Q . Workshop : What population health researchers ’ need and how PHIRI federated research infrastructure

citation ID: ckac129.465 Association between institutional affiliations of academic editors and authors in medical journals


Background:
Most of the literature on conflict of interest (COI) has not focused on the role of academic editors and their possible COIs, although academic editors often hold senior faculty positions at universities, which might be considered a COI if this influences towards a more favourable processing to articles submitted by institutional colleagues. The current study aims to assess whether academic editor affiliation, a potential COI, can influence academic institution ranking as top contributor in the biomedical field.

Methods:
We conducted a cross-sectional analysis extracting publicly available data from the 2019 Clarivate InCites Journal Citation Reports for journals in the ''Medicine, General & Internal'' category and from each journal website. We constructed the following study outcomes: i) being a top 5 academic contributor for the peer-review journal of interest (yes/no), ii) being a top 10 academic contributor for the peer-review journal of interest (yes/no), and iii) ranking position as top 50 academic contributor for the peer-review journal of interest. Mixed-effect linear and logistic regression models were employed, as appropriate.

Results:
We included 114 journals in our analysis, 49% were openaccess only. Sharing same affiliation of any of the editorial board members was associated with a 6.7 and 5.6 greater likelihood of being top 5 and top 10 contributors, respectively . Similarly, sharing same affiliation was associated with being 12.1 places higher as top contributor . When considering the editor in chief affiliation solely, association was even stronger.

Conclusions:
We found that academic editors sharing the same institutional affiliation with authors was strongly associated with the likelihood of that institution of being a top contributor. Shared institutional affiliations between editors and authors should be clearly stated as part of an open and transparent peer-review process. Key messages: Editors sharing same affiliation with authors was strongly associated with the likelihood for the institution the editor was affiliated with of being top contributor for academic medical journals. Shared institutional affiliations between editors and authors should be clearly stated as part of an open and transparent peer-review process. What population health researchers' need? The population health research community has a vast experience in the reuse of data for health monitoring and surveillance or healthcare performance assessment. However, there is a gap in the extensive reuse of individual sensitive data, particularly when mobilising these data requires the linkage of multiple data sources curated in different sites. The gap is greater when it comes to using sensitive data in cross-national research. The usual arguments to explain the scarce extensive and continuous mobilisation of sensitive data are data privacy and safety issues, the difficulty to discover data sources of value, complex accessing rules, uneven data quality (particularly, nonharmonized data), and limited capacity (personnel and dedicated resources). In InfAct Joint Action, Information for Action, we demonstrated at a very small scale that mobilising individual sensitive data is possible, it is compliant with the legal and ethical requirements, and it yields the expected outputs. The instrument used for such an achievement was the design, implementation and deployment of a very small-scale federated infrastructure, where we could pilot all the legal, organisational, data quality and technological issues related to the mobilisation of individual sensitive data. (https://doi.org/ 10.1186/s13690-021-00731-z). Building on those achievements In PHIRI (see here https://www.phiri.eu/wp7) we are paving the way for a large-scale research infrastructure where multiple population health researchers with multiple research questions will need the mobilisation of multiple data sources from multiple sites across Europe. The PHIRI enhanced infrastructure will have to be prepared to provide a variety of services for data discovery, data access, data analysis and research outputs FAIR publication, while improving the capacity of population health researchers community in the use of advanced computing tools. In this workshop we will start describing the PHIRI federated research infrastructure achievements, the governance step-wise approach and the technological solutions provided. The workshop will discuss how an enhanced PHIRI could improve its services for the community of population health researchers; in particular improving the analytical capacity and the associated technological solutions. Finally, the workshop will touch ground on the future developments, in particular, the interaction of the PHIRI infrastructure with existen European-wide services providers, as EGI, and research infrastructures.

