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This publication contains the slidedeck of the presentation with the title "Continuously evolving demonstrator", and subtitled "Baseline use case: Prototyping a workflow that is standard to population health research" given by Enrique Bernal-Delgado at theBY-COVID Spring 23 Use Cases Workshop: Integration of socioeconomic data in observational studies on vaccine effectiveness organised as part ofBY-COVID WP5 activities. The use cases explored in the BY-COVID project are thought to be developed as demonstrators showcasing methodologies and tools incresing preparedness for tackling the next pandemics via the analysis of health and life sciences data. The presentation introduces the baseline use case as a support to prototype a standard workflow aiming at responding relevant population health research questions, assessing public health interventions by reusing routinely collected health data (i.e., real-world data) from multiple sources in different countries using a federated approach. &nbsp

This publication corresponds to the preliminary outputs for the Baseline Use Case proposed in T.5.2 (WP5) in the BY-COVID project on "SARS-CoV-2 Vaccine(s) effectiveness in preventing SARS-CoV-2 infection" in Aragon, Spain. Research Question: "How effective have the SARS-CoV-2 vaccination programmes been in preventing SARS-CoV-2 infections?" Intervention (exposure): COVID-19 vaccine(s) Outcome: SARS-CoV-2 infection Subgroup analysis: Vaccination schedule (type of vaccine) Study Design: An observational retrospective longitudinal study to assess the effectiveness of the SARS-CoV-2 vaccine in preventing SARS-CoV-2 infections using routinely collected social, health and care data from several countries. A causal model was established using Directed Acyclic Graphs (DAGs) to map domain knowledge, theories and assumptions about the causal relationship between exposure and outcome. The DAG developed for the research question of interest is shown below. Cohort definition: All people eligible to be vaccinated (from 5 to 115 years old, included) or with, at least, one dose of a SARS-CoV-2 vaccine (any of the available brands) having or not a previous SARS-CoV-2 infection. Inclusion criteria: All people vaccinated with at least one dose of the COVID-19 vaccine (any available brands) in an area of residence. Any person eligible to be vaccinated (from 5 to 115 years old, included) with a positive diagnosis (irrespective of the type of test) for SARS-CoV-2 infection (COVID-19) during the period of study. Exclusion criteria: People not eligible for the vaccine (from 0 to 4 years old, included) Study period: From the date of the first documented SARS-CoV-2 infection in each country to the most recent date in which data is available at the time of analysis, roughly from 01-03-2020 to 30-06-2022, depending on the country. Outcome: Time from primary vaccination to confirmed SARS-CoV-2 infection. These outputs correspond with the interactive reports of each main step of the analytical pipeline described in 'Methods analytical pipeline' and have been produced using routinely collected data from public health and the national health system in the region of Aragon (Spain) with population coverage (~1.3 million lives). The scripts implementing the statistical analyses in the analytical pipeline are available on GitHub, with additional documentation supporting the development and implementation of the baseline use case. #### v.0.0.2 changelog #### Corrected issues with original data with multiple registries failing the validation rule checking 'pregnant_bl'., These are preliminary outputs of a pilot to be completed with data from the region of Aragon (Spain) and from Belgium. Both interactive reports and their content are expected to change and be updated once data and statistical analyses are expanded and refined.

This publication corresponds to the study protocol of the Baseline Use Case proposed in T.5.2 (WP5) in the BY-COVID project on “SARS-CoV-2 Vaccine effectiveness in preventing SARS-CoV-2 infection.” Research question: We aim to investigate the real-world effectiveness of SARS-CoV-2 primary vaccination as compared to partial or no vaccination in preventing SARS-CoV-2 infection in virtually all resident populations spanning different countries. Study design: An observational retrospective longitudinal (cohort) study using routinely collected social, health and care data from several countries/regions in Europe. We have designed the observational study to emulate a hypothetical Target Trial (TT) of the causal effect of SARS-CoV-2 vaccination programs in preventing SARS-CoV-2 infections. File included in this publication: Study protocol (PDF): The protocol provides a work plan, including details on the research team, study rationale, methodology, and ethical aspects. The implementation of this study is supported by supplementary documents including a causal model (DAG), a common data model specification, and a simulated dataset following the specification of the common data model. This information is available at https://doi.org/10.5281/zenodo.6913045., The contents of this document are the sole responsibility of their authors. All rights reserved. Copying and distribution by any means is permitted provided that the authors are acknowledged, that no commercial use is made of the works and that no modifications are made (CC-BY INT 4.0.).

