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Related Concept Videos

Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
Pharmacovigilance01:19

Pharmacovigilance

Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

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.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Causality in Epidemiology01:21

Causality in Epidemiology

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...
Vaccinations01:51

Vaccinations

Overview

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Related Experiment Video

Updated: May 23, 2026

Rapid In Vivo Assessment of Adjuvant's Cytotoxic T Lymphocytes Generation Capabilities for Vaccine Development
09:03

Rapid In Vivo Assessment of Adjuvant's Cytotoxic T Lymphocytes Generation Capabilities for Vaccine Development

Published on: June 19, 2018

Algorithm to assess causality after individual adverse events following immunizations.

Neal A Halsey1, Kathryn M Edwards, Cornelia L Dekker

  • 1Institute for Vaccine Safety, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States. tproveau@jhsph.edu

Vaccine
|April 18, 2012
PubMed
Summary

A new algorithm standardizes the assessment of adverse events following immunizations (AEFI), improving data interpretation and causality determination for healthcare providers and scientists. This structured approach enhances transparency and aids in understanding vaccine safety.

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13:47

Opsono-Adherence Assay to Evaluate Functional Antibodies in Vaccine Development Against Bacillus anthracis and Other Encapsulated Pathogens

Published on: May 19, 2020

Area of Science:

  • Immunology
  • Vaccinology
  • Public Health

Background:

  • Assessing individual adverse events following immunizations (AEFI) is complex.
  • Current methods often rely on expert opinions lacking clear methodology.
  • Understanding causality is crucial for vaccine safety surveillance.

Purpose of the Study:

  • To develop a standardized algorithm for collecting and interpreting AEFI data.
  • To enhance the assessment of causality for individual adverse events post-vaccination.
  • To provide a transparent and trackable method for AEFI analysis.

Main Methods:

  • Development of a standardized algorithm for AEFI assessment.
  • Inclusion of key diagnostic and etiological questions.
  • Emphasis on standardized data collection and interpretation.
  • Illustrative examples provided for algorithm application.

Main Results:

  • The algorithm provides a structured approach to AEFI causality assessment.
  • It addresses key questions regarding diagnosis, alternative causes, and vaccine association.
  • Standardization improves transparency and reassessment capabilities.

Conclusions:

  • The developed algorithm offers a systematic method for evaluating AEFI.
  • It can assist healthcare providers and scientists in determining vaccine causality.
  • The approach supports educational efforts in vaccine safety and AEFI analysis.