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Segregation indices for disease clustering.

Elvan Ceyhan1

  • 1Department of Mathematics, Koç University, Sarıyer, 34450, Istanbul, Turkey.

Statistics in Medicine
|December 6, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces two distance-based segregation indices for detecting disease clustering in populations. One index shows robustness to population differences and strong power for disease mapping.

Keywords:
Cuzick-Edwards's testscell-specific testsempirical powerempirical sizenearest neighbor contingency tableoverall testrandom labelingspatial clustering

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Area of Science:

  • Epidemiology
  • Spatial Statistics
  • Biostatistics

Background:

  • Disease clustering is a significant concern in epidemiology.
  • Understanding spatial patterns of diseases is crucial for public health.
  • Existing methods for disease cluster detection have limitations.

Purpose of the Study:

  • To propose and evaluate two novel distance-based segregation indices for testing disease clustering.
  • To compare the performance of these indices against existing methods using Monte Carlo simulations.
  • To assess the robustness of the indices to various population and disease distribution patterns.

Main Methods:

  • Derivation of asymptotic distributions for the proposed segregation indices.
  • Extensive Monte Carlo simulations to compare empirical size and power.
  • Evaluation of test sensitivity to background patterns, clustering levels, and relative abundances.
  • Comparison with random labeling (RL) null hypothesis and non-RL alternative patterns.

Main Results:

  • Differences in relative abundances significantly influence the empirical sizes of disease clustering tests.
  • One proposed index demonstrates robustness to relative abundance variations.
  • This robust index exhibits power comparable to leading methods in disease cluster detection.
  • The methods were illustrated using two real-world epidemiological datasets.

Conclusions:

  • The proposed distance-based segregation indices offer a valuable tool for spatial disease clustering analysis.
  • The robust index provides reliable disease mapping even with differing case/control ratios.
  • These methods enhance the ability to identify and understand disease spatial distributions in epidemiology.