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Multivariate scan statistics for disease surveillance.

Martin Kulldorff1, Farzad Mostashari, Luiz Duczmal

  • 1Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, 133 Brookline Avenue, 6th Floor, Boston, MA 02215, USA. martin_kulldorff@hms.harvard.edu

Statistics in Medicine
|January 12, 2007
PubMed
Summary
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This study introduces a new scan statistic method to improve disease surveillance by combining multiple data sets. The enhanced approach increases the power to detect outbreaks across various data sources.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Disease surveillance often involves analyzing numerous disparate datasets.
  • Analyzing datasets separately can reduce statistical power to detect widespread outbreaks.
  • Combining datasets by simple summation can obscure signals present in individual datasets.

Purpose of the Study:

  • To develop an extended spatial and space-time scan statistic for integrating multiple data sets.
  • To enhance the detection of disease outbreaks occurring in one or multiple data sources simultaneously.
  • To introduce a method for adjusting for covariates using combined likelihoods.

Main Methods:

  • An extension of the scan statistic that incorporates multiple datasets into a single likelihood function.

Related Experiment Videos

  • The combined log likelihood is defined as the sum of individual log likelihoods for datasets with observed counts exceeding expected counts.
  • A secondary extension utilizes combined likelihoods to adjust for covariates.

    • Main Results:

    • The new method successfully integrates multiple data streams for enhanced disease surveillance.
    • Demonstrated utility using diverse data sources: physician telephone calls, urgent care visits, and regular physician visits.
    • Identified a familial outbreak of pinworm disease as the strongest signal, with other signals arising from combined datasets.

    Conclusions:

    • The proposed scan statistic extension effectively combines multiple data sets for improved disease outbreak detection.
    • This integrated approach enhances statistical power and reduces the risk of masking outbreak signals.
    • The method is applicable to various data types and can incorporate covariate adjustments for more robust surveillance.