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Quantifying gaps in the tuberculosis care cascade in Brazil: A mathematical model study using national program data.

Sivaram Subaya EmaniKleydson AlvesLayana Costa AlvesDaiane Alves da SilvaPatrícia Bartholomay OliveiraMarcia Caldas de CastroTed CohenRodrigo de Macedo CoutoMauro Niskier SanchezNicolas A Menzies
Published in: PLoS medicine (2024)
In this study, we observed that delays to diagnosis, post-disease sequelae and treatment loss to follow-up were primary contributors to the TB burden of disease in Brazil. Reducing delays to diagnosis, improving healthcare after TB cure, and reducing treatment loss to follow-up should be prioritized to improve the burden of TB disease in Brazil.
Keyphrases
  • machine learning
  • artificial intelligence
  • healthcare
  • mycobacterium tuberculosis
  • quality improvement
  • emergency department
  • hepatitis c virus
  • pulmonary tuberculosis
  • risk factors
  • hiv infected