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Published on: August 31, 2022
Pooled Cohort Profile: ReCoDID Consortium's Harmonized Acute Febrile Illness Arbovirus Meta-Cohort
Gustavo Gómez1, Heather Hufstedler2, Carlos Montenegro Morales3
1Grupo de Epidemiología Clínica, Universidad Industrial de Santander, Bucaramanga, Colombia.
Insights
Infectious disease cohorts now have harmonized clinical and laboratory data from nine Latin American arbovirus studies. This meta-cohort enables cross-population research and long-term disease interaction monitoring.
Area of Science:
- Public Health
- Infectious Diseases
- Data Science
Background:
- Infectious disease (ID) cohorts are crucial for public health surveillance and pandemic response.
- Limited funding hinders long-term storage and sharing of clinical-epidemiological (CE) and high-dimensional laboratory (HDL) data.
- Lack of data standardization and linkage impedes pooling smaller cohorts for cross-disease interaction studies.
Purpose of the Study:
- To create a harmonized and standardized meta-cohort of CE and HDL data from arbovirus studies.
- To facilitate cross-population inference and data reuse for infectious disease research.
- To enable joint research projects on arboviral diseases and potential biomarkers.
Main Methods:
- Retrospective harmonization of CE data from 9 arbovirus cohorts in Latin America using the Maelstrom Research methodology.
- Standardization of data to Clinical Data Interchange Standards Consortium (CDISC) standards.
- Creation of a meta-cohort integrating CE and HDL data.
Main Results:
- A harmonized and standardized meta-cohort of CE and HDL data from 9 Latin American arbovirus studies was successfully created.
- Data dictionaries are available via Bio Studies, detailing variables across datasets.
- Linked, harmonized, and curated human cohort data will be accessible via the European Genome-phenome Archive upon request evaluation.
Conclusions:
- The ReCoDID (Reconciliation of Cohort Data for Infectious Diseases) Consortium established a valuable meta-cohort for arbovirus research.
- This initiative addresses the critical need for standardized, accessible data in infectious disease research.
- The meta-cohort will support advanced research, including immunological interactions and biomarker discovery for arboviral diseases.
Abstract:
Infectious disease (ID) cohorts are key to advancing public health surveillance, public policies, and pandemic responses. Unfortunately, ID cohorts often lack funding to store and share clinical-epidemiological (CE) data and high-dimensional laboratory (HDL) data long term, which is evident when the link between these data elements is not kept up to date. This becomes particularly apparent when smaller cohorts fail to successfully address the initial scientific objectives due to limited case numbers, which also limits the potential to pool these studies to monitor long-term cross-disease interactions within and across populations. CE data from 9 arbovirus (arthropod-borne viruses) cohorts in Latin America were retrospectively harmonized using the Maelstrom Research methodology and standardized to Clinical Data Interchange Standards Consortium (CDISC). We created a harmonized and standardized meta-cohort that contains CE and HDL data from 9 arbovirus studies from Latin America. To facilitate advancements in cross-population inference and reuse of cohort data, the Reconciliation of Cohort Data for Infectious Diseases (ReCoDID) Consortium harmonized and standardized CE and HDL from 9 arbovirus cohorts into 1 meta-cohort. Interested parties will be able to access data dictionaries that include information on variables across the data sets via Bio Studies. After consultation with each cohort, linked harmonized and curated human cohort data (CE and HDL) will be made accessible through the European Genome-phenome Archive platform to data users after their requests are evaluated by the ReCoDID Data Access Committee. This meta-cohort can facilitate various joint research projects (eg, on immunological interactions between sequential flavivirus infections and for the evaluation of potential biomarkers for severe arboviral disease).
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