment directly to the Open Science Framework. Researchers can configure data storage options for an experiment on the DataPipe website and then use the DataPipe API to send data to the Open Science Framework from any Internet-connected experiment. DataPipe is free to use and open-source. This paper describes the design of DataPipe and how it can help researchers adopt the practice of born-open data collection.
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Affiliation(s)
- Joshua R de Leeuw
- Department of Cognitive Science, Vassar College, Poughkeepsie, NY, USA.
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Fan Z, Zhou Z, Zhang W. Game analysis of enterprise data sharing from a supply chain perspective. Heliyon 2024; 10:e25678. [PMID: 38370251 PMCID: PMC10869863 DOI: 10.1016/j.heliyon.2024.e25678] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 01/19/2024] [Accepted: 01/31/2024] [Indexed: 02/20/2024] Open
Abstract
[Research Purpose] In the era of the digital economy, there is an urgent need to explore solutions to various problems faced by enterprises in their digital transformation, such as the lack of data resources, data silos, and information asymmetry within supply chains. [Method/Contribution] Leveraging evolutionary game theory and adopting a supply chain perspective, this study integrates the government and upstream/downstream enterprises into a unified analysis framework. In this study, a three-party evolutionary game model under government coordination aimed at fostering data openness and sharing among supply chain enterprises is constructed. Simulation analyses are conducted on decision-making strategies concerning data sharing between the government and supply chain enterprises across different scenarios. [Research Conclusion] It is observed that the high level of benefits and low costs associated with data sharing incentivize supply chain enterprises to actively open and share their data. Notably, government incentives significantly encourage data openness among these enterprises by subsidizing the cost of data sharing, "especially evident when the incentive coefficient exceeds 0.6," thereby guiding them toward collaborative data-sharing initiatives. Finally, it is also found that data sharing further promotes the digital transformation of the supply chain, optimizing decision-making processes, resource allocation, and operational efficiency. Through data sharing, better forecasting, inventory management, and risk mitigation strategies can be implemented. Moreover, data sharing fosters collaboration among supply chain partners enhances transparency and trust, and makes the supply chain more synchronized and responsive, which leads to value cocreation within the supply chain, with downstream enterprises being more incentivized than upstream enterprises by this value cocreation.
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Affiliation(s)
- Zifu Fan
- School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Zhiqiang Zhou
- School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Wei Zhang
- School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
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Ramos YFM, Rice SJ, Ali SA, Pastrello C, Jurisica I, Rai MF, Collins KH, Lang A, Maerz T, Geurts J, Ruiz-Romero C, June RK, Thomas Appleton C, Rockel JS, Kapoor M. Evolution and advancements in genomics and epigenomics in OA research: How far we have come. Osteoarthritis Cartilage 2024:S1063-4584(24)00054-2. [PMID: 38428513 DOI: 10.1016/j.joca.2024.02.656] [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: 01/29/2024] [Accepted: 02/25/2024] [Indexed: 03/03/2024]
Abstract
OBJECTIVE Osteoarthritis (OA) is the most prevalent musculoskeletal disease affecting articulating joint tissues, resulting in local and systemic changes that contribute to increased pain and reduced function. Diverse technological advancements have culminated in the advent of high throughput "omic" technologies, enabling identification of comprehensive changes in molecular mediators associated with the disease. Amongst these technologies, genomics and epigenomics - including methylomics and miRNomics, have emerged as important tools to aid our biological understanding of disease. DESIGN In this narrative review, we selected articles discussing advancements and applications of these technologies to OA biology and pathology. We discuss how genomics, deoxyribonucleic acid (DNA) methylomics, and miRNomics have uncovered disease-related molecular markers in the local and systemic tissues or fluids of OA patients. RESULTS Genomics investigations into the genetic links of OA, including using genome-wide association studies, have evolved to identify 100+ genetic susceptibility markers of OA. Epigenomic investigations of gene methylation status have identified the importance of methylation to OA-related catabolic gene expression. Furthermore, miRNomic studies have identified key microRNA signatures in various tissues and fluids related to OA disease. CONCLUSIONS Sharing of standardized, well-annotated omic datasets in curated repositories will be key to enhancing statistical power to detect smaller and targetable changes in the biological signatures underlying OA pathogenesis. Additionally, continued technological developments and analysis methods, including using computational molecular and regulatory networks, are likely to facilitate improved detection of disease-relevant targets, in-turn, supporting precision medicine approaches and new treatment strategies for OA.
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Affiliation(s)
- Yolande F M Ramos
- Dept. Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Sarah J Rice
- Biosciences Institute, International Centre for Life, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Shabana Amanda Ali
- Henry Ford Health + Michigan State University Health Sciences, Detroit, MI, USA
| | - Chiara Pastrello
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, Ontario, Canada
| | - Igor Jurisica
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, Ontario, Canada; Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Muhammad Farooq Rai
- Department of Biological Sciences, Center for Biotechnology, College of Medicine & Health Sciences, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Kelsey H Collins
- Department of Orthopaedic Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Annemarie Lang
- Departments of Orthopaedic Surgery and Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Tristan Maerz
- Department of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Jeroen Geurts
- Rheumatology, Department of Musculoskeletal Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Cristina Ruiz-Romero
- Grupo de Investigación de Reumatología (GIR), Unidad de Proteómica, INIBIC -Hospital Universitario A Coruña, SERGAS, A Coruña, Spain
| | - Ronald K June
- Department of Mechanical & Industrial Engineering, Montana State University, Bozeman, MT, USA
| | - C Thomas Appleton
- Department of Medicine, University of Western Ontario, London, Ontario, Canada
| | - Jason S Rockel
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, Ontario, Canada
| | - Mohit Kapoor
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, Ontario, Canada.
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van Roode MY, Dos S Ribeiro C, Farag E, Nour M, Moustafa A, Ahmed M, Haringhuizen G, Koopmans MPG, van de Burgwal LHM. Six dilemmas for stakeholders inherently affecting data sharing during a zoonotic (re-)emerging infectious disease outbreak response. BMC Infect Dis 2024; 24:185. [PMID: 38347527 PMCID: PMC10863217 DOI: 10.1186/s12879-024-09054-0] [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: 08/15/2023] [Accepted: 01/24/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Timely access to outbreak related data, particularly in the early events of a spillover, is important to support evidence based control measures in response to outbreaks of zoonotic Emerging Infectious Diseases (EID). Yet, this is impeded by several barriers that need to be understood to promote timely sharing of data. Using the MERS epidemic as a model for a zoonotic EID outbreak, this study sought to provide an in-depth understanding of data sharing practices. METHODS Semi-structured interviews with 25 experts were conducted, along with Focus Group Discussions with 15 additional experts. A root-cause analysis was performed to examine the causal relationships between barriers. Enablers were mapped to the root-cause analysis to understand their influence on the barriers. Finally, root causes were placed in context of core dilemmas identified from the qualitative analysis. FINDINGS Eight barriers to data sharing were identified, related to collaboration, technical preparedness, regulations, and (conflict of) interests, and placed in the context of six dilemmas inherent to the multi-stakeholder collaboration required for a zoonotic outbreak response. Fourteen identified enablers showed the willingness of stakeholders to overcome or circumvent these barriers, but also indicated the inherent trial and error nature of implementing such enablers. INTERPRETATION Addressing the barriers requires solutions that must consider the complexity and interconnectedness of the root causes underlying them, and should consider the distinct scopes and interests of the different stakeholders. Insights provided by this study can be used to encourage data sharing practices for future outbreaks FUNDING: Wellcome Trust and UK Aid; EU-H2020 Societal Challenges (grant agreement no. 643476), Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VI.Veni.201S.044).
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Affiliation(s)
- Martine Y van Roode
- Department of Viroscience, Erasmus University Medical Center (Erasmus MC), Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.
| | - Carolina Dos S Ribeiro
- Center for Infectious Disease Control, The Netherlands National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Vrije Universiteit Amsterdam (VU Amsterdam), Faculty of Science, Athena Institute for Research On Innovation and Communication in Health and Life Sciences, Amsterdam, The Netherlands
| | - Elmoubasher Farag
- Department of Health Protection & Communicable Diseases, Ministry of Public Health, Doha, Qatar
| | - Mohamed Nour
- Department of Health Protection & Communicable Diseases, Ministry of Public Health, Doha, Qatar
| | - Aya Moustafa
- Department of Health Protection & Communicable Diseases, Ministry of Public Health, Doha, Qatar
| | - Minahil Ahmed
- Department of Health Protection & Communicable Diseases, Ministry of Public Health, Doha, Qatar
| | - George Haringhuizen
- Center for Infectious Disease Control, The Netherlands National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Marion P G Koopmans
- Department of Viroscience, Erasmus University Medical Center (Erasmus MC), Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Pandemic and Disaster Preparedness Center (PDPC), Rotterdam, The Netherlands
| | - Linda H M van de Burgwal
- Vrije Universiteit Amsterdam (VU Amsterdam), Faculty of Science, Athena Institute for Research On Innovation and Communication in Health and Life Sciences, Amsterdam, The Netherlands
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11
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Rahman N, O'Cathail C, Zyoud A, Sokolov A, Oude Munnink B, Grüning B, Cummins C, Amid C, Nieuwenhuijse DF, Visontai D, Yuan DY, Gupta D, Prasad DK, Gulyás GM, Rinck G, McKinnon J, Rajan J, Knaggs J, Skiby JE, Stéger J, Szarvas J, Gueye K, Papp K, Hoek M, Kumar M, Ventouratou MA, Bouquieaux MC, Koliba M, Mansurova M, Haseeb M, Worp N, Harrison PW, Leinonen R, Thorne R, Selvakumar S, Hunt S, Venkataraman S, Jayathilaka S, Cezard T, Maier W, Waheed Z, Iqbal Z, Aarestrup FM, Csabai I, Koopmans M, Burdett T, Cochrane G. Mobilisation and analyses of publicly available SARS-CoV-2 data for pandemic responses. Microb Genom 2024; 10:001188. [PMID: 38358325 PMCID: PMC10926692 DOI: 10.1099/mgen.0.001188] [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: 05/04/2023] [Accepted: 01/14/2024] [Indexed: 02/16/2024] Open
Abstract
The COVID-19 pandemic has seen large-scale pathogen genomic sequencing efforts, becoming part of the toolbox for surveillance and epidemic research. This resulted in an unprecedented level of data sharing to open repositories, which has actively supported the identification of SARS-CoV-2 structure, molecular interactions, mutations and variants, and facilitated vaccine development and drug reuse studies and design. The European COVID-19 Data Platform was launched to support this data sharing, and has resulted in the deposition of several million SARS-CoV-2 raw reads. In this paper we describe (1) open data sharing, (2) tools for submission, analysis, visualisation and data claiming (e.g. ORCiD), (3) the systematic analysis of these datasets, at scale via the SARS-CoV-2 Data Hubs as well as (4) lessons learnt. This paper describes a component of the Platform, the SARS-CoV-2 Data Hubs, which enable the extension and set up of infrastructure that we intend to use more widely in the future for pathogen surveillance and pandemic preparedness.
