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Introduction: The public–private ADVANCE collaboration developed and tested a system to generate evidence on vaccine benefits and risks using European electronic healthcare databases. In the safety of vaccines, background incidence rates are key to allow proper monitoring and assessment. The goals of this study were to compute age-, sex-, and calendar-year stratified incidence rates of nine autoimmune diseases in seven European healthcare databases from four countries and to assess validity by comparing with published data. Methods: Event rates were calculated for the following outcomes: acute disseminated encephalomyelitis, Bell’s palsy, Guillain–Barré syndrome, immune thrombocytopenia purpura, Kawasaki disease, optic neuritis, narcolepsy, systemic lupus erythematosus, and transverse myelitis. Cases were identified by diagnosis codes. Participating organizations/databases originated from Denmark, Italy, Spain, and the UK. The source population comprised all persons registered, with at least 1 year of data prior to the study start, or follow-up from birth. Stratified incidence rates were computed per database over the period 2003 to 2014. Results: Between 2003 and 2014, 148,947 incident cases of nine autoimmune diseases were identified. Crude incidence rates were highest for Bell’s palsy [23.8/100,000 person-years (PYs), 95% confidence interval (CI) 23.6–24.1] and lowest for Kawasaki disease (0.7/100,000 PYs, 95% CI 0.6–0.7). Specific patterns were observed by sex, age, calendar time, and data sources. Rates were comparable with published estimates. Conclusion: A range of autoimmune events could be identified in the ADVANCE system. Estimation of rates indicated consistency across selected European healthcare databases, as well as consistency with US published data.

Introduction: The public-private ADVANCE consortium (Accelerated development of vaccine benefit-risk collaboration in Europe) aimed to assess if electronic healthcare databases can provide fit-for purpose data for collaborative, distributed studies and monitoring of vaccine coverage, benefits and risks of vaccines. Objective: To evaluate if European healthcare databases can be used to estimate vaccine coverage, benefit and/or risk using pertussis-containing vaccines as an example. Methods: Characterisation was conducted using open-source Java-based (Jerboa) software and R scripts. We obtained: (i) The general characteristics of the database and data source (meta-data) and (ii) a detailed description of the database population (size, representatively of age/sex of national population, rounding of birth dates, delay between birth and database entry), vaccinations (number of vaccine doses, recording of doses, pattern of doses by age and coverage) and events of interest (diagnosis codes, incidence rates). A total of nine databases (primary care, regional/national record linkage) provided data on events (pertussis, pneumonia, death, fever, convulsions, injection site reactions, hypotonic hypo-responsive episode, persistent crying) and vaccines (acellular pertussis and whole cell pertussis) related to the pertussis proof of concept studies. Results: The databases contained data for a total population of 44 million individuals. Seven databases had recorded doses of vaccines. The pertussis coverage estimates were similar to those reported by the World Health Organisation (WHO). Incidence rates of eve

Background: The Accelerated Development of VAccine beNefit-risk Collaboration in Europe (ADVANCE) is a public-private collaboration aiming to develop and test a system for rapid benefit-risk (B/R) monitoring of vaccines using European healthcare databases. Event misclassification can result in biased estimates. Using different algorithms for identifying cases of Bordetella pertussis (BorPer) infection as a test case, we aimed to describe a strategy to quantify event misclassification, when manual chart review is not feasible. Methods: Four participating databases retrieved data from primary care (PC) setting: BIFAP: (Spain), THIN and RCGP RSC (UK) and PEDIANET (Italy); SIDIAP (Spain) retrieved data from both PC and hospital settings. BorPer algorithms were defined by healthcare setting, data domain (diagnoses, drugs, or laboratory tests) and concept sets (specific or unspecified pertussis). Algorithm- and database-specific BorPer incidence rates (IRs) were estimated in children aged 0–14 years enrolled in 2012 and 2014 and followed up until the end of each calendar year and compared with IRs of confirmed pertussis from the ECDC surveillance system (TESSy). Novel formulas were used to approximate validity indices, based on a small set of assumptions. They were applied to approximately estimate positive predictive value (PPV) and sensitivity in SIDIAP. Results: The number of cases and the estimated BorPer IRs per 100,000 person-years in PC, using data representing 3,173,268 person-years, were 0 (IR = 0.0), 21 (IR = 4.3), 21 (IR = 5.1), 79 (IR = 5.7), and 2 (IR = 2.3) in BIFAP, SIDIAP, THIN, RCGP RSC and PEDIANET respectively. The IRs for combined specific/unspecified pertussis were higher than TESSy, suggesting that some false positives had been included. In SIDIAP the estimated IR was 45.0 when discharge diagnoses were included. The sensitivity and PPV of combined PC specific and unspecific diagnoses for BorPer cases in SIDIAP were approximately 85% and 72%, respecti

Background: Many healthcare databases have been routinely collected over the past decades, to support clinical practice and administrative services. However, their secondary use for research is often hindered by restricted governance rules. Furthermore, health research studies typically involve many participants with complementary roles and responsibilities which require proper process management. Results: From a wide set of requirements collected from European clinical studies, we developed TASKA, a task/workflow management system that helps to cope with the socio-technical issues arising when dealing with multidisciplinary and multi-setting clinical studies. The system is based on a two-layered architecture: 1) the backend engine, which follows a micro-kernel pattern, for extensibility, and RESTful web services, for decoupling from the web clients; 2) and the client, entirely developed in ReactJS, allowing the construction and management of studies through a graphical interface. TASKA is a GNU GPL open source project, accessible at https://github.com/bioinformatics-ua/taska. A demo version is also available at https://bioinformatics.ua.pt/taska. Conclusions: The system is currently used to support feasibility studies across several institutions and countries, in the context of the European Medical Information Framework (EMIF) project. The tool was shown to simplify the set-up of health studies, the management of participants and their roles, as well as the overall governance process.

Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sou