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Activity Number: 417 - Statistical Methods for Discovering Latent Structures in High-Dimensional and Complex Data
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #322826
Title: Latent Class Models for Prevalence Estimation of Emerging Diseases Using Verbal Autopsy Data
Author(s): Zehang Richard Li*
Companies: UCSC
Keywords: data shift; dependent binary data; quantification learning

A widely-used tool to obtain information on causes-of-death when a medically certified cause-of-death is not available is verbal autopsies (VA). VA involves a structured questionnaire administered to family members or caregivers of a recently deceased person. During public health emergencies when new diseases emerge, VAs are usually the only feasible tool to gather information on cause-of-death in many low- and middle-income countries. Existing statistical methods for analyzing VAs, however, are not suitable for such situations. We propose a novel latent class model framework to estimate the prevalence of a new disease using limited VAs collected under an informative selection process. We will also extend the framework to monitor the dynamics of mortality rates over time.

Authors who are presenting talks have a * after their name.

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