Prolonged increase in psychotropic drug use among young women following the COVID-19 pandemic: a French nationwide retrospective study.

BACKGROUND: The COVID-19 pandemic has had a significant impact on mental health, with evidence suggesting an enduring mental health crisis. Studies worldwide observed increased usage of antidepressants, anxiolytics, and hypnotics during the pandemic, notably among young people and women. However, few studies tracked consumption post-2021. Our study aimed to fill this gap by investigating whether the surge in the number psychotropic drug consumers in France persisted 2 years after the first lockdown, particularly focusing on age and gender differences. METHODS: We conducted a national retrospective observational study based on the French national insurance database. We retrieved all prescriptions of anxiolytics, hypnotics, and antidepressants dispensed in pharmacies in France for the period 2015-2022. We performed interrupted time series analyses based on Poisson models for five age classes (12-18; 19-25; 26-50; 51-75; 76 and more) to assess the trend before lockdown, the gap induced and the change in trend after. RESULTS: In the overall population, the number of consumers remained constant for antidepressants while it decreased for anxiolytics and hypnotics. Despite this global trend, a long-term increase was observed in the 12-18 and 19-25 groups for the three drug classes. Moreover, for these age classes, the increases were more pronounced for women than men, except for hypnotics where the trends were similar. CONCLUSIONS: The number of people using antidepressants continues to increase more than 2 years after the first lockdown, showing a prolonged effect on mental health. This effect is particularly striking among adolescents and young adults confirming the devastating long-term impact of the pandemic on their mental health.

Home Care Nursing on Long-Term Illness Management in the French Healthcare System.

This study explores the role of home care nurses in managing long-term illnesses (L-TI) within the French healthcare system, utilizing data from the SNDS. Focused on data from 2022, it categorizes nursing actions into medical procedures, care procedures, and nursing processes, revealing significant involvement in patient care. The findings highlight the crucial, evolving role of home care nurses in addressing the complex needs of millions suffering from chronic conditions like diabetes and cardiovascular diseases in France.

Data Lake, Data Warehouse, Datamart, and Feature Store: Their Contributions to the Complete Data Reuse Pipeline.

Unlabelled: The growing adoption and use of health information technology has generated a wealth of clinical data in electronic format, offering opportunities for data reuse beyond direct patient care. However, as data are distributed across multiple software, it becomes challenging to cross-reference information between sources due to differences in formats, vocabularies, and technologies and the absence of common identifiers among software. To address these challenges, hospitals have adopted data warehouses to consolidate and standardize these data for research. Additionally, as a complement or alternative, data lakes store both source data and metadata in a detailed and unprocessed format, empowering exploration, manipulation, and adaptation of the data to meet specific analytical needs. Subsequently, datamarts are used to further refine data into usable information tailored to specific research questions. However, for efficient analysis, a feature store is essential to pivot and denormalize the data, simplifying queries. In conclusion, while data warehouses are crucial, data lakes, datamarts, and feature stores play essential and complementary roles in facilitating data reuse for research and analysis in health care.

Researchers' perceptions of the trustworthiness, for reuse purposes, of government health data in Victoria, Australia: Implications for policy and practice.

In 2022 the Australian Data Availability and Transparency Act (DATA) commenced, enabling accredited "data users" to access data from "accredited data service providers." However, the DATA Scheme lacks guidance on "trustworthiness" of the data to be utilised for reuse purposes. Objectives: To determine: (i) Do researchers using government health datasets trust the data? (ii) What factors influence their perceptions of data trustworthiness? and (iii) What are the implications for government and data custodians? Method: Authors of published studies (2008-2020) that utilised Victorian government health datasets were surveyed via a case study approach. Twenty-eight trust constructs (identified via literature review) were grouped into data factors, management properties and provider factors. Results: Fifty experienced health researchers responded. Most (88%) believed that Victorian government health data were trustworthy. When grouped, data factors and management properties were more important than data provider factors in building trust. The most important individual trust constructs were: "compliant with ethical regulation" (100%) and "monitoring privacy and confidentiality" (98%). Constructs of least importance were knowledge of "participant consent" (56%) and "major focus of the data provider was research" (50%). Conclusion: Overall, the researchers trusted government health data, but data factors and data management properties were more important than data provider factors in building trust. Implications: Government should ensure the DATA Scheme incorporates mechanisms to validate those data utilised by accredited data users and data providers have sufficient quality (intrinsic and extrinsic) to meet the requirements of "trustworthiness," and that evidentiary documentation is provided to support these "accredited data."