Context: International comparisons of the health of mothers and babies provide essential benchmarks for guiding health practice and policy, but statistics are not routinely compiled in a comparable way. These data are especially critical during health emergencies, such as the coronavirus disease (COVID-19) pandemic. The Population Health Information Research Infrastructure (PHIRI) project aimed to promote the exchange of population data in Europe and included a Use Case on perinatal health. Objective: To develop and test a protocol for federated analysis of population birth data in Europe. Methods: The Euro-Peristat network with participants from 31 countries developed a Common Data Model (CDM) and R scripts to exchange and analyse aggregated data on perinatal indicators. Building on recommended Euro-Peristat indicators, complemented by a three-round consensus process, the network specified variables for a CDM and common outputs. The protocol was tested using routine birth data for 2015 to 2020; a survey was conducted assessing data provider experiences and opinions. Results: The CDM included 17 core data items for the testing phase and 18 for a future expanded phase. 28 countries and the four UK nations created individual person-level databases and ran R scripts to produce anonymous aggregate tables. Seven had all core items, 17 had 13-16, while eight had ≤12. Limitations were not having all items in the same database, required for this protocol. Infant death and mode of birth were most frequently missing. Countries took from under a day to several weeks to set up the CDM, after which the protocol was easy and quick to use. Conclusion: This open-source protocol enables rapid production and analysis of perinatal indicators and constitutes a roadmap for a sustainable European information system. It also provides minimum standards for improving national data systems and can be used in other countries to facilitate comparison of perinatal indicators.

As referred in BY-COVID Work Package (WP) description, WP2 brings together data resources and catalogues across domains, captures data governance and access procedures. It aims at aligning metadata descriptions and other relevant semantic information first within domains (e.g., biomolecular and imaging, clinical and health, survey, etc.) and at a second stage (in alignment with WP3 developments) expose a reference catalogue with harmonised metadata descriptions across domains. Deliverable (D) 2.2, part of BY-COVID WP2, will develop a common sense of the preferred mechanisms already available for accessing real-world data and transfer it across research domains and jurisdictions, describing technical, legal and organisational barriers across data sources and identified solutions for real-world data access including the procedures to manage users permissions for controlled access data. This milestone, which focuses on patient clinical and health data, starts paving the way of this Deliverable (D) 2.2 providing insight on issues affecting access and use of sensitive real-world data. In this milestone we do refer to routine health data reuse not to the reuse of data primarily collected for research purposes. The milestone starts with a definition of key concepts, shedding light on the legal bases for access and reuse when the purpose is research, and provides a template for the description and assessment of RWD access and use policies and procedures using, as examples, three real-world data initiatives collected in milestone 2.1.

This publication corresponds to the studyprotocolof the Baseline Use Case proposed in T.5.2 (WP5) in theBY-COVID projecton“SARS-CoV-2Vaccineeffectiveness in preventing SARS-CoV-2 infection.” Research question: We aim to investigate the real-world effectiveness of SARS-CoV-2 primary vaccination as compared to partial or no vaccination in preventing SARS-CoV-2 infection in virtually all resident populations spanning different countries. Study design: An observational retrospective longitudinal (cohort) study using routinely collected social, health and care data from several countries/regions in Europe. We have designed the observational study to emulate a hypothetical Target Trial (TT) of the causal effect of SARS-CoV-2 vaccination programs in preventing SARS-CoV-2 infections. Fileincluded in this publication: Study protocol (PDF): The protocol provides a work plan, including details on the research team, study rationale, methodology, and ethical aspects. The implementation of this study is supported by supplementary documents including a causal model (DAG), a common data model specification, and a simulated dataset following the specification of the common data model. This information is available athttps://doi.org/10.5281/zenodo.6913045. &nbsp

