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

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:

Study Designs in Epidemiology

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.

Principles of Disease Surveillance

Principles of Disease Surveillance

Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...

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,...

Statistical Methods for Analyzing Epidemiological Data

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:

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...

You might also read

Related Articles

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

Sort by

Same author

Shared leadership team: the creation, journey, lessons learned.

Frontiers in public health·2026

Same author

Chronic Conditions and Pediatric Healthcare Utilization during Warm Weather Days in New York City.

The journal of applied research on children : informing policy for children at risk·2022

Same author

Evidence of a range expansion in sunfish from 47 years of coastal sightings.

Marine biology·2022

Same author

High ambient temperature and child emergency and hospital visits in New York City.

Paediatric and perinatal epidemiology·2021

Same author

Geocoding Error, Spatial Uncertainty, and Implications for Exposure Assessment and Environmental Epidemiology.

International journal of environmental research and public health·2020

Same author

Cancer incidence surrounding the former Apollo nuclear facility 1990-2010.

Journal of exposure science & environmental epidemiology·2018

Search research articles

Related Experiment Video

Updated: Jun 15, 2026

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

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

Data completeness and quality in a community-based and participatory epidemiologic study.

Leah Schinasi¹, Rachel Avery Horton, Steve Wing

¹Department of Epidermiology, School of Public Health, Unversity of North Caroline, NC, USA.

Progress in Community Health Partnerships : Research, Education, and Action

|March 9, 2010

Summary

Community members collected high-quality data in a participatory research study. Improved training and researcher support can further enhance data completeness and consistency in community-based participatory research.

Related Experiment Videos

Last Updated: Jun 15, 2026

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

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

Area of Science:

Epidemiology
Public Health Research
Community-Based Participatory Research (CBPR)

Background:

Community-based participatory research (CBPR) challenges traditional scientific objectivity.
Limited evaluation exists for data quality in CBPR studies.

Purpose of the Study:

Examine factors influencing data completeness and quality in a CBPR study.
Assess data collected by community members for the Community Health Effects of Industrial Hog Operations (CHEIHO) study.

Main Methods:

101 residents collected daily data on odor, health, and mood for 2 weeks.
Data completeness and errors were analyzed using mixed models.
Factors examined included time of day, odor, participation week, and assistance.

Main Results:

Overall data completeness was high (2% missing/out of order).
Lung function data had the most errors; missing data decreased after week 1.
Missing saliva samples were linked to reported odor; missing data increased for women in week 2.

Conclusions:

Community members demonstrated the capacity to collect relatively complete and consistent data.
Enhanced training and researcher interaction can improve data quality in CBPR.
Findings support the validity of data collected through CBPR methods.