Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Causality in Epidemiology

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...

Criteria for Causality: Bradford Hill Criteria - II

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:

Criteria for Causality: Bradford Hill Criteria - I

Criteria for Causality: Bradford Hill Criteria - I

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:

Introduction to Epidemiology

Introduction to Epidemiology

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,...

Confounding in Epidemiological Studies

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 phenomenon...

Bias in Epidemiological Studies

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:

You might also read

Related Articles

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

Sort by

Same author

Socioeconomic disparities in site-specific cancer incidence and mortality: Golestan cohort study.

BMJ public health·2026

Same author

The association between dietary total, animal and plant protein and macronutrient substitution; and the risk of all-cause, cardiovascular or cancer mortality in the Golestan cohort study.

BMC public health·2026

Same author

‌Biomarkers of opioids, volatile organic compounds, and polycyclic aromatic hydrocarbons and lung cancer incidence among opium users.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026

Same author

Estimating opium use prevalence at the national and provincial levels in Iran: a modelling study.

Scientific reports·2026

Same author

The association between waterpipe smoking and head and neck squamous cell carcinoma: A multicenter case-control study in Iran.

International journal of cancer·2025

Same author

Regular use of pharmaceutical opioids and subsequent risk of cancer: a prospective cohort study and Mendelian randomization analysis.

EClinicalMedicine·2025

Same journal

Association of Serum Lipids with 10-Year CVD and All-Cause Mortality in Iranian Adults: A Prospective Cohort Study.

Archives of Iranian medicine·2026

Same journal

Pancreatic PEComa: Case Report of an Extremely Rare Tumor.

Archives of Iranian medicine·2026

Same journal

Exploring the Role of Central Venous Pressure in Cardiac Surgery-Associated Acute Kidney Injury: A Comprehensive Scoping Review.

Archives of Iranian medicine·2026

Same journal

Medical Treatment of Hyperthyroidism; Efficacy and Safety Considerations.

Archives of Iranian medicine·2026

Same journal

Performance of ChatGPT and Gemini Compared with Emergency Physicians in NSTEMI Cases: A Prospective Cross-sectional Study.

Archives of Iranian medicine·2026

Same journal

Stent Patency and Survival after PTBD and Biliary Stenting for Pancreatic Cancer: A 5-Year Retrospective Cohort Study.

Archives of Iranian medicine·2026

See all related articles

Search research articles

Related Experiment Videos

Causality in epidemiology.

Farin Kamangar¹

¹School of Community Health and Policy, Morgan State University, Baltimore, MD, USA. farin.kamangar@morgan.edu

Archives of Iranian Medicine

|October 2, 2012

Summary

This summary is machine-generated.

This article explains causality in epidemiology, detailing methods to differentiate causal links from coincidental associations. It reviews established guidelines and modern approaches for assessing cause-and-effect relationships in public health research.

Related Experiment Videos

Area of Science:

Epidemiology
Public Health
Scientific Methodology

Background:

Understanding causality is fundamental in epidemiology for establishing disease origins and effective interventions.
Distinguishing true causal associations from spurious correlations is a persistent challenge in public health research.

Purpose of the Study:

To introduce the concept of causality within the field of epidemiology.
To delineate methods used by epidemiologists to identify causal relationships.
To review established and contemporary criteria for assessing causality.

Main Methods:

Literature review of established epidemiological principles.
Discussion of criteria for causal inference, including Hill's guidelines.
Exploration of alternative explanations for observed associations.

Main Results:

Causality in epidemiology involves rigorous methods to ascertain true cause-and-effect relationships.
Key criteria, such as temporality, strength, and consistency, aid in distinguishing causal from non-causal associations.
Recent developments refine the assessment of causality beyond traditional frameworks.

Conclusions:

Accurate identification of causality is essential for evidence-based public health policy and disease prevention.
Epidemiologists employ a range of methods and criteria to strengthen causal inference.
The interpretation of causality is influenced by scientific, philosophical, and historical perspectives.