CohortCharacteristics: an R package for population characterisation in observational studies using the OMOP common data model.

Abstract

Describing cohort characterisation ensures comparability and reproducibility in multi-database observational studies. To address this need, we developed CohortCharacteristics, an open-source R package that facilitates standardised cohort characterisation in datasets mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). This study aims to explain the development of the package and demonstrate its core functionality. We developed CohortCharacteristics, an open-source R package that can perform cohort characterisation for various types of databases. To demonstrate its functionality, we then used CohortCharacteristics to generate descriptive statistics on demographics, comorbidities, medication exposures, cohort overlap, and timing of cohort entries. The study included data from CPRD GOLD (UK), DK-DHR (Denmark), IPCI (Netherlands), IQVIA Longitudinal Patient Database Belgium (IQVIA LPD Belgium), IQVIA DA Germany, NAJS (Croatia), and SIDIAP (Spain), all mapped to the OMOP CDM. The CohortCharacteristics R package is freely available on CRAN with detailed vignettes and documentation on its functionality. Cohort characteristics were generally consistent across databases, with similar age distributions and female representation. CPRD GOLD, NAJS, and SIDIAP exhibited higher prescribing rates for respiratory, cardiovascular, and nervous system medications, while IQVIA databases and DK-DHR reported lower rates. Timing analysis showed that dementia diagnoses typically followed insomnia diagnoses in several databases, supporting existing literature. Antipsychotic prescriptions often occurred after dementia diagnosis, reflecting prescribing practices aligned with clinical guidelines. CohortCharacteristics enables consistent cohort characterisation across a network of data mapped to the OMOP CDM, thereby improving transparency in multi-database research. The package's functionality, demonstrated in this study, illustrates its applicability in observational studies with OMOP CDM data.