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

Introduction to Epidemiology01:26

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,...
Bias in Epidemiological Studies01:29

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:
Confounding in Epidemiological Studies01:27

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...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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

Leveraging voice memos recorded via smartphones for qualitative data collection.

Journal of public health research·2026
Same author

The Gap in Integrated Pediatric Care: A Systematic Review of Family-Based Weight Management for Children Living with HIV.

Sage open pediatrics·2026
Same author

Content and statistical validation of the FNPA survey in Modern Standard Arabic (MSA) for use with Palestinian populations in the West Bank.

BMC public health·2026
Same author

Implementation science evaluation of an eHealth pediatric primary-care overweight and obesity intervention using the RE-AIM evaluation framework.

PloS one·2026
Same author

The Use of Electronic Health Record Data to Identify Variation in Referral, Consent, and Engagement in a Pediatric Intervention for Overweight and Obesity: A Cross-Sectional Study.

Population health management·2023
Same author

Family-based pediatric weight management interventions in US primary care settings targeting children ages 6-12 years old: A systematic review guided by the RE-AIM framework.

Translational behavioral medicine·2023
Same journal

The developing impacts of the creative health quality framework on practice and partnership.

Perspectives in public health·2026
Same journal

Junction Arts uses creative approaches to tackle loneliness and public health issues in post-industrial communities.

Perspectives in public health·2026
Same journal

Creative Health in practice: Hub-and-Spoke consortium working across West Yorkshire.

Perspectives in public health·2026
Same journal

Defining, identifying and regulating dark kitchens in the North of England: perspectives from consumer, local authority and food business stakeholders.

Perspectives in public health·2026
Same journal

Engaging the multi-agency workforce in targeted prevention of sudden unexpected infant deaths (SUDI): implementing <i>Eyes on the Baby</i>.

Perspectives in public health·2026
Same journal

Editorial.

Perspectives in public health·2026
See all related articles

Related Experiment Video

Updated: May 24, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Reframing our data: promoting positive epidemiology.

J Yudkin1, B Smith2

  • 1Texas A&M University, 212 Adriance Lab Road, College Station, TX 77843, USA.

Perspectives in Public Health
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Positive epidemiology studies protective factors to promote well-being. This approach reframes health narratives by measuring protective elements alongside risks for empowerment.

More Related Videos

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Related Experiment Videos

Last Updated: May 24, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Area of Science:

  • Public Health
  • Epidemiology
  • Psychology

Background:

  • Traditional epidemiology often focuses on risk factors for disease.
  • There is a growing need to understand factors that promote health and resilience.

Purpose of the Study:

  • To introduce and define positive epidemiology.
  • To advocate for the study and communication of protective factors.

Main Methods:

  • Conceptual framework development.
  • Literature review on protective factors and well-being.

Main Results:

  • Positive epidemiology offers a complementary perspective to risk-focused research.
  • Measuring protective factors can empower individuals and communities.

Conclusions:

  • Positive epidemiology shifts focus towards health promotion and empowerment.
  • Integrating protective factors into research reframes health narratives positively.