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The impact of active case finding on transmission dynamics of tuberculosis: A modelling study.
Ayabina, Diepreye Victoria; Gomes, M Gabriela M; Nguyen, Nhung Viet; Vo, Luan; Shreshta, Suvesh; Thapa, Anil; Codlin, Andrew James; Mishra, Gokul; Caws, Maxine.
Afiliação
  • Ayabina DV; Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
  • Gomes MGM; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.
  • Nguyen NV; Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
  • Vo L; CIBIO-InBIO, Centro de Investiga¸c˜ao em Biodiversidade e Recursos Gen´eticos, and CMUP, Centro de Matem´atica da Universidade do Porto, Porto, Portugal.
  • Shreshta S; National Tuberculosis Control Programme of Vietnam- National Lung Hospital (VNTP-NLH), Hanoi, Vietnam.
  • Thapa A; Friends for International TB Relief (FIT), Ho Chi Minh City, Vietnam.
  • Codlin AJ; Save the Children, Kathmandu, Nepal.
  • Mishra G; National TB Control Centre, Thimi, Kathmandu, Nepal.
  • Caws M; Friends for International TB Relief (FIT), Ho Chi Minh City, Vietnam.
PLoS One ; 16(11): e0257242, 2021.
Article em En | MEDLINE | ID: mdl-34797864
BACKGROUND: In the last decade, active case finding (ACF) strategies for tuberculosis (TB) have been implemented in many diverse settings, with some showing large increases in case detection and reporting at the sub-national level. There have also been several studies which seek to provide evidence for the benefits of ACF to individuals and communities in the broader context. However, there remains no quantification of the impact of ACF with regards to reducing the burden of transmission. We sought to address this knowledge gap and quantify the potential impact of active case finding on reducing transmission of TB at the national scale and further, to determine the intensification of intervention efforts required to bring the reproduction number (R0) below 1 for TB. METHODS: We adopt a dynamic transmission model that incorporates heterogeneity in risk to TB to assess the impact of an ACF programme (IMPACT TB) on reducing TB incidence in Vietnam and Nepal. We fit the models to country-level incidence data using a Bayesian Markov Chain Monte Carlo approach. We assess the impact of ACF using a parameter in our model, which we term the treatment success rate. Using programmatic data, we estimate how much this parameter has increased as a result of IMPACT TB in the implementation districts of Vietnam and Nepal and quantify additional efforts needed to eliminate transmission of TB in these countries by 2035. RESULTS: Extending the IMPACT TB programme to national coverage would lead to moderate decreases in TB incidence and would not be enough to interrupt transmission by 2035. Decreasing transmission sufficiently to bring the reproduction number (R0) below 1, would require a further intensification of current efforts, even at the sub-national level. CONCLUSIONS: Active case finding programmes are effective in reducing TB in the short term. However, interruption of transmission in high-burden countries, like Vietnam and Nepal, will require comprehensive incremental efforts. Complementary measures to reduce progression from infection to disease, and reactivation of latent infection, are needed to meet the WHO End TB incidence targets.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Tuberculose / Modelos Biológicos Tipo de estudo: Diagnostic_studies / Etiology_studies / Incidence_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País como assunto: Asia Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Tuberculose / Modelos Biológicos Tipo de estudo: Diagnostic_studies / Etiology_studies / Incidence_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País como assunto: Asia Idioma: En Ano de publicação: 2021 Tipo de documento: Article