The OMOP Common Date Model has been selected as one of three new Global Goods, gaining recognition as one of the top digital health tools for data interoperability to support the digital transformation efforts of countries across the world. 

The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), an OHDSI open community data standard designed to standardize the structure and content of observational data and enable efficient analyses that can produce reliable evidence, is one of 45 Global Goods — digital health tools that are adaptable to different countries and contexts to help address key health system challenges — recognized by Digital Square at PATH.

“We are very excited that Digital Square recognizes that the OMOP CDM is a tool that can improve healthcare around the world,” said Cynthia Sung, Adjunct Associate Professor at Duke-NUS Medical School and the 2023 OHDSI Titan Award recipient for Community Collaboration. "OHDSI’s mission is to improve health by empowering a community to collaboratively generate the evidence that promotes better health decisions and better care. The OMOP CDM is critical to that mission. Nearly one billion unique anonymized patient records from 49 different countries exist in an OMOP CDM, which allows for researchers to generate robust and reliable real-world evidence in a distributed network.

The Titan Awards recognize collaborators (or collaborating institutions) for their contributions towards OHDSI’s mission. Now in its seventh year, the Titan Awards will be announced during the State of the Community hour at the Global Symposium. The nomination period will remain open until Sept. 13, so please let us know what individuals or teams have made significant impacts on the community this year!

Topic: Observational methods for COVID-19 vaccine effectiveness research: an empirical evaluation and target trial emulation

Presenter: Martí Català Sabaté, Medical Statistician and Data Scientist, University of Oxford

Abstract: There are scarce data on best practices to control for confounding in observational studies assessing vaccine effectiveness to prevent COVID-19. We compared the performance of three well-established methods [overlap weighting, inverse probability treatment weighting and propensity score (PS) matching] to minimize confounding when comparing vaccinated and unvaccinated people. Subsequently, we conducted a target trial emulation to study the ability of these methods to replicate COVID-19 vaccine trials.

Polina Talapova is the lead of the Medical Team at SciForce, based in Ukraine. She is also a OMOP CDM Consultant for Tufts Medical Center. As a medical doctor with a PhD in pathology, Polina has made significant contributions to the OHDSI Vocabulary team. Since 2017, she has played a key role in developing mappings and machinery for an extensive list of medical terminologies, including ICD9CM, ICDs, LOINC, ATC, CPT4, HCPCS, NCCD, CVX, OMOP Extension, LPDs and more. Her most notable work centers on the improvement of Condition, Measurement, and Drug domains.

Polina is an active member of various working groups within OHDSI, including Vocabulary, Psychiatry, Vaccine, GIS, CDM, and THEMIS. Her extensive experience in ontology engineering, ETL and semantic mapping has led to the development of numerous validity checks and the authoring of substantial OHDSI Vocabulary documentation.

She discusses her career journey, OHDSI standardized vocabularies, her passion to build a Ukraine National Node and more in the latest collaborator spotlight.