A Hierarchical Framework for Correcting Under-Reporting in Count Data

Tuberculosis poses a global health risk and Brazil is among the top 20 countries by absolute mortality. However, this epidemiological burden is masked by under-reporting, which impairs planning for effective intervention. We present a comprehensive investigation and application of a Bayesian hierarc...

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Bibliographic Details
Published in:Journal of the American Statistical Association 2019-10, Vol.114 (528), p.1481-1492
Main Authors: Stoner, Oliver, Economou, Theo, Drummond Marques da Silva, Gabriela
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian methods
Censoring
Computer simulation
Elicitation
Epidemiology
Partial information
Prediction models
Regression analysis
Sensitivity analysis
Simulation
Statistical methods
Statistics
Tuberculosis
Under-detection
Under-recording
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Summary:Tuberculosis poses a global health risk and Brazil is among the top 20 countries by absolute mortality. However, this epidemiological burden is masked by under-reporting, which impairs planning for effective intervention. We present a comprehensive investigation and application of a Bayesian hierarchical approach to modeling and correcting under-reporting in tuberculosis counts, a general problem arising in observational count data. The framework is applicable to fully under-reported data, relying only on an informative prior distribution for the mean reporting rate to supplement the partial information in the data. Covariates are used to inform both the true count-generating process and the under-reporting mechanism, while also allowing for complex spatio-temporal structures. We present several sensitivity analyses based on simulation experiments to aid the elicitation of the prior distribution for the mean reporting rate and decisions relating to the inclusion of covariates. Both prior and posterior predictive model checking are presented, as well as a critical evaluation of the approach. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
ISSN:0162-1459
1537-274X
DOI:10.1080/01621459.2019.1573732