Implementing a Biomedical Data Warehouse From Blueprint to Bedside in a Regional French University Hospital Setting: Unveiling Processes, Overcoming Challenges, and Extracting Clinical Insight.

Background: Biomedical data warehouses (BDWs) have become an essential tool to facilitate the reuse of health data for both research and decisional applications. Beyond technical issues, the implementation of BDWs requires strong institutional data governance and operational knowledge of the European and national legal framework for the management of research data access and use. Objective: In this paper, we describe the compound process of implementation and the contents of a regional university hospital BDW. Methods: We present the actions and challenges regarding organizational changes, technical architecture, and shared governance that took place to develop the Nantes BDW. We describe the process to access clinical contents, give details about patient data protection, and use examples to illustrate merging clinical insights. Unlabelled: More than 68 million textual documents and 543 million pieces of coded information concerning approximately 1.5 million patients admitted to CHUN between 2002 and 2022 can be queried and transformed to be made available to investigators. Since its creation in 2018, 269 projects have benefited from the Nantes BDW. Access to data is organized according to data use and regulatory requirements. Conclusions: Data use is entirely determined by the scientific question posed. It is the vector of legitimacy of data access for secondary use. Enabling access to a BDW is a game changer for research and all operational situations in need of data. Finally, data governance must prevail over technical issues in institution data strategy vis-à-vis care professionals and patients alike.

A Community Data Sharing Resource: The LDbase Data Repository.

The purpose of this invited paper is to show the learning disabilities field what LDbase is, why it's important for the field, what it offers the field, and examples of how you can leverage LDbase in your own work.

The National Sleep Research Resource: making data findable, accessible, interoperable, reusable and promoting sleep science.

This paper presents a comprehensive overview of the National Sleep Research Resource (NSRR), a National Heart Lung and Blood Institute-supported repository developed to share data from clinical studies focused on the evaluation of sleep disorders. The NSRR addresses challenges presented by the heterogeneity of sleep-related data, leveraging innovative strategies to optimize the quality and accessibility of available datasets. It provides authorized users with secure centralized access to a large quantity of sleep-related data including polysomnography, actigraphy, demographics, patient-reported outcomes, and other data. In developing the NSRR, we have implemented data processing protocols that ensure de-identification and compliance with FAIR (Findable, Accessible, Interoperable, Reusable) principles. Heterogeneity stemming from intrinsic variation in the collection, annotation, definition, and interpretation of data has proven to be one of the primary obstacles to efficient sharing of datasets. Approaches employed by the NSRR to address this heterogeneity include (1) development of standardized sleep terminologies utilizing a compositional coding scheme, (2) specification of comprehensive metadata, (3) harmonization of commonly used variables, and (3) computational tools developed to standardize signal processing. We have also leveraged external resources to engineer a domain-specific approach to data harmonization. We describe the scope of data within the NSRR, its role in promoting sleep and circadian research through data sharing, and harmonization of large datasets and analytical tools. Finally, we identify opportunities for approaches for the field of sleep medicine to further support data standardization and sharing.

Lessons learned from the development of a national registry on dementia care and support based on linked national health and administrative data.

Introduction: This paper provides insight into the development of the Dutch Dementia Care and Support Registry and the lessons that can be learned from it. The aim of this Registry was to contribute to quality improvement in dementia care and support. Methods: This paper describes how the Registry was set up in four stages, reflecting the four FAIR principles: the selection of data sources (Findability); obtaining access to the selected data sources (Accessibility); data linkage (Interoperability); and the reuse of data (Reusability). Results: The linkage of 16 different data sources, including national routine health and administrative data appeared to be technically and legally feasible. The linked data in the Registry offers rich information about (the use of) care for persons with dementia across various healthcare settings, including but not limited to primary care, secondary care, long-term care and medication use, that cannot be obtained from single data sources. Conclusions: A key lesson learned is that in order to reuse the data for quality improvement in practice, it is essential to involve healthcare professionals in setting up the Registry and to guide them in the interpretation of the data.

