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Impact of the GeneXpert(®) MTB/RIF rapid molecular test on tuberculosis detection: temporal trends and vulnerable territories
OBJECTIVE: to assess the impact of the GeneXpert(®) MTB/RIF rapid molecular test on tuberculosis detection, to analyze the temporal trend of the event and to identify vulnerable territories in a Brazilian municipality. METHOD: an ecological study carried out in Ribeirão Preto, São Paulo, Brazil, a m...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Escola de Enfermagem de Ribeirão Preto / Universidade de São
Paulo
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294793/ https://www.ncbi.nlm.nih.gov/pubmed/34287540 http://dx.doi.org/10.1590/1518.8345.4412.3441 |
Sumario: | OBJECTIVE: to assess the impact of the GeneXpert(®) MTB/RIF rapid molecular test on tuberculosis detection, to analyze the temporal trend of the event and to identify vulnerable territories in a Brazilian municipality. METHOD: an ecological study carried out in Ribeirão Preto, São Paulo, Brazil, a municipality considered a priority in tuberculosis control due to the high number of cases. To classify the temporal trend, the Prais-Winsten method and the Interrupted Time Series were used to identify changes in the disease incidence. Kernel intensity analysis was applied to identify vulnerable areas. RESULTS: the temporal trend of tuberculosis decreased by 18.1%/year and by 6.9%/year for children under 15 years old. The North District decreased by 6.67%/year and the East District increased by 17.5%/year in the incidence of tuberculosis. Resistant tuberculosis, after the implementation of the Rapid Molecular Test, increased by 0.6% per year. The South and West Districts showed a higher density of cases, with a range from 45 to 79 tuberculosis cases per square kilometer (km(2)). CONCLUSION: although resistant tuberculosis is not a problem in the scenario, the study showed an increase in its incidence, which puts it on alert. The use of spatial analysis enabled the identification of priority areas, putting them in evidence for health surveillance actions. |
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