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Evaluating tuberculosis treatment outcomes in Haiti from 2018 to 2019: A competing risk analysis.
Raphael, Nernst-Atwood; Garraud, Pierre Anthony; Roelens, Maroussia; Alfred, Jean Patrick; Richard, Milo; Estill, Janne; Keiser, Olivia; Merzouki, Aziza.
Afiliação
  • Raphael NA; Institute of Global Health, University of Geneva, Geneva, Switzerland.
  • Garraud PA; Strategic Health Information System, DAI Global LLC, Port-au-Prince, Haiti.
  • Roelens M; Strategic Health Information System, DAI Global LLC, Port-au-Prince, Haiti.
  • Alfred JP; Institute of Global Health, University of Geneva, Geneva, Switzerland.
  • Richard M; Unit of Studies and Programming, Ministry of Health, Port-au-Prince, Haiti.
  • Estill J; National Tuberculosis Program, Ministry of Health, Port-au-Prince, Haiti.
  • Keiser O; Institute of Global Health, University of Geneva, Geneva, Switzerland.
  • Merzouki A; Institute of Global Health, University of Geneva, Geneva, Switzerland.
IJID Reg ; 11: 100350, 2024 Jun.
Article em En | MEDLINE | ID: mdl-38577553
ABSTRACT

Objectives:

This study assesses tuberculosis (TB) treatment outcomes in Haiti.

Methods:

Data from drug-susceptible patients with TB (2018-2019) were analyzed using the Fine & Gray model with multiple imputation.

Results:

Of the 16,545 patients, 14.7% had concurrent HIV coinfection, with a 66.2% success rate. The median treatment duration was 5 months, with patients averaging 30 years (with an interquartile range of 22-42 years). The estimated hazard of achieving a successful treatment outcome decreased by 2.5% and 8.1% for patients aged 45 and 60 years, respectively, compared with patients aged 30 years. Male patients had a 6.5% lower estimated hazard of success than their female counterparts. In addition, patients coinfected with HIV experienced a 35.3% reduction in the estimated hazard of achieving a successful treatment outcome compared with those with a negative HIV serologic status.

Conclusions:

Integrated health care approaches should be implemented, incorporating innovative solutions, such as machine learning algorithms combined with geographic information systems and non-conventional data sources (including social media), to identify TB hotspots and high-burden households.
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Texto completo: 1 Bases de dados: MEDLINE País/Região como assunto: Haiti Idioma: En Revista: IJID Reg Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Suíça

Texto completo: 1 Bases de dados: MEDLINE País/Região como assunto: Haiti Idioma: En Revista: IJID Reg Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Suíça