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

Covariate adjusted mixture models and disease mapping with the program DismapWin

P Schlattmann1, E Dietz, D Böhning

  • 1Institute for Social Medicine, Medical University of Luebeck, St. Juergen-Ring, Luebeck, Germany.

Statistics in Medicine
|April 15, 1996
PubMed
Summary

This study introduces a novel epidemiological approach using mixture models and empirical Bayes methods to detect and map spatial disease clustering. It also presents a mixed Poisson regression for incorporating explanatory variables, enhancing disease risk analysis.

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

[SARS-CoV-2-associated deaths in adult persons up to 50 years of age].

Rechtsmedizin (Berlin, Germany)·2021
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

Area of Science:

  • Epidemiology
  • Spatial Statistics
  • Biostatistics

Background:

  • Analyzing spatial disease clustering is crucial in epidemiology.
  • Identifying geographical patterns of disease risk aids public health interventions.

Purpose of the Study:

  • To describe an approach for identifying spatial heterogeneity in disease risk using mixture models.
  • To present methods for mapping disease risk and incorporating explanatory variables.

Main Methods:

  • Utilized mixture models within an empirical Bayes framework to detect spatial disease clustering.
  • Employed mixed Poisson regression to include covariates and analyze disease risk factors.

Main Results:

  • Successfully identified spatial heterogeneity in disease risk.

Related Experiment Videos

  • Demonstrated the application of the methods using tuberculosis data from Berlin, 1991.

    • Conclusions:

    • The described methods effectively analyze spatial disease clustering and risk.
    • The integration of covariates refines epidemiological models for better public health insights.