Impact of strategic public health interventions to reduce tuberculosis incidence in Brazil: a Bayesian structural time-series scenario analysis.

ABSTRACT:

Background

Despite government efforts, tuberculosis (TB) remains a major public health threat in Brazil. In 2023, TB incidence was 39.8 cases per 100,000 population, far above the WHO's target of 6.7 cases per 100,000. Using national-level datasets, we investigated and forecasted the potential impact of proposed public health interventions aimed at reducing TB incidence in Brazil.

Methods

Monthly TB surveillance data (January 2018-December 2023) were collected from Brazilian national reporting systems: SINAN-TB (TB cases), SITE-TB (TB drug resistance), and IL-TB (preventive therapy). These data were used to create a multivariable Bayesian Structural Time-Series (BSTS) model, with 5000 Monte-Carlo simulations, which identified key predictors of TB incidence and forecasted the

SUBMITTER: Villalva-Serra K 

PROVIDER: S-EPMC11697790 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature