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A functional-model-adjusted spatial scan statistic.

Mohamed-Salem Ahmed1,2, Michaël Genin1,2

  • 1EA2694 - Santé publique : épidemiologie et qualité des soins, University of Lille, Lille, France.

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|January 23, 2020
PubMed
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This study presents a novel spatial scan statistic to accurately detect disease clusters while accounting for longitudinal confounding factors. The new method improves cluster detection accuracy, particularly in epidemiological studies.

Keywords:
cluster detectionconfounding factorfunctional data analysisgeneralized functional linear modellongitudinal data

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Spatial Analysis

Background:

  • Accurate spatial cluster detection is crucial in public health for identifying disease outbreaks.
  • Traditional methods may not adequately adjust for time-varying confounding factors that influence spatial patterns.
  • Longitudinal confounding factors, indexed in space, pose a challenge for standard spatial scan statistics.

Purpose of the Study:

  • To introduce a new spatial scan statistic that adjusts for longitudinal confounding factors.
  • To develop a flexible framework applicable to various probability models.
  • To enhance the accuracy of spatial cluster detection in the presence of time-varying covariates.

Main Methods:

  • Developed a functional-model-adjusted spatial scan statistic using generalized functional linear models.
  • Incorporated longitudinal confounding factors as functional covariates within the statistical framework.
  • Applied the method to a Poisson probability model and evaluated its performance via simulation studies.

Main Results:

  • The new method demonstrated equivalence to conventional spatial scan statistics when applied to a Poisson model.
  • Simulation studies indicated superior accuracy in adjusting the cluster detection procedure compared to existing methods.
  • The functional-model-adjusted statistic effectively accounts for longitudinal confounding factors in spatial analyses.

Conclusions:

  • The novel spatial scan statistic provides a more accurate approach for cluster detection when longitudinal confounding factors are present.
  • This method offers a robust framework for epidemiological research involving spatial and temporal data.
  • The adjusted statistic enhances the reliability of identifying disease hotspots influenced by time-varying covariates.