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 Experiment Videos

Disease cluster statistics for imprecise space-time locations

G M Jacquez1

  • 1BioMedware, Inc., Ann Arbor, MI 48104-1236, USA.

Statistics in Medicine
|April 15, 1996
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Criteria for the evaluation of alternative environmental monitoring variables: Theory and an application using winter flounder (Pleuronectes americanus) and Dover sole (Microstomus pacificus).

Environmental monitoring and assessment·2013
Same author

A k nearest neighbour test for space-time interaction.

Statistics in medicine·1996
Same author

The analysis of disease clusters, Part II: Introduction to techniques.

Infection control and hospital epidemiology·1996
Same author

The analysis of disease clusters, Part I: State of the art.

Infection control and hospital epidemiology·1996
Same author

Statistical software for the clustering of health events.

Statistics in medicine·1996
Same author

The map comparison problem: tests for the overlap of geographic boundaries.

Statistics in medicine·1995

This study introduces a new statistical approach for analyzing disease clusters using imprecise location data. It enhances the accuracy of cluster detection in real-world public health investigations.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS) in Public Health

Background:

  • Health professionals increasingly investigate disease clusters, relying on statistical tests for analysis.
  • Current statistical methods for disease cluster analysis often assume precise data, which is frequently unavailable in real-world scenarios.
  • Imprecise and uncertain health event data (e.g., approximate locations, uncertain timeframes) lead to inaccurate statistical test results.

Purpose of the Study:

  • To develop a general statistical approach for disease cluster analysis that incorporates uncertainty in space-time locations.
  • To address the incompatibility between precise analytical methods and imprecise real-world health data.
  • To improve the accuracy and applicability of cluster statistics in public health investigations.

Related Experiment Videos

Main Methods:

  • The proposed approach generalizes existing cluster statistics by modifying the relationship matrices (nearest-neighbor, distance, adjacency).
  • It incorporates uncertainty regarding space-time locations directly into these relationship matrices.
  • The method is designed to be compatible with most existing cluster analysis tests.

Main Results:

  • The developed approach provides a framework to account for imprecise location data in cluster analysis.
  • This method enhances the accuracy of statistical tests when dealing with uncertain or approximate spatial and temporal information.
  • The generalized approach is adaptable to various existing cluster detection methodologies.

Conclusions:

  • The new statistical approach offers a robust solution for analyzing disease clusters with imprecise data.
  • It is well-suited for real-world public health investigations where data uncertainty is common.
  • This methodology promises to improve the reliability of disease cluster detection and analysis.