Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Causality in Epidemiology01:21

Causality in Epidemiology

1.8K
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...
1.8K
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

1.4K
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:
1.4K
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

1.2K
The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
1.2K
Introduction to Epidemiology01:26

Introduction to Epidemiology

2.0K
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
2.0K
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

881
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...
881
Cause and Effect01:53

Cause and Effect

12.6K
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
12.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Prediction of low birth weight using machine learning-based analysis of environmental and maternal risk factors: insights from the Korean CHildren's ENvironmental health study (Ko-CHENS).

Environmental research·2026
Same author

Pilot study of a community-based PM<sub>2.5</sub> educational intervention: impacts on behavior, exposure, and pulmonary function.

BMC public health·2026
Same author

Corrigendum to "Association between prenatal exposure to air pollution and risk of Kawasaki disease in young children of South Korea: Big CHildren's ENvironmental health Study" [J Hazard Mater Volume 511 (2026) 142225].

Journal of hazardous materials·2026
Same author

Association between relative handgrip strength and glycemic control among male automobile manufacturing workers using vibration tools in South Korea.

Annals of occupational and environmental medicine·2026
Same author

Association between prenatal exposure to air pollution and risk of Kawasaki disease in young children of South Korea: Big children's environmental health study.

Journal of hazardous materials·2026
Same author

Paraben mixture exposure and liver function in pregnant women: Findings from the Korean CHildren's ENvironmental health Study (Ko-CHENS).

Environmental pollution (Barking, Essex : 1987)·2026
Same journal

Assessment of relationship between the use of household products and atopic dermatitis in Seoul: focused on products with associated risks.

Environmental health and toxicology·2019
Same journal

Inhalation risk assessment of naphthalene emitted from deodorant balls in public toilets.

Environmental health and toxicology·2019
Same journal

Eye irritation tests of polyhexamethylene guanidine phosphate (PHMG) and chloromethylisothiazolinone/methylisothiazolinone (CMIT/MIT) using a tissue model of reconstructed human cornea-like epithelium.

Environmental health and toxicology·2019
Same journal

Acute toxicity of copper hydroxide and glyphosate mixture in Clarias gariepinus: interaction and prediction using mixture assessment models.

Environmental health and toxicology·2019
Same journal

Environmental Health Studies in the Korean National Industrial Complexes (EHSNIC): Focus-Group Interviews.

Environmental health and toxicology·2019
Same journal

One more leap.

Environmental health and toxicology·2019
See all related articles

Related Experiment Video

Updated: Feb 21, 2026

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

3.0K

Causal inference in environmental epidemiology.

Sanghyuk Bae1, Hwan-Cheol Kim2, Byeongjin Ye3

  • 1Department of Preventive Medicine, Dankook University College of Medicine, Cheonan, Korea.

Environmental Health and Toxicology
|October 14, 2017
PubMed
Summary
This summary is machine-generated.

This study outlines a three-step process for inferring causality in epidemiology to identify disease causes. It proposes methods for evaluating study validity, general causality, and individual causality for scientific truth.

Keywords:
CausalityEnvironmental exposureEpidemiologyValidity

More Related Videos

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
08:09

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease

Published on: January 7, 2014

8.0K

Related Experiment Videos

Last Updated: Feb 21, 2026

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

3.0K
Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
08:09

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease

Published on: January 7, 2014

8.0K

Area of Science:

  • Epidemiology
  • Causal Inference
  • Biostatistics

Background:

  • Elucidating disease causes is a primary goal of epidemiology.
  • Causal inference is essential for understanding disease etiology.
  • Existing methods for causal inference require a structured approach.

Purpose of the Study:

  • To present a systematic framework for causal inference in epidemiology.
  • To provide practical tools for evaluating study validity and inferring causality.
  • To establish a foundation for scientifically sound causal conclusions.

Main Methods:

  • A three-step approach: study validity evaluation, general causality inference, and individual causality inference.
  • Utilizing a checklist for assessing study biases and generalizability.
  • Applying Hill's 9 viewpoints for general causal inference.

Main Results:

  • A proposed checklist enhances the evaluation of study validity.
  • Hill's viewpoints provide a robust method for general causal inference.
  • Individual causality can be inferred by combining general causality with exposure evidence.

Conclusions:

  • The proposed framework offers a structured method for epidemiological causal inference.
  • This approach ensures scientific rigor in determining disease causes.
  • Findings support evidence-based conclusions for public health and legal contexts.