European Data Research Articles

The data quality problem (in the European Financial Data Space)

Abstract By creating the European Financial Data Space (EFDS), the European Commission aims to foster the development of data-driven innovation in the financial sector. Artificial intelligence (AI) is the most notable innovation that could benefit from enhanced access to data through the EFDS. However, the current policy framework establishing the EFDS suffers from a major limitation—it neglects the question of data quality or whether the data shared in the EFDS will be fit for the purpose of the development of AI applications. The AI Act and General Data Protection Regulation could play a role in fostering data quality in this area, but the two frameworks are also unlikely to do that to a satisfactory extent. To address the data quality problem, this article proposes policymakers should develop a framework for data quality that encompasses both a conceptual and an organizational dimension aimed at promoting the free flow of high-quality data in the EFDS.

P102 Methicillin-resistant Staphylococcus aureus and pulmonary outcome in people with cystic fibrosis: a European Cystic Fibrosis Patient Registry data analysis

P102 Methicillin-resistant Staphylococcus aureus and pulmonary outcome in people with cystic fibrosis: a European Cystic Fibrosis Patient Registry data analysis

Modeling the impacts of extreme climate scenarios on soil acidity (pH and exchangeable aluminum) in Abbay River Basin, Ethiopia

The Abbay River Basin faces the looming threat of extreme climate events, including prolonged droughts and erratic rainfall patterns, which can significantly affect soil health and fertility. This study aimed to explore the influence of extreme climate conditions on soil pH and exchangeable aluminum, aiming to promote sustainable agricultural practices in Ethiopia. The Africa Soil Information Service (ASIS) provided datasets on soil pH and exchangeable aluminum. The European Copernicus Climate Change Data Store was used to download historical and future datasets of extreme climatic indices from 1980 to 2010 and 2015–2050, respectively. The Coupled Model Intercomparison Project Phase 6 model ensemble was used to predict future climate impacts under three shared socioeconomic scenarios: SSP1-2.6, SSP2-4.3, and SSP5-8.5. Data extraction, quality control, and clustering were conducted before analysis, and the model was validated for its accuracy and reliability in predicting soil parameter changes. An artificial neural network model was utilized to predict the effects of extreme climate indices on soil pH and exchangeable aluminum concentrations. The model was designed to accurately and reliably predict changes in soil parameters. This study compared the changes in soil pH and aluminum concentrations using paired t tests. The model's diagnostic results indicated a significant impact of extreme climate scenarios on soil pH and exchangeable aluminum. Extreme climate factors such as heavy precipitation and cooler night time temperatures significantly contribute to soil acidification and an increase in aluminum concentration. Under the SSP1-2.6 and SSP2-4.5 emission scenarios, soil pH levels are expected to increase by 8.38 % and 3.79 %, respectively. These changes in soil pH are expected to have significant impacts on the exchangeable aluminum content in the soil, with increases of 37 % and 5.38 %, respectively, under the same emission scenarios. However, the SSP5.8 scenario predicted a 45 % increase in exchangeable aluminum and a 9.36 % decrease in soil pH. Therefore, this study significantly enhances our understanding of the influence of climate change on soil health. The development of strategies to mitigate climate change impacts on agriculture in the region must consider the effects of extreme climate indices.

Protocolo diagnóstico y tratamiento del nódulo tiroideo

A thyroid nodule consists of the growth of a thyroid lesion that is delimited and distinguished from the normal thyroid parenchyma. Its incidence is increasing due to the increase in imaging tests (incidental thyroid tumors), with no impact on thyroid cancer survival. A thyroid function test with a thyroid stimulating hormone (TSH) measurement should be performed, thus verifying nodule functionality. In addition, an initial approach should be made to the probability of malignancy via a thyroid ultrasound that categorizes each nodule according to the European Thyroid Imaging and Reporting Data System (EU-TIRADS), indicating the risk of malignancy and also the size at which a thyroid cytology (via fine-needle aspiration) should be performed. Cytology allows for classifying the nodules according to the Bethesda categories that will provide an estimate of the risk of malignancy, which will be of utmost importance when deciding on management (observation versus surgery).

Synthetic data and European General Data Protection Regulation: Ethics, quality and legality of data sharing

Synthetic data is increasingly being used across the financial services, clinical research, manufacturing and transport industries. In clinical research, use cases for synthetic data include secondary analysis to identify novel treatment pathways, to develop healthcare policies, to evaluate research methods and, importantly, to evaluate research hypotheses without exposing real patients to potentially harmful experimental treatments. Methods for creating synthetic data in a manner that can reconcile the privacy of clinical trial participants while preserving the utility of data for analysis are rapidly evolving. However, challenges remain that include obtaining appropriate consent for the use of real patient data in the creation of synthetic datasets, eliminating bias in synthetic data and ensuring that data privacy concerns can be addressed.

Pre- and post-operative lung cancer recurrence prediction following curative surgery: A retrospective study using European radiomics and clinical data.

Pre- and post-operative lung cancer recurrence prediction following curative surgery: A retrospective study using European radiomics and clinical data.

Implementation and Execution of Big Data-based Studies in Ophthalmology within the Framework of the GDPR.

The processing of the retrospective data pool for ophthalmology holds huge potential, especially for the research sector. "Big Data" enables medical science to draw conclusions for the future from historical data. Based on the evaluation of such data, algorithms could be trained, for instance, that are capable of making decisions with the help of artificial intelligence. As a result, the medical decision-making process on certain issues could be accelerated, enriched in qualitative and quantitative terms, or even completely be taken over. Ophthalmology is a rapidly evolving field. Due to the multitude of partly automated medical imaging technologies and the predestined accessibility of the eye for such technologies, ophthalmology, similarly to radiology or dermatology, is well suited for artificial intelligence-assisted image data analysis and the frequently associated initiation of diagnosis and therapy. Meanwhile, numerous studies exist based on AI-assisted image data analysis of ophthalmological image data. To the extent that the algorithms filter out results from the data pools by means of calculation rules and are even capable of making independent decisions on the basis of decision trees, the enormous benefit and, simultaneously, the profit for scientific research is quite obvious. Accordingly, it would be desirable to have unrestricted and comprehensive possibility of corresponding data processing of these health data for ophthalmological research. In spite of the potential for ophthalmology, for which there is only fragmentary evidence, the question of practical feasibility arises. In particular, the legal requirements and limits of European and national data protection law must be taken into account, prior to any unreflected processing of personal (health) data. Only by doing so can we circumvent existing obstacles and pitfalls, which can lead to severe fines. Most important are to date the requirements of two legal texts: The General Data Protection Regulation (GDPR) and the Federal Data Protection Act (BDSG). This article provides an overview of the relevant legal requirements applicable in the field of ophthalmology and highlights the major pitfalls and implementation requirements.

Analysis of GDPR Compliance in Utilizing Personal Data for Public Health Purposes in Romania.

The General Data Protection Regulation (GDPR), which became effective on May 25, 2016, underscored the significance of confidentiality across various economic and social domains. Within the medical sector, confidentiality of patient health information is meticulously governed by laws, e.g., no. 95/2006 and no. 46/2003. While these laws address numerous privacy aspects within the doctor-patient relationship, it becomes necessary to update them to align with the latest advancements in emerging technologies, particularly in the context of telemedicine. Upon reviewing the overview of rules pertaining to health data processing in Romania, as published by the European Data Protection Board (EDPB) in 2021, and comparing it with the current public health and research laws in Romania, it becomes apparent that there is a regulatory gap concerning the secondary use of health data. This gap is particularly notable in terms of planning, managing and enhancing the healthcare system, as well as utilizing such data for scientific and historical research purposes, leading to the necessity of developing and regulating the European Health Data Space. Although steps have been taken to align the GDPR legislation in Romania, there is still a disproportionality in the regulation of privacy and cyber security with the implementation of new technologies that will collect, process and store sensitive medical data.

EMPOWERING INDIVIDUALS: A DEEP DIVE INTO THE DIGITAL PERSONAL DATA PROTECTION ACT, 2023

The Digital Personal Data Protection Act (DPDPA) represents a significant milestone in Indias legislative journey towards safeguarding personal data. Previous attempts, notably the Draft Personal Data Protection Bill of 2019 and the Draft Data Protection Bill of 2022, laid the groundwork for this landmark act. The DPDPA aligns with global standards, drawing comparisons to the European Unions General Data Protection Regulation (GDPR). Its enactment is rooted in the Supreme Courts Puttaswamy judgment (2017), rapid digitalization, global data protection trends, economic considerations, and extensive public and stakeholder feedback. Key features of the DPDPA include its broad applicability, a nuanced consent framework, and robust individual rights such as access, rectification, erasure, and data portability. The Act imposes stringent obligations on data fiduciaries concerning data collection, processing, storage, security, and disclosure, overseen by the Data Protection Board (DPB). The DPB is empowered with investigative and enforcement capabilities to ensure compliance. The Act also incorporates specific exemptions to balance various interests. Analyzing the DPDPA reveals its potential to empower individuals by providing control over their personal data, although it presents implementation challenges such as compliance burdens for businesses and potential ambiguities. Comparing the DPDPA with frameworks like the GDPR and the California Consumer Privacy Act (CCPA) highlights its strengths and weaknesses. The Acts impact on Indias digital economy and innovation suggests both potential benefits and challenges, emphasizing the need for a balanced approach to foster growth while ensuring data protection.

Deciphering the Puzzle: Literature Insights on Chlamydia trachomatis-Mediated Tumorigenesis, Paving the Way for Future Research.

Some infectious agents have the potential to cause specific modifications in the cellular microenvironment that could be propitious to the carcinogenesis process. Currently, there are specific viruses and bacteria, such as human papillomavirus (HPV) and Helicobacter pylori, that are well established as risk factors for neoplasia. Chlamydia trachomatis (CT) infections are one of the most common bacterial sexually transmitted infections worldwide, and recent European data confirmed a continuous rise across Europe. The infection is often asymptomatic in both sexes, requiring a screening program for early detection. Notwithstanding, not all countries in Europe have it. Chlamydia trachomatis can cause chronic and persistent infections, resulting in inflammation, and there are plausible biological mechanisms that link the genital infection with tumorigenesis. Herein, we aimed to understand the epidemiological and biological plausibility of CT genital infections causing endometrial, ovarian, and cervical tumors. Also, we covered some of the best suitable in vitro techniques that could be used to study this potential association. In addition, we defend the point of view of a personalized medicine strategy to treat those patients through the discovery of some biomarkers that could allow it. This review supports the need for the development of further fundamental studies in this area, in order to investigate and establish the role of chlamydial genital infections in oncogenesis.

Effects of Implementation of European Thyroid Imaging Reporting and Data System Risk Stratification in a Thyroid Cancer Program in Western Sweden: A Retrospective Cohort Study

ObjectiveThe European Thyroid Imaging Reporting and Data System (EU-TIRADS) allows for selective fine needle aspiration cytology (FNAC). In 2017, EU-TIRADS was implemented as part of a nationwide standardized care bundle for thyroid cancer in Western Sweden with a population of approximately 1.7 million. The objective of this study was to investigate the clinical value of EU-TIRADS attempting to reduce the number of unnecessary FNACs in referred patients with thyroid nodules. MethodsThe study cohort consisted of all patients referred to Sahlgrenska University Hospital due to a palpable, newly detected or growing thyroid nodules or a positron emission tomography–positive finding for examination with thyroid ultrasound and selective cytology between 2018 and 2022. Medical records on EU-TIRADS classification, corresponding FNAC results, and histopathologic diagnosis were retrospectively collected. Adherence to the EU-TIRADS guidelines, use of selective FNAC, and rate of malignancy in patients who underwent surgery were assessed. ResultsIn total, 1246 thyroid nodules in 990 patients were evaluated. The distributions of EU-TIRADS 2 to 5 (number [percentage]) for all examined nodules were 63 (5%), 462 (37%), 443 (36%), and 278 (22%), respectively. FNAC was omitted in 7% of the investigated patients. FNAC was performed in 124 nodules (10%) despite not fulfilling the EU-TIRADS criteria or absence of positron emission tomography–positive findings. The rate of malignancy was 33% and 1/50 in patients who underwent “unnecessary” FNAC. ConclusionImplementation of EU-TIRADS in routine management of thyroid nodules led to the selective use of FNAC; however, the clinical impact was limited. This study provides real-world data on the value and magnitude of diagnostic improvement by implementing EU-TIRADS in clinical practice.

