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A Bayesian nonparametric approach to correct for underreporting in count data.

Serena Arima1, Silvia Polettini2, Giuseppe Pasculli3

  • 1Department of Human and Social Sciences, University of Salento, Via di Valesio, 73100, LECCE, Italy.

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Summary
This summary is machine-generated.

This study introduces a new statistical model to accurately estimate underreported disease prevalence, like chronic kidney disease in Italy. The model improves disease surveillance and management by accounting for data quality issues.

Keywords:
Chronic kidney disease (CKD)Compound Poisson distributionData qualityDependent Dirichlet processMCMCunderreporting

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Public Health

Background:

  • Accurate disease prevalence estimation is crucial for public health monitoring and management.
  • Count data often suffers from underreporting, particularly in heterogeneous regions.
  • Existing methods may not adequately address data quality issues and underreporting.

Purpose of the Study:

  • To propose a novel nonparametric compound Poisson model for underreported count data.
  • To incorporate latent clustering of reporting probabilities into the model.
  • To accurately estimate disease prevalence, using chronic kidney disease in Apulia, Italy as a case study.

Main Methods:

  • Developed a nonparametric compound Poisson model with latent clustering for reporting probabilities.
  • Estimated model parameters using expert opinion and a proxy for the reporting process.
  • Applied the model to a unique database of 258 municipalities in Apulia, Italy.

Main Results:

  • The model provided accurate prevalence estimates for chronic kidney disease in Apulia.
  • Results revealed interesting geographical patterns of the disease within the region.
  • The model demonstrated accuracy and suitability for data with partial quality information when compared to existing approaches using simulated and real data.

Conclusions:

  • The proposed model effectively addresses underreported count data by modeling reporting probability heterogeneity.
  • It offers a valuable tool for accurate disease surveillance and management, especially in data-scarce or data-quality-challenged settings.
  • The approach is versatile and validated through application to both chronic kidney disease and early neonatal mortality risk data.