Blockchain-Based Dynamic Consent and its Applications for Patient-Centric Research and Health Information Sharing: Protocol for an Integrative Review.

BACKGROUND: Blockchain has been proposed as a critical technology to facilitate more patient-centric research and health information sharing. For instance, it can be applied to coordinate and document dynamic informed consent, a procedure that allows individuals to continuously review and renew their consent to the collection, use, or sharing of their private health information. Such has been suggested to facilitate ethical, compliant longitudinal research, and patient engagement. However, blockchain-based dynamic consent is a relatively new concept, and it is not yet clear how well the suggested implementations will work in practice. Efforts to critically evaluate implementations in health research contexts are limited. OBJECTIVE: The objective of this protocol is to guide the identification and critical appraisal of implementations of blockchain-based dynamic consent in health research contexts, thereby facilitating the development of best practices for future research, innovation, and implementation. METHODS: The protocol describes methods for an integrative review to allow evaluation of a broad range of quantitative and qualitative research designs. The PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) framework guided the review's structure and nature of reporting findings. We developed search strategies and syntax with the help of an academic librarian. Multiple databases were selected to identify pertinent academic literature (CINAHL, Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science) and gray literature (Electronic Theses Online Service, ProQuest Dissertations and Theses, Open Access Theses and Dissertations, and Google Scholar) for a comprehensive picture of the field's progress. Eligibility criteria were defined based on PROSPERO (International Prospective Register of Systematic Reviews) requirements and a criteria framework for technology readiness. A total of 2 reviewers will independently review and extract data, while a third reviewer will adjudicate discrepancies. Quality appraisal of articles and discussed implementations will proceed based on the validated Mixed Method Appraisal Tool, and themes will be identified through thematic data synthesis. RESULTS: Literature searches were conducted, and after duplicates were removed, 492 articles were eligible for screening. Title and abstract screening allowed the removal of 312 articles, leaving 180 eligible articles for full-text review against inclusion criteria and confirming a sufficient body of literature for project feasibility. Results will synthesize the quality of evidence on blockchain-based dynamic consent for patient-centric research and health information sharing, covering effectiveness, efficiency, satisfaction, regulatory compliance, and methods of managing identity. CONCLUSIONS: The review will provide a comprehensive picture of the progress of emerging blockchain-based dynamic consent technologies and the rigor with which implementations are approached. Resulting insights are expected to inform best practices for future research, innovation, and implementation to benefit patient-centric research and health information sharing. TRIAL REGISTRATION: PROSPERO CRD42023396983; http://tinyurl.com/cn8a5x7t. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50339.

Using Bayesian Dynamic Borrowing to Maximize the Use of Existing Data: A Case-Study.

Bayesian Dynamic Borrowing (BDB) designs are being increasingly used in clinical drug development. These methods offer a mathematically rigorous and robust approach to increase efficiency and strengthen evidence by integrating existing trial data into a new clinical trial. The regulatory acceptability of BDB is evolving and varies between and within regulatory agencies. This paper describes how BDB can be used to design a new randomised clinical trial including external data to supplement the planned sample size and discusses key considerations related to data re-use and BDB in drug development programs. A case-study illustrating the planning and evaluation of a BDB approach to support registration of a new medicine with the Center for Drug Evaluation in China will be presented. Key steps and considerations for the use of BDB will be discussed and evaluated, including how to decide whether it is appropriate to borrow external data, which external data can be re-used, the weight to put on the external data and how to decide if the new study has successfully demonstrated treatment benefit.

In France, the organization of perinatal care has a direct influence on the outcome of the mother and the newborn: Contribution from a French nationwide study.

