Flimma: a federated and privacy-aware tool for differential gene expression analysis.

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma ( https://exbio.wzw.tum.de/flimma/ ) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.

Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example.

OBJECTIVES: Critical care medicine is a natural environment for machine learning approaches to improve outcomes for critically ill patients as admissions to ICUs generate vast amounts of data. However, technical, legal, ethical, and privacy concerns have so far limited the critical care medicine community from making these data readily available. The Society of Critical Care Medicine and the European Society of Intensive Care Medicine have identified ICU patient data sharing as one of the priorities under their Joint Data Science Collaboration. To encourage ICUs worldwide to share their patient data responsibly, we now describe the development and release of Amsterdam University Medical Centers Database (AmsterdamUMCdb), the first freely available critical care database in full compliance with privacy laws from both the United States and Europe, as an example of the feasibility of sharing complex critical care data. SETTING: University hospital ICU. SUBJECTS: Data from ICU patients admitted between 2003 and 2016. INTERVENTIONS: We used a risk-based deidentification strategy to maintain data utility while preserving privacy. In addition, we implemented contractual and governance processes, and a communication strategy. Patient organizations, supporting hospitals, and experts on ethics and privacy audited these processes and the database. MEASUREMENTS AND MAIN RESULTS: AmsterdamUMCdb contains approximately 1 billion clinical data points from 23,106 admissions of 20,109 patients. The privacy audit concluded that reidentification is not reasonably likely, and AmsterdamUMCdb can therefore be considered as anonymous information, both in the context of the U.S. Health Insurance Portability and Accountability Act and the European General Data Protection Regulation. The ethics audit concluded that responsible data sharing imposes minimal burden, whereas the potential benefit is tremendous. CONCLUSIONS: Technical, legal, ethical, and privacy challenges related to responsible data sharing can be addressed using a multidisciplinary approach. A risk-based deidentification strategy, that complies with both U.S. and European privacy regulations, should be the preferred approach to releasing ICU patient data. This supports the shared Society of Critical Care Medicine and European Society of Intensive Care Medicine vision to improve critical care outcomes through scientific inquiry of vast and combined ICU datasets.

[Health data in France: Abundant but complex]. / Les données de santé en France - Abondantes mais complexes.

TITLE: Les données de santé en France - Abondantes mais complexes. ABSTRACT: Alors que l'application de traçage des contacts (contact tracing) StopCovid (transformée à la mi-octobre 2020 en TousAntiCovid), débattue au Parlement1 en raison des inquiétudes qu'elle suscitait concernant la confidentialité des données personnelles et les libertés individuelles du fait qu'elle permet d'alerter un utilisateur s'il s'est trouvé à proximité d'une personne atteinte de la COVID-19, a été adoptée par près de 12 millions de personnes2, un dispositif concernant les données individuelles de santé, aux conséquences potentiellement beaucoup plus importantes pour les citoyens et leurs données personnelles, a commencé à se mettre en place suite à la Loi du 24 juillet 2019 (Loi n° 2019-774) relative à l'organisation et à la transformation du système de santé3 : la plateforme des données de santé, communément appelée Health Data Hub, constituée sous la forme d'un groupement d'intérêt public (GIP). Il ne s'agit plus de simplement signaler qu'on a croisé une personne anonyme infectée par le SARS-Cov-2, mais de réunir, dans une infrastructure informatique unique, un immense ensemble de données personnelles particulièrement sensibles concernant la totalité de la population française. Ce projet suscite désormais un certain intérêt médiatique et un début d'inquiétude. Mais cette inquiétude ne concerne presque uniquement que le fait que ces données sont déposées et gérées dans un cloud appartenant à une société américaine, un nuage informatique qui tombe sous le coup de la loi américaine de 2018 dite « CLOUD act4 ¼, qui ouvre la possibilité d'un transfert des données personnelles vers les États-Unis, comme s'en est inquiété récemment le Conseil d'État.5 Cet aspect est certes très important, mais il masque également de très nombreux enjeux liés au partage des données de santé, et qui sont largement méconnus de la population. Nous nous proposons de rappeler, tout d'abord, ce que sont les données de santé, ce qu'elles apportent et la nécessité d'en faciliter le partage, mais aussi les difficultés rencontrées pour leur accès et leur utilisation. Nous expliquerons ensuite, dans un deuxième article, en quoi cette plateforme des données de santé, telle qu'elle est conçue et pilotée par les pouvoirs publics pour répondre à ces difficultés et pour promouvoir l'intelligence artificielle en santé, est un projet qui soulève de fortes inquiétudes pour les citoyens et la société dans son ensemble. Même si les problèmes posés se présentent sous une forme différente selon les pays, notre propos concernera spécifiquement la situation en France.

Review of policies of companies and databases regarding access to customers' genealogy data for law enforcement purposes.

The rapidly evolving popularity of direct-to-consumer genetic genealogy companies has made it possible to retrieve genomic information for unintended reasons by third parties, including the emerging use for law enforcement purposes. The question remains whether users of direct-to-consumer genetic genealogy companies and genealogical databases are aware that their genetic and/or genealogical data could be used as means to solving forensic cases. Our review of 22 companies' and databases' policies showed that only four companies have provided additional information on how law enforcement agencies should request permission to use their services for law enforcement purposes. Moreover, two databases have adopted a different approach by providing a special service for law enforcement. Although all companies and databases included in the study provide at least some provisions about police access, there is an ongoing debate over the ethics of these practices, and how to balance users' privacy with law enforcement requests.

Limitations for health research with restricted data collection from UK primary care.

PURPOSE: UK primary care provides a rich data source for research. The impact of proposed data collection restrictions is unknown. This study aimed to assess the impact of restricting the scope of electronic health record (EHR) data collection on the ability to conduct research. The study estimated the consequences of restricted data collection on published Clinical Practice Research Datalink studies from high impact journals or referenced in clinical guidelines. METHODS: A structured form was used to systematically analyse the extent to which individual studies would have been possible using a database with data collection restrictions in place: (1) retrospective collection of specified diseases only; (2) retrospective collection restricted to a 6- or 12-year period; (3) prospective and retrospective collection restricted to non-sensitive data. Outcomes were categorised as unfeasible (not reproducible without major bias); compromised (feasible with design modification); or unaffected. RESULTS: Overall, 91% studies were compromised with all restrictions in place; 56% studies were unfeasible even with design modification. With restrictions on diseases alone, 74% studies were compromised; 51% were unfeasible. Restricting collection to 6/12 years had a major impact, with 67 and 22% of studies compromised, respectively. Restricting collection of sensitive data had a lesser but marked impact with 10% studies compromised. CONCLUSION: EHR data collection restrictions can profoundly reduce the capacity for public health research that underpins evidence-based medicine and clinical guidance. National initiatives seeking to collect EHRs should consider the implications of restricting data collection on the ability to address vital public health questions.

Evaluating the Use of Nonrandomized Real-World Data Analyses for Regulatory Decision Making.

The analysis of longitudinal healthcare data outside of highly controlled parallel-group randomized trials, termed real-world evidence (RWE), has received increasing attention in the medical literature. In this paper, we discuss the potential role of RWE in drug regulation with a focus on the analysis of healthcare databases. We present several cases in which RWE is already used and cases in which RWE could potentially support regulatory decision making. We summarize key issues that investigators and regulators should consider when designing or evaluating such studies, and we propose a structured process for implementing analyses that facilitates regulatory review. We evaluate the empirical evidence base supporting the validity, transparency, and reproducibility of RWE from analysis of healthcare databases and discuss the work that still needs to be done to ensure that such analyses can provide decision-ready evidence on the effectiveness and safety of treatments.

Fourth Amendment Protections of Health Information After Carpenter v. United States: The Devil's In The Database.