Effectiveness of cannabis use and cannabis use disorder interventions: a European and international data synthesis.

This data synthesis examined the effectiveness of behavioural and pharmacological approaches for cannabis treatment. We integrated findings from high level evidence studies and prioritised data from Europe when available. The synthesis found that only a relatively small number of published behavioural and pharmacological studies on cannabis interventions have been conducted in Europe. Applying both European and non-European data, it was found that Cognitive Behavioural Therapy (CBT) and/or Motivational Enhancement Therapy (MET) improved short-term outcomes in the frequency of cannabis use and dependency severity, although abstinence outcomes were less consistent. These improvements were typically not maintained nine months after treatment. CBT and MET (or combined CBT + MET) treatments that extend beyond four sessions were more effective than fewer sessions over a shorter duration. Combining CBT or MET (or combined CBT + MET) with adjunctive Contingency Management (CM) improved therapeutic outcomes. No pharmacotherapies have been approved for the management of cannabis use, cannabis use disorders or cannabis withdrawal. Despite only weak evidence to support the use of pharmacological agents, some are used 'off-label' to manage withdrawal symptoms outside clinical trials.

#506 Long-term exposure to air pollution and CKD-associated mortality: results from the pooled cohort of the European multicentre ELAPSE-study

Abstract Background and Aims Air pollution has been shown to be associated with cause-specific mortality. However, mortality due to chronic kidney disease (CKD) is understudied in this context, with no European data available so far. We investigated the relation between long-term exposure to air pollution and CKD-associated mortality in the Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE). Method Data of five population-based cohorts from four European countries from the pooled ELAPSE cohort were linked to data from local mortality registries. CKD-associated mortality was defined using ICD10 codes N18-N19 or corresponding ICD9 codes. Mean annual exposure at home address was determined with Europe-wide hybrid land use regression models for nitrogen dioxide (NO2), black carbon (BC), ozone (O3), particulate matter ≤2.5 µm (PM2.5) and several elemental constituents of PM2.5 with a spatial resolution of 100 m x 100 m. Cox regression models were adjusted for age (time-axis), cohort and sex (both as strata), calendar year of recruitment, smoking status, marital status, employment status and neighborhood mean income. Results Over a mean follow-up time of 20.4 years, 313 of 289,564 persons died from CKD. Associations were observed for all pollutants, positive for PM2.5 (hazard ratio (HR) with 95% confidence interval (CI) of 1.31 (1.03-1.66) per 5 µg/m3 increase), BC (1.26 (1.03-1.53) per 0.5 × 10− 5/m), NO2 (1.13 (0.93-1.38) per 10 µg/m3) and negative for O3 (0.71 (0.54-0.93) per 10 µg/m3). Results were robust when additionally adjusting for smoking intensity and duration, BMI, education or residential greenness, but susceptible to cohort selection. Findings are mainly based on the large Austrian cohort, that contributed 226 cases. Among the elemental constituents, Cu, Fe, K, Ni, S and Zn, representing different PM2.5 sources including traffic, industry, biomass and oil burning and long-range transport, were associated with CKD-related mortality. Conclusion In conclusion, our results suggest a possible association between air pollution from different sources with CKD-related mortality. Our results indicate that measures to improve air quality and decrease air pollution might also be beneficial from a renal perspective.

Sharing sensitive data in life sciences: an overview of centralized and federated approaches.

Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.