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Affiliation(s)
- Nadim Rahman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Colman O'Cathail
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Ahmad Zyoud
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Alexey Sokolov
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Bas Oude Munnink
- Erasmus Medical Center, Wytemaweg 80, 3015 CN Rotterdam, Netherlands
| | - Björn Grüning
- University of Freiburg, Friedrichstr. 39, 79098 Freiburg, Germany
| | - Carla Cummins
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Clara Amid
- Erasmus Medical Center, Wytemaweg 80, 3015 CN Rotterdam, Netherlands
| | | | - Dávid Visontai
- Eötvös Loránd University, H-1053 Budapest, Egyetem tér 1-3, Hungary
| | - David Yu Yuan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Dipayan Gupta
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Divyae K. Prasad
- Erasmus Medical Center, Wytemaweg 80, 3015 CN Rotterdam, Netherlands
| | - Gábor Máté Gulyás
- Technical University of Denmark, Anker Engelunds Vej 101, 2800 Kongens Lyngby, Denmark
| | - Gabriele Rinck
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Jasmine McKinnon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Jeena Rajan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Jeff Knaggs
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Jeffrey Edward Skiby
- Technical University of Denmark, Anker Engelunds Vej 101, 2800 Kongens Lyngby, Denmark
| | - József Stéger
- Eötvös Loránd University, H-1053 Budapest, Egyetem tér 1-3, Hungary
| | - Judit Szarvas
- Technical University of Denmark, Anker Engelunds Vej 101, 2800 Kongens Lyngby, Denmark
| | - Khadim Gueye
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Krisztián Papp
- Eötvös Loránd University, H-1053 Budapest, Egyetem tér 1-3, Hungary
| | - Maarten Hoek
- Erasmus Medical Center, Wytemaweg 80, 3015 CN Rotterdam, Netherlands
| | - Manish Kumar
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Marianna A. Ventouratou
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | | | - Martin Koliba
- Technical University of Denmark, Anker Engelunds Vej 101, 2800 Kongens Lyngby, Denmark
| | - Milena Mansurova
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Muhammad Haseeb
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Nathalie Worp
- Erasmus Medical Center, Wytemaweg 80, 3015 CN Rotterdam, Netherlands
| | - Peter W. Harrison
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Rasko Leinonen
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Ross Thorne
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Sandeep Selvakumar
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Sarah Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Sundar Venkataraman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Suran Jayathilaka
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Timothée Cezard
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Wolfgang Maier
- University of Freiburg, Friedrichstr. 39, 79098 Freiburg, Germany
| | - Zahra Waheed
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Zamin Iqbal
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | | | - Istvan Csabai
- Eötvös Loránd University, H-1053 Budapest, Egyetem tér 1-3, Hungary
| | - Marion Koopmans
- Erasmus Medical Center, Wytemaweg 80, 3015 CN Rotterdam, Netherlands
| | - Tony Burdett
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Guy Cochrane
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
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12
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Ruan T, Paavola J, Chan FKS, Xu Y, Baldacchini C, Calfapietra C. A lack of focus on data sharing, stakeholders, and economic benefits in current global green infrastructure planning. J Environ Manage 2024; 351:119849. [PMID: 38134507 DOI: 10.1016/j.jenvman.2023.119849] [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: 04/25/2023] [Revised: 11/26/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023]
Abstract
Green infrastructure (GI) is increasingly popular in solving urban environmental challenges and enhancing ecosystem services. Yet the research status and challenges of GI planning have not been comprehensively benchmarked to date. We explored the GI types, actions, goals, and spatiotemporal characteristics of GI planning cases worldwide based on the available literature. The challenges of GI planning were also investigated by the cases included in this manuscript. Additionally, the urban governance solutions to address these challenges were proposed. We found that multi-type GI planning is the most popular. Data sharing, stakeholder participation, economic benefits and research funding for GI planning research were generally inadequate, although they have improved trend over time. Multiple-goal GI planning frequently has higher levels of data sharing, stakeholder participation and economic benefits than GI planning that just takes into account one purpose. We conclude that the future transformation of GI planning requires efficient data sharing mechanisms, effective co-design among stakeholders, systematic business models, and available research funding.
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Affiliation(s)
- Tian Ruan
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, PR China; University of Chinese Academy of Sciences, Beijing, 100049, PR China; Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo, 315830, PR China
| | - Jouni Paavola
- Centre for Climate Change Economics and Policy (CCCEP), School of Earth and Environment, University of Leeds, Leeds, LS29JT, UK
| | - Faith Ka Shun Chan
- School of Geographical Sciences, University of Nottingham Ningbo China, Ningbo, 315100, PR China; Water@Leeds Research Institute, University of Leeds, Leeds, LS29JT, UK
| | - Yaoyang Xu
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, PR China; Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo, 315830, PR China.
| | - Chiara Baldacchini
- Department of Ecological and Biological Sciences (DEB), University of Tuscia, 01100 Viterbo, Italy; Institute of Research on Terrestrial Ecosystem (IRET), National Research Council (CNR), 05010 ,Porano (TR), Italy
| | - Carlo Calfapietra
- Institute of Research on Terrestrial Ecosystem (IRET), National Research Council (CNR), 05010 ,Porano (TR), Italy
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13
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Billingham L, Brown L, Framke T, Greystoke A, Hovig E, Mathur S, Page P, Pean E, Barjesteh van Waalwijk van Doorn-Khosrovani S, Vonk R, Wissink S, Zander H, Plummer R. Histology independent drug development - Is this the future for cancer drugs? Cancer Treat Rev 2024; 123:102674. [PMID: 38176220 DOI: 10.1016/j.ctrv.2023.102674] [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: 10/30/2023] [Revised: 12/18/2023] [Accepted: 12/21/2023] [Indexed: 01/06/2024]
Abstract
The Cancer Drug Development Forum (CDDF)'s 'Histology independent drug development - is this the future for cancer drugs?' workshop was set up to explore the current landscape of histology independent drug development, review the current regulatory landscape and propose recommendations for improving the conduct of future trials. The first session considered lessons learnt from previous trials, including innovative solutions for reimbursement. The session explored why overall survival represents the most valuable endpoint, and the importance of duration of response, which can be captured with swimmer and spider plots. The second session on biomarker development and treatment optimisation considered current regulations for companion diagnostics, FDA guidance on histology independent drug development in oncology, and the need to establish cut-offs for the biomarker of tumour mutational burden to identify the patients most likely to benefit from PDL1 treatment. The third session reviewed novel trial designs, including basket, umbrella and platform trials, and statistical approaches of hierarchical modelling where homogeneity between study cohorts enables information to be borrowed between cohorts. The discussion highlighted the need to agree 'common assessment standards' to facilitate pooling of data across studies. In the fourth session, the sharing of data sets was recognised as a key step for improving equity of access to precision medicines across Europe. The session considered how the European Health Data Space (EHDS) could streamline access to medical records, emphasizing the importance of introducing greater accountability into the digital space. In conclusion the workshop proposed 11 recommendations to facilitate histology agnostic drug development.
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Affiliation(s)
- Lucinda Billingham
- Cancer Research UK Clinical Trials Unit, Institute of Cancer and Genomic Sciences, University of Birmingham, UK.
| | - Lynn Brown
- Oncology Division, Merck & Co., Inc., Rahway, NJ, USA.
| | - Theodor Framke
- European Medicines Agency, Amsterdam, The Netherlands. Institute for Biostatistics, Hannover Medical School, Hannover, Germany.
| | - Alastair Greystoke
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK.
| | - Eivind Hovig
- Centre for Bioinformatics, University of Oslo, P.O. Box 1080 Blindern, 0316 OSLO, Norway; Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway.
| | | | | | - Elias Pean
- European Medicines Agency, Amsterdam, the Netherlands.
| | - Sahar Barjesteh van Waalwijk van Doorn-Khosrovani
- National Funder's Committee for Evaluation of Specialised Medicines and Companion Diagnostics (CieBAG), the Netherlands; Department of Oncology, Leiden University Medical Centre The Netherlands CZ, Postbus 90152, 5000 LD, Tilburg, the Netherlands.
| | | | | | | | - Ruth Plummer
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK.
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14
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Leigh S, Baines R, Stevens S, Garba-Sani Z, Austin D, Chatterjee A. Walk a mile in my shoes: perspectives towards sharing of health and experience data among individuals living with sickle cell disorder. Mhealth 2024; 10:4. [PMID: 38323148 PMCID: PMC10839506 DOI: 10.21037/mhealth-23-18] [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] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 11/29/2023] [Indexed: 02/08/2024] Open
Abstract
Background Advancements in digital health technologies (DHTs) mean people are increasingly recording and managing personal health data. As observed during the COVID-19 pandemic, sharing of such data may provide unrivalled opportunities in advancing our understanding of conditions otherwise poorly understood, including rare conditions. Methods A semi-structured focus group (n=25) explored perspectives and experiences of sharing health data among those with a group of rare haematological conditions, sickle cell disorder (SCD). The focus group explored (I) what 'feeling well' looks like; (II) how this could be monitored using DHTs; (III) which data healthcare professionals (HCPs) should pay greater attention to and; (IV) types of data willing to be shared, with whom, and under which conditions. Key themes were further assessed via an online survey (n=50). Results Patient-relevant measures of condition-management focused on "everything else that comes with" SCD, suggesting HCPs did not pay sufficient attention to day-to-day symptom variability. This was juxtaposed against the "fixed and one-off" electronic health record (EHR), collecting pre-specified data at pre-determined snapshots of time, not considered reflective of outcomes associated with "feeling well" day-to-day. Forty-four-point-seven percent of respondents had previously shared health data. Most were willing to share data concerning symptoms and health service utilisation, but were less willing to share genomic and EHR data. Sixty-one-point-seven percent believed HCPs did not pay enough attention to daily fluctuations in mental and physical health. Financial benefits (74.5%), trust in organisations seeking data (72.3%), and knowing how data will be used (61.7%) were key facilitators of data sharing. Seventy-one percent, 70% and 65.2% had not previously shared health data with the pharmaceutical industry, charitable organisations and digital health interventions respectively, but were open to doing so in the future. Conclusions Those living with the rare condition SCD were supportive of collecting and sharing data to foster research and improve understanding and outcomes. However, specific requirements were identified to respect privacy and informational needs regarding future use of data. DHTs can be a valuable tool in improving understanding of the day-to-day impact of health conditions, but understanding patient needs is critical in ensuring involvement in the process, as not all data types are considered of equal value, benefit, or risk.
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Affiliation(s)
- Simon Leigh
- Prometheus Health Technologies, Mor Workspace, Newquay, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Rebecca Baines
- Centre for Health Technology, University of Plymouth, Plymouth, UK
| | - Sebastian Stevens
- Prometheus Health Technologies, Mor Workspace, Newquay, UK
- Centre for Health Technology, University of Plymouth, Plymouth, UK
| | | | - Daniella Austin
- School of Psychology, Faculty of Health, University of Plymouth, Plymouth, UK
| | - Arunangsu Chatterjee
- Centre for Health Technology, University of Plymouth, Plymouth, UK
- School of Medicine, University of Leeds, Leeds, UK
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15
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Cawthorne CJ, Volpe A, Fruhwirth GO. The Basics of Visualizing, Analyzing, and Reporting Preclinical PET/CT Imaging Data. Methods Mol Biol 2024; 2729:195-220. [PMID: 38006498 DOI: 10.1007/978-1-0716-3499-8_12] [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: 11/27/2023]
Abstract
Positron emission tomography (PET) has transformed medical imaging, and while first developed and applied to the human setting, it has found widespread application at the preclinical level over the past two decades. Its strength is that it offers noninvasive 3D tomographic imaging in a quantitative manner at very high sensitivity. Paired with the right molecular probes, invaluable insights into physiology and pathophysiology have been accessible and therapeutic development has been enhanced through preclinical PET imaging. PET imaging is now often routinely combined with either computed tomography (CT) or magnetic resonance imaging (MRI) to provide additional anatomical context. All these developments were accompanied by the provision of ever more complex and powerful analysis software enabling users to visualize and quantify signals from PET imaging data. Aside from experimental complexities, there are also various pitfalls in PET image data analysis, which can negatively impact on reporting and reproducibility.Here, we provide a protocol intended to guide the inexperienced user through PET/CT data analysis. We describe the general principles and workflows required for PET/CT image data visualization and quantitative analysis using various software packages popular in the field. Moreover, we present recommendations for reporting of preclinical PET/CT data including examples of good and poor practice.
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Affiliation(s)
- Christopher J Cawthorne
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, Katholieke Universiteit Leuven, Leuven, Belgium.
| | - Alessia Volpe
- Molecular Imaging Group, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Gilbert O Fruhwirth
- Imaging Therapies and Cancer Group, Comprehensive Cancer Centre, School of Cancer and Pharmaceutical Sciences, King's College London, London, UK.
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16
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Collins GS, Whittle R, Bullock GS, Logullo P, Dhiman P, de Beyer JA, Riley RD, Schlussel MM. Open science practices need substantial improvement in prognostic model studies in oncology using machine learning. J Clin Epidemiol 2024; 165:111199. [PMID: 37898461 DOI: 10.1016/j.jclinepi.2023.10.015] [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: 08/02/2023] [Revised: 10/06/2023] [Accepted: 10/20/2023] [Indexed: 10/30/2023]
Abstract
OBJECTIVE To describe the frequency of open science practices in a contemporary sample of studies developing prognostic models using machine learning methods in the field of oncology. STUDY DESIGN AND SETTING We conducted a systematic review, searching the MEDLINE database between December 1, 2022, and December 31, 2022, for studies developing a multivariable prognostic model using machine learning methods (as defined by the authors) in oncology. Two authors independently screened records and extracted open science practices. RESULTS We identified 46 publications describing the development of a multivariable prognostic model. The adoption of open science principles was poor. Only one study reported availability of a study protocol, and only one study was registered. Funding statements and conflicts of interest statements were common. Thirty-five studies (76%) provided data sharing statements, with 21 (46%) indicating data were available on request to the authors and seven declaring data sharing was not applicable. Two studies (4%) shared data. Only 12 studies (26%) provided code sharing statements, including 2 (4%) that indicated the code was available on request to the authors. Only 11 studies (24%) provided sufficient information to allow their model to be used in practice. The use of reporting guidelines was rare: eight studies (18%) mentioning using a reporting guideline, with 4 (10%) using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis statement, 1 (2%) using Minimum Information About Clinical Artificial Intelligence Modeling and Consolidated Standards Of Reporting Trials-Artificial Intelligence, 1 (2%) using Strengthening The Reporting Of Observational Studies In Epidemiology, 1 (2%) using Standards for Reporting Diagnostic Accuracy Studies, and 1 (2%) using Transparent Reporting of Evaluations with Nonrandomized Designs. CONCLUSION The adoption of open science principles in oncology studies developing prognostic models using machine learning methods is poor. Guidance and an increased awareness of benefits and best practices of open science are needed for prediction research in oncology.
