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Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using

Zhenke Wu1,2, Zehang R Li3, Irena Chen1

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, United States.

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Summary
This summary is machine-generated.

This study introduces a novel domain-adaptive method for verbal autopsy (VA) to improve cause-specific mortality fraction (CSMF) estimation. The approach effectively leverages similarities between different populations for more accurate death cause assignment.

Keywords:
domain adaptationlatent class modelsspike-and-slab priorvariational Bayesverbal autopsy

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Area of Science:

  • Biostatistics
  • Public Health
  • Epidemiology

Background:

  • Determining causes of death (COD) outside vital statistics systems is difficult.
  • Verbal autopsy (VA) is a common method, but requires methods adaptable to new populations (domains).
  • Existing statistical methods for cause-specific mortality fractions (CSMFs) may not fully utilize between-domain similarities.

Purpose of the Study:

  • To propose a domain-adaptive method for VA that integrates external information on between-domain similarities.
  • To improve the accuracy of CSMF estimation and individual COD assignment in diverse populations.
  • To provide a scalable and data-driven approach for analyzing VA data across different domains.

Main Methods:

  • Developed a domain-adaptive method using a prespecified rooted weighted tree to encode between-domain similarity.
  • Employed latent class models to characterize domain-specific response distributions.
  • Utilized a logistic stick-breaking Gaussian diffusion process prior and spike-and-slab priors for information pooling.
  • Conducted posterior inference using a scalable variational Bayes algorithm.

Main Results:

  • Simulation studies demonstrated that the domain adaptation method improves CSMF estimation.
  • The proposed method enhances the accuracy of individual COD assignment.
  • Validation using a real-world dataset confirmed the method's effectiveness.

Conclusions:

  • The proposed domain-adaptive method offers a significant advancement for verbal autopsy analysis.
  • This approach effectively balances domain-specific characteristics with shared information across populations.
  • The method has the potential to improve global health surveillance by providing more accurate mortality data.