POTENTIAL CONFLICTS IN PERSONAL DATA PROTECTION UNDER CURRENT LEGISLATION IN VIETNAM COMPARED WITH EUROPEAN GENERAL DATA PROTECTION REGULATION

Background: Transatlantic data transfers are a critical component of the global digital economy, facilitating commerce and communication among countries worldwide. However, these transfers have been fraught with legal and regulatory challenges, particularly concerning protecting personal data due to the lack of a comprehensive global privacy law. Methods: This comparative, descriptive study exploits secondary resources by comparing and contrasting the principles of the European General Data Protection Regulation and the new Decree on personal data protection in Vietnam to provide deep insights into the differences between them. Results and Conclusions: Although the Decree takes advantage of many of the European General Data Protection Regulation's principles, i.e., the rights of data subjects, consent requirements, and the need for impact assessments, it has its provisions specific to the Vietnamese context, such as the absence of "legitimate interests" as a legal basis for processing and the unique enforcement mechanisms. Despite many similarities, the specific requirements around consent, data subject rights, breach notification, extraterritorial data transfers, and enforcement mechanisms might result in conflicts among these legislative documents. The Decree, which would become more effective, shall rely on its enforcement mechanisms and the ability to impose meaningful sanctions for non-compliance; thus, it should incorporate a more detailed sanctions regime to deter violations effectively.

Artificial intelligence based data curation: enabling a patient-centric European health data space.

The emerging European Health Data Space (EHDS) Regulation opens new prospects for large-scale sharing and re-use of health data. Yet, the proposed regulation suffers from two important limitations: it is designed to benefit the whole population with limited consideration for individuals, and the generation of secondary datasets from heterogeneous, unlinked patient data will remain burdensome. AIDAVA, a Horizon Europe project that started in September 2022, proposes to address both shortcomings by providing patients with an AI-based virtual assistant that maximises automation in the integration and transformation of their health data into an interoperable, longitudinal health record. This personal record can then be used to inform patient-related decisions at the point of care, whether this is the usual point of care or a possible cross-border point of care. The personal record can also be used to generate population datasets for research and policymaking. The proposed solution will enable a much-needed paradigm shift in health data management, implementing a 'curate once at patient level, use many times' approach, primarily for the benefit of patients and their care providers, but also for more efficient generation of high-quality secondary datasets. After 15 months, the project shows promising preliminary results in achieving automation in the integration and transformation of heterogeneous data of each individual patient, once the content of the data sources managed by the data holders has been formally described. Additionally, the conceptualization phase of the project identified a set of recommendations for the development of a patient-centric EHDS, significantly facilitating the generation of data for secondary use.

Deep learning-based harmonization and super-resolution of Landsat-8 and Sentinel-2 images

Multi-spectral satellite images of the Earth’s surface are used in various applications, from water quality assessment and urban planning to climate monitoring, disaster response, infrastructure oversight, and agricultural surveillance. Many of these applications would benefit from higher spatial and temporal resolution of observations, which could be achieved by combining observations from several sources. This study introduces a deep learning-based pipeline to harmonize the spectral and spatial discrepancies between the Landsat-8 and Sentinel-2 Earth Observation satellites. Through established image quality metrics, we demonstrate a significant enhancement in the spatial resolution of Landsat-8 images. Field observation experiments show that leveraging unified images from both satellites increases the availability of cloud-free images by 21% annually on average in our study area. Additionally, our pipeline enhances the Normalized Difference Vegetation Index (NDVI) correlation between Landsat-8 and Sentinel-2 observations by about 4.9%, offering significant performance gains in a downstream crop segmentation task. Our 100M parameter model, trained on European data, generalizes to most regions with only minor limitations. Furthermore, we show that the pipeline can provide uncertainty estimates for its outputs, which are valuable for decision-making in downstream applications. Our code base is available online: https://github.com/venkatesh-thiru/Deep-Harmonization.

