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A geostatistical framework for combining spatially referenced disease prevalence data from multiple diagnostics.

Benjamin Amoah1, Peter J Diggle1, Emanuele Giorgi1

  • 1CHICAS, Lancaster University Medical School, Lancaster, UK.

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|August 27, 2019
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Summary

This study introduces a new geostatistical framework to accurately combine data from multiple disease diagnostic tests, improving disease prevalence mapping. The method accounts for spatial correlations, leading to more reliable and precise epidemiological inferences.

Keywords:
disease mappinggeostatisticsmalariamultiple diagnostic testsneglected tropical disesaesprevalence

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Area of Science:

  • Epidemiology
  • Geostatistics
  • Biostatistics

Background:

  • Multiple diagnostic tests are frequently employed in disease surveillance due to resource constraints or to gather complementary epidemiological data.
  • Current statistical methods for combining prevalence data from multiple diagnostics often overlook spatial correlations, potentially leading to inaccurate overdispersion.
  • Accurate disease prevalence mapping is crucial for effective public health interventions and resource allocation.

Purpose of the Study:

  • To develop a geostatistical framework for joint modeling of data from multiple diagnostic tests, addressing limitations of existing methods.
  • To enable accurate prediction of disease prevalence using both gold-standard and alternative diagnostic tests.
  • To enhance the precision and reliability of disease prevalence inferences through integrated geostatistical analysis.

Main Methods:

  • Development of a geostatistical framework for joint modeling of multiple diagnostic test data.
  • Application of the framework to map Loa loa and Plasmodium falciparum malaria prevalence in African case studies.
  • Utilizing a Monte Carlo procedure based on the variogram for parsimonious geostatistical model selection.

Main Results:

  • The proposed framework successfully integrates data from diverse diagnostic tests, including microscopy, RAPLOA, PCR, and rapid diagnostic tests.
  • Accounting for diagnostic-specific residual spatial variation is crucial for accurate prevalence estimation.
  • Joint geostatistical modeling significantly improves the reliability and precision of disease prevalence mapping.

Conclusions:

  • The developed geostatistical framework offers a robust approach for combining data from multiple diagnostic tests in disease prevalence studies.
  • The findings underscore the importance of considering spatial correlations and diagnostic-specific variations for precise epidemiological insights.
  • This methodology provides a valuable tool for enhancing disease surveillance and informing public health strategies globally.