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

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...
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
Relative Risk01:12

Relative Risk

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...
Hazard Rate01:11

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
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...

You might also read

Related Articles

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

Sort by
Same author

Multilevel Analysis of the Food and Physical Activity Environment and Adult Obesity Across U.S. Counties and States.

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

Environmental health disparities in pediatric cancer: a report from the Fourth Symposium on Childhood Cancer Health Disparities.

Pediatric hematology and oncology·2025
Same author

Dimension reduction of 911 Good Samaritan Laws: Drawing inferences from policy surveillance.

Drug and alcohol dependence·2023
Same author

Review and inventory of 911 Good Samaritan Law Provisions in the United States.

The International journal on drug policy·2022
Same author

Case growth analysis to inform local response to COVID-19 epidemic in a diverse U.S community.

Scientific reports·2022
Same author

Trends and Correlates of Breakthrough Infections With SARS-CoV-2.

Frontiers in public health·2022

Related Experiment Video

Updated: May 28, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Conceptual models for cumulative risk assessment.

Stephen H Linder1, Ken Sexton

  • 1Institute for Health Policy, The University of Texas School of Public Health, Houston TX 77030, USA. stephen.h.linder@uth.tmc.edu

American Journal of Public Health
|October 25, 2011
PubMed
Summary

Theoretical frameworks are crucial for cumulative risk assessment. This study discusses social determinant, health disparity, and multiple stressor models to improve understanding of health inequalities and environmental exposures.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

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

Published on: May 15, 2020

Related Experiment Videos

Last Updated: May 28, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

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

Published on: May 15, 2020

Area of Science:

  • Environmental health science
  • Social epidemiology
  • Health risk assessment

Background:

  • Cumulative risk assessment currently lacks a unified theoretical framework, leading to reliance on speculative models.
  • Existing research often uses conceptual models without robust theoretical backing, hindering progress.

Purpose of the Study:

  • To advocate for the importance of theoretical foundations in cumulative risk assessment.
  • To explore three distinct theoretical frameworks applicable to cumulative risk models: social determinant, health disparity, and multiple stressor models.

Main Methods:

  • Literature review and conceptual analysis of existing theoretical frameworks.
  • Discussion of how each framework conceptualizes the pathways linking exposures to health outcomes.

Main Results:

  • Social determinant models link health outcomes to structural inequalities.
  • Health disparity models connect social/contextual factors to behaviors and biology.
  • Multiple stressor models integrate environmental agents and other stressors.

Conclusions:

  • Establishing a strong theoretical basis is essential for accurate cumulative risk assessment.
  • Adopting established theoretical frameworks will enhance the characterization of cumulative risk and health disparities.
  • Improved theoretical grounding will lead to better accounting for disproportionate adverse health effects.