OBJECTIVE: To investigate maternal and neonatal outcomes after a delivery in France in 2019, according to hospital characteristics and the impact of distance and time of travel on mother and newborn. METHODS: All parturients above 18 years of age who delivered in 2019 and were identified in the French health insurance database were included, with their newborns, in this retrospective cohort study. Main outcome measures were Severe Maternal Morbidity score and the Neonatal Adverse Outcome Indicator (NAOI). RESULTS: Among the 733 052 pregnancies included, 10 829 presented a severe maternal morbidity (1.48%) and 77 237 had a neonatal adverse outcome (10.4%). Factors associated with an unfavorable maternal or neonatal outcome were Obstetric Comorbidity Index, primiparity, and cesarean or instrumental delivery. Prematurity was associated with less severe maternal morbidity but more neonatal adverse outcomes. Time of travel above 30 min was associated with a higher NAOI rate. CONCLUSIONS: Results suggest the efficiency of regionalization of perinatal care in France, although a difference in both outcomes persists according to unit volume, suggesting the need for a further step in concentrating perinatal care. Perinatal care organization should focus on mapping the territory with high-level, high-volume maternity throughout the territory; this suggests closing down high-volume units and improving low-volume ones to maintain coherent mapping.

Animal Metabolite Database: Metabolite Concentrations in Animal Tissues and Convenient Comparison of Quantitative Metabolomic Data.

The Animal Metabolite Database (AMDB, https://amdb.online) is a freely accessible database with built-in statistical analysis tools, allowing one to browse and compare quantitative metabolomics data and raw NMR and MS data, as well as sample metadata, with a focus on the metabolite concentrations rather than on the raw data itself. AMDB also functions as a platform for the metabolomics community, providing convenient deposition and exchange of quantitative metabolomic data. To date, the majority of the data in AMDB relate to the metabolite content of the eye lens and blood of vertebrates, primarily wild species from Siberia, Russia and laboratory rodents. However, data on other tissues (muscle, heart, liver, brain, and more) are also present, and the list of species and tissues is constantly growing. Typically, every sample in AMDB contains concentrations of 60-90 of the most abundant metabolites, provided in nanomoles per gram of wet tissue weight (nmol/g). We believe that AMDB will become a widely used tool in the community, as typical metabolite baseline concentrations in tissues of animal models will aid in a wide variety of fundamental and applied scientific fields, including, but not limited to, animal modeling of human diseases, assessment of medical formulations, and evolutionary and environmental studies.

Data sharing and re-use in the traumatic stress field: An international survey of trauma researchers.

BACKGROUND: The FAIR data principles aim to make scientific data more Findable, Accessible, Interoperable, and Reusable. In the field of traumatic stress research, FAIR data practices can help accelerate scientific advances to improve clinical practice and can reduce participant burden. Previous studies have identified factors that influence data sharing and re-use among scientists, such as normative pressure, perceived career benefit, scholarly altruism, and availability of data repositories. No prior study has examined researcher views and practices regarding data sharing and re-use in the traumatic stress field. OBJECTIVE: To investigate the perspectives and practices of traumatic stress researchers around the world concerning data sharing, re-use, and the implementation of FAIR data principles in order to inform development of a FAIR Data Toolkit for traumatic stress researchers. METHOD: A total of 222 researchers from 28 countries participated in an online survey available in seven languages, assessing their views on data sharing and re-use, current practices, and potential facilitators and barriers to adopting FAIR data principles. RESULTS: The majority of participants held a positive outlook towards data sharing and re-use, endorsing strong scholarly altruism, ethical considerations supporting data sharing, and perceiving data re-use as advantageous for improving research quality and advancing the field. Results were largely consistent with prior surveys of scientists across a wide range of disciplines. A significant proportion of respondents reported instances of data sharing and re-use, but gold standard practices such as formally depositing data in established repositories were reported as infrequent. The study identifies potential barriers such as time constraints, funding, and familiarity with FAIR principles. CONCLUSIONS: These results carry crucial implications for promoting change and devising a FAIR Data Toolkit tailored for traumatic stress researchers, emphasizing aspects such as study planning, data preservation, metadata standardization, endorsing data re-use, and establishing metrics to assess scientific and societal impact.

Traumatic stress researchers worldwide responding to a survey held generally positive views on data sharing, endorsing scholarly altruism and pro-sharing ethical considerations, and rating data re-use as useful for advancing the field.While many respondents reported instances of sharing or re-using data, gold standard practices such as formally depositing data in established repositories were reported as infrequent.Barriers to data sharing and re-use included time constraints, funding, and a lack of familiarity with practices to make data more Findable, Accessible, Interoperable, and Re-usable (FAIR).

