A novel Bayesian geospatial method for estimating tuberculosis incidence reveals many missed TB cases in Ethiopia.

ABSTRACT:

Background

Reported tuberculosis (TB) incidence globally continues to be heavily influenced by expert opinion of case detection rates and ecological estimates of disease duration. Both approaches are recognised as having substantial variability and inaccuracy, leading to uncertainty in true TB incidence and other such derived statistics.

Methods

We developed Bayesian binomial mixture geospatial models to estimate TB incidence and case detection rate (CDR) in Ethiopia. In these models the underlying true incidence was formulated as a partially observed Markovian process following a mixed Poisson distribution and the detected (observed) TB cases as a binomial distribution, conditional on CDR and true incidence. The models use notification data from multiple areas over several

SUBMITTER: Shaweno D 

PROVIDER: S-EPMC5625624 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature