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

Mixture models and disease mapping

P Schlattmann1, D Böhning

  • 1Department of Epidemiology, Institute for Social Medicine, Free University Berlin, Germany.

Statistics in Medicine
|October 1, 1993
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

Cost-effectiveness-analysis of oral health remotivation and reinstruction in nursing homes in a cluster-randomized controlled trial.

Journal of dentistry·2024
Same author

Assessing injury risk in male and female Royal Navy recruits: does the Functional Movement Screen provide understanding to inform effective injury mitigation?

BMJ military health·2023
Same author

Sensitivity of contact-tracing for COVID-19 in Thailand: a capture-recapture application.

BMC infectious diseases·2022
Same author

Incidence and mortality of hospital- and ICU-treated sepsis: results from an updated and expanded systematic review and meta-analysis.

Intensive care medicine·2020
Same author

Advantages of a multi-state approach in surgical research: how intermediate events and risk factor profile affect the prognosis of a patient with locally advanced rectal cancer.

BMC medical research methodology·2018
Same author

Laryngo- rhino- otologie·2017
Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
See all related articles

This study introduces a novel mixture model approach for mapping disease clusters, improving upon traditional epidemiological methods. The new technique enhances the identification of population heterogeneity in disease distribution.

Area of Science:

  • Epidemiology
  • Spatial Analysis
  • Statistical Modeling

Background:

  • Disease cluster analysis and spatial mapping are foundational epidemiological challenges.
  • Traditional methods exist for visualizing disease patterns geographically.
  • Identifying population heterogeneity is crucial for understanding disease distribution.

Purpose of the Study:

  • To present traditional methods for disease cluster mapping.
  • To introduce an alternative approach using mixture models and empirical Bayes for enhanced spatial analysis.
  • To evaluate the proposed method using real-world hepatitis B data.

Main Methods:

  • Review of traditional spatial disease mapping techniques.
  • Application of mixture models to identify population heterogeneity.

Related Experiment Videos

  • Implementation within an empirical Bayes framework for map construction.
  • Parametric bootstrap for method evaluation.

    • Main Results:

    • A spatial map of hepatitis B cases in Berlin (1989) was generated.
    • The performance of the novel mixture model approach was compared to traditional methods.
    • The empirical Bayes framework provided a robust approach to spatial analysis.

    Conclusions:

    • Mixture models offer a powerful alternative for analyzing population heterogeneity in disease mapping.
    • The empirical Bayes framework enhances the reliability of spatial epidemiological studies.
    • This approach improves the understanding of disease clustering and spatial patterns.