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

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:

You might also read

Related Articles

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

Sort by
Same author

Effectiveness and tolerability of fenfluramine in pediatric and adult patients with developmental and epileptic encephalopathies: A multicenter, retrospective, real-world clinical-practice study.

Epilepsia·2026
Same author

A Bayesian spatially-clustered coefficient model with temporal structures for hepatitis A data in South Korea.

Journal of applied statistics·2026
Same author

A spatiotemporal optimization engine for prescribed burning in the Southeast US.

Ecological informatics·2026
Same author

Multivariate and Online Transfer Learning With Uncertainty Quantification.

Statistics in medicine·2026
Same author

A two-stage approach for segmenting spatial point patterns applied to multiplex imaging.

Biostatistics (Oxford, England)·2026
Same author

When are novel methods for analyzing complex chemical mixtures in epidemiology beneficial?

Environmental epidemiology (Philadelphia, Pa.)·2026

Related Experiment Video

Updated: Jun 21, 2026

Measuring Carbon Content in Airway Macrophages Exposed to Carbon-Containing Particulate Matters
05:18

Measuring Carbon Content in Airway Macrophages Exposed to Carbon-Containing Particulate Matters

Published on: July 12, 2024

Spatial-temporal association between fine particulate matter and daily mortality.

Jungsoon Choi1, Montserrat Fuentes, Brian J Reich

  • 1Department of Statistics, North Carolina State University, Raleigh, NC, 27695-8203, USA.

Computational Statistics & Data Analysis
|August 5, 2009
PubMed
Summary

Fine particulate matter (PM2.5) exposure is linked to mortality, with associations varying by location and season. This study developed a Bayesian framework to analyze these complex spatiotemporal relationships.

More Related Videos

Quantification of three DNA Lesions by Mass Spectrometry and Assessment of Their Levels in Tissues of Mice Exposed to Ambient Fine Particulate Matter
12:15

Quantification of three DNA Lesions by Mass Spectrometry and Assessment of Their Levels in Tissues of Mice Exposed to Ambient Fine Particulate Matter

Published on: May 29, 2019

Related Experiment Videos

Last Updated: Jun 21, 2026

Measuring Carbon Content in Airway Macrophages Exposed to Carbon-Containing Particulate Matters
05:18

Measuring Carbon Content in Airway Macrophages Exposed to Carbon-Containing Particulate Matters

Published on: July 12, 2024

Quantification of three DNA Lesions by Mass Spectrometry and Assessment of Their Levels in Tissues of Mice Exposed to Ambient Fine Particulate Matter
12:15

Quantification of three DNA Lesions by Mass Spectrometry and Assessment of Their Levels in Tissues of Mice Exposed to Ambient Fine Particulate Matter

Published on: May 29, 2019

Area of Science:

  • Environmental Health Sciences
  • Biostatistics
  • Atmospheric Science

Background:

  • Fine particulate matter (PM2.5) is a complex mixture of air pollutants associated with significant health risks, including premature death.
  • The chemical composition and concentration of PM2.5 fluctuate spatially and temporally, potentially altering its impact on mortality rates.
  • Existing methods for analyzing PM2.5 and mortality associations can be sensitive to assumptions about seasonal trends.

Purpose of the Study:

  • To develop and implement a flexible, multi-stage Bayesian statistical framework for investigating spatiotemporal associations between PM2.5 exposure and mortality.
  • To accurately map ambient PM2.5 concentrations across various spatial and temporal scales.
  • To analyze the dynamic relationships between PM2.5 exposure and mortality, accounting for confounding factors and uncertainty.

Main Methods:

  • A two-stage Bayesian framework was employed, integrating air quality modeling (CMAQ) with monitoring data (IMPROVE, FRM) in stage 1.
  • Stage 2 utilized a spatial-temporal generalized Poisson regression model to examine health endpoints and PM2.5 exposures.
  • A space-time stochastic search variable selection approach was used within the Bayesian model to flexibly handle seasonal trends, treating the number of basis functions as an unknown parameter.

Main Results:

  • The study successfully mapped PM2.5 concentrations and analyzed their spatiotemporal relationships with mortality in North Carolina for 2001.
  • The Bayesian framework provided a robust method for accounting for various sources of uncertainty in the analysis.
  • The flexible approach to modeling seasonal trends improved the analysis of time-varying confounders.

Conclusions:

  • The developed Bayesian framework offers a powerful and adaptable tool for studying the complex links between air pollution and public health.
  • Accurate assessment of PM2.5 exposure across space and time is crucial for understanding its health impacts.
  • This methodology enhances the ability to analyze dynamic environmental health associations, informing public health interventions.