Impact of Xpert MTB/RIF implementation in tuberculosis case detection and control in Brazil: a nationwide intervention time-series analysis (2011–2022)

Universidade Salvador. Curso de Medicina. Salvador, BA, Brasil / Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative. Salvador, BA, Brazil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Pesquisa Clínica e Translacional. Salvador, BA, Brasil.
Universidade Salvador. Curso de Medicina. Salvador, BA, Brasil / Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative. Salvador, BA, Brazil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Pesquisa Clínica e Translacional. Salvador, BA, Brasil / Universidade Federal do Rio de Janeiro. Faculdade de Medicina. Programa Pós-graduação de Clínica Médica. Rio de Janeiro, RJ, Brasil / Instituto de Pesquisa Clínica e Translacional. Faculdade Zarns. Clariens Educação. Salvador, BA, Brasil.
Universidade Salvador. Curso de Medicina. Salvador, BA, Brasil / Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative. Salvador, BA, Brazil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Pesquisa Clínica e Translacional. Salvador, BA, Brasil.
Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative. Salvador, BA, Brazil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Pesquisa Clínica e Translacional. Salvador, BA, Brasil.
Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative. Salvador, BA, Brazil / Fundação Oswaldo Cruz. Laboratório de Análise e Visualização de Dados. Porto Velho, RO, Brasil.
Division of Infectious Diseases & Epidemiology. Department of Medicine. Vanderbilt University School of Medicine. Nashville, TN, USA / Department of Biostatistics. Vanderbilt University School of Medicine. Nashville, TN, USA.
Department of Biostatistics. Vanderbilt University School of Medicine. Nashville, TN, USA.
Fundação Medicina Tropical Dr. Heitor Vieira Dourado. Manaus, AM, Brasil / Universidade do Estado do Amazonas. Programa de Pós-Graduação em Medicina Tropical. Manaus, AM, Brasil / Universidade Nilton Lins. Manaus, AM, Brasil.
Division of Infectious Diseases & Epidemiology. Department of Medicine. Vanderbilt University School of Medicine. Nashville, TN, USA.
Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative. Salvador, BA, Brazil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Pesquisa Clínica e Translacional. Salvador, BA, Brasil / Universidade Federal do Rio de Janeiro. Faculdade de Medicina. Programa Pós-graduação de Clínica Médica. Rio de Janeiro, RJ, Brasil.
Universidade Salvador. Curso de Medicina. Salvador, BA, Brasil / Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative. Salvador, BA, Brazil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Pesquisa Clínica e Translacional. Salvador, BA, Brasil / Universidade Federal do Rio de Janeiro. Faculdade de Medicina. Programa Pós-graduação de Clínica Médica. Rio de Janeiro, RJ, Brasil / Instituto de Pesquisa Clínica e Translacional. Faculdade Zarns. Clariens Educação. Salvador, BA, Brasil / Division of Infectious Diseases & Epidemiology. Department of Medicine. Vanderbilt University School of Medicine. Nashville, TN, USA / Escola Bahiana de Medicina e Saúde Pública. Salvador, BA, Brasil.

Abstract

Background: Since 2014, Brazil has gradually implemented the Xpert MTB/RIF (Xpert) test to enhance early tuberculosis (TB) and drug-resistant (DR-TB) detection and control, yet its nationwide impact remains underexplored. Our study conducts an intervention time-series analysis (ITSA) to evaluate how the Xpert’s implementation has improved TB and DR-TB detection nationwide. Methods: 1,061,776 cases from Brazil’s National TB Registry (2011–2022) were reviewed and ITSA (2011–2019) was used to gauge the impact of the Xpert’s adoption on TB and DR-TB notification. Granger Causality and dynamic regression modelling determined if incorporating Xpert testing as an external regressor enhanced forecasting accuracy for Brazil’s future TB trends. Findings: Xpert implementation resulted in a 9.7% increase in TB notification and substantial improvements in DR-TB (63.6%) and drug-susceptible TB (92.1%) detection compared to expected notifications if it had not been implemented. Xpert testing counts also presented a time-dependent relationship with DR-TB detection postimplementation, and improved predictions in forecasting models, which depicted a potential increase in TB and DR-TB detection in the next six years. Interpretation: This study underscores the critical role of Xpert’s adoption in boosting TB and DR-TB detection in Brazil, reinforcing the case for its widespread use in disease control. Improvements in prediction accuracy resulting from integrating Xpert data are crucial for allocating resources and reducing the incidence of TB. By acknowledging Xpert’s role in both disease control and improving predictions, we advocate for its expanded use and further research into advanced molecular diagnostics for effective TB and DR-TB control.

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Elsevier

Citation

SERRA, Klauss Villalva et al. Impact of Xpert MTB/RIF implementation in tuberculosis case detection and control in Brazil: a nationwide intervention time-series analysis (2011–2022). The Lancet Regional Health – Americas, v. 36, p. 1-14, 2024.

DOI

10.1016/j.lana.2024.100804

ISSN

2667-193X

Please use this identifier to cite or link to this item: https://www.arca.fiocruz.br/handle/icict/65388

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