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A hierarchical model for analyzing multisite individual-level disease surveillance data from multiple systems.

Yuzi Zhang1, Howard H Chang1, Qu Cheng2

  • 1Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USA.

Biometrics
|February 22, 2022
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Summary

This study introduces a new Bayesian modeling framework to improve disease surveillance accuracy. The enhanced model corrects for undercounting in passive surveillance systems, leading to better public health insights.

Keywords:
Bayesian hierarchical modelingheterogeneous capture probabilitiesindividual-level disease surveillance

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Area of Science:

  • Epidemiology and Public Health
  • Biostatistics and Mathematical Modeling

Background:

  • Passive surveillance systems are cost-effective for monitoring diseases over large areas but suffer from imperfect case ascertainment and variable capture probabilities.
  • Factors like differential access to healthcare contribute to heterogeneous capture probabilities, potentially underestimating true disease incidence.

Purpose of the Study:

  • To develop a hierarchical Bayesian modeling framework to analyze data from multiple surveillance systems.
  • To estimate the true number of incident disease cases by accounting for individual-level covariate-dependent heterogeneous capture probabilities.
  • To improve the estimation of true disease incidence by borrowing information across surveillance sites.

Main Methods:

  • Development of a hierarchical modeling framework for analyzing data from multiple surveillance systems.
  • Implementation of a two-stage Bayesian procedure for inference.
  • Incorporation of individual-level covariate-dependent heterogeneous capture probabilities.
  • Information borrowing across surveillance sites to enhance estimation accuracy.

Main Results:

  • Simulation studies demonstrated superior performance of the proposed approach over models that do not borrow information across sites, showing reduced bias and root mean square error.
  • Application to pulmonary tuberculosis (PTB) surveillance data in China yielded bias-corrected estimates of PTB cases.
  • Identification of risk factors associated with PTB rates and factors influencing surveillance system operating characteristics.

Conclusions:

  • The proposed hierarchical Bayesian framework effectively improves the accuracy of disease surveillance by correcting for undercounting in passive systems.
  • The model provides bias-corrected estimates and identifies key risk factors for diseases like PTB, enhancing public health surveillance and intervention strategies.
  • Borrowing information across sites significantly improves the reliability of disease incidence estimates in areas with heterogeneous surveillance data.