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Affiliation(s)
- Gary S Collins
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom.
| | - Rebecca Whittle
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
| | - Garrett S Bullock
- Department of Orthopaedic Surgery, Wake Forest School of Medicine, Winston-Salem, NC, USA; Centre for Sport, Exercise and Osteoarthritis Research Versus Arthritis, University of Oxford, Oxford, United Kingdom
| | - Patricia Logullo
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
| | - Paula Dhiman
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
| | - Jennifer A de Beyer
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Michael M Schlussel
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
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17
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Catalá-López F, Ridao M, Tejedor-Romero L, Caulley L, Hutton B, Husereau D, Alonso-Arroyo A, Bernal-Delgado E, Drummond MF, Moher D. Transparency, openness, and reproducible research practices are frequently underused in health economic evaluations. J Clin Epidemiol 2024; 165:111208. [PMID: 37939742 DOI: 10.1016/j.jclinepi.2023.10.024] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/15/2023] [Accepted: 10/31/2023] [Indexed: 11/10/2023]
Abstract
OBJECTIVES To investigate the extent to which articles of economic evaluations of healthcare interventions indexed in MEDLINE incorporate research practices that promote transparency, openness, and reproducibility. STUDY DESIGN AND SETTING We evaluated a random sample of health economic evaluations indexed in MEDLINE during 2019. We included articles written in English reporting an incremental cost-effectiveness ratio in terms of costs per life years gained, quality-adjusted life years, and/or disability-adjusted life years. Reproducible research practices, openness, and transparency in each article were extracted in duplicate. We explored whether reproducible research practices were associated with self-report use of a guideline. RESULTS We included 200 studies published in 147 journals. Almost half were published as open access articles (n = 93; 47%). Most studies (n = 150; 75%) were model-based economic evaluations. In 109 (55%) studies, authors self-reported use a guideline (e.g., for study conduct or reporting). Few studies (n = 31; 16%) reported working from a protocol. In 112 (56%) studies, authors reported the data needed to recreate the incremental cost-effectiveness ratio for the base case analysis. This percentage was higher in studies using a guideline than studies not using a guideline (72/109 [66%] with guideline vs. 40/91 [44%] without guideline; risk ratio 1.50, 95% confidence interval 1.15-1.97). Only 10 (5%) studies mentioned access to raw data and analytic code for reanalyses. CONCLUSION Transparency, openness, and reproducible research practices are frequently underused in health economic evaluations. This study provides baseline data to compare future progress in the field.
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Affiliation(s)
- Ferrán Catalá-López
- Department of Health Planning and Economics, National School of Public Health, Institute of Health Carlos III, Madrid, Spain; Department of Medicine, University of Valencia/INCLIVA Health Research Institute and CIBERSAM, Valencia, Spain; Clinical Epidemiology Program, Ottawa Hospital Research Institute (OHRI), Ottawa, Ontario, Canada.
| | - Manuel Ridao
- Institute for Health Research in Aragon (IISA), Zaragoza, Spain; Data Science for Health Services and Policy Research, Aragon Health Sciences Institute (IACS), Zaragoza, Spain; Research Network on Chronicity, Primary Care, and Health Promotion (RICAPPS), Institute of Health Carlos III, Madrid, Spain
| | - Laura Tejedor-Romero
- Department of Health Planning and Economics, National School of Public Health, Institute of Health Carlos III, Madrid, Spain; Preventive Medicine Unit, La Princesa University Teaching Hospital, Madrid, Spain; Division of Pharmacoepidemiology and Pharmacovigilance, Spanish Medicines and Healthcare Products Agency (AEMPS), Madrid, Spain
| | - Lisa Caulley
- Clinical Epidemiology Program, Ottawa Hospital Research Institute (OHRI), Ottawa, Ontario, Canada; Otolaryngology-Head and Neck Surgery Department, Ottawa Hospital, Ottawa, Ontario, Canada; Department of Clinical Medicine and Otolaryngology-Head and Neck Surgery, Aarhus University, Aarhus, Denmark
| | - Brian Hutton
- Clinical Epidemiology Program, Ottawa Hospital Research Institute (OHRI), Ottawa, Ontario, Canada; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Don Husereau
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada; Institute of Health Economics, Edmonton, Alberta, Canada
| | - Adolfo Alonso-Arroyo
- Department of History of Science and Documentation, University of Valencia, Valencia, Spain; Information and Social and Health Research (UISYS) Joint Research Unit, Spanish National Research Council (CSIC), University of Valencia, Valencia, Spain
| | - Enrique Bernal-Delgado
- Data Science for Health Services and Policy Research, Aragon Health Sciences Institute (IACS), Zaragoza, Spain; Research Network on Chronicity, Primary Care, and Health Promotion (RICAPPS), Institute of Health Carlos III, Madrid, Spain
| | | | - David Moher
- Clinical Epidemiology Program, Ottawa Hospital Research Institute (OHRI), Ottawa, Ontario, Canada; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
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18
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Abstract
On September 12, 2022, President Biden issued Executive Order 14081 to enable the progress of biomanufacturing and biotechnology. This timely initiative will help overcome many challenging issues, and its potential impacts will be huge. This article discusses eight recommendations to make this US national initiative successful, encourage other nations to consider similar initiatives, and create a better world for the next generations.
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Affiliation(s)
- Tae Seok Moon
- Moonshot Bio, Inc., 73 Turnpike Street, North Andover, MA 01845, USA.
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19
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Monachino G, Zanchi B, Fiorillo L, Conte G, Auricchio A, Tzovara A, Faraci FD. Deep Generative Models: The winning key for large and easily accessible ECG datasets? Comput Biol Med 2023; 167:107655. [PMID: 37976830 DOI: 10.1016/j.compbiomed.2023.107655] [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: 07/24/2023] [Revised: 10/04/2023] [Accepted: 10/31/2023] [Indexed: 11/19/2023]
Abstract
Large high-quality datasets are essential for building powerful artificial intelligence (AI) algorithms capable of supporting advancement in cardiac clinical research. However, researchers working with electrocardiogram (ECG) signals struggle to get access and/or to build one. The aim of the present work is to shed light on a potential solution to address the lack of large and easily accessible ECG datasets. Firstly, the main causes of such a lack are identified and examined. Afterward, the potentials and limitations of cardiac data generation via deep generative models (DGMs) are deeply analyzed. These very promising algorithms have been found capable not only of generating large quantities of ECG signals but also of supporting data anonymization processes, to simplify data sharing while respecting patients' privacy. Their application could help research progress and cooperation in the name of open science. However several aspects, such as a standardized synthetic data quality evaluation and algorithm stability, need to be further explored.
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Affiliation(s)
- Giuliana Monachino
- Institute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Via la Santa 1, Lugano 6900, Switzerland; Institute of Informatics, University of Bern, Neubrückstrasse 10, Bern 3012, Switzerland.
| | - Beatrice Zanchi
- Institute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Via la Santa 1, Lugano 6900, Switzerland; Department of Quantitative Biomedicine, University of Zurich, Schmelzbergstrasse 26, Zurich 8091, Switzerland
| | - Luigi Fiorillo
- Institute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Via la Santa 1, Lugano 6900, Switzerland
| | - Giulio Conte
- Division of Cardiology, Fondazione Cardiocentro Ticino, Via Tesserete 48, Lugano 6900, Switzerland; Centre for Computational Medicine in Cardiology, Faculty of Informatics, Università della Svizzera Italiana, Via la Santa 1, Lugano 6900, Switzerland
| | - Angelo Auricchio
- Division of Cardiology, Fondazione Cardiocentro Ticino, Via Tesserete 48, Lugano 6900, Switzerland; Centre for Computational Medicine in Cardiology, Faculty of Informatics, Università della Svizzera Italiana, Via la Santa 1, Lugano 6900, Switzerland
| | - Athina Tzovara
- Institute of Informatics, University of Bern, Neubrückstrasse 10, Bern 3012, Switzerland; Sleep Wake Epilepsy Center | NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16, Bern 3010, Switzerland
| | - Francesca Dalia Faraci
- Institute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Via la Santa 1, Lugano 6900, Switzerland
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20
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Hill D, Dunham C, Larsen DA, Collins M. Operationalizing an open-source dashboard for communicating results of wastewater-based surveillance. MethodsX 2023; 11:102299. [PMID: 37554289 PMCID: PMC10404718 DOI: 10.1016/j.mex.2023.102299] [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: 05/31/2023] [Accepted: 07/25/2023] [Indexed: 08/10/2023] Open
Abstract
COVID-19 saw the expansion of public health tools to manage the pandemic. One tool that saw extensive use was the public health dashboard, web-based visualization tools that communicate information to users in easy-to-read graphics. Dashboards were widely used prior to the pandemic, but COVID-19 saw expanded use and development. To date, dashboards have become an important part of public health surveillance programs around the world helping decisionmakers use data to evaluate different public health metrics including caseloads, hospitalizations, and environmental surveillance results from testing wastewater. Wastewater surveillance provides community-based, spatially relevant data on disease trends within communities to assess the scale of infection in a region, which makes it an excellent candidate for dashboard development to improve public health. We developed a dashboard for New York State's wastewater surveillance program using open-source, reproducible web programming. The dashboard we developed has been used for the COVID-19 response in New York, and our methods can be adapted to other programs and pathogens. We provide:•descriptions of how the dashboard was developed and maintained•specific guidance for reproducing our dashboard in other areas and for other pathogens•fully reproducible code with step-by-step instructions for researchers and professionals to make their own data dashboards.
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Affiliation(s)
- Dustin Hill
- Department of Public Health, Syracuse University, Syracuse, NY, USA
| | | | - David A. Larsen
- Department of Public Health, Syracuse University, Syracuse, NY, USA
| | - Mary Collins
- School of Marine and Atmospheric Sciences, Sustainability Studies Division, Stony Brook University, Stony Brook, NY, USA
- Institute for Advanced Computational Science, Stony Brook University, Stony Brook, NY, USA
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21
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Polesie S, Alinaghi F, Egeberg A. A systematic review investigating at what proportion clinical images are shared in prospective randomized controlled trials involving patients with psoriasis and biological agents. J DERMATOL TREAT 2023; 34:2281261. [PMID: 37965743 DOI: 10.1080/09546634.2023.2281261] [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: 10/19/2023] [Accepted: 11/02/2023] [Indexed: 11/16/2023]
Abstract
For many patients including those with psoriasis, scientific manuscripts comprising clinical outcomes including psoriasis area severity index (PASI) and/or physician global assessment (PGA) may be difficult to understand. However, most patients can relate to images at baseline and follow-up, particularly for dermatological diseases. This study aimed to assess the proportion of shared clinical images in psoriasis trials. A systematic review adhering to the PRISMA guidelines was performed. The review was limited to randomized controlled trials, and among these, only investigations involving biological agents for treatment of psoriasis were included. The Embase, MEDLINE and Scopus databases were searched for eligible studies published from inception to October 26, 2021. In total, 152 studies were included. When combining these, 62,871 patients were randomized. Overall, 203 images were shared depicting 60 patients in the manuscripts yielding an overall sharing rate of 0.1%. Patient images are seldom incorporated in clinical trial manuscripts which impairs interpretation for patients. Inclusion of image material would strengthen the patients' perspective and understanding on what treatment effects that can be expected. As such, this systematic review should be an invitation to the pharmaceutical industry, other sponsors, and editorial offices to improve easy transfer of information to patients using image 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
| | - Farzad Alinaghi
- National Allergy Research Centre, Department of Dermatology and Allergy, Herlev and Gentofte Hospital, University of Copenhagen, Hellerup, Denmark
| | - Alexander Egeberg
- Department of Dermatology, Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark
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22
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Müller H, Lopes-Dias C, Holub P, Plass M, Jungwirth E, Reihs R, Torke PR, Malatras A, Berger A, Coombs H, Dillner J, Merino-Martinez R. BIBBOX, a FAIR toolbox and App Store for life science research. N Biotechnol 2023; 77:12-19. [PMID: 37295722 DOI: 10.1016/j.nbt.2023.06.001] [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: 03/03/2023] [Revised: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 06/12/2023]
Abstract
Data quality has recently become a critical topic for the research community. European guidelines recommend that scientific data should be made FAIR: findable, accessible, interoperable and reusable. However, as FAIR guidelines do not specify how the stated principles should be implemented, it might not be straightforward for researchers to know how actually to make their data FAIR. This can prevent life-science researchers from sharing their datasets and pipelines, ultimately hindering the progress of research. To address this difficulty, we developed the BIBBOX, which is a platform that supports researchers publishing their datasets and the associated software in a FAIR manner.
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Affiliation(s)
- Heimo Müller
- Medical University of Graz, Neue Stiftingtalstraße 6, A-8010 Graz, Austria.
| | | | - Petr Holub
- BBMRI-ERIC, Neue Stiftingtalstraße 2/B/6, A-8010 Graz, Austria
| | - Markus Plass
- Medical University of Graz, Neue Stiftingtalstraße 6, A-8010 Graz, Austria
| | - Emilian Jungwirth
- Medical University of Graz, Neue Stiftingtalstraße 6, A-8010 Graz, Austria
| | - Robert Reihs
- Medical University of Graz, Neue Stiftingtalstraße 6, A-8010 Graz, Austria
| | - Paul R Torke
- Medical University of Graz, Neue Stiftingtalstraße 6, A-8010 Graz, Austria
| | | | - Anouk Berger
- International Agency for Research on Cancer (IARC), 25 avenue Tony Garnier, 69366 Lyon, France
| | - Heather Coombs
- International Agency for Research on Cancer (IARC), 25 avenue Tony Garnier, 69366 Lyon, France
| | - Joakim Dillner
- Karolinska Institutet, Alfred Nobels Allé 8, 14152 Huddinge, Sweden
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23
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Rinaldi E, Thun S, Stellmach C. ISO/TS 21564:2019- based Evaluation of a Semantic Map between Variables in the ISARIC Freestanding Follow Up Survey and ORCHESTRA Studies. J Med Syst 2023; 47:115. [PMID: 37962711 PMCID: PMC10645626 DOI: 10.1007/s10916-023-02012-4] [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: 04/03/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023]
Abstract
The COVID-19 pandemic has led to tremendous investment in clinical studies to generate much-needed knowledge on the prevention, diagnosis, treatment and long-term effects of the disease. Case report forms, comprised of questions and answers (variables), are commonly used to collect data in clinical trials. Maximizing the value of study data depends on data quality and on the ability to easily pool and share data from several sources. ISARIC, in collaboration with the WHO, has created a case report form that is available for use by the scientific community to collect COVID-19 trial data. One of such research initiatives collecting and analyzing multi-country and multi-cohort COVID-19 study data is the Horizon 2020 project ORCHESTRA. Following the ISO/TS 21564:2019 standard, a mapping between five ORCHESTRA studies' variables and the ISARIC Freestanding Follow-Up Survey elements was created. Measures of correspondence of shared semantic domain of 0 (perfect match), 1 (fully inclusive match), 2 (partial match), 4 (transformation required) or 4* (not present in ORCHESTRA) as compared to the target code system, ORCHESTRA study variables, were assigned to each of the elements in the ISARIC FUP case report form (CRF) which was considered the source code system. Of the ISARIC FUP CRF's variables, around 34% were found to show an exact match with corresponding variables in ORCHESTRA studies and about 33% showed a non-inclusive overlap. Matching variables provided information on patient demographics, COVID-19 testing, hospital admission and symptoms. More in-depth details are covered in ORCHESTRA variables with regards to treatment and comorbidities. ORCHESTRA's Long-Term Sequelae and Fragile population studies' CRFs include 32 and 27 variables respectively which were evaluated as a perfect match to variables in the ISARIC FUP CRF. Our study serves as an example of the kind of maps between case report form variables from different research projects needed to link ongoing COVID-19 research efforts and facilitate collaboration and data sharing. To enable data aggregation across two data systems, the information they contain needs to be connected through a map to determine compatibility and transformation needs. Combining data from various clinical studies can increase the power of analytical insights.
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Affiliation(s)
- Eugenia Rinaldi
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Anna-Louisa-Karsch-Str.2, 10178, Berlin, Germany.
| | - Sylvia Thun
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Anna-Louisa-Karsch-Str.2, 10178, Berlin, Germany
| | - Caroline Stellmach
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Anna-Louisa-Karsch-Str.2, 10178, Berlin, Germany
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24
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Scheibner J, Kroesche N, Wakefield L, Cockburn T, McPhail SM, Richards B. Does Legislation Impede Data Sharing in Australia Across Institutions and Jurisdictions? A Scoping Review. J Med Syst 2023; 47:116. [PMID: 37962613 DOI: 10.1007/s10916-023-02009-z] [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: 08/10/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023]
Abstract
In Australia, regulations governing data, including formal legislation and policies promulgated by private and public agencies, are often seen as a barrier to data sharing. This sharing can include between institutions, as well as across jurisdictional borders in a federated jurisdiction such as Australia. In some cases, these regulations place a barrier to sharing data across borders or between institutions without a prerequisite requirement. In other cases, these regulations may be perceived as a justification not to share data. The objective of this review was to analyse published literature from Australia to see what regulations were used to justify not sharing data, along with any other factors that might discourage data sharing. We searched PubMed, Scopus and Web of Science for empirical and policy articles discussing data sharing in Australia. We then filtered these results via abstract and conducted a full text assessment to include 33 articles for analysis. Although there are a few areas of notable regulatory divergence with respect to legislation governing health data, most regulations in Australia are relatively consistent. Further, the absence of uniform ethics approval between sites in different states was frequently cited as a barrier to data sharing.
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Affiliation(s)
- James Scheibner
- College of Business, Government and Law, Flinders University, Adelaide, Australia.
| | - Nicole Kroesche
- Australian Centre for Health Law Research (ACHLR), School of Law, Faculty of Business and Law, Queensland University of Technology, Brisbane, Australia
| | - Luke Wakefield
- Australian Centre for Health Law Research (ACHLR), School of Law, Faculty of Business and Law, Queensland University of Technology, Brisbane, Australia
| | - Tina Cockburn
- Australian Centre for Health Law Research (ACHLR), School of Law, Faculty of Business and Law, Queensland University of Technology, Brisbane, Australia
| | - Steven M McPhail
- Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
- Digital Health and Informatics Directorate, Metro South Health, Brisbane, Australia
| | - Bernadette Richards
- Associate Professor of Ethics and Professionalism, Medical School, Academy for Medical Education, University of Queensland, Brisbane, Australia
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25
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Modi ND, Kichenadasse G, Hoffmann TC, Haseloff M, Logan JM, Veroniki AA, Venchiarutti RL, Smit AK, Tuffaha H, Jayasekara H, Manning-Bennet A, Morton E, McKinnon RA, Rowland A, Sorich MJ, Hopkins AM. A 10-year update to the principles for clinical trial data sharing by pharmaceutical companies: perspectives based on a decade of literature and policies. BMC Med 2023; 21:400. [PMID: 37872545 PMCID: PMC10594907 DOI: 10.1186/s12916-023-03113-0] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 10/13/2023] [Indexed: 10/25/2023] Open
Abstract
Data sharing is essential for promoting scientific discoveries and informed decision-making in clinical practice. In 2013, PhRMA/EFPIA recognised the importance of data sharing and supported initiatives to enhance clinical trial data transparency and promote scientific advancements. However, despite these commitments, recent investigations indicate significant scope for improvements in data sharing by the pharmaceutical industry. Drawing on a decade of literature and policy developments, this article presents perspectives from a multidisciplinary team of researchers, clinicians, and consumers. The focus is on policy and process updates to the PhRMA/EFPIA 2013 data sharing commitments, aiming to enhance the sharing and accessibility of participant-level data, clinical study reports, protocols, statistical analysis plans, lay summaries, and result publications from pharmaceutical industry-sponsored trials. The proposed updates provide clear recommendations regarding which data should be shared, when it should be shared, and under what conditions. The suggested improvements aim to develop a data sharing ecosystem that supports science and patient-centred care. Good data sharing principles require resources, time, and commitment. Notwithstanding these challenges, enhancing data sharing is necessary for efficient resource utilization, increased scientific collaboration, and better decision-making for patients and healthcare professionals.
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Affiliation(s)
- Natansh D Modi
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Ganessan Kichenadasse
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
- Flinders Centre for Innovation in Cancer, Department of Medical Oncology, Flinders Medical Centre, Adelaide, SA, Australia
| | - Tammy C Hoffmann
- Institute for Evidence-Based Healthcare, Faculty of Health Sciences and Medicine, Bond University, Gold Coast, QLD, Australia
| | | | - Jessica M Logan
- Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Areti A Veroniki
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
| | - Rebecca L Venchiarutti
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia
- Department of Head and Neck Surgery, Chris O'Brien Lifehouse, Sydney, NSW, Australia
| | - Amelia K Smit
- The Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, NSW, Australia
| | - Haitham Tuffaha
- Centre for the Business and Economics of Health, The University of Queensland, Brisbane, QLD, Australia
| | - Harindra Jayasekara
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, VIC, Australia
| | | | - Erin Morton
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Ross A McKinnon
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Andrew Rowland
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Michael J Sorich
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Ashley M Hopkins
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.
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Zannad F, Crea F, Keaney J, Spencer S, Hill JA, Pfeffer MA, Pocock S, Raderschadt E, Ross JS, Sacks CA, Van Spall HGC, Winslow R, Jessup M. Rapid, accurate publication and dissemination of clinical trial results: benefits and challenges. Eur Heart J 2023; 44:4220-4229. [PMID: 37165687 DOI: 10.1093/eurheartj/ehad279] [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: 03/06/2023] [Revised: 03/13/2023] [Accepted: 04/02/2023] [Indexed: 05/12/2023] Open
Abstract
Large-scale clinical trials are essential in cardiology and require rapid, accurate publication, and dissemination. Whereas conference presentations, press releases, and social media disseminate information quickly and often receive considerable coverage by mainstream and healthcare media, they lack detail, may emphasize selected data, and can be open to misinterpretation. Preprint servers speed access to research manuscripts while awaiting acceptance for publication by a journal, but these articles are not formally peer-reviewed and sometimes overstate the findings. Publication of trial results in a major journal is very demanding but the use of existing checklists can help accelerate the process. In case of rejection, procedures such as easing formatting requirements and possibly carrying over peer-review to other journals could speed resubmission. Secondary publications can help maximize benefits from clinical trials; publications of secondary endpoints and subgroup analyses further define treatment effects and the patient populations most likely to benefit. These rely on data access, and although data sharing is becoming more common, many challenges remain. Beyond publication in medical journals, there is a need for wider knowledge dissemination to maximize impact on clinical practice. This might be facilitated through plain language summary publications. Social media, websites, mainstream news outlets, and other publications, although not peer-reviewed, are important sources of medical information for both the public and for clinicians. This underscores the importance of ensuring that the information is understandable, accessible, balanced, and trustworthy. This report is based on discussions held on December 2021, at the 18th Global Cardiovascular Clinical Trialists meeting, involving a panel of editors of some of the top medical journals, as well as members of the lay press, industry, and clinical trialists.
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Affiliation(s)
- Faiez Zannad
- Université de Lorraine, INSERM, CIC 1439, Institut Lorrain du Coeur et des Vaisseaux, CHU 54500, Vandoeuvre-lès-Nancy, France
| | - Filippo Crea
- Department of Cardiovascular and Pneumological Sciences, Catholic University of the Sacred Heart, Rome 00168, Italy
| | - John Keaney
- Division of Cardiovascular Medicine, Heart and Vascular Center, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | | | - Joseph A Hill
- Department of Internal Medicine and Department of Molecular Biology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Marc A Pfeffer
- Cardiovascular Division, Brigham and Women's Hospital, and Harvard Medical School Boston, MA 02115, USA
| | - Stuart Pocock
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Emma Raderschadt
- Global Medical Affairs, Boehringer Ingelheim, Siegburg, 55218, Germany
| | - Joseph S Ross
- Department of Medicine, Yale School of Medicine, New Haven, 06510, USA
| | | | - Harriette G C Van Spall
- Department of Medicine, and Department of Health Research Methods, Evidence, and Impact, McMaster University; Population Health Research Institute; Research Institute of St. Joseph's, Hamilton, ON L8N 4A6, Canada
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27
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Etchegary H, Darmonkov G, Simmonds C, Pullman D, Rahman P. Public attitudes towards genomic data sharing: results from a provincial online survey in Canada. BMC Med Ethics 2023; 24:81. [PMID: 37805493 PMCID: PMC10560413 DOI: 10.1186/s12910-023-00967-0] [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: 01/23/2023] [Accepted: 10/04/2023] [Indexed: 10/09/2023] Open
Abstract
BACKGROUND While genomic data sharing can facilitate important health research and discovery benefits, these must be balanced against potential privacy risks and harms to individuals. Understanding public attitudes and perspectives on data sharing is important given these potential risks and to inform genomic research and policy that aligns with public preferences and needs. METHODS A cross sectional online survey measured attitudes towards genomic data sharing among members of the general public in an Eastern Canadian province. RESULTS Results showed a moderate comfort level with sharing genomic data, usually into restricted scientific databases with controlled access. Much lower comfort levels were observed for sharing data into open or publicly accessible databases. While respondents largely approved of sharing genomic data for health research permitted by a research ethics board, many general public members were concerned with who would have access to their data, with higher rates of approval for access from clinical or academic actors, but much more limited approval of access from commercial entities or governments. Prior knowledge about sequencing and about research ethics boards were both related to data sharing attitudes. CONCLUSIONS With evolving regulations and guidelines for genomics research and data sharing, it is important to consider the perspectives of participants most impacted by these changes. Participant information materials and informed consent documents must be explicit about the safeguards in place to protect genomic data and the policies governing the sharing of data. Increased public awareness of the role of research ethics boards and of the need for genomic data sharing more broadly is also needed.
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Affiliation(s)
- Holly Etchegary
- Faculty of Medicine, Memorial University, St. John's, NL, A1B 3V6, Canada.
| | - Georgia Darmonkov
- Faculty of Medicine, Memorial University, St. John's, NL, A1B 3V6, Canada
| | - Charlene Simmonds
- Research Initiatives and Services, Memorial University, St. John's, NL, A1B 3V6, Canada
| | - Daryl Pullman
- Faculty of Medicine, Memorial University, St. John's, NL, A1B 3V6, Canada
| | - Proton Rahman
- Faculty of Medicine, Memorial University, St. John's, NL, A1B 3V6, Canada
- Eastern Regional Health Authority, Memorial University and Rheumatologist, St. John's, NL, A1B 3V6, Canada
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28
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Law M, Couturier DL, Choodari-Oskooei B, Crout P, Gamble C, Jacko P, Pallmann P, Pilling M, Robertson DS, Robling M, Sydes MR, Villar SS, Wason J, Wheeler G, Williamson SF, Yap C, Jaki T. Medicines and Healthcare products Regulatory Agency's "Consultation on proposals for legislative changes for clinical trials": a response from the Trials Methodology Research Partnership Adaptive Designs Working Group, with a focus on data sharing. Trials 2023; 24:640. [PMID: 37798805 PMCID: PMC10552399 DOI: 10.1186/s13063-023-07576-7] [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: 10/27/2022] [Accepted: 08/04/2023] [Indexed: 10/07/2023] Open
Abstract
In the UK, the Medicines and Healthcare products Regulatory Agency consulted on proposals "to improve and strengthen the UK clinical trials legislation to help us make the UK the best place to research and develop safe and innovative medicines". The purpose of the consultation was to help finalise the proposals and contribute to the drafting of secondary legislation. We discussed these proposals as members of the Trials Methodology Research Partnership Adaptive Designs Working Group, which is jointly funded by the Medical Research Council and the National Institute for Health and Care Research. Two topics arose frequently in the discussion: the emphasis on legislation, and the absence of questions on data sharing. It is our opinion that the proposals rely heavily on legislation to change practice. However, clinical trials are heterogeneous, and as a result some trials will struggle to comply with all of the proposed legislation. Furthermore, adaptive design clinical trials are even more heterogeneous than their non-adaptive counterparts, and face more challenges. Consequently, it is possible that increased legislation could have a greater negative impact on adaptive designs than non-adaptive designs. Overall, we are sceptical that the introduction of legislation will achieve the desired outcomes, with some exceptions. Meanwhile the topic of data sharing - making anonymised individual-level clinical trial data available to other investigators for further use - is entirely absent from the proposals and the consultation in general. However, as an aspect of the wider concept of open science and reproducible research, data sharing is an increasingly important aspect of clinical trials. The benefits of data sharing include faster innovation, improved surveillance of drug safety and effectiveness and decreasing participant exposure to unnecessary risk. There are already a number of UK-focused documents that discuss and encourage data sharing, for example, the Concordat on Open Research Data and the Medical Research Council's Data Sharing Policy. We strongly suggest that data sharing should be the norm rather than the exception, and hope that the forthcoming proposals on clinical trials invite discussion on this important topic.
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Affiliation(s)
- Martin Law
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
- Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK.
| | - Dominique-Laurent Couturier
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | | | - Phillip Crout
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Carrol Gamble
- Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK
| | - Peter Jacko
- Lancaster University Management School, Lancaster University, Lancaster, UK
- Berry Consultants, Abingdon, UK
| | | | - Mark Pilling
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - David S Robertson
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | | | - Matthew R Sydes
- University College London, London, UK
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Sofía S Villar
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - James Wason
- Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Graham Wheeler
- Imperial Clinical Trials Unit, Imperial College London, London, W12 7RH, UK
| | - S Faye Williamson
- Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Christina Yap
- Clinical Trials and Statistics Unit, The Institute of Cancer Research, London, UK
| | - Thomas Jaki
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Faculty for Informatics and Data Science, University of Regensburg, Regensburg, Germany
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Radabaugh HL, Ferguson AR, Bramlett HM, Dietrich WD. Increasing Rigor of Preclinical Research to Maximize Opportunities for Translation. Neurotherapeutics 2023; 20:1433-1445. [PMID: 37525025 PMCID: PMC10684440 DOI: 10.1007/s13311-023-01400-5] [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] [Accepted: 06/09/2023] [Indexed: 08/02/2023] Open
Abstract
The use of animal models in pre-clinical research has significantly broadened our understanding of the pathologies that underlie traumatic brain injury (TBI)-induced damage and deficits. However, despite numerous pre-clinical studies reporting the identification of promising neurotherapeutics, translation of these therapies to clinical application has so far eluded the TBI research field. A concerted effort to address this lack of translatability is long overdue. Given the inherent heterogeneity of TBI and the replication crisis that continues to plague biomedical research, this is a complex task that will require a multifaceted approach centered around rigor and reproducibility. Here, we discuss the role of three primary focus areas for better aligning pre-clinical research with clinical TBI management. These focus areas are (1) reporting and standardization of protocols, (2) replication of prior knowledge including the confirmation of expected pharmacodynamics, and (3) the broad application of open science through inter-center collaboration and data sharing. We further discuss current efforts that are establishing the core framework needed for successfully addressing the translatability crisis of TBI.
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Affiliation(s)
- Hannah L Radabaugh
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Adam R Ferguson
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA
| | - Helen M Bramlett
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA
| | - W Dalton Dietrich
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA.
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30
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Wu Y, Sun Y, Liu Y, Levis B, Krishnan A, He C, Neupane D, Patten SB, Cuijpers P, Ziegelstein RC, Benedetti A, Thombs BD. Depression screening tool accuracy individual participant data meta-analyses: data contribution was associated with multiple factors. J Clin Epidemiol 2023; 162:63-71. [PMID: 37619800 DOI: 10.1016/j.jclinepi.2023.08.006] [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: 05/16/2023] [Revised: 08/12/2023] [Accepted: 08/16/2023] [Indexed: 08/26/2023]
Abstract
OBJECTIVES To examine the proportion of eligible primary studies that contributed data, study characteristics associated with data contribution, and reasons for noncontribution using diagnostic test accuracy Individual Participant Data Meta-Analysis (IPDMA) data sets from the DEPRESsion Screening Data project. STUDY DESIGN AND SETTING We reviewed data set contributions from four IPDMAs. A multivariable logistic regression model was fitted to evaluate study factors associated with data contribution. RESULTS Of 456 eligible studies from four included IPDMAs, 295 (65%) contributed data. More recent year of publication and higher journal impact factor were associated with greater odds of data contribution. Studies conducted in Europe (excluding the United Kingdom), Oceania, Canada, the Middle East, Africa, and Central or South America (reference = the United States), that have recruitment from inpatient care or nonmedical settings (reference = outpatient), that reported screening accuracy results, or that drew negative conclusions (reference = positive conclusions) were more likely to contribute data. Studies of the Geriatric Depression Scale (reference = the Patient Health Questionnaire) or lacking funding information were negatively associated with data contribution. Over 80% of noncontributions were due to authors being unreachable or data being unavailable. CONCLUSION The study identified factors associated with data contribution that may support future research to promote data contribution to IPDMAs.
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Affiliation(s)
- Yin Wu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Ying Sun
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Yi Liu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Brooke Levis
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, UK
| | - Ankur Krishnan
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Chen He
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Dipika Neupane
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Scott B Patten
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Pim Cuijpers
- Department of Clinical, Neuro and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Roy C Ziegelstein
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Andrea Benedetti
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada; Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre, Montreal, Quebec, Canada; Department of Medicine, McGill University, Montreal, Quebec, Canada
| | - Brett D Thombs
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada; Department of Medicine, McGill University, Montreal, Quebec, Canada; Department of Psychology, McGill University, Montreal, Quebec, Canada; Department of Educational and Counselling Psychology, McGill University, Montreal, Quebec, Canada; Biomedical Ethics Unit, McGill University, Montreal, Quebec, Canada.
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31
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Stephenson D, Belfiore-Oshan R, Karten Y, Keavney J, Kwok DK, Martinez T, Montminy J, Müller MLTM, Romero K, Sivakumaran S. Transforming Drug Development for Neurological Disorders: Proceedings from a Multidisease Area Workshop. Neurotherapeutics 2023; 20:1682-1691. [PMID: 37823970 PMCID: PMC10684834 DOI: 10.1007/s13311-023-01440-x] [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] [Accepted: 09/06/2023] [Indexed: 10/13/2023] Open
Abstract
Neurological disorders represent some of the most challenging therapeutic areas for successful drug approvals. The escalating global burden of death and disability for such diseases represents a significant worldwide public health challenge, and the rate of failure of new therapies for chronic progressive disorders of the nervous system is higher relative to other non-neurological conditions. However, progress is emerging rapidly in advancing the drug development landscape in both rare and common neurodegenerative diseases. In October 2022, the Critical Path Institute (C-Path) and the US Food and Drug Administration (FDA) organized a Neuroscience Annual Workshop convening representatives from the drug development industry, academia, the patient community, government agencies, and regulatory agencies regarding the future development of tools and therapies for neurological disorders. This workshop focused on five chronic progressive diseases: Alzheimer's disease, Parkinson's disease, Huntington's disease, Duchenne muscular dystrophy, and inherited ataxias. This special conference report reviews the key points discussed during the three-day dynamic workshop, including shared learnings, and recommendations that promise to catalyze future advancement of novel therapies and drug development tools.
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32
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Bayer JM, Scully RA, Dlabola EK, Courtwright JL, Hirsch CL, Hockman-Wert D, Miller SW, Roper BB, Saunders WC, Snyder MN. Sharing FAIR monitoring program data improves discoverability and reuse. Environ Monit Assess 2023; 195:1141. [PMID: 37665400 DOI: 10.1007/s10661-023-11788-4] [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: 10/14/2022] [Accepted: 08/24/2023] [Indexed: 09/05/2023]
Abstract
Data resulting from environmental monitoring programs are valuable assets for natural resource managers, decision-makers, and researchers. These data are often collected to inform specific reporting needs or decisions with a specific timeframe. While program-oriented data and related publications are effective for meeting program goals, sharing well-documented data and metadata allows users to research aspects outside initial program intentions. As part of an effort to integrate data from four long-term large-scale US aquatic monitoring programs, we evaluated the original datasets against the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and offer recommendations and lessons learned. Differences in data governance across these programs resulted in considerable effort to access and reuse the original datasets. Requirements, guidance, and resources available to support data publishing and documentation are inconsistent across agencies and monitoring programs, resulting in various data formats and storage locations that are not easily found, accessed, or reused. Making monitoring data FAIR will reduce barriers to data discovery and reuse. Programs are continuously striving to improve data management, data products, and metadata; however, provision of related tools, consistent guidelines and standards, and more resources to do this work is needed. Given the value of these data and the significant effort required to access and reuse them, actions and steps intended on improving data documentation and accessibility are described.
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Affiliation(s)
- Jennifer M Bayer
- U.S. Geological Survey, Pacific Northwest Aquatic Monitoring Partnership, Cook, WA, 98605, USA.
| | - Rebecca A Scully
- U.S. Geological Survey, Pacific Northwest Aquatic Monitoring Partnership, Cook, WA, 98605, USA
| | - Erin K Dlabola
- U.S. Geological Survey, Forest and Rangeland Ecosystem Science Center, Corvallis, OR, 97331, USA
| | - Jennifer L Courtwright
- Watershed Sciences Department, College of Natural Resources, Utah State University, Logan, UT, 84322, USA
| | - Christine L Hirsch
- United States Forest Service, Pacific Northwest Research Station, Corvallis, OR, 97331, USA
| | - David Hockman-Wert
- United States Forest Service, Pacific Northwest Research Station, Corvallis, OR, 97331, USA
| | - Scott W Miller
- Bureau of Land Management, National Operations Center, Denver, CO, 80225, USA
| | - Brett B Roper
- United States Forest Service, National Stream and Aquatic Ecology Center, Logan, UT, 84332, USA
| | - W Carl Saunders
- PACFISH/INFISH Biological Opinion Monitoring Program, United States Forest Service, Logan, UT, 84332, USA
| | - Marcía N Snyder
- United States Forest Service, Pacific Northwest Research Station, Corvallis, OR, 97331, USA
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Marelli L, Stevens M, Sharon T, Van Hoyweghen I, Boeckhout M, Colussi I, Degelsegger-Márquez A, El-Sayed S, Hoeyer K, van Kessel R, Zając DK, Matei M, Roda S, Prainsack B, Schlünder I, Shabani M, Southerington T. The European health data space: Too big to succeed? Health Policy 2023; 135:104861. [PMID: 37399677 PMCID: PMC10448378 DOI: 10.1016/j.healthpol.2023.104861] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [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: 04/04/2023] [Revised: 06/01/2023] [Accepted: 06/19/2023] [Indexed: 07/05/2023]
Abstract
In May 2022, the European Commission issued the Proposal for a Regulation on the European Health Data Space (EHDS), with the aims of granting citizens increased access to and control of their (electronic) health data across the EU, and facilitating health data re-use for research, innovation, and policymaking. As the first in a series of European domain-specific "data spaces", the EHDS is a high-stakes development that will transform health data governance in the EU region. As an international consortium of experts from health policy, law, ethics and the social sciences, we are concerned that the EHDS Proposal will detract from, rather than lead to the achievement of, its stated aims. We are in no doubt on the benefits of using health data for secondary purposes, and we appreciate attempts to facilitate such uses across borders in a carefully curated manner. Based on the current draft Regulation, however, the EHDS risks undermining rather than enhancing patient control over data; hindering rather than facilitating the work of health professionals and researchers; and eroding rather than increasing the public value generated through health data sharing. Therefore, significant adjustments are needed if the EHDS is to realize its promised benefits. Besides analyzing the implications for key groups and European societies at large who will be affected by the implementation of the EHDS, this contribution advances targeted policy recommendations to address the identified shortcomings of the EHDS Proposal.
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Affiliation(s)
- Luca Marelli
- Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, 20129, Italy; Centre for Sociological Research, KU Leuven, Leuven, 3000, Belgium.
| | - Marthe Stevens
- Department of Ethics and Political Philosophy and Interdisciplinary Hub for Digitalization and Society, Radboud University, Nijmegen, 6525, HT, the Netherlands
| | - Tamar Sharon
- Department of Ethics and Political Philosophy and Interdisciplinary Hub for Digitalization and Society, Radboud University, Nijmegen, 6525, HT, the Netherlands
| | | | | | - Ilaria Colussi
- Biobanking and BioMolecular resources Research Infrastructure. European Research Infrastructure Consortium (BBMRI-ERIC), Graz, 8010, Austria
| | - Alexander Degelsegger-Márquez
- Department of International Affairs, Policy, Evaluation and Digitalization, Gesundheit Österreich GmbH (Austrian National Public Health Institute), Vienna, 1010, Austria
| | - Seliem El-Sayed
- Department of Political Science and Research Platform Governance of Digital Practices, University of Vienna, Vienna, 1010, Austria
| | - Klaus Hoeyer
- Department of Public Health, University of Copenhagen, Copenhagen, 1014, Denmark
| | - Robin van Kessel
- LSE Health, Department of Health Policy, London School of Economics and Political Science, London, WC2A 2AE, United Kingdom; Department of International Health, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, 6211LK, Netherlands
| | - Dorota Krekora Zając
- Department of Comparative Civil Law, Faculty of Law and Administration University of Warsaw, Warsaw, 00-927, Poland
| | - Mihaela Matei
- European Clinical Research Infrastructure Network (ECRIN), Paris, 75013, France
| | - Sara Roda
- Standing Committee of European Doctors (CPME), Brussels, 1040, Belgium; LSTS - Law, Science, Technology & Society Research Group, and HALL - Health and Ageing Law Lab, Vrije University Brussels, Brussels, 1050, Belgium
| | - Barbara Prainsack
- Department of Political Science and Research Platform Governance of Digital Practices, University of Vienna, Vienna, 1010, Austria
| | | | - Mahsa Shabani
- Faculty of Law, Ghent University, Ghent, 9000-B, Belgium; Faculty of Law, University of Amsterdam, 1018, WV, Amsterdam, the Netherlands
| | - Tom Southerington
- Finnish Biobank Cooperation (FINBB), Turku, 20540, Finland; Faculty of Law, University of Turku, 20014 Turun yliopisto, Finland
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34
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Guo L, Li Y, Qi Y, Huang Z, Han K, Liu X, Liu X, Xu M, Fan G. VT3D: a visualization toolbox for 3D transcriptomic data. J Genet Genomics 2023; 50:713-719. [PMID: 37054878 DOI: 10.1016/j.jgg.2023.04.001] [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: 01/12/2023] [Revised: 04/03/2023] [Accepted: 04/04/2023] [Indexed: 04/15/2023]
Abstract
Data visualization empowers researchers to communicate their results that support scientific reasoning in an intuitive way. Three-dimension (3D) spatially resolved transcriptomic atlases constructed from multi-view and high-dimensional data have rapidly emerged as a powerful tool to unravel spatial gene expression patterns and cell type distribution in biological samples, revolutionizing the understanding of gene regulatory interactions and cell niches. However, limited accessible tools for data visualization impede the potential impact and application of this technology. Here we introduce VT3D, a visualization toolbox that allows users to explore 3D transcriptomic data, enabling gene expression projection to any 2D plane of interest, 2D virtual slice creation and visualization, and interactive 3D data browsing with surface model plots. In addition, it can either work on personal devices in standalone mode or be hosted as a web-based server. We apply VT3D to multiple datasets produced by the most popular techniques, including both sequencing-based approaches (Stereo-seq, spatial transcriptomics, and Slide-seq) and imaging-based approaches (MERFISH and STARMap), and successfully build a 3D atlas database that allows interactive data browsing. We demonstrate that VT3D bridges the gap between researchers and spatially resolved transcriptomics, thus accelerating related studies such as embryogenesis and organogenesis processes. The source code of VT3D is available at https://github.com/BGI-Qingdao/VT3D, and the modeled atlas database is available at http://www.bgiocean.com/vt3d_example.
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Affiliation(s)
- Lidong Guo
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China
| | - Yao Li
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China
| | - Yanwei Qi
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China
| | - Zhi Huang
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China
| | - Kai Han
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China
| | - Xiaobin Liu
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China
| | - Xin Liu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mengyang Xu
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China; BGI-Shenzhen, Shenzhen, Guangdong 518083, China.
| | - Guangyi Fan
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China; BGI-Shenzhen, Shenzhen, Guangdong 518083, China; State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, Guangdong 518083, China.
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35
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Tik N, Gal S, Madar A, Ben-David T, Bernstein-Eliav M, Tavor I. Generalizing prediction of task-evoked brain activity across datasets and populations. Neuroimage 2023; 276:120213. [PMID: 37268097 DOI: 10.1016/j.neuroimage.2023.120213] [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: 04/27/2023] [Revised: 05/28/2023] [Accepted: 05/30/2023] [Indexed: 06/04/2023] Open
Abstract
Predictions of task-based functional magnetic resonance imaging (fMRI) from task-free resting-state (rs) fMRI have gained popularity over the past decade. This method holds a great promise for studying individual variability in brain function without the need to perform highly demanding tasks. However, in order to be broadly used, prediction models must prove to generalize beyond the dataset they were trained on. In this work, we test the generalizability of prediction of task-fMRI from rs-fMRI across sites, MRI vendors and age-groups. Moreover, we investigate the data requirements for successful prediction. We use the Human Connectome Project (HCP) dataset to explore how different combinations of training sample sizes and number of fMRI datapoints affect prediction success in various cognitive tasks. We then apply models trained on HCP data to predict brain activations in data from a different site, a different MRI vendor (Phillips vs. Siemens scanners) and a different age group (children from the HCP-development project). We demonstrate that, depending on the task, a training set of approximately 20 participants with 100 fMRI timepoints each yields the largest gain in model performance. Nevertheless, further increasing sample size and number of timepoints results in significantly improved predictions, until reaching approximately 450-600 training participants and 800-1000 timepoints. Overall, the number of fMRI timepoints influences prediction success more than the sample size. We further show that models trained on adequate amounts of data successfully generalize across sites, vendors and age groups and provide predictions that are both accurate and individual-specific. These findings suggest that large-scale publicly available datasets may be utilized to study brain function in smaller, unique samples.
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Affiliation(s)
- Niv Tik
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Shachar Gal
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Asaf Madar
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Tamar Ben-David
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Michal Bernstein-Eliav
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Ido Tavor
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel; Strauss Center for Computational Neuroimaging, Tel Aviv University, Tel Aviv, Israel.
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36
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Kim J, Lee JM, Kang J. Smart cities and disaster risk reduction in South Korea by 2022: The case of daegu. Heliyon 2023; 9:e18794. [PMID: 37576205 PMCID: PMC10415893 DOI: 10.1016/j.heliyon.2023.e18794] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 07/16/2023] [Accepted: 07/27/2023] [Indexed: 08/15/2023] Open
Abstract
Smart cities have been introduced globally. It involves technical development and economic, social, and environmental objectives. In response to the Fourth Industrial Revolution (Industry 4.0) and global trends, Korea has prepared legal and institutional measures for smart city composition. This study reviewed the importance of key documents and agreements in Daegu Metropolitan City to reduce disaster risk for the vulnerable in the context of smart cities. 25 research studies were critically and systematically reviewed from the perspective of disaster risk reduction in smart cities. In its disaster safety areas, Daegu Metropolitan City aims to reduce property damage and casualties that may occur because of physical events such as collapse, water-related disasters, and heatwaves by up to 20%. Smart disaster mitigation involves data collection, sharing, and propagation. The entire process is handled on a safety platform called Data hub. According to the Daegu Metropolitan City government, solving social problems and managing disasters is key to a smart city, and it is striving to improve the efficiency of other cities. However, Daegu has limitations because it is a service-oriented smart city, and it is necessary to engage citizens to participate, raise awareness of the smart city, and educate them on the platform. The study results recommend future research that focus on disaster risk reduction and resilience in smart cities worldwide.
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Affiliation(s)
- Jaekyoung Kim
- Interdisciplinary Program in Landscape Architecture, Seoul National University, Seoul 08826, Republic of Korea
- Transdisciplinary Program in Smart City Global Convergence, Seoul National University, Seoul 08826, Republic of Korea
| | - Jung-Min Lee
- Land & Housing Institute, Daejeon 34047, Republic of Korea
| | - Junsuk Kang
- Interdisciplinary Program in Landscape Architecture, Seoul National University, Seoul 08826, Republic of Korea
- Transdisciplinary Program in Smart City Global Convergence, Seoul National University, Seoul 08826, Republic of Korea
- Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of Korea
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Liu F, Wang P. A novel privacy protection method of residents' travel trajectories based on federated blockchain and InterPlanetary file systems in smart cities. PeerJ Comput Sci 2023; 9:e1495. [PMID: 37547411 PMCID: PMC10403164 DOI: 10.7717/peerj-cs.1495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 06/27/2023] [Indexed: 08/08/2023]
Abstract
The government does have to record and analyze the travel trajectories of urban residents aiming to effectively control the epidemic during COVID-19. However, these privacy-related data are usually stored in centralized cloud databases, which are prone to be vulnerable to cyber attacks leading to personal trajectory information leakage. In this article, we proposed a novel secure sharing and storing method of personal travel trajectory data based on BC and InterPlanetary File System (IPFS). We adopt the Hyperledger Fabric, the representative of Federated BC framework, combined with the IPFS storage to form a novel mode of querying on-chain and storing off-chain aiming to both achieve the effectiveness of data processing and protect personal privacy-related information. This method firstly solves the efficiency problem of traditional public BC and ensures the security of stored data by storing the ciphertext of complete personal travel trajectory data in decentralized IPFS storage. Secondly, considering the huge amount of information of residents' travel trajectories, the method proposed in this article can obtain the complete information under the chain stored in IPFS by querying the index on the chain, which significantly improves the data processing efficiency of residents' travel trajectories and thus promotes the effective control of the new crown pneumonia epidemic. Finally, the feasibility of the proposed solution is verified through performance evaluation and security analysis.
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Berrios C, Neal S, Zion T, Pastinen T. Comparing Attitudes About Genomic Privacy and Data Sharing in Adolescents and Parents of Children Enrolled in a Genomic Research Repository. AJOB Empir Bioeth 2023; 15:33-40. [PMID: 37487180 PMCID: PMC10805964 DOI: 10.1080/23294515.2023.2232780] [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: 07/26/2023]
Abstract
BACKGROUND Sharing of genomic data aims to make efficient use of limited resources, which may be particularly valuable in rare disease research. Adult research participants and parents of pediatric research participants have shown support for data sharing with protections, but little is known about adolescent attitudes on genomic privacy and data sharing. METHODS In-depth interviews were conducted with 10 adolescents and 18 parents of children enrolled in a pediatric genomic research repository. Interview transcripts were analyzed for themes on attitudes toward genomic privacy, restricted-access data sharing, and open-access data sharing. Findings in adolescent and parent participants were compared and contrasted. RESULTS No adolescents endorsed privacy concerns for restricted-access data sharing. Both adolescents and parents saw value in data sharing for reaching the goals of research and discussed trust in institutions and researchers to protect their data and use it as intended. Adolescents were more likely than parents to accept open-access data sharing, including after risks were discussed. CONCLUSIONS In this exploratory study, adolescents and parents enrolled in a genomic research repository shared many attitudes about genomic data sharing, but adolescents were less concerned about privacy and more agreeable toward open-access data sharing. Future research is needed to investigate this hypothesis in expanded populations and settings, and to clarify whether adolescent attitudes change with age and experiences.
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Affiliation(s)
- Courtney Berrios
- Genomic Medicine Center, Children’s Mercy Kansas City, Kansas City, MO 64108
- School of Medicine, University of Missouri Kansas City, Kansas City, MO 64110
| | - Shelby Neal
- Genomic Medicine Center, Children’s Mercy Kansas City, Kansas City, MO 64108
- School of Medicine, University of Missouri Kansas City, Kansas City, MO 64110
| | - Tricia Zion
- Genomic Medicine Center, Children’s Mercy Kansas City, Kansas City, MO 64108
| | - Tomi Pastinen
- Genomic Medicine Center, Children’s Mercy Kansas City, Kansas City, MO 64108
- School of Medicine, University of Missouri Kansas City, Kansas City, MO 64110
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Kerasidou A, Kerasidou CX. Data-driven research and healthcare: public trust, data governance and the NHS. BMC Med Ethics 2023; 24:51. [PMID: 37452393 PMCID: PMC10349411 DOI: 10.1186/s12910-023-00922-z] [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: 03/24/2023] [Accepted: 06/13/2023] [Indexed: 07/18/2023] Open
Abstract
It is widely acknowledged that trust plays an important role for the acceptability of data sharing practices in research and healthcare, and for the adoption of new health technologies such as AI. Yet there is reported distrust in this domain. Although in the UK, the NHS is one of the most trusted public institutions, public trust does not appear to accompany its data sharing practices for research and innovation, specifically with the private sector, that have been introduced in recent years. In this paper, we examine the question of, what is it about sharing NHS data for research and innovation with for-profit companies that challenges public trust? To address this question, we draw from political theory to provide an account of public trust that helps better understand the relationship between the public and the NHS within a democratic context, as well as, the kind of obligations and expectations that govern this relationship. Then we examine whether the way in which the NHS is managing patient data and its collaboration with the private sector fit under this trust-based relationship. We argue that the datafication of healthcare and the broader 'health and wealth' agenda adopted by consecutive UK governments represent a major shift in the institutional character of the NHS, which brings into question the meaning of public good the NHS is expected to provide, challenging public trust. We conclude by suggesting that to address the problem of public trust, a theoretical and empirical examination of the benefits but also the costs associated with this shift needs to take place, as well as an open conversation at public level to determine what values should be promoted by a public institution like the NHS.
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Affiliation(s)
- Angeliki Kerasidou
- Ethox Centre, Oxford Population Health (Nuffield Department of Population Health, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery Oxford, University of Oxford, Oxford, UK.
| | - Charalampia Xaroula Kerasidou
- Ethox Centre, Oxford Population Health (Nuffield Department of Population Health, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery Oxford, University of Oxford, Oxford, UK
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Maxwell L, Shreedhar P, Levis B, Chavan SA, Akter S, Carabali M. Overlapping research efforts in a global pandemic: a rapid systematic review of COVID-19-related individual participant data meta-analyses. BMC Health Serv Res 2023; 23:735. [PMID: 37415216 PMCID: PMC10327330 DOI: 10.1186/s12913-023-09726-8] [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: 10/24/2022] [Accepted: 06/20/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND Individual participant data meta-analyses (IPD-MAs), which involve harmonising and analysing participant-level data from related studies, provide several advantages over aggregate data meta-analyses, which pool study-level findings. IPD-MAs are especially important for building and evaluating diagnostic and prognostic models, making them an important tool for informing the research and public health responses to COVID-19. METHODS We conducted a rapid systematic review of protocols and publications from planned, ongoing, or completed COVID-19-related IPD-MAs to identify areas of overlap and maximise data request and harmonisation efforts. We searched four databases using a combination of text and MeSH terms. Two independent reviewers determined eligibility at the title-abstract and full-text stages. Data were extracted by one reviewer into a pretested data extraction form and subsequently reviewed by a second reviewer. Data were analysed using a narrative synthesis approach. A formal risk of bias assessment was not conducted. RESULTS We identified 31 COVID-19-related IPD-MAs, including five living IPD-MAs and ten IPD-MAs that limited their inference to published data (e.g., case reports). We found overlap in study designs, populations, exposures, and outcomes of interest. For example, 26 IPD-MAs included RCTs; 17 IPD-MAs were limited to hospitalised patients. Sixteen IPD-MAs focused on evaluating medical treatments, including six IPD-MAs for antivirals, four on antibodies, and two that evaluated convalescent plasma. CONCLUSIONS Collaboration across related IPD-MAs can leverage limited resources and expertise by expediting the creation of cross-study participant-level data datasets, which can, in turn, fast-track evidence synthesis for the improved diagnosis and treatment of COVID-19. TRIAL REGISTRATION 10.17605/OSF.IO/93GF2.
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Affiliation(s)
- Lauren Maxwell
- Heidelberger Institut Für Global Health, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 130/3, 69120, Heidelberg, Germany.
| | - Priya Shreedhar
- Heidelberger Institut Für Global Health, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 130/3, 69120, Heidelberg, Germany
| | - Brooke Levis
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Cote Ste Catherine Road, Montreal, QC, H3T 1E2, Canada
| | - Sayali Arvind Chavan
- Institute of Tropical Medicine and Public Health, Charité - Universitätsmedizin Berlin, Südring 2-3, 13353, Berlin, Germany
| | - Shaila Akter
- Heidelberger Institut Für Global Health, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 130/3, 69120, Heidelberg, Germany
| | - Mabel Carabali
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, 2001 McGill College Avenue, Montréal, H3A 1G1, Canada
- Department of Social and Preventive Medicine, School of Public Health, Universite de Montreal, 7101 Parc Avenue, Montreal, H3N 1X9, Canada
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Hamzah N, Malim NHAH, Abdullah JM, Sumari P, Mokhtar AM, Rosli SNS, Ibrahim SAS, Idris Z. Big Brain Data Initiatives in Universiti Sains Malaysia: Data Stewardship to Data Repository and Data Sharing. Neuroinformatics 2023; 21:589-600. [PMID: 37344699 DOI: 10.1007/s12021-023-09637-3] [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] [Accepted: 05/30/2023] [Indexed: 06/23/2023]
Abstract
The sharing of open-access neuroimaging data has increased significantly during the last few years. Sharing neuroimaging data is crucial to accelerating scientific advancement, particularly in the field of neuroscience. A number of big initiatives that will increase the amount of available neuroimaging data are currently in development. The Big Brain Data Initiative project was started by Universiti Sains Malaysia as the first neuroimaging data repository platform in Malaysia for the purpose of data sharing. In order to ensure that the neuroimaging data in this project is accessible, usable, and secure, as well as to offer users high-quality data that can be consistently accessed, we first came up with good data stewardship practices. Then, we developed MyneuroDB, an online repository database system for data sharing purposes. Here, we describe the Big Brain Data Initiative and MyneuroDB, a data repository that provides the ability to openly share neuroimaging data, currently including magnetic resonance imaging (MRI), electroencephalography (EEG), and magnetoencephalography (MEG), following the FAIR principles for data sharing.
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Affiliation(s)
- Nurfaten Hamzah
- Department of Neurosciences, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
- Brain and Behaviour Cluster, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
| | | | - Jafri Malin Abdullah
- Department of Neurosciences, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
- Brain and Behaviour Cluster, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
- Department of Neurosciences & Brain Behaviour Cluster, Hospital Universiti Sains Malaysia, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
| | - Putra Sumari
- School of Computer Sciences, Universiti Sains Malaysia, 11800, Gelugor, Pulau Pinang, Malaysia
| | - Ariffin Marzuki Mokhtar
- Hospital Management System Unit, Hospital Universiti Sains Malaysia, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
| | - Siti Nur Syamila Rosli
- Department of Neurosciences, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
| | | | - Zamzuri Idris
- Department of Neurosciences, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
- Brain and Behaviour Cluster, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
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42
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Tucci DL. NIDCD's 5-Year Strategic Plan Describes Scientific Priorities and Commitment to Basic Science. J Assoc Res Otolaryngol 2023:10.1007/s10162-023-00902-5. [PMID: 37341886 DOI: 10.1007/s10162-023-00902-5] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2023] Open
Abstract
The National Institute on Deafness and Other Communication Disorders (NIDCD) recently issued a new strategic plan that describes the institute's scientific priorities over the next five years. Developed in collaboration with informed stakeholders, the 2023-2027 NIDCD Strategic Plan: Advancing the Science of Communication to Improve Lives creates a unified vision to stimulate discoveries in basic research, model systems, innovative technologies, individualized treatment approaches, scientific data sharing, and translation of research findings into clinical practice. To further accelerate scientific discoveries, the institute encourages collaborations and information sharing among interdisciplinary teams conducting research in these priority areas, and advocates for the utilization of biomedical databases to share scientific findings. NIDCD also welcomes investigator-driven applications that capitalize on advances in basic research to better understand normal and disordered processes; develop or improve model systems to inform research; or facilitate the use of biomedical data utilizing best practices. Through these efforts, NIDCD will continue to conduct and support research that improves the quality of life for the millions of American impacted by conditions affecting hearing, balance, taste, smell, voice, speech, or language.
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Affiliation(s)
- Debara L Tucci
- National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, USA.
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Ramdjee B, Husson M, Hajage D, Tubach F, Estellat C, Dechartres A. COVID-19 trials were not more likely to report intent to share individual data than non-COVID-19 trials in ClinicalTrials.gov. J Clin Epidemiol 2023; 158:10-17. [PMID: 36965602 PMCID: PMC10036148 DOI: 10.1016/j.jclinepi.2023.03.015] [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: 09/07/2022] [Revised: 01/30/2023] [Accepted: 03/21/2023] [Indexed: 03/27/2023]
Abstract
OBJECTIVES To compare intent to share individual participant data (IPD) between COVID-19 and non-COVID-19 trials registered at ClinicalTrials.gov between 01/09/2020, and 01/03/2021. We also evaluated factors independently associated with intent to share IPD and whether intent to share IPD has improved as compared with the prepandemic period. METHODS We searched ClinicalTrials.gov for all interventional phase 3 studies registered between 01/09/2020, and 01/03/2021. Then, we identified COVID-19 trials and selected a random sample of non-COVID-19 trials with a ratio 2:1. We compared the intent to share IPD between these trials and with 292 trials registered between 01/12/2019, and 01/03/2020 (prepandemic period). RESULTS We included 148 COVID-19 trials and 296 non-COVID-19 trials. Intent to share IPD did not significantly differ between COVID-19 and non-COVID-19 trials (22.3% vs. 27.0%, P = 0.3). Intent to share IPD was independently associated with industry-sponsorship (odds ratio [OR] = 2.92; 95% confidence interval [CI]: 1.65-5.27) and location in the United States (OR = 2.93; 95% CI: 1.64-5.41) or the European Union (OR = 2.06; 95% CI: 1.03-4.19). The intent to share IPD has not significantly improved compared with the prepandemic period (P = 0.16). CONCLUSION Data-sharing intent at registration does not seem better for COVID-19 trials.
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Affiliation(s)
- Bruno Ramdjee
- AP-HP, Hôpital Pitié Salpêtrière, Département de Santé Publique, F75013, Paris, France
| | - Mathilde Husson
- AP-HP, Hôpital Pitié Salpêtrière, Département de Santé Publique, F75013, Paris, France
| | - David Hajage
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, AP-HP, Hôpital Pitié Salpêtrière, Département de Santé Publique, CIC-1901, F75013, Paris, France
| | - Florence Tubach
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, AP-HP, Hôpital Pitié Salpêtrière, Département de Santé Publique, CIC-1901, F75013, Paris, France
| | - Candice Estellat
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, AP-HP, Hôpital Pitié Salpêtrière, Département de Santé Publique, CIC-1901, F75013, Paris, France
| | - Agnès Dechartres
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, AP-HP, Hôpital Pitié Salpêtrière, Département de Santé Publique, CIC-1901, F75013, Paris, France.
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Bak MAR, Ploem MC, Tan HL, Blom MT, Willems DL. Towards trust-based governance of health data research. Med Health Care Philos 2023; 26:185-200. [PMID: 36633724 PMCID: PMC9835739 DOI: 10.1007/s11019-022-10134-8] [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] [Subscribe] [Scholar Register] [Accepted: 12/12/2022] [Indexed: 05/13/2023]
Abstract
Developments in medical big data analytics may bring societal benefits but are also challenging privacy and other ethical values. At the same time, an overly restrictive data protection regime can form a serious threat to valuable observational studies. Discussions about whether data privacy or data solidarity should be the foundational value of research policies, have remained unresolved. We add to this debate with an empirically informed ethical analysis. First, experiences with the implementation of the General Data Protection Regulation (GDPR) within a European research consortium demonstrate a gap between the aims of the regulation and its effects in practice. Namely, strictly formalised data protection requirements may cause routinisation among researchers instead of substantive ethical reflection, and may crowd out trust between actors in the health data research ecosystem; while harmonisation across Europe and data sharing between countries is hampered by different interpretations of the law, which partly stem from different views about ethical values. Then, building on these observations, we use theory to argue that the concept of trust provides an escape from the privacy-solidarity debate. Lastly, the paper details three aspects of trust that can help to create a responsible research environment and to mitigate the encountered challenges: trust as multi-agent concept; trust as a rational and democratic value; and trust as method for priority setting. Mutual cooperation in research-among researchers and with data subjects-is grounded in trust, which should be more explicitly recognised in the governance of health data research.
