Position paper on management of personal data in environment and health research in Europe

Govarts, Eva; Gilles, Liese; Stephanie, Bopp; Holub, Petr; Matalonga, Leslie; Vermeulen, Roel; Vrijheid, Martine; Beltrán, Sergi; Mette, Hartlev; Sarah, Jones; Laura, Rodriguez Martin; Standaert, Arnout; Morris, Swertz A.; Theunis, Jan; Xenia, Trier; Vogel, Nina; Koert, Van Espen; Remy, Sylvie; Greet, Schoeters

doi:10.1016/j.envint.2022.107334

Cited by 13 publications

(54 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studying the bene ts, the barriers to use with large clinical databases, the policy frameworks that have been formulated, and the challenges makes the study of data management and governance essential to promote the data sharing and reuse [18,19]. To address the purpose of this study, a de nition of health data management [20] and health data hub patterns of data governance was covered through the analysis in-depth of a dedicated survey of a representative list of National, European, and Worldwide health data hubs.…”

Section: Discussionmentioning

confidence: 99%

Desiderata for the governance of health data hubs for research

Alvarez-Romero

Martínez-García

Bernabéu-Wittel

et al. 2022

Preprint

View full text Add to dashboard Cite

Background Digital transformation in healthcare and the growth of health data generation and collection include an important challenge for the secondary use of healthcare records in the health research field. Likewise, due to the ethical and legal constraints for using sensitive data, understanding how health data is managed by dedicated infrastructures called data hubs is essential to facilitate data sharing and reuse. Methods In order to capture the different data governance behind health data hubs across Europe, a survey focused on analysing the feasibility of linking individual-level data between data collections and the generation of health data governance patterns was carried out. The target audience of this study was National, European, and Worldwide data hubs. In total, the designed survey was sent to a representative list of 99 health data hubs in January 2022. Results In total, 41 survey responses received till June 2022 were analysed in-depth. Stratification methods were performed to cover the different levels of granularity identified in some data hubs’ characteristics. Firstly, a general pattern of data governance for data hubs was defined. Afterward, specific profiles were defined, generating specific patterns of data governance through the stratifications in terms of the kind of organisation (centralised vs. decentralised), and role (data controller or data processor) of the health data hubs interviewees. Conclusions The in-depth analysis of the responses from health data hubs interviewees across Europe provided a list of the most frequent aspects that concluded a set of specific recommendations on data management and governance, taking into account the constraints of sensitive data. In summary, a data hub should work in a centralised way providing a Data Processing Agreement and a formal procedure to identify data providers, as well as data quality control, data integrity and anonymisation methods.

show abstract

Section: Discussionmentioning

confidence: 99%

Desiderata for the governance of health data hubs for research

Alvarez-Romero

Martínez-García

Bernabéu-Wittel

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…); SAS (R) VIYA for data visualization dashboard; R (R) and Python (R) open-source libraries for the prototyping and implementation of statistical analysis and machine learning. Interoperability represents a key point in data management for properly exploiting their resources, for example, to visualize them for different stakeholders, or through joint statistical analysis at the European level ( 10 ). Our approach to interoperability is twofold, as shown in another area of application ( 11 ).…”

Section: Methodsmentioning

confidence: 99%

GENERATOR HEART FAILURE DataMart: An integrated framework for heart failure research

D’Amario

Laborante

Delvinioti

et al. 2023

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

BackgroundHeart failure (HF) is a multifaceted clinical syndrome characterized by different etiologies, risk factors, comorbidities, and a heterogeneous clinical course. The current model, based on data from clinical trials, is limited by the biases related to a highly-selected sample in a protected environment, constraining the applicability of evidence in the real-world scenario. If properly leveraged, the enormous amount of data from real-world may have a groundbreaking impact on clinical care pathways. We present, here, the development of an HF DataMart framework for the management of clinical and research processes.MethodsWithin our institution, Fondazione Policlinico Universitario A. Gemelli in Rome (Italy), a digital platform dedicated to HF patients has been envisioned (GENERATOR HF DataMart), based on two building blocks: 1. All retrospective information has been integrated into a multimodal, longitudinal data repository, providing in one single place the description of individual patients with drill-down functionalities in multiple dimensions. This functionality might allow investigators to dynamically filter subsets of patient populations characterized by demographic characteristics, biomarkers, comorbidities, and clinical events (e.g., re-hospitalization), enabling agile analyses of the outcomes by subsets of patients. 2. With respect to expected long-term health status and response to treatments, the use of the disease trajectory toolset and predictive models for the evolution of HF has been implemented. The methodological scaffolding has been constructed in respect of a set of the preferred standards recommended by the CODE-EHR framework.ResultsSeveral examples of GENERATOR HF DataMart utilization are presented as follows: to select a specific retrospective cohort of HF patients within a particular period, along with their clinical and laboratory data, to explore multiple associations between clinical and laboratory data, as well as to identify a potential cohort for enrollment in future studies; to create a multi-parametric predictive models of early re-hospitalization after discharge; to cluster patients according to their ejection fraction (EF) variation, investigating its potential impact on hospital admissions.ConclusionThe GENERATOR HF DataMart has been developed to exploit a large amount of data from patients with HF from our institution and generate evidence from real-world data. The two components of the HF platform might provide the infrastructural basis for a combined patient support program dedicated to continuous monitoring and remote care, assisting patients, caregivers, and healthcare professionals.

show abstract

“…In order to feed HBM4EU data into the Information Platform for Chemical Monitoring (IPCHEM), HBM4EU has harmonised vocabularies and formats for reporting (meta)data (Bopp et al 2020). HBM4EU has managed to post-harmonise existing data from former studies and produced harmonised data in the HBM4EU Aligned Studies (Eva et al 2022). HBM4EU data are also available via IPCHEM.…”

Section: Increasing Findability Of Hbm Data Suitable For Regulatory P...mentioning

confidence: 99%

“…In all the activities of data sharing, data protection plays an important role. If HBM data are aggregated, they can be considered anonymous and are not considered personal data under the European GDPR (EC, 2018;Eva et al, 2022). Single measurement HBM data must be handled in a stringent way to prevent the risk of re-identification of data subjects.…”

Section: Strategic Objective 4 -Improve Harmonisation Throughout the ...mentioning

confidence: 99%

“…At the same time, the knowledge about associations between exposure and health effects will help in identifying mitigation measures for people that are already exposed. Exposure to harmful substances, that are subject to the toxicologically relevant dose-response principle or that have a primarily sensitising effect, can be tracked by the physician by means of evaluating the patient's medical history, anamnesis, and specific laboratory tests (if relevant) (EC, 2018;Eva et al, 2022). Regular physical exams can provide additional information about the onset and diagnosis of the disease.…”

Section: Analysis Of Published Texts/papersmentioning

confidence: 99%

See 1 more Smart Citation