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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Confounding in Epidemiological Studies

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...

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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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A call to address complexity in prevention science research.

Kristen Hassmiller Lich¹, Elizabeth M Ginexi, Nathaniel D Osgood

¹Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. klich@unc.edu

Prevention Science : the Official Journal of the Society for Prevention Research

|September 18, 2012

Summary

Prevention science faces complex challenges. A systems science approach offers new methods to analyze intricate interactions between biology, behavior, and environment for better developmental outcomes.

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Last Updated: May 18, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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Published on: May 15, 2020

Area of Science:

Prevention Science
Systems Science
Developmental Psychology

Background:

Preventive interventions address complex issues within multi-level social and environmental contexts across the lifespan.
Current prevention science lacks analytical approaches to fully address these complexities.
Existing methods struggle to capture the dynamic interplay of factors influencing developmental risk and protective factors.

Purpose of the Study:

Introduce a systems science approach for prevention science.
Propose methods to handle complex, evolving relationships in prevention research.
Enhance progress in prevention science by examining multi-level interactions.

Main Methods:

Applying systems science methodologies to prevention research.
Analyzing complex systems interactions among biology, behavior, and environment.
Examining the evolution of relationships over time within developmental contexts.

Main Results:

Systems science offers a framework to address the complexity inherent in prevention science.
This approach can analyze a wide array of interactions yielding unique risk and protective factor combinations.
Potential utility demonstrated through examples of current prevention research challenges.

Conclusions:

A systems science approach can significantly enhance prevention science.
It complements traditional methods by providing tools to analyze complex systems.
Adopting systems science methodologies can augment research objectives and improve understanding of developmental outcomes.