Reusing Biomedical Data as Agreed - Towards Structured Metadata for Data Use Agreements.

INTRODUCTION: With increasing availability of reusable biomedical data - from cohort studies to clinical routine data, data re-users face the problem to manage transferred data according to the heterogeneous data use agreements. While structured metadata is addressed in many contexts including informed consent, contracts are to date still unstructured text documents. In particular within collaborative and active working groups the actual usage agreement's regulations are highly relevant for the daily practice - can I share the data with colleagues from the same university or the same research network, can they be stored on a PHD student's laptop, can I store the data for further approved data usage requests? METHODS: In this article, we inspect and review seven different data usage agreements. We focus on digital data that is copied and transferred to the requester's environment. RESULTS: We identified 24 metadata items in the four main categories data usage, storage, and sharing, as well as publication of results. DISCUSSION: While the topics are largely overlap in the data use agreements, the actual regulations of the topics are diverse. Although we do not explicitly investigate trusted research environments, where data is offered within an analytics platform, we consider them a as subgroup, where most of the practical questions from the data scientist's perspective also arise. CONCLUSION: With a limited set of structured metadata items, data scientists could have information about the data use agreement at hand along with the transferred data in an easily accessible way.

Adaptability of Existing Feasibility Tools for Clinical Study Research Data Platforms.

INTRODUCTION: The increasing need for secondary use of clinical study data requires FAIR infrastructures, i.e. provide findable, accessible, interoperable and reusable data. It is crucial for data scientists to assess the number and distribution of cohorts that meet complex combinations of criteria defined by the research question. This so-called feasibility test is increasingly offered as a self-service, where scientists can filter the available data according to specific parameters. Early feasibility tools have been developed for biosamples or image collections. They are of high interest for clinical study platforms that federate multiple studies and data types, but they pose specific requirements on the integration of data sources and data protection. METHODS: Mandatory and desired requirements for such tools were acquired from two user groups - primary users and staff managing a platform's transfer office. Open Source feasibility tools were sought by different literature search strategies and evaluated on their adaptability to the requirements. RESULTS: We identified seven feasibility tools that we evaluated based on six mandatory properties. DISCUSSION: We determined five feasibility tools to be most promising candidates for adaption to a clinical study research data platform, the Clinical Communication Platform, the German Portal for Medical Research Data, the Feasibility Explorer, Medical Controlling, and the Sample Locator.

Development and validation of automated electronic health record data reuse for a multidisciplinary quality dashboard.

Objective: To describe the development and validation of automated electronic health record data reuse for a multidisciplinary quality dashboard. Materials and methods: Comparative study analyzing a manually extracted and an automatically extracted dataset with 262 patients treated for HNC cancer in a tertiary oncology center in the Netherlands in 2020. The primary outcome measures were the percentage of agreement on data elements required for calculating quality indicators and the difference between indicators results calculated using manually collected and indicators that used automatically extracted data. Results: The results of this study demonstrate high agreement between manual and automatically collected variables, reaching up to 99.0% agreement. However, some variables demonstrate lower levels of agreement, with one variable showing only a 20.0% agreement rate. The indicator results obtained through manual collection and automatic extraction show high agreement in most cases, with discrepancy rates ranging from 0.3% to 3.5%. One indicator is identified as a negative outlier, with a discrepancy rate of nearly 25%. Conclusions: This study shows that it is possible to use routinely collected structured data to reliably measure the quality of care in real-time, which could render manual data collection for quality measurement obsolete. To achieve reliable data reuse, it is important that relevant data is recorded as structured data during the care process. Furthermore, the results also imply that data validation is conditional to development of a reliable dashboard.

In France, distance from hospital and health care structure impact on outcome after arthroplasty of the hip for proximal fractures of the femur.