Every day, companies collect health information from customers and analyze it for commercial purposes. This poses a significant threat to privacy, particularly as the Fourth Amendment protection of this deeply personal information is limited. Generally, law enforcement officers do not need probable cause and a warrant to access these private health information databases; only a subpoena is required, and sometimes nothing at all. The Fourth Amendment protections for health information may, however, have changed after the Supreme Court's 2018 decision in Carpenter v. United States, which held that the Fourth Amendment protects people from warrantless searches of historical cell-site location information possessed by their cell phone providers. The Court explained that, because of the nature of historical cell-site location information, individuals retain a reasonable expectation of privacy despite the information being in the possession of a third party. In reaching its holding, the Supreme Court considered the type of data, the uniqueness of cell-site location information, the impact of technological advancement on privacy, the voluntariness of the disclosure, and the property rights associated with the records. Many of these factors could support heightened Fourth Amendment protection for health information. This Article argues that Carpenter v. United States provides additional protections for future searches of health information in private databases.

The impact of the General Data Protection Regulation on health research.

Background: On the May 25, 2018 the General Data Protection Regulation (hereafter the GDPR or the Regulation) came into force, replacing the Data Protection Directive 95/46/EC (upon which the Data Protection Act 1998 is based), and imposing new responsibilities on organizations which process the data of European Union citizens. Sources of data: This piece examines the impact of the Regulation on health research. Areas of agreement: The Regulation seeks to harmonize data privacy laws across Europe, to protect and empower all EU citizen's data privacy and to reshape the way that organizations approach data privacy (See the GDPR portal at: https://www.eugdpr.org/ (accessed 8 May 2018). As a Regulation the GDPR is directly applicable in all member states as opposed to a directive which requires national implementing measures (In the UK the Data Protection Act 1998 was the implementing legislation for the Data Protection Directive 95/46/EC.). Areas of controversy: The Regulation is sector wide, but its impact on organizations us sector specific. In some sectors, the Regulation inhibits the processing of personal data, whilst in others it enables that processing. The Regulation takes the position that the 'processing of data should be designed to serve mankind' (Recital 4). Whilst it does not spell out what exactly is meant by this, it indicates that a proportionate approach will be taken to the protection of personal data, where that data can be processed for common goods such as healthcare. Thus, the protection of personal data is not absolute, but considered in relation to its function in society and balance with other fundamental rights in accordance with the principle of proportionality (Recital 4). Differing interpretations of proportionality can detract from the harmonization objective of the Regulation. Growing points: Reflecting the commitment to proportionality, scientific research holds a privileged position in the Regulation. Throughout the Regulation provision is made for organizations that process personal data for scientific research purposes to avoid restrictive measures which might impede the increase of knowledge. However, the application of the Regulation differs across health research sectors and across jurisdictions. Transparency and engagement across the health research sector is required to promote alignment. Areas timely for developing research: Research which focuses on the particular problems which arise in the context of the regulation's application to health research would be welcome. Particularly in the context of the operation of the Regulation alongside the duty of confidentiality and the variation in approaches across Member States.

Public Regulatory Databases as a Source of Insight for Neuromodulation Devices Stimulation Parameters.

OBJECTIVE: The Shannon model is often used to define an expected boundary between non-damaging and damaging modes of electrical neurostimulation. Numerous preclinical studies have been performed by manufacturers of neuromodulation devices using different animal models and a broad range of stimulation parameters while developing devices for clinical use. These studies are mostly absent from peer-reviewed literature, which may lead to this information being overlooked by the scientific community. We aimed to locate summaries of these studies accessible via public regulatory databases and to add them to a body of knowledge available to a broad scientific community. METHODS: We employed web search terms describing device type, intended use, neural target, therapeutic application, company name, and submission number to identify summaries for premarket approval (PMA) devices and 510(k) devices. We filtered these records to a subset of entries that have sufficient technical information relevant to safety of neurostimulation. RESULTS: We identified 13 product codes for 8 types of neuromodulation devices. These led us to devices that have 22 PMAs and 154 510(k)s and six transcripts of public panel meetings. We found one PMA for a brain, peripheral nerve, and spinal cord stimulator and five 510(k) spinal cord stimulators with enough information to plot in Shannon coordinates of charge and charge density per phase. CONCLUSIONS: Analysis of relevant entries from public regulatory databases reveals use of pig, sheep, monkey, dog, and goat animal models with deep brain, peripheral nerve, muscle and spinal cord electrode placement with a variety of stimulation durations (hours to years); frequencies (10-10,000 Hz) and magnitudes (Shannon k from below zero to 4.47). Data from located entries indicate that a feline cortical model that employs acute stimulation might have limitations for assessing tissue damage in diverse anatomical locations, particularly for peripheral nerve and spinal cord simulation.

