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

185
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,...
185
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

164
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...
164
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

574
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:
574
Healthcare Associated Infections II: Preventive Measures01:22

Healthcare Associated Infections II: Preventive Measures

2.9K
Essential infection prevention measures are based on the knowledge of the infection chain, the modes of transmission in healthcare settings, and the use of the best practices in all healthcare settings. Compulsory public reporting of healthcare-associated infection rates is needed to allow individuals and the community to make informed choices regarding selecting a healthcare facility.
The best practices for preventing healthcare-associated infections include hand hygiene, patient risk...
2.9K
Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

2.2K
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...
2.2K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

733
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:  
733

You might also read

Related Articles

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

Sort by
Same author

[Position paper. The role of epidemiology in urban health].

Epidemiologia e prevenzione·2026
Same author

Long-term exposure to ambient air pollution and incidence of type 2 diabetes in an industrial contaminated area in Central Italy.

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

Heat-Related Mental Health Hospitalizations in Italy:A Global Sensitivity Analysis Approach to Evaluate Generalized Additive Model Assumptions.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same author

Mid-term effects of faecal immunochemical test screening on colorectal cancer incidence by tumour site.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver·2026
Same author

External validation of a data-driven algorithm to identify breast cancer recurrences from administrative healthcare data.

Breast (Edinburgh, Scotland)·2026
Same author

[Occupational cancers: open issues and perspectives in cancer research, surveillance, detection, and prevention tools].

Epidemiologia e prevenzione·2026

Related Experiment Video

Updated: Sep 25, 2025

Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes
07:42

Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes

Published on: January 10, 2022

4.3K

Health impact assessment should be based on correct methods.

Carla Ancona1, Giorgio Assennato2, Fabrizio Bianchi3

  • 1Epidemiology Department Lazio Regional Health Authority, Rome, Italy. c.ancona@deplazio.it.

La Medicina Del Lavoro
|April 28, 2022
PubMed
Summary

Health Impact Assessment (HIA) methodology is challenged. This commentary argues that using only project-related risk factors, instead of the total baseline disease rate, is epidemiologically unsound and may underestimate health impacts.

More Related Videos

High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents HPHC
11:38

High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents HPHC

Published on: May 10, 2016

12.4K
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

2.4K

Related Experiment Videos

Last Updated: Sep 25, 2025

Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes
07:42

Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes

Published on: January 10, 2022

4.3K
High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents HPHC
11:38

High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents HPHC

Published on: May 10, 2016

12.4K
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

2.4K

Area of Science:

  • Environmental Health
  • Epidemiology
  • Public Health Policy

Background:

  • Health Impact Assessment (HIA) is a WHO-proposed methodology to predict health effects of community projects.
  • Calculating health impacts often relies on the baseline disease rate within a community.
  • A recent paper challenged the traditional HIA methodology, questioning the use of the full baseline rate.

Purpose of the Study:

  • To critique a recent proposal that suggests modifying the calculation of attributable cases in Health Impact Assessment.
  • To argue against the exclusion of non-project-related risk factors from baseline disease rate calculations.
  • To defend the epidemiological soundness of the traditional HIA methodology.

Main Methods:

  • This study presents a commentary and critique of a recently proposed methodological change to HIA.
  • The critique is based on logical reasoning and established epidemiological principles.
  • The authors analyze the implications of using a fraction of the baseline rate versus the total baseline rate.

Main Results:

  • The proposed modification to HIA methodology is deemed logically and epidemiologically unsound.
  • The assumption that traditional HIA overestimates health impacts is rejected due to flawed reasoning.
  • The proposed method is likely to produce a significant underestimation of attributable health cases.

Conclusions:

  • The traditional use of baseline disease rates in Health Impact Assessment is defended.
  • The proposed alternative method is scientifically unfounded and risks underestimating public health impacts.
  • Accurate prediction of health impacts requires consideration of all relevant risk factors, not just project-specific ones.