Opportunity to identify, define, and quantify predictors of clinical outcomes.
The Center for Disease Control (CDC) has awarded its Pregnant People-Infant Linked Longitudinal Surveillance grant to a multidisciplinary group of data scientists, computer scientists, biostatisticians, epidemiologists, informaticians, physicians, and patient advocates from Johns Hopkins University (JHU), University of Alabama Birmingham (UAB), and the Critical Path Institute (C-Path) CURE Drug Repurposing Collaboratory (CDRC).
This funding opportunity aims to build a robust data infrastructure to capture information on key exposures and outcomes for pregnant people and their infants. The CDC will work with selected applicants to sustain, improve, and expand surveillance efforts to ensure timely reporting of key exposures and outcomes that impact pregnant people and infants, improve data quality and share evolving outcome data, innovate clinical strategies, and build a strong collaborative network.
This funding opportunity consists of three components:
- Component A funds entities that directly maintain and enter information into the electronic health records, are able to demonstrate existing linkages between pregnant people and infant records, and are able to provide data for children through six years of age.
- Component B funds entities that have access to population-based state or jurisdiction-wide established public health data systems to identify pregnant people and pregnancy outcomes and conduct longitudinal follow-up.
- Component C funds entities that have informatics and health data science expertise and can provide technical assistance to all entities within Components A and B.
Johns Hopkins is a Component C entity and has been awarded $1.8 million/year for up to $8 million over four years to build this important surveillance network. There is an unmet critical need to identify, define, and quantify predictors of clinical outcomes in pregnant people, neonates, and infants. According to Co-PI Dr. Khyzer Aziz, “Although there is a wealth of available data, these frequently excluded populations are grossly understudied, resulting in empiric treatment and exposures rather than patient specific treatment paradigms.”
Researchers will leverage the institutional expertise at Johns Hopkins in disseminating the infrastructure and architecture necessary to utilize real-world data via the Observational Health Data Sciences and Informatics Observational Medical Outcomes Partnership, Common Data Model (OHDSI OMOP CDM). Co-PI Dr. Paul Nagy explains that “OMOP, a CDM based upon standard clinical terminologies, enables extraction, ingestion, and collation of variables of interest into an observational research registry which has the capability for data storage, security, analysis, and transfer among participating sites.”
The OMOP CDM will facilitate data collection in a standardized and efficient manner that enables reproducible research in phenotype definitions and analysis, yet is timely and readily computable, scalable, and deployable across institutions and departments for pregnant people, neonates, and infants.