Genotypic and Spatial Analysis of Mycobacterium tuberculosis Transmission in a High-Incidence Urban Setting

Autores

Ribeiro, F.K., Pan, W.K.Y., Bertolde, A., Alves Vinhas, S., Lyrio Peres, R., Riley, L., Palaci, M. and Maciel, E.L.

Publicado en

Clinical Infectious Diseases, v. 61(5):758-766

Año de publicación

2015

Afiliaciones

•Graduate Program in Infectious Diseases, Federal University of Espírito Santo, Vitória, Brazil
•Duke Global Health Institute and Nicholas School of Environment, Duke University, Durham, North Carolina
•Department of Statistics, Federal University of Espírito Santo, Vitória, Brazil
•Division of Infectious Disease and Vaccinology, School of Public Health, University of California, Berkeley
•Graduate Program in Public Health, Federal University of Espírito Santo, Vitória, Brazil

Programa

CRN3

Proyecto

CRN3056

Keywords

molecular epidemiology, spatial analysis, transmission, tuberculosis

DOI

https://doi.org/10.1093/cid/civ365
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Resumen

Background

Genotyping Mycobacterium tuberculosis isolates allows study of dynamics of tuberculosis transmission, while geoprocessing allows spatial analysis of clinical and epidemiological data. Here, genotyping data and spatial analysis were combined to characterize tuberculosis transmission in Vitória, Brazil, to identify distinct neighborhoods and risk factors associated with recent tuberculosis transmission.

Methods

From 2003 to 2007, 503 isolates were genotyped by IS6110 restriction fragment length polymorphism (RFLP) and spoligotyping. The analysis included kernel density estimation, K-function analysis, and a t test distance analysis. Mycobacterium tuberculosis isolates belonging to identical RFLP patterns (clusters) were considered to represent recent tuberculosis infection (cases).

Results

Of 503 genotyped isolates, 242 (48%) were categorized into 70 distinct clusters belonging to 12 RFLP families. The proportion of recent transmission was 34.2%. Kernel density maps indicated 3 areas of intense concentration of cases. K-function analysis of the largest RFLP clusters and families showed they co-localized in space. The distance analysis confirmed these results and demonstrated that unique strain patterns (controls) randomly distributed in space. A logit model identified young age, positive smear test, and lower Index of Quality of Urban Municipality as risk factors for recent transmission. The predicted probabilities for each neighborhood were mapped and identified neighborhoods with high risk for recent transmission.

Conclusions

Spatial and genotypic clustering of M. tuberculosis isolates revealed ongoing active transmission of tuberculosis caused by a small subset of strains in specific neighborhoods of the city. Such information provides an opportunity to target tuberculosis transmission control, such as through rigorous and more focused contact investigation programs.