Correction: Good practices for clinical data warehouse implementation: A case study in France.

[This corrects the article DOI: 10.1371/journal.pdig.0000298.].

Good practices for clinical data warehouse implementation: A case study in France.

Real-world data (RWD) bears great promises to improve the quality of care. However, specific infrastructures and methodologies are required to derive robust knowledge and brings innovations to the patient. Drawing upon the national case study of the 32 French regional and university hospitals governance, we highlight key aspects of modern clinical data warehouses (CDWs): governance, transparency, types of data, data reuse, technical tools, documentation, and data quality control processes. Semi-structured interviews as well as a review of reported studies on French CDWs were conducted in a semi-structured manner from March to November 2022. Out of 32 regional and university hospitals in France, 14 have a CDW in production, 5 are experimenting, 5 have a prospective CDW project, 8 did not have any CDW project at the time of writing. The implementation of CDW in France dates from 2011 and accelerated in the late 2020. From this case study, we draw some general guidelines for CDWs. The actual orientation of CDWs towards research requires efforts in governance stabilization, standardization of data schema, and development in data quality and data documentation. Particular attention must be paid to the sustainability of the warehouse teams and to the multilevel governance. The transparency of the studies and the tools of transformation of the data must improve to allow successful multicentric data reuses as well as innovations in routine care.

Tip of the Iceberg? Country- and Company-Level Analysis of Drug Company Payments for Research and Development in Europe.

BACKGROUND: Creating new therapies often involves drug companies paying healthcare professionals and institutions for research and development (R&D) activities, including clinical trials. However, industry sponsorship can create conflicts of interest (COIs). We analysed approaches to drug company R&D payment disclosure in European countries and the distribution of R&D payments at the country and company level. METHODS: Using documentary sources and a stakeholder survey we identified country- regulatory approaches to R&D payment disclosure. We reviewed company-level descriptions of disclosure practices in the United Kingdom, a country with a major role in Europe's R&D. We obtained country-level R&D payment data from industry trade groups and public authorities and company-level data from eurosfordocs.eu, a publicly available payments database. We conducted content analysis and descriptive statistical analysis. RESULTS: In 32 of 37 studied countries, all R&D payments were reported without named recipients, following a self-regulatory approach developed by the industry. The methodological descriptions from 125 companies operating in the United Kingdom suggest that within the self-regulatory approach companies had much leeway in deciding what activities and payments were considered as R&D. In five countries, legislation mandated the disclosure of R&D payment recipients, but only in two were payments practically identifiable and analysable. In 17 countries with available data, R&D constituted 19%-82% of all payments reported, with self-regulation associated with higher shares. Available company-level data from three countries with self-regulation suggests that R&D payments were concentrated by big funders, and some companies reported all, or nearly all, payments as R&D. CONCLUSION: The lack of full disclosure of R&D payments in countries with industry self-regulation leaves considerable sums of money unaccounted for and potentially many COIs undetected. Disclosure mandated by legislation exists in few countries and rarely enhances transparency practically. We recommend a unified European approach to R&D payment disclosure, including clear definitions and a centralised database.

Accessibility and quality of drug company disclosures of payments to healthcare professionals and organisations in 37 countries: a European policy review.

OBJECTIVES: To examine the accessibility and quality of drug company payment data in Europe. DESIGN: Comparative policy review of payment data in countries with different regulatory approaches to disclosure. SETTING: 37 European countries. PARTICIPANTS: European Federation of Pharmaceutical Industries and Associations, its trade group and their drug company members; eurosfordocs.eu, an independent database integrating payments disclosed by companies and trade groups; regulatory bodies overseeing payment disclosure. MAIN OUTCOME MEASURES: Regulatory approaches to disclosure (self-regulation, public regulation, combination of the two); data accessibility (format, structure, searchability, customisable summary statistics, downloadability) and quality (spectrum of disclosed characteristics, payment aggregation, inclusion of taxes, recipient or donor identifiers). RESULTS: Of 30 countries with self-regulation, five had centralised databases, with Disclosure UK displaying the highest accessibility and quality. In 23 of the remaining countries with self-regulation and available data, disclosures were published in the portable document format (PDF) on individual company websites, preventing the public from understanding payment patterns. Eurosfordocs.eu had greater accessibility than any industry-run database, but the match between the value of payments integrated in eurosfordocs.eu and summarised separately by industry in seven countries ranged between 56% and 100% depending on country. Eurosfordocs.eu shared quality shortcomings with the underlying industry data, including ambiguities in identifying payments and their recipients. Public regulation was found in 15 countries, used either alone (3), in combination (4) or in parallel with (8) self-regulation. Of these countries, 13 established centralised databases with widely ranging accessibility and quality, and sharing some shortcomings with the industry-run databases. The French database, Transparence Santé, had the highest accessibility and quality, exceeding that of Disclosure UK. CONCLUSIONS: The accessibility and quality of payment data disclosed in European countries are typically low, hindering investigation of financial conflicts of interest. Some improvements are straightforward but reaching the standards characterising the widely researched US Open Payments database requires major regulatory change.

Pharmaceutical industry self-regulation and non-transparency: country and company level analysis of payments to healthcare professionals in seven European countries.