Key messages:
In the domain of population health sciences, the reuse of individual sensitive data for research purposes is very limited.
15th European Public Health Conference 2022 iii189 The PHIRI federated research infrastructure is paving the way for population health researchers to enhance their research when reusing individual sensitive data.
Abstract citation ID: ckac129.467 What the PHIRI federated research infrastructure has achieved so far?
Juan Gonzalez-Garcia J Gonzalez-Garcia 1 1 Biocomputing Unit, Institute for Health Sciences in Aragon, Zaragoza, Spain Contact: jgonzalezgarc.iacs@aragon.es PHIRI infrastructure follows a federated approach that is governed following the European Interoperability Framework. The vision of PHIRI is to create an infrastructure for individual level data processing following the privacy-bydesign principle in a data-centric approach. As a basis to legal interoperability and compliance with the GDPR, the queries or algorithms are moved to the data instead of moving the data. So far, the PHIRI technological developments have focused on a client-server architecture. In this architecture a Coordinator Hub, the server, is in charge of orchestrating the deployment of the data-centric analysis solutions, in the form of R and Python scripts, that will be later executed in the partner nodes (data hubs), the clients. To perform the orchestration the Coordinator Hub encapsulates the scripts in software containers, using Docker images; all the outputs are published in Zenodo. The software containers are then deployed manually from Zenodo in the partner nodes and executed by its IT specialists using their own individual level data -the software containers have represented the technical interoperability layer. The data used on each partner has been previously adapted to a common data model (CDM) and the quality of the dataset has been assessed against the data model by each partner -this has represented the semantic interoperability layer. Finally, the outputs of the analysis's execution are aggregated data that are sent back to the Coordinator Hub to perform a comparative analysis. This stepwise approach has been tested in various research questions promoted by a leading researcher and agreed by the partner nodes who act as data hubs. A help-desk services and a developer's forum and a help-desk service have been set up to ease the implementation and deployment of the research queries -these both have represented the organisational interoperability layer The PHIRI federated approach has consisted of the development of four research queries (use cases) mobilising individual data from a number of data hubs (nodes in the federation). Methodologically speaking, use cases have required the creation of specific cohorts of patients, population subgroups or populations, and the identification of events of interestover-time differences in health status and care healthcare utilisation before and during the pandemic. Technologically speaking, PHIRI infrastructure consists of a distributed endto-end analytical pipeline containing the statistical analysis workflow, including data quality assessment at origin and the mathematical algorithms. Once datasets are prepared in each data hub, partners run the analyses and produce a research output (dashboards containing the research results and tables with aggregated data) that is shared for results compilation and comparative analysis. An enhanced version of the PHIRI infrastructure should allow more complex data distribution. The research questions covered so far are aiming inference on populations or providers, which implies a very simple distribution methodology, as described. However, when the research questions requires inference on the individuals (eg, quasi-experimental study on the effectiveness of a real-life intervention), when the inference requires a hierarchical approach (ie, part of the variance is at individual level and part at cluster level) or when, several rounds of training are needed (eg, validation of an artificial intelligence) the approach would require sharing coefficients, distances in n-dimensional spaces or models, and, some times various rounds of distribution. Finally, an enhanced version of the PHIRI infrastructure should generalise the current FAIR approach limited to the publication of the analytical pipeline in ZENODO, setting up the services and tools required for an improved version of the PHIRI open-science strategy. The proof of concept tested by PHIRI consisted of the development of several research questions in multiple data hubs using a federated approach. It was possible to embed the use cases' analytical pipelines in a portable standalone (i.e. docker image) and distribute it in different health data hubs and technological environments sources for execution. The tested solution has the advantage of not moving sensitive data out of the silos and thus protecting privacy -the code meets data and not the opposite. Some precious lessons provide guidance on how to further develop the PHIRI infrastructure. 1) A deep knowledge on what data is available in the different data hubs of a federation is key since the basis for the development of a research query is the construction of a data model that is common to all the nodes in the federation. In an eventual enhanced PHIRI infrastructure, a solution will be implementing a semantic information system that allows the exchange of metadata using federated and interoperable metadata catalogues based on Semantic RDF graph databases, compliant with the W3C DCAT metadata standard and exposing the end-points of the SPARQL querying language of the Web of linked-data. 2) Making available training samples mimicking real-world data within the docker image has been of high added-value for the development of the use cases' analytical pipelines. In an eventual enhanced PHIRI infrastructure, a generalisation could consist of setting up a ''knowledge hub'' where synthetic data, twinning the population, data would allow any expert users to search and find data through federated queries and prepare and train their analytical pipelines; the ''knowledge hub'' would provide a computational environment (e.g. Jupyter as a service playground), the necessary tools (i.e. cookbooks and capacity building services) and training samples to answer research questions, with the advantage of using data that is anonymous by nature and open access.