Triggering Violence? The Impact of Firearms on the Nature and Lethality of Violent Encounters: A European Perspective

Firearms are used in around 25% of homicides in Europe. More so than other weapons commonly used in violent encounters, firearms have the ability to inflict lethal injuries. Yet, theoretical approaches to understanding the impact of firearms on the prevalence, lethality, and nature of violent encounters are scarce and have been developed almost exclusively within and for the context of the United States, where the levels of firearm violence and firearm availability are much higher than in Europe, limiting their applicability to the European context. To address this issue, European empirical data are reviewed in this paper to critically assess the few existing theoretical approaches focusing on the use of firearms in lethal and non-lethal violence. Based on the obtained findings, future directions for empirical research, as well as suggestions for new conceptualizations of firearm violence in Europe are presented.

The white paper on artificial intelligence as a source for the formation of European Union legislation in the field of artificial intelligence

The article analyzes the provisions of the White Paper on artificial intelligence as a source of the formation of European Union legislation in the field of artificial intelligence. It is noted that the study of legal regulation of artificial intelligence in the European Union will contribute to the formation of the domestic source base in the field of application of artificial intelligence. It has been analyzed that in scientific circles, the provisions of the White Book on artificial intelligence are highlighted by domestic scientists in a fragmentary manner, which makes it difficult to comprehensively perceive the content and meaning of the White Book as a whole. The article states that the legislation of Ukraine needs improvement, which consists in compliance with European standards in the field of artificial intelligence. It has been established that it is advisable to enshrine provisions on the use of artificial intelligence at the legislative level. The article examines the provisions of the European Data Strategy, which recognizes artificial intelligence as an important component of the data economy. It has been studied that the provisions of the European Data Strategy provide for a combination of technology, industry, digital infrastructure and regulatory framework. It was noted that the provisions of the Strategy regarding artificial intelligence were further developed in the White Book. The article specifies the concept of artificial intelligence in accordance with the provisions of the White Book. It is noted that the Provisions of the White Book are aimed at ensuring consultation of interested parties, collection of feedback as a basis for future decision-making processes. It has been proven that the White Paper can be considered a political document containing the goals and possible actions of the European Commission regarding the regulation of artificial intelligence and contains a list of political proposals, intentions and recommendations. It is noted that the White Book is a framework for future legislative proposals on the formation of a legal framework related to artificial intelligence in the European Union. The article defines the structure of the main provisions of the White Book and establishes the fundamental principles that determine the development and implementation of artificial intelligence. Attention is focused on chapter 5 of the White Paper, which is devoted to the «Ecosystem of Trust: Regulatory Framework for Artificial Intelligence», which is devoted to the key elements of the regulatory framework for artificial intelligence in Europe.

Exploring the interconnected between type 2 diabetes mellitus and nonalcoholic fatty liver disease: Genetic correlation and Mendelian randomization analysis.

Epidemiological and clinical studies have indicated a higher risk of nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM), implying a potentially shared genetic etiology, which is still less explored. Genetic links between T2DM and NAFLD were assessed using linkage disequilibrium score regression and pleiotropic analysis under composite null hypothesis. European GWAS data have identified shared genes, whereas SNP-level pleiotropic analysis under composite null hypothesis has explored pleiotropic loci. generalized gene-set analysis of GWAS data determines pleiotropic pathways and tissue enrichment using eQTL mapping to identify associated genes. Mendelian randomization analysis was used to investigate the causal relationship between NAFLD and T2DM. Linkage disequilibrium score regression analysis revealed a strong genetic correlation between T2DM and NAFLD, and identified 24 pleiotropic loci. These single-nucleotide polymorphisms are primarily involved in biosynthetic regulation, RNA biosynthesis, and pancreatic development. generalized gene-set analysis of GWAS data analysis revealed significant enrichment in multiple brain tissues. Gene mapping using these 3 methods led to the identification of numerous pleiotropic genes, with differences observed in liver and kidney tissues. These genes were mainly enriched in pancreas, brain, and liver tissues. The Mendelian randomization method indicated a significantly positive unidirectional causal relationship between T2DM and NAFLD. Our study identified a shared genetic structure between NAFLD and T2DM, providing new insights into the genetic pathogenesis and mechanisms of NAFLD and T2DM comorbidities.