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Background: The socioeconomic status is often an important confounder in the context of epidemiological studies. However, this information is typically not included in studies based on claims data, possibly due to a lack of knowledge regarding the completeness and validity of the available information.Objectives: Our aim was to assess and evaluate the socioeconomic status in the German Pharmacoepidemiological Research Database (GePaRD).Methodology: First, an algorithm was developed to assign an educational status to individuals insured in 2017 in order to estimate the socioeconomic status at the individual level. For comparison, the socioeconomic status was estimated based on the place of residence. For plausibility, we examined whether the known association between the presence of obesity and the socioeconomic status could be reproduced.Results: Based on different age groups it was possible to dichotomously estimate the socioeconomic status for 86–93% of those under 60 years of age and for 67% of the 60- to 69-year-olds. For individuals 70 years and older, the proportion of missing values was very high. In all subgroups, the prevalence of obesity was higher among persons of lower socioeconomic status than among persons of higher socioeconomic status. The estimation based on the place of residence produced plausible results, but with smaller differences by socioeconomic status.Discussion and conclusion: Overall, the study results suggest that in terms of plausibility and completeness the information available in GePaRD is well suited for estimating the individual socioeconomic status for age groups up to 69 years.

Introduction: Pharmacovigilance is vital for drug safety. The process typically involves two key steps: initial signal generation from spontaneous reporting systems (SRSs) and subsequent expert review to assess the signals' (potential) causality and decide on the appropriate action. Methods: We propose a novel discovery and verification approach to pharmacovigilance based on electronic healthcare data. We enhance the signal detection phase by introducing an ensemble of methods which generated signals are combined using Borda count ranking; a method designed to emphasize consensus. Ensemble methods tend to perform better when data is noisy and leverage the strengths of individual classifiers, while trying to mitigate some of their limitations. Additionally, we offer the committee of medical experts with the option to perform an in-depth investigation of selected signals through tailored pharmacoepidemiological studies to evaluate their plausibility or spuriousness. To illustrate our approach, we utilize data from the German Pharmacoepidemiological Research Database, focusing on drug reactions to the direct oral anticoagulant rivaroxaban. Results: In this example, the ensemble method is built upon the Bayesian confidence propagation neural network, longitudinal Gamma Poisson shrinker, penalized regression and random forests. We also conduct a pharmacoepidemiological verification study in the form of a nested active comparator case-control study, involving patients diagnosed with atrial fibrillation who initiated anticoagulant treatment between 2011 and 2017. Discussion: The case study reveals our ability to detect known adverse drug reactions and discover new signals. Importantly, the ensemble method is computationally efficient. Hasty false conclusions can be avoided by a verification study, which is, however, time-consuming to carry out. We provide an online tool for easy application: https://borda.bips.eu. [ABSTRACT FROM AUTHOR]

Purpose: Large health‐care databases are increasingly used for research on drug utilization and safety in pregnancy. For the German Pharmacoepidemiological Research Database (GePaRD), covering ~20% of the German population, algorithms have been developed to identify pregnancies, to estimate their date of onset and to link mothers to their babies. Using this methodology, we aimed to conduct a proof‐of‐concept analysis on the known association between valproate (VPA) exposure in early pregnancy and spina bifida in the exposed child. Methods: We identified all pregnancies in GePaRD between 2006 and 2016 in women aged 12 to 50 years. To each VPA dispensation of these women, an exposure period was assigned, based on the dispensation date and the number of defined daily doses in the dispensed package. A pregnancy was classified as exposed to VPA in the critical time window if this exposure period overlapped with the first 55 days of pregnancy. Risk ratios were calculated for spina bifida in live births and induced abortions comparing VPA‐exposed ones to all pregnancies. Results: Overall, we identified 1 271 384 pregnancies fulfilling the inclusion criteria. Of these, 668 pregnancies (0.053%) were classified as exposed to VPA in the critical time window regarding spina bifida. An induced abortion accompanied by a diagnosis of spina bifida was observed in one of the VPA‐exposed pregnancies (0.15%) and in 154 of all pregnancies (0.012%), yielding a risk ratio of 12.4 (95% confidence interval [CI]: 1.7–88.2). Out of 775 875 pregnancies ending in a live birth, 366 (0.047%) were classified as VPA exposed. A diagnosis of spina bifida was coded in 3 of 366 VPA‐exposed live births (0.82%) and in 260 of all live births (0.03%), yielding a relative risk of 24.5 (95% CI: 7.9–76.0). Conclusions: Our proof‐of‐concept analysis based on GePaRD showed a strong association between intrauterine exposure to VPA and occurrence of spina bifida. The results are plausible and consistent with the literature, supporting the suitability of GePaRD and the developed algorithms to conduct studies on drug safety in pregnancy. [ABSTRACT FROM AUTHOR]