Authors
Francles Blanco-Guillot, M Lucía Castañeda-Cediel, Pablo Cruz-Hervert, Leticia Ferreyra-Reyes, Guadalupe Delgado-Sanchez, Elizabeth Ferreira-Guerrero, Rogelio Montero-Campos, Miriam Bobadilla-del-Valle, Rosa Areli Martínez-Gamboa, Pedro Torres-Gonzalez, Norma Tellez-Vazquez, Sergio Canizales-Quintero, Mercedes Yanes-Lane, Norma Mongua-Rodriguez, Alfredo Ponce-de-Leon, Jose Sifuentes-Osornio, Lourdes Garcia-Garcia
Publication date
2018/3/13
Journal
PLoS One
Volume
13
Issue
3
Pages
e0193911
Publisher
Public Library of Science
Description
Background
Genotyping and georeferencing in tuberculosis (TB) have been used to characterize the distribution of the disease and occurrence of transmission within specific groups and communities.
Objective
The objective of this study was to test the hypothesis that diabetes mellitus (DM) and pulmonary TB may occur in spatial and molecular aggregations.
Material and methods
Retrospective cohort study of patients with pulmonary TB. The study area included 12 municipalities in the Sanitary Jurisdiction of Orizaba, Veracruz, México. Patients with acid-fast bacilli in sputum smears and/or Mycobacterium tuberculosis in sputum cultures were recruited from 1995 to 2010. Clinical (standardized questionnaire, physical examination, chest X-ray, blood glucose test and HIV test), microbiological, epidemiological, and molecular evaluations were carried out. Patients were considered “genotype-clustered” if two or more isolates from different patients were identified within 12 months of each other and had six or more IS6110 bands in an identical pattern, or < 6 bands with identical IS6110 RFLP patterns and spoligotype with the same spacer oligonucleotides. Residential and health care centers addresses were georeferenced. We used a Jeep hand GPS. The coordinates were transferred from the GPS files to ArcGIS using ArcMap 9.3. We evaluated global spatial aggregation of patients in IS6110-RFLP/ spoligotype clusters using global Moran´s I. Since global distribution was not random, we evaluated “hotspots” using Getis-Ord Gi* statistic. Using bivariate and multivariate analysis we analyzed sociodemographic, behavioral, clinic and bacteriological …
Total citations
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