The European pharmaceutical industry uses the alleged efficacy of self-regulation to question the need for transparency laws similar to the US Physician Payment Sunshine Act. We conducted a comparative analysis of 20 large companies' payment disclosures in seven European countries in 2017-2019. The data was extracted as part of eurosfordocs.eu, a novel transparency project that scrapes and integrates publicly available databases and disclosures. Our analysis of EUR 735 million showed marked differences in country payment patterns. For example, payment totals per registered doctor were substantially larger in Spain and lowest in Sweden. There were significant country and company differences in individualized data completeness. Only 19% of totals were reported with recipient names in Germany, compared to Ireland (59%), the United Kingdom (60%), Italy (67%), Switzerland (73%), Sweden (79%) and Spain (100%), with little or no improvement over time. Payment data in Spain was particularly difficult to extract. Thus, in no country did self-regulation generate comprehensive individualized data allowing for building an accurate picture of financial relationships between the industry and healthcare professionals. We conclude that the cultures and policies of countries and companies create structural problems of data inaccessibility and incompleteness within the self-regulatory framework. Therefore, this study supports calls for a Europe-wide "Sunshine Act" to achieve real transparency of drug company payments.

Extensive gene remodeling in the viral world: new evidence for nongradual evolution in the mobilome network.

Complex nongradual evolutionary processes such as gene remodeling are difficult to model, to visualize, and to investigate systematically. Despite these challenges, the creation of composite (or mosaic) genes by combination of genetic segments from unrelated gene families was established as an important adaptive phenomena in eukaryotic genomes. In contrast, almost no general studies have been conducted to quantify composite genes in viruses. Although viral genome mosaicism has been well-described, the extent of gene mosaicism and its rules of emergence remain largely unexplored. Applying methods from graph theory to inclusive similarity networks, and using data from more than 3,000 complete viral genomes, we provide the first demonstration that composite genes in viruses are 1) functionally biased, 2) involved in key aspects of the arm race between cells and viruses, and 3) can be classified into two distinct types of composite genes in all viral classes. Beyond the quantification of the widespread recombination of genes among different viruses of the same class, we also report a striking sharing of genetic information between viruses of different classes and with different nucleic acid types. This latter discovery provides novel evidence for the existence of a large and complex mobilome network, which appears partly bound by the sharing of genetic information and by the formation of composite genes between mobile entities with different genetic material. Considering that there are around 10E31 viruses on the planet, gene remodeling appears as a hugely significant way of generating and moving novel sequences between different kinds of organisms on Earth.

A pluralistic account of homology: adapting the models to the data.

Defining homologous genes is important in many evolutionary studies but raises obvious issues. Some of these issues are conceptual and stem from our assumptions of how a gene evolves, others are practical, and depend on the algorithmic decisions implemented in existing software. Therefore, to make progress in the study of homology, both ontological and epistemological questions must be considered. In particular, defining homologous genes cannot be solely addressed under the classic assumptions of strong tree thinking, according to which genes evolve in a strictly tree-like fashion of vertical descent and divergence and the problems of homology detection are primarily methodological. Gene homology could also be considered under a different perspective where genes evolve as "public goods," subjected to various introgressive processes. In this latter case, defining homologous genes becomes a matter of designing models suited to the actual complexity of the data and how such complexity arises, rather than trying to fit genetic data to some a priori tree-like evolutionary model, a practice that inevitably results in the loss of much information. Here we show how important aspects of the problems raised by homology detection methods can be overcome when even more fundamental roots of these problems are addressed by analyzing public goods thinking evolutionary processes through which genes have frequently originated. This kind of thinking acknowledges distinct types of homologs, characterized by distinct patterns, in phylogenetic and nonphylogenetic unrooted or multirooted networks. In addition, we define "family resemblances" to include genes that are related through intermediate relatives, thereby placing notions of homology in the broader context of evolutionary relationships. We conclude by presenting some payoffs of adopting such a pluralistic account of homology and family relationship, which expands the scope of evolutionary analyses beyond the traditional, yet relatively narrow focus allowed by a strong tree-thinking view on gene evolution.

MosaicFinder: identification of fused gene families in sequence similarity networks.

MOTIVATION: Gene fusion is an important evolutionary process. It can yield valuable information to infer the interactions and functions of proteins. Fused genes have been identified as non-transitive patterns of similarity in triplets of genes. To be computationally tractable, this approach usually imposes an a priori distinction between a dataset in which fused genes are searched for, and a dataset that may have provided genetic material for fusion. This reduces the 'genetic space' in which fusion can be discovered, as only a subset of triplets of genes is investigated. Moreover, this approach may have a high-false-positive rate, and it does not identify gene families descending from a common fusion event. RESULTS: We represent similarities between sequences as a network. This leads to an efficient formulation of previous methods of fused gene identification, which we implemented in the Python program FusedTriplets. Furthermore, we propose a new characterization of families of fused genes, as clique minimal separators of the sequence similarity network. This well-studied graph topology provides a robust and fast method of detection, well suited for automatic analyses of big datasets. We implemented this method in the C++ program MosaicFinder, which additionally uses local alignments to discard false-positive candidates and indicates potential fusion points. The grouping into families will help distinguish sequencing or prediction errors from real biological fusions, and it will yield additional insight into the function and history of fused genes. AVAILABILITY: FusedTriplets and MosaicFinder are published under the GPL license and are freely available with their source code at this address: http://sourceforge.net/projects/mosaicfinder. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.