Using Bayesian spatial models to map and to identify geographical hotspots of multidrug-resistant tuberculosis in Portugal between 2000 and 2016

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Oliveira, O (Autor) Outra; Ribeiro, AI (Autor) Outra; Krainski, ET (Autor) Outra; Rito, T (Autor) Outra; Duarte R (Autor) FMUP; Correia Neves, M (Autor) Outra

Revista

Título: Scientific Reports

Vol. 10

ISSN: 2045-2322

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00S-TF6

DOI: 10.1038/s41598-020-73759-w

Abstract (EN): Multidrug-resistant tuberculosis (MDR-TB) is a major threat to the eradication of tuberculosis. TB control strategies need to be adapted to the necessities of different countries and adjusted in high-risk areas. In this study, we analysed the spatial distribution of the MDR- and non-MDR-TB cases across municipalities in Continental Portugal between 2000 and 2016. We used Bayesian spatial models to estimate age-standardized notification rates and standardized notification ratios in each area, and to delimitate high- and low-risk areas, those whose standardized notification ratio is significantly above or below the country's average, respectively. The spatial distribution of MDR- and non-MDR-TB was not homogeneous across the country. Age-standardized notification rates of MDR-TB ranged from 0.08 to 1.20 and of non-MDR-TB ranged from 7.73 to 83.03 notifications per 100,000 population across the municipalities. We identified 36 high-risk areas for non-MDR-TB and 8 high-risk areas for MDR-TB, which were simultaneously high-risk areas for non-MDR-TB. We found a moderate correlation (rho =0.653; 95% CI 0.457-0.728) between MDR- and non-MDR-TB standardized notification ratios. We found heterogeneity in the spatial distribution of MDR-TB across municipalities and we identified priority areas for intervention against TB. We recommend including geographical criteria in the application of molecular drug resistance to provide early MDR-TB diagnosis, in high-risk areas.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9