BACKGROUND: Hip arthroplasty is a frequently performed procedure in orthopedic surgery, carried out in almost all health structures for two main issues: fracture and coxarthrosis. Even if volume-outcome relationship appeared associated in many surgeries recently, data provided are not sufficient to set surgical thresholds neither than closing down low-volumes centers. QUESTION: With this study, we wanted to identify surgical, health care-related and territorial factors influencing patient' mortality and readmission after a HA for a femoral fracture in 2018 in France. PATIENTS AND METHODS: Data were anonymously collected from French nationwide administrative databases. All patients who underwent a hip arthroplasty for a femoral fracture through 2018 were included. Patient outcome was 90-day mortality and 90-day readmission rate after surgery. RESULTS: Of the 36,252 patients that underwent a HA for fracture in France in 2018, 0.7% died within 90-day year and 1.2% were readmitted. Male and Charlson comorbidity index were associated with a higher 90-day mortality and readmission rate in multivariate analysis. High volume was associated with a lower mortality rate. Neither time of travel nor distance upon health facility were associated with mortality nor with readmission rate in the analysis. CONCLUSION: Even if volume appears to be associated with lower mortality rate even for longer distance and time of travel, the persistence of exogenous factors not documented in the French databases suggests that regionalization of hip arthroplasty should be organized with caution. CLINICAL RELEVANCE: As volume-outcome relationship must be interpreted with caution, policy makers should not regionalize such surgery without further investigation.

Opening the black box of hospitalizations in French high-secure psychiatric forensic units.

INTRODUCTION: Basic epidemiological data are rare concerning the activity of specialized forensic psychiatric facilities in France. Here, we investigated the activity of the ten (640 beds) French "units for difficult patients" (unités pour malades difficiles [UMDs]). METHOD: We used the Programme de médicalisation des systèmes d'information (PMSI) database to describe the characteristics and evolution of psychiatric hospitalisations in UMDs between 2012 and 2021, as well as the age, sex, and principal diagnoses of the patients hospitalized in these facilities. RESULTS: Between 2012 and 2021, 4857 patients were hospitalized in UMDs (6082 stays). Among them, 897 (18.5%) had more than one stay. The number of admissions ranged from a minimum of 434 to a maximum of 632 per year. The number of discharges ranged from a minimum of 473 to a maximum of 609 per year. The mean length of stay was 13.5 (SD: 22.64) months with a median of 7.3 months (IQR: 4.0-14.4). Among the 6082 stays, 5721 (94.1%) involved male patients. The median age was 33 (IQR: 26-41) years. The most frequent principal psychiatric diagnoses were psychotic disorders and personality disorders. CONCLUSION: The number of individuals hospitalized in specialized forensic psychiatric facilities has been stable for 10 years in France and remains lower than in most European countries.

FAIRness through automation: development of an automated medical data integration infrastructure for FAIR health data in a maximum care university hospital.

BACKGROUND: Secondary use of routine medical data is key to large-scale clinical and health services research. In a maximum care hospital, the volume of data generated exceeds the limits of big data on a daily basis. This so-called "real world data" are essential to complement knowledge and results from clinical trials. Furthermore, big data may help in establishing precision medicine. However, manual data extraction and annotation workflows to transfer routine data into research data would be complex and inefficient. Generally, best practices for managing research data focus on data output rather than the entire data journey from primary sources to analysis. To eventually make routinely collected data usable and available for research, many hurdles have to be overcome. In this work, we present the implementation of an automated framework for timely processing of clinical care data including free texts and genetic data (non-structured data) and centralized storage as Findable, Accessible, Interoperable, Reusable (FAIR) research data in a maximum care university hospital. METHODS: We identify data processing workflows necessary to operate a medical research data service unit in a maximum care hospital. We decompose structurally equal tasks into elementary sub-processes and propose a framework for general data processing. We base our processes on open-source software-components and, where necessary, custom-built generic tools. RESULTS: We demonstrate the application of our proposed framework in practice by describing its use in our Medical Data Integration Center (MeDIC). Our microservices-based and fully open-source data processing automation framework incorporates a complete recording of data management and manipulation activities. The prototype implementation also includes a metadata schema for data provenance and a process validation concept. All requirements of a MeDIC are orchestrated within the proposed framework: Data input from many heterogeneous sources, pseudonymization and harmonization, integration in a data warehouse and finally possibilities for extraction or aggregation of data for research purposes according to data protection requirements. CONCLUSION: Though the framework is not a panacea for bringing routine-based research data into compliance with FAIR principles, it provides a much-needed possibility to process data in a fully automated, traceable, and reproducible manner.