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Affiliation(s)
- Marieke A R Bak
- Department of Ethics, Law and Humanities, Amsterdam UMC (Location AMC), University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands.
| | - M Corrette Ploem
- Department of Ethics, Law and Humanities, Amsterdam UMC (Location AMC), University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands
| | - Hanno L Tan
- Department of Cardiology, Amsterdam UMC (Location AMC), University of Amsterdam, Amsterdam, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - M T Blom
- Department of Cardiology, Amsterdam UMC (Location AMC), University of Amsterdam, Amsterdam, The Netherlands
| | - Dick L Willems
- Department of Ethics, Law and Humanities, Amsterdam UMC (Location AMC), University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands
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45
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Löbe M, Kuntz A, Henke C, Meinke F, Sax U, Winter A. Concept for a Basic ISO 14721 Archive Information Package for Clinical Studies. Stud Health Technol Inform 2023; 302:721-725. [PMID: 37203477 DOI: 10.3233/shti230247] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Secondary use of medical data for research is desirable for intrinsic, ethical and financial reasons. In this context, the question becomes relevant as to how such datasets are to be made accessible to a larger target group in the long term. Typically, datasets are not extracted ad hoc from the primary systems, because they are processed qualitatively (FAIR data). Special data repositories are currently being built for this purpose. This paper examines the requirements for the reuse of clinical trial data in a data repository utilizing the Open Archiving Information System (OAIS) reference model. In particular, a concept for an Archive Information Package (AIP) is developed with the central focus on a cost-effective trade-off between the effort of creation for the data producer and the comprehensibility of the data for the data consumer.
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Affiliation(s)
- Matthias Löbe
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Germany
| | - Alessandra Kuntz
- Department of Medical Informatics at the University Medical Center Göttingen, Germany
| | - Christian Henke
- Department of Medical Informatics at the University Medical Center Göttingen, Germany
| | - Frank Meinke
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Germany
| | - Ulrich Sax
- Department of Medical Informatics at the University Medical Center Göttingen, Germany
- Campus-Institute of Data Science (CIDAS), Göttingen, Germany
| | - Alfred Winter
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Germany
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Landers C, Ormond KE, Blasimme A, Brall C, Vayena E. Talking Ethics Early in Health Data Public Private Partnerships. J Bus Ethics 2023; 190:649-659. [PMID: 38487176 PMCID: PMC10933190 DOI: 10.1007/s10551-023-05425-w] [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] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 04/25/2023] [Indexed: 03/17/2024]
Abstract
Data access and data sharing are vital to advance medicine. A growing number of public private partnerships are set up to facilitate data access and sharing, as private and public actors possess highly complementary health data sets and treatment development resources. However, the priorities and incentives of public and private organizations are frequently in conflict. This has complicated partnerships and sparked public concerns around ethical issues such as trust, justice or privacy-in turn raising an important problem in business and data ethics: how can ethical theory inform the practice of public and private partners to mitigate misaligned incentives, and ensure that they can deliver societally beneficial innovation? In this paper, we report on the development of the Swiss Personalized Health Network's ethical guidelines for health data sharing in public private partnerships. We describe the process of identifying ethical issues and engaging core stakeholders to incorporate their practical reality on these issues. Our report highlights core ethical issues in health data public private partnerships and provides strategies for how to overcome these in the Swiss health data context. By agreeing on and formalizing ethical principles and practices at the beginning of a partnership, partners and society can benefit from a relationship built around a mutual commitment to ethical principles. We present this summary in the hope that it will contribute to the global data sharing dialogue.
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Affiliation(s)
- Constantin Landers
- Health Ethics and Policy Lab, ETH Zurich, Hottingerstrasse 10, 8032 Zurich, Switzerland
| | - Kelly E. Ormond
- Health Ethics and Policy Lab, ETH Zurich, Hottingerstrasse 10, 8032 Zurich, Switzerland
| | - Alessandro Blasimme
- Health Ethics and Policy Lab, ETH Zurich, Hottingerstrasse 10, 8032 Zurich, Switzerland
| | - Caroline Brall
- Ethics and Policy Lab, Multidisciplinary Center for Infectious Diseases, University of Bern, Länggassstrasse 49a, 3012 Bern, Switzerland
- Institute of Philosophy, University of Bern, Länggassstrasse 49a, 3012 Bern, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Lab, ETH Zurich, Hottingerstrasse 10, 8032 Zurich, Switzerland
- ELSI Advisory Group, Swiss Personalized Health Network, Laupenstrasse 7, 3001 Bern, Switzerland
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Mang JM, Prokosch HU, Kapsner LA. Reproducibility in 2023 - An End-to-End Template for Analysis and Manuscript Writing. Stud Health Technol Inform 2023; 302:58-62. [PMID: 37203609 DOI: 10.3233/shti230064] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Reproducibility imposes some special requirements at different stages of each project, including reproducible workflows for the analysis including to follow best practices regarding code style and to make the creation of the manuscript reproducible as well. Available tools therefore include version control systems such as Git and document creation tools such as Quarto or R Markdown. However, a re-usable project template mapping the entire process from performing the data analysis to finally writing the manuscript in a reproducible manner is yet lacking. This work aims to fill this gap by presenting an open source template for conducting reproducible research projects utilizing a containerized framework for both developing and conducting the analysis and summarizing the results in a manuscript. This template can be used instantly without any customization.
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Affiliation(s)
- Jonathan M Mang
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Hans-Ulrich Prokosch
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Lorenz A Kapsner
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
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Aplin T, Radauer A, Bader MA, Searle N. The Role of EU Trade Secrets Law in the Data Economy: An Empirical Analysis. IIC Int Rev Ind Prop Copyr Law 2023; 54:1-33. [PMID: 37359758 PMCID: PMC10170042 DOI: 10.1007/s40319-023-01325-8] [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] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 06/28/2023]
Abstract
This article draws on a recently completed study for the European Commission on trade secrets in the data economy. It distils the main findings of that Study and advances it by reflecting on and analyzing these findings in the context of existing legal, management and economics literature, as well as their implications for EU legal policymaking when it comes to trade secrets law. In order to facilitate data sharing, the article argues for a cautious approach, with very modest legislative reforms to the EU Trade Secrets Directive, instead preferring soft law and practical steps to be taken. There is, however, greater scope to reform legal regimes that are complementary to EU trade secrets law, such as the sui generis database right.
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Affiliation(s)
- Tanya Aplin
- Dr.; Professor of Intellectual Property Law, Dickson Poon School of Law, King’s College London, London, UK
| | - Alfred Radauer
- Dr.; Head of Institute Business Administration and Management, IMC Krems, University of Applied Sciences, Krems an der Donau, Austria
| | - Martin A. Bader
- Dr.; Professor of Technology Management and Entrepreneurship, European and Swiss Patent Attorney, THI Business School, Technische Hochschule Ingolstadt, University of Applied Sciences, Ingolstadt, Germany
| | - Nicola Searle
- Dr.; EPRSC Digital Economy Fellow and Senior Lecturer at Institute for Creative and Cultural Entrepreneurship, Goldsmiths, University of London, London, UK
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Wint W, Mitchell A, Alexander N, Ellerbeck J, Enticott G, Hogarth P, Prosser A, Lambert L, Hackett D, Tait N, Tiller J, Upton P. Challenges and opportunities of sharing animal health data for research and disease management: a case study of bovine tuberculosis. REV SCI TECH OIE 2023; 42:75-82. [PMID: 37232317 DOI: 10.20506/rst.42.3350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
The sharing of animal disease data should be encouraged. The analysis of such data will broaden our knowledge of animal diseases and potentially provide insights into their management. However, the need to conform to data protection rules in the sharing of such data for analysis purposes often poses practical difficulties. This paper sets out the challenges and the methods used for the sharing of animal health data in England, Scotland and Wales - Great Britain - using bovine tuberculosis (bTB) data as a case study. The data sharing described is undertaken by the Animal and Plant Health Agency on behalf of the Department for Environment, Food and Rural Affairs and the Welsh and Scottish Governments. It should be noted that animal health data are held at the level of Great Britain (rather than the United Kingdom - which includes Northern Ireland), as Northern Ireland's Department of Agriculture, Environment and Rural Affairs has its own separate data systems. Bovine tuberculosis is the most significant and costly animal health problem facing cattle farmers in England and Wales. It can be devastating for farmers and farming communities and the control costs for taxpayers in Great Britain are over £150 million a year. The authors describe two methods of data sharing - first, where data are requested by, and delivered to, an academic institution for epidemiological or scientific analysis, and second, where data are proactively published in an accessible and meaningful way. They provide details of an example of the second method, namely, the free-to-access website ‘information bovine TB' (https://ibtb.co.uk), which publishes bTB data for the benefit of the farming community and veterinary health professionals.
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50
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Miller NA, Ehmann MM, Hagerman CJ, Forman E, Arigo D, Spring B, LaFata E, Zhang Z, Milliron BJ, Butryn ML. Sharing digital self-monitoring data with others to enhance long-term weight loss: A randomized controlled trial. Contemp Clin Trials 2023; 129:107201. [PMID: 37080355 DOI: 10.1016/j.cct.2023.107201] [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: 01/19/2023] [Revised: 03/24/2023] [Accepted: 04/16/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND Participants in behavioral weight loss (BWL) programs increasingly use digital tools to self-monitor weight, physical activity, and dietary intake. Data collected with these tools can be systematically shared with other parties in ways that might support behavior change. METHODS Adults age 18 to 70 with overweight/obesity (BMI 27-50 kg/m2) will enroll in a remotely delivered, 24-month BWL program designed to produce and maintain a 10% weight loss. Participants will be asked to use a wireless body weight scale, wearable activity sensor, and dietary intake app daily. All participants will receive individual and group counseling, engage in text messaging with members of their group, and appoint a friend or family member to serve in a support role. A 2x2x2 factorial design will test the effects of three types of data sharing partnerships: 1) Coach Share: The behavioral coach will regularly view digital self-monitoring data and address data observations. 2) Group Share: Participants will view each other's self-monitoring data in small-group text messages. 3) Friend/Family Share: A friend or family member will view the participant's data via automated message. The primary outcome is weight loss at 24 months. Mediators and moderators of intervention effects will be tested. CONCLUSION This study will provide a clear indication of whether data sharing can improve long-term weight loss. This study will be the first to discern the mechanisms of action through which each type of data sharing may be beneficial, and elucidate conditions under which the benefits of data sharing may be maximized.
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Affiliation(s)
- Nicole A Miller
- Center for Weight, Eating, and Lifestyle Science, Drexel University, 3201 Chestnut Street Stratton Hall, Philadelphia, PA 19104, United States; Department of Nutrition Sciences, Drexel University, 60 N 36th St, 11(th) floor, Philadelphia, PA 19104, United States.
| | - Marny M Ehmann
- Center for Weight, Eating, and Lifestyle Science, Drexel University, 3201 Chestnut Street Stratton Hall, Philadelphia, PA 19104, United States; Department of Psychological and Brain Sciences, Drexel University, 3201 Chestnut Street Stratton Hall, Philadelphia, PA 19104, United States
| | - Charlotte J Hagerman
- Center for Weight, Eating, and Lifestyle Science, Drexel University, 3201 Chestnut Street Stratton Hall, Philadelphia, PA 19104, United States; Department of Psychological and Brain Sciences, Drexel University, 3201 Chestnut Street Stratton Hall, Philadelphia, PA 19104, United States
| | - Evan Forman
- Center for Weight, Eating, and Lifestyle Science, Drexel University, 3201 Chestnut Street Stratton Hall, Philadelphia, PA 19104, United States; Department of Psychological and Brain Sciences, Drexel University, 3201 Chestnut Street Stratton Hall, Philadelphia, PA 19104, United States
| | - Danielle Arigo
- Department of Psychology, Rowan University, 201 Mullica Hill Rd, Robinson Hall, Glassboro, NJ 08028, United States
| | - Bonnie Spring
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Evanston, IL, United States
| | - Erica LaFata
- Center for Weight, Eating, and Lifestyle Science, Drexel University, 3201 Chestnut Street Stratton Hall, Philadelphia, PA 19104, United States; Department of Psychological and Brain Sciences, Drexel University, 3201 Chestnut Street Stratton Hall, Philadelphia, PA 19104, United States
| | - Zoe Zhang
- Department of Psychological and Brain Sciences, Drexel University, 3201 Chestnut Street Stratton Hall, Philadelphia, PA 19104, United States
| | - Brandy-Joe Milliron
- Center for Weight, Eating, and Lifestyle Science, Drexel University, 3201 Chestnut Street Stratton Hall, Philadelphia, PA 19104, United States
| | - Meghan L Butryn
- Center for Weight, Eating, and Lifestyle Science, Drexel University, 3201 Chestnut Street Stratton Hall, Philadelphia, PA 19104, United States
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