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

Relative Risk01:12

Relative Risk

2.6K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
2.6K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.7K
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:  
1.7K
Causality in Epidemiology01:21

Causality in Epidemiology

2.1K
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...
2.1K
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

1.1K
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...
1.1K
Hazard Ratio01:12

Hazard Ratio

737
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
737
Fundamental Attribution Error01:14

Fundamental Attribution Error

14.1K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
14.1K

You might also read

Related Articles

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

Sort by
Same author

Impacts of Social Environments on Neighborhood Depression Incidence: Fully Accounting for Spatial Effects.

International journal of environmental research and public health·2026
Same author

The causal impact of urbanicity on neighbourhood psychosis prevalence.

Spatial and spatio-temporal epidemiology·2025
Same author

Suicide variations between English neighbourhoods over 2017-21: The role of spatial scale.

Social science & medicine (1982)·2024
Same author

The association between county-level mental health provider shortage areas and suicide rates in the United States during the COVID-19 pandemic.

General hospital psychiatry·2024
Same author

Editorial: Epidemiological considerations in COVID-19 forecasting.

Frontiers in epidemiology·2024
Same author

Psychosis prevalence in London neighbourhoods; A case study in spatial confounding.

Spatial and spatio-temporal epidemiology·2024

Related Experiment Video

Updated: Apr 16, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K

Spatial variation in attributable risks.

Peter Congdon1

  • 1School of Geography, QMUL, United Kingdom.

Spatial and Spatio-Temporal Epidemiology
|March 18, 2015
PubMed
Summary
This summary is machine-generated.

This study reveals significant spatial variation in attributable risk (AR) for diabetes linked to elevated BMI. It highlights how neighborhood factors influence diabetes rates, emphasizing the need for geographically tailored public health strategies.

Keywords:
Attributable riskBayesianDeprivationDiabetesObesitySpatial

More Related Videos

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

19.6K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Related Experiment Videos

Last Updated: Apr 16, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K
Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

19.6K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Area of Science:

  • Epidemiology
  • Spatial Analysis
  • Public Health

Background:

  • Attributable risk (AR) quantifies a risk factor's contribution to disease.
  • Previous research explored AR variability across demographics.
  • This study investigates spatial variability in ARs using multilevel data.

Purpose of the Study:

  • To assess spatial variability in attributable risk (AR) for diabetes.
  • To analyze the impact of individual and geographic confounders on AR.
  • To examine area-specific diabetes rates attributable to excess weight.

Main Methods:

  • Utilized multilevel data analysis with individual and geographic confounders.
  • Employed spatial statistical methods to account for unobserved spatial influences.
  • Incorporated contextual adjustment using area variables and spatial heterogeneity for effect modification.

Main Results:

  • Demonstrated clear evidence of spatial variation in attributable risk (AR) for diabetes.
  • Identified significant spatial heterogeneity in the effects of elevated BMI across deprivation categories.
  • Revealed variations in small area diabetes rates attributable to excess weight.

Conclusions:

  • Attributable risk (AR) for diabetes exhibits significant spatial variability.
  • Neighborhood deprivation modifies the effect of elevated BMI on diabetes risk.
  • Geographically specific interventions are crucial for managing diabetes burden related to excess weight.