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Título:
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Geographic predictors of primary multidrug-resistant tuberculosis cases in an endemic area of Lima, Peru
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Autores:
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Shah, L. ;
Choi, H. W. ;
Berrang-Ford, L. ;
Henostroza, G. ;
Krapp, F. ;
Zamudio, C. ;
Heymann, S. J. ;
Kaufman, J. S. ;
Ciampi, A. ;
Seas, C. ;
Gotuzzo, E. ;
Brewer, T. F.
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Tipo de documento:
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texto impreso
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Editorial:
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International Union Against Tuberculosis and Lung Disease, 2020-06-10T18:11:30Z
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Nota general:
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info:eu-repo/semantics/restrictedAccess
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
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Idiomas:
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Inglés
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Palabras clave:
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Editados por otras instituciones
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Artículos
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Artículos en revistas indizadas
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Resumen:
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SETTING: Peru reports among the highest multidrug-resistant tuberculosis (MDR-TB) rates in the Americas, with a growing proportion in previously untreated tuberculosis (TB) cases. The identification of clusters of primary MDR-TB compared with drug-susceptible TB (DS-TB) could help prioritize interventions. OBJECTIVE: To examine the clustering of primary MDR-TB case residences and their proximity to high-risk locations in San Juan de Lurigancho District, Lima, Peru. DESIGN: Enrolled primary MDR-TB and primary DS-TB cases were interviewed and their primary residence was recorded using handheld Global Positioning System devices. Kuldorff's spatial scan statistic was used for cluster detection (SaTScan(TM), v. 9.1.1). Identified clusters were visualized in Quantum Geographic Information Systems software (v1.8.0). The following cluster centers were tested: a health centre with the highest TB and MDR-TB rates (Clinic X), a hospital and two prisons. Using regression analyses, we examined predictors of primary MDR-TB cases. RESULTS: A statistically significant cluster of primary
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En línea:
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http://doi.org/10.5588/ijtld.14.0011
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