The INFACT Data Model contains: - the common data model for the Acute Stroke Care Process Mining Analysis in two formats a) human-readable (PPTX file) and machine-ready (EXCEL file), - the instructions to locally deploy and run the Analytic Pipeline composed by the Event Log Builder and the Process Mining Pipeline, - the consensus on the definition specifications in ICD-10th and ICD-9th codes for stroke case selection and characterization, - a synthetic sample data set following the specifications of the common data model to test the Analytic Pipeline, and - the execution scripts for locally deploying the Docker container for the analyses., This documentation was produced by the IACS team as part of the Coordination Hub within the development of the Stroke Care pilot in task 10.4 (T.10.4) in Work Package 10 of InfAct (Information for Action!), the Joint Action on Health Information., {"references":["González-García, Juan, Estupiñán-Romero, Francisco, Tellería-Orriols, Carlos, & Bernal-Delgado, Enrique. (2020, July 20). Stroke Process Mining Analysis Virtual Machine Image (OVFv2 / VHD formats) (Version Data Model Slides v13 (Effective Data Model v11). Run Slides v7.). Zenodo. http://doi.org/10.5281/zenodo.3952495"]}

The European Collaboration for Healthcare Optimisation (ECHO) Data Model Specification corresponds to a technical report documenting the conceptual, logical and physical data models and main requirements and decisions guiding the final design of the ECHO knowledge infrastructure. This document provides further technical details on the data management of the ECHO project, adding useful hints on how the ECHO Consortium solved main technical and semantic interoperability challenges to enable international health system performance comparison based on the re-use of healthcare administrative data., ECHO Consortium: IACS's team (E. Bernal-Delgado, S. Garc��a-Armesto, N. Mart��nez, M. Seral, F. Estupi����n, M. Comendeiro, E. Angulo-Pueyo, M. Ridao and C. Baixaul��, J. Librero J as affiliated researchers), University of Southern Denmark's team (T. Christiansen, L.C. Thygesen), University of York's team (K. Bloor, R. Cookson and N. Gutacker), University Nova de Lisboa 's team (C. Mateus, C. Nunes and I. Joaquim), Institute of Public Health of Ljubljana's team (A.M. Yazbeck, M. Galsworthy and T. Albrecht), UMIT's team (J. Munck and B. Gu��ntert) and EHMA's team (J. Bremner, P. Giepmans and O. Dix). This report has been entirely generated with R, using RMarkdown package among others (see sessionInfo() output below) --- R version 3.6.0 (2019-04-26) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 7 x64 (build 7601) Service Pack 1 Matrix products: default locale: [1] LC_COLLATE=Spanish_Spain.1252 LC_CTYPE=Spanish_Spain.1252 LC_MONETARY=Spanish_Spain.1252 LC_NUMERIC=C [5] LC_TIME=Spanish_Spain.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] htmlwidgets_1.3 htmltools_0.3.6 png_0.1-7 DiagrammeR_1.0.1 forcats_0.4.0 [6] stringr_1.4.0 dplyr_0.8.1 purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 [11] tibble_2.1.1 ggplot2_3.1.1 tidyverse_1.2.1 datamodelr_0.2.2.9002 loaded via a namespace (and not attached): [1] tidyselect_0.2.5 xfun_0.7 Rook_1.1-1 haven_2.1.0 lattice_0.20-38 colorspace_1.4-1 [7] generics_0.0.2 viridisLite_0.3.0 XML_3.98-1.20 rlang_0.3.4 pillar_1.4.0 glue_1.3.1 [13] withr_2.1.2 RColorBrewer_1.1-2 modelr_0.1.4 readxl_1.3.1 plyr_1.8.4 munsell_0.5.0 [19] gtable_0.3.0 cellranger_1.1.0 rvest_0.3.4 visNetwork_2.0.7 knitr_1.22 broom_0.5.2 [25] Rcpp_1.0.1 scales_1.0.0 backports_1.1.4 jsonlite_1.6 rgexf_0.15.3 gridExtra_2.3 [31] brew_1.0-6 hms_0.4.2 digest_0.6.18 stringi_1.4.3 grid_3.6.0 influenceR_0.1.0 [37] cli_1.1.0 tools_3.6.0 magrittr_1.5 lazyeval_0.2.2 crayon_1.3.4 pkgconfig_2.0.2 [43] xml2_1.2.0 downloader_0.4 lubridate_1.7.4 assertthat_0.2.1 httr_1.4.0 rstudioapi_0.10 [49] viridis_0.5.1 R6_2.4.0 igraph_1.2.4.1 nlme_3.1-139 compiler_3.6.0