State Tobacco Policies as Predictors of Evidence-Based Cessation Method Usage: Results From a Large, Nationally Representative Dataset.

Introduction: Evidence-based cessation methods including nicotine replacement therapy (NRT), non-NRT medications, quitlines, and behavioral treatments are underutilized by smokers attempting to quit. Although a number of studies have demonstrated a relationship between state-level tobacco policies (eg, taxation, appropriations) and cessation, whether such state-level factors influence likelihood of using an evidence-based treatment is unclear. Accordingly, the aims of the present study were: (1) to describe evidence-based cessation method utilization by state and (2) to examine the effect of state-level factors on cessation method utilization above and beyond individual-level predictors. Methods: Data were utilized from the 2010-2011 Tobacco Use Supplement to the Current Population Survey (TUS-CPS). Participants included 9232 smokers who reported a past-year quit attempt. Data on 11 state-level predictors were collated from national datasets. Analyses were based on: (1) descriptive characterization of quit method usage, (2) logistic regression models to determine state-level factors as predictors of quit method utilization, controlling for individual-level predictors, (3) cluster analyses grouping states with similar state-level factors, and (4) examination of cluster as a predictor of cessation method. Results: Tobacco control appropriations significantly predicted NRT, quitline, and behavioral treatment utilization. Additional state-level factors that demonstrated significant relationships included Medicaid coverage of non-NRT medications and behavioral treatment, tobacco tax rate, smoking prevalence, and percentage of population uninsured. State clustering significantly predicted quit method across all four methods. Conclusions: State-level factors influence the likelihood of residents utilizing evidence-based quit methods. Results are discussed in terms of implications for tobacco policy at the state level. Implications: Results from the present study highlight state tobacco control appropriations as a robust predictor of evidence-based cessation method utilization. Other significant state-level predictors of evidence-based cessation method utilization included Medicaid coverage of non-NRT medications and behavioral treatment, tobacco tax rate, smoking prevalence, and percentage of population uninsured. Moreover, state-level predictors clustered together to significantly predict evidence-based cessation method utilization. Thus, increasing tobacco control appropriations, extending health insurance coverage, maximizing revenue from tobacco taxation and tobacco settlements, and ultimately decreasing smoking prevalence are important targets for individual states to promote utilization of evidence-based cessation methods.

The European Union Policy in the Field of Rare Diseases.

Rare diseases, are defined by the European Union as life-threatening or chronically debilitating diseases with low prevalence (less than 5 per 10,000). The specificities of rare diseases - limited number of patients and scarcity of relevant knowledge and expertise - single them out as a unique domain of very high European added-value.The legal instruments at the disposal of the European Union, in terms of the Article 168 of the Treaties, are very limited. However a combination of instruments using the research and the pharmaceutical legal basis and an intensive and creative use of funding from the Health Programmes has permitted to create a solid basis that Member States have considered enough to put rare diseases in a privileged position in the health agenda.The adoption of the Commission Communication, in November 2008, and of the Council Recommendation, in June 2009, and in 2011 the adoption of the Directive on Cross-border healthcare., have created an operational framework to act in the field of rare disease with European coordination in several areas (classification and codification, European Reference Networks, orphan medicinal products, the Commission expert group on rare diseases, etc.).Rare diseases is an area with high and practical potential for the European cooperation.

