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.

Related Concept Videos

Study Designs in Epidemiology01:20

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
726
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
382
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
787
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.1K
Patterns of Fever01:26

Patterns of Fever

Before understanding the types and patterns of fever, it is essential to know its phases.
3.5K
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
470