Conducting an Epidemiologic Study and Making It FAIR: Reusable Tools and Procedures from a Population-Based Cohort Study.

Conducting large-scale epidemiologic studies requires powerful software for electronic data capture, data management, data quality assessments, and participant management. There is also an increasing need to make studies and the data collected findable, accessible, interoperable, and reusable (FAIR). However, reusable software tools from major studies, underlying such needs, are not necessarily known to other researchers. Therefore, this work gives an overview on the main tools used to conduct the internationally highly networked population-based project Study of Health in Pomerania (SHIP), as well as approaches taken to improve its FAIRness. Deep phenotyping, formalizing processes from data capture to data transfer, with a strong emphasis on cooperation and data exchange have laid the foundation for a broad scientific impact with more than 1500 published papers to date.

Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R.

BACKGROUND: No standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments. METHODS: Developments were guided by the evaluation of an existing data quality framework and literature reviews. Functions for the computation of data quality indicators were written in R. The concept and implementations are illustrated based on data from the population-based Study of Health in Pomerania (SHIP). RESULTS: The data quality framework comprises 34 data quality indicators. These target four aspects of data quality: compliance with pre-specified structural and technical requirements (integrity); presence of data values (completeness); inadmissible or uncertain data values and contradictions (consistency); unexpected distributions and associations (accuracy). R functions calculate data quality metrics based on the provided study data and metadata and R Markdown reports are generated. Guidance on the concept and tools is available through a dedicated website. CONCLUSIONS: The presented data quality framework is the first of its kind for observational health research data collections that links a formal concept to implementations in R. The framework and tools facilitate harmonized data quality assessments in pursue of transparent and reproducible research. Application scenarios comprise data quality monitoring while a study is carried out as well as performing an initial data analysis before starting substantive scientific analyses but the developments are also of relevance beyond research.

Hospitalization rates of generic metoprolol compared with the original beta-blocker in an epidemiological database study.

PURPOSE: A less favourable galenic profile of generic formulations of the beta-blocker metoprolol raised the concern of a higher risk for serious cardiovascular (CV) events. We assessed hospital admission rates for CV diseases and prescription prevalences of various drugs using claims data of statutory health insurances (SHIs) to compare the incidence of serious CV events among users of original and generic metoprolol. Index events included hospitalization due to myocardial infarction, hypertensive crisis and stroke. METHODS: Data files of three SHIs were linked with dispensing data of drug prescriptions from each pharmacy's electronic data processing centre on an individual basis. Incidences of hospital admissions among patients receiving original metoprolol and among patients treated with the generic equivalent were compared by logistic regression, stratified for Bremen and the rest of Northern Germany. Risk estimates and confidence intervals were adjusted for confounders. RESULTS: A total of 49,673 patients receiving metoprolol were identified within a cohort of 3,649,285 insurance members. While the crude analysis revealed a higher risk for index events in patients receiving the generic drug (Bremen: RR 1.45; Northern Germany: RR 1.14), no elevated risk remained after confounder adjustment (Bremen: OR 1.06; Northern Germany: OR 1.04). Among co-morbid conditions considered as confounders, a previous CV event and an elevated thromboembolic risk exerted the strongest effect on index events. CONCLUSIONS: SHI data are a valuable source for pharmacoepidemiology and health services research in Germany. Incidence rates of serious CV events did not reveal any noticeable differences between the original and the generic group after confounder adjustment.