[Required Framework for the Collection of Real-life Data: An Example from University Eye Hospital Munich]. / Rahmenbedingungen zur Sammlung von "Real-Life"-Daten am Beispiel der Augenklinik der Universität München.

Background The importance of evaluating real-life data is constantly increasing. Currently available computer systems better allow for analyses of data, as more and more data is available in a digital form. Before a project for real-life data analyses is started, technical considerations and staff, legal, and data protection procedures need to be addressed. In this manuscript, experiences made at the University Eye Hospital in Munich will be shared. Materials and Methods Legal requirements, as found in laws and guidelines governing documentation and data privacy, are highlighted. Technical requirements for information technology infrastructure and software are defined. A survey conducted by the German Ophthalmological Society, among German eye hospitals investigating the current state of digitalization, was conducted. Also, staff requirements are outlined. Results A database comprising results of 330,801 patients was set up. It includes all diagnoses, procedures, clinical findings and results from diagnostic devices. This database was approved by the local data protection officer. In less than half of German eye hospitals (n = 21) that participated in the survey (n = 54), a complete electronic documentation is done. Fourteen institutions are completely paper-based, and the remainder of the hospitals used a mixed system. Conclusion In this work, we examined the framework that is required to develop a comprehensive database containing real-life data from clinics. In future, these databases will become increasingly important as more and more innovation are made in decision support systems. The base for this is comprehensive and well-curated databases.

[Big data and their perspectives in radiation therapy]. / Métadonnées et leurs applications possibles en radiothérapie.

The concept of big data indicates a change of scale in the use of data and data aggregation into large databases through improved computer technology. One of the current challenges in the creation of big data in the context of radiation therapy is the transformation of routine care items into dark data, i.e. data not yet collected, and the fusion of databases collecting different types of information (dose-volume histograms and toxicity data for example). Processes and infrastructures devoted to big data collection should not impact negatively on the doctor-patient relationship, the general process of care or the quality of the data collected. The use of big data requires a collective effort of physicians, physicists, software manufacturers and health authorities to create, organize and exploit big data in radiotherapy and, beyond, oncology. Big data involve a new culture to build an appropriate infrastructure legally and ethically. Processes and issues are discussed in this article.

Deciphering the Sunshine Act: Transparency Regulation and Financial Conflicts in Health Care.

The Physician Payments Sunshine Act ("Sunshine Act"), enacted to address financial conflicts in health care, is the first comprehensive federal legislation mandating public reporting of payments between drug companies, device manufacturers, and medicine. This article analyzes the Sunshine Act's uneven record, exploring how the law serves as an intriguing example of the uncertain case for transparency regulation in health care. The Sunshine Act's bumpy rollout demonstrates that commanding transparency through legislation can be arduous because of considerable implementation challenges. Capturing all the relevant information about financial relationships and reporting it with sufficient contextual and comparative data has proven disappointingly difficult. In addition, the law suffers from uncertainty and poor design as to the intended audience. Indeed, there is strong reason to believe that it will not significantly impact decision-making of primary recipients like patients. Yet the Sunshine Act nonetheless retains important and perhaps underappreciated value. From the almost four years of information generated, we have learned that industry-medicine financial ties vary significantly by physician specialty, and somewhat by physician gender. In many medical fields the distribution of top dollar payments tends to be heavily skewed to a few recipients, all of which have important implications for optimal management of financial conflicts and for health policy more generally. Accordingly, the Sunshine Act's greatest potential is not guiding decisions of individual patients or physicians, but its downstream effects. This Article traces how secondary audiences, such as regulators, watchdogs, and counsel are already starting to make productive use of Sunshine Act information. Public reporting has, for example, made more feasible linking industry payment information with Medicare reimbursement data. As a result, policymakers can more closely examine correlations between industry spending directed at individual physicians and their prescribing and referral decisions. Moreover, savvy counsel are recognizing that Sunshine Act information provides explosive evidence in private civil litigation and this Article explores the first wave of cases.