With increasing availability, the use of healthcare databases as complementary data source for drug safety signal detection has been explored to circumvent the limitations inherent in spontaneous reporting. Areas covered: To review the methods proposed for safety signal detection in healthcare databases and their performance. Expert opinion: Fifteen different data mining methods were identified. They are based on disproportionality analysis, traditional pharmacoepidemiological designs (e.g. self-controlled designs), sequence symmetry analysis (SSA), sequential statistical testing, temporal association rules, supervised machine learning (SML), and the tree-based scan statistic. When considering the performance of these methods, the self-controlled designs, the SSA, and the SML seemed the most interesting approaches. In the perspective of routine signal detection from healthcare databases, pragmatic aspects such as the need for stakeholders to understand the method in order to be confident in the results must be considered. From this point of view, the SSA could appear as the most suitable method for signal detection in healthcare databases owing to its simple principle and its ability to provide a risk estimate. However, further developments, such as automated prioritization, are needed to help stakeholders handle the multiplicity of signals.

In 2019, the Innovative Medicines Initiative (IMI) funded the ConcePTION project—Building an ecosystem for better monitoring and communicating safety of medicines use in pregnancy and breastfeeding: validated and regulatory endorsed workflows for fast, optimised evidence generation—with the vision that there is a societal obligation to rapidly reduce uncertainty about the safety of medication use in pregnancy and breastfeeding. The present paper introduces the set of concepts used to describe the European data sources involved in the ConcePTION project and illustrates the ConcePTION Common Data Model (CDM), which serves as the keystone of the federated ConcePTION network. Based on data availability and content analysis of 21 European data sources, the ConcePTION CDM has been structured with six tables designed to capture data from routine healthcare, three tables for data from public health surveillance activities, three curated tables for derived data on population (e.g., observation time and mother‐child linkage), plus four metadata tables. By its first anniversary, the ConcePTION CDM has enabled 13 data sources to run common scripts to contribute to major European projects, demonstrating its capacity to facilitate effective and transparent deployment of distributed analytics, and its potential to address questions about utilization, effectiveness, and safety of medicines in special populations, including during pregnancy and breastfeeding, and, more broadly, in the general population.

Introduction COVID-19 vaccines were rapidly authorised, thus requiring intense post-marketing re-evaluation of their benefit-risk profile. A multi-national European collaboration was established with the aim to prospectively monitor safety of the COVID-19 vaccines through web-based survey of vaccinees. Methods A prospective cohort event monitoring study was conducted with primary consented data collection in seven European countries. Through the web applications, participants received and completed baseline and up to six follow-up questionnaires on self-reported adverse reactions for at least 6 months following the first dose of COVID-19 vaccine (Netherlands, France, Belgium, UK, Italy) and baseline and up to ten follow-up questionnaires for one year in Germany and Croatia. Rates of adverse reactions have been described by type (solicited, non-solicited; serious/non-serious; and adverse events of special interest) and stratified by vaccine brand. We calculated the frequency of adverse reaction after dose 1 and prior to dose 2 among all vaccinees who completed at least one follow-up questionnaire. Results Overall, 117,791 participants were included and completed the first questionnaire in addition to the baseline: 88,196 (74.9%) from Germany, 27,588 (23.4%) from Netherlands, 984 (0.8%) from France, 570 (0.5%) from Italy, 326 (0.3%) from Croatia, 89 (0.1%) from the UK and 38 (0.03%) from Belgium. There were 89,377 (75.9%) respondents who had received AstraZeneca vaccines, 14,658 (12.4%) BioNTech/Pfizer, 11,266 (9.6%) Moderna and 2490 (2.1%) Janssen vaccines as a first dose. Median age category was 40–49 years for all vaccines except for Pfizer where median age was 70–79 years. Most vaccinees were female with a female-to-male ratio of 1.34, 1.96 and 2.50 for AstraZeneca, Moderna and Janssen, respectively. BioNtech/Pfizer had slightly more men with a ratio of 0.82. Fatigue and headache were the most commonly reported solicited systemic adverse reactions and injection-site pain was the most common solicited local reaction. The rates of adverse events of special interest (AESIs) were 0.1–0.2% across all vaccine brands. Conclusion This large-scale prospective study of COVID-19 vaccine recipients showed, for all the studied vaccines, a high frequency of systemic reactions, related to the immunogenic response, and local reactions at the injection site, while serious reactions or AESIs were uncommon, consistent with those reported on product labels. This study demonstrated the feasibility of setting up and conducting cohort event monitoring across multiple European countries to collect safety data on novel vaccines that are rolled out at scale in populations which may not have been included in pivotal trials.

There is a lack of information about the burden of metastatic castration-resistant prostate cancer (mCRPC). The present work aims to estimate the incidence and prevalence of mCRPC in 2014 using the French nationwide healthcare database (SNDS). Prevalence and incidence were estimated based on an SNDS extraction of men covered by the general healthcare insurance (86 % of the French population), and aged ≥40. Patients with mCRPC were identified amongst prostate cancer cases using an algorithm estimating a date of first metastasis management and a date of castration resistance. This algorithm was validated by clinical experts through a blind review of 200 anonymized medical charts from SNDS data. Prevalence and incidence were standardized on the European Standard Population (2013 edition). Prevalence and incidence of mCRPC were estimated as, respectively, 62 and 21 cases per 100 000 men in 2014. Less than one mCRPC case per 100 000 was observed in men aged 40-49. Maximum mCRPC incidence was in men aged 80-89 (175 per 100 000). The algorithm used for mCRPC identification had 97 % positive and 99 % negative predictive values. The good performances of the algorithm for mCRPC identification and the consistency of the generated results with the existing data highlight the robustness of these first estimates of mCRPC prevalence and incidence. Future updates will call for algorithm adjustment as practices evolve over time. These first real-life data will serve for future follow-up of the impact of changes in the management of prostate cancer.

Background: Diagnosis performances of case-identifying algorithms developed in healthcare database are usually assessed by comparing identified cases with an external data source. When this is not feasible, intra-database validation can present an appropriate alternative. Objectives: To illustrate through two practical examples how to perform intra-database validations of case-identifying algorithms using reconstituted Electronic Health Records (rEHRs). Methods: Patients with 1) multiple sclerosis (MS) relapses and 2) metastatic castration-resistant prostate cancer (mCRPC) were identified in the French nationwide healthcare database (SNDS) using two case-identifying algorithms. A validation study was then conducted to estimate diagnostic performances of these algorithms through the calculation of their positive predictive value (PPV) and negative predictive value (NPV). To that end, anonymized rEHRs were generated based on the overall information captured in the SNDS over time (e.g. procedure, hospital stays, drug dispensing, medical visits) for a random selection of patients identified as cases or non-cases according to the predefined algorithms. For each disease, an independent validation committee reviewed the rEHRs of 100 cases and 100 non-cases in order to adjudicate on the status of the selected patients (true case/ true non-case), blinded with respect to the result of the corresponding algorithm. Results: Algorithm for relapses identification in MS showed a 95% PPV and 100% NPV. Algorithm for mCRPC identification showed a 97% PPV and 99% NPV. Conclusion: The use of rEHRs to conduct an intra-database validation appears to be a valuable tool to estimate the performances of a case-identifying algorithm and assess its validity, in the absence of alternative.