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Using GIS technology to identify areas of tuberculosis transmission and incidence

Patrick K Moonan12, Manuel Bayona2, Teresa N Quitugua3, Joseph Oppong4, Denise Dunbar5, Kenneth C Jost5, Gerry Burgess6, Karan P Singh2 and Stephen E Weis126*

Author Affiliations

1 Department of Medicine, 3500 Camp Bowie Blvd. University of North Texas Health Science Center at Fort Worth, Fort Worth, Texas 76107, USA

2 School of Public Health, 3500 Camp Bowie Blvd. University of North Texas Health Science Center at Fort Worth, Fort Worth, Texas 76107, USA

3 Department of Microbiology and Immunology, 15355 Lambda Drive. University of Texas Health Science Center at San Antonio South Texas Center for Biology in Medicine Bldg, Room 2.100.04, San Antonio, TX 78245, USA

4 Department of Geography, 1704 W. Mulberry. University of North Texas, P.O. Box 305279 Denton, Texas 76203, USA

5 Bureau of Laboratories, Texas Department of Health Austin, Texas 78756, USA

6 Tarrant County Public Health Department, 1101 S. Main St. Fort Worth, Texas 76104, Suite 1600, USA

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International Journal of Health Geographics 2004, 3:23  doi:10.1186/1476-072X-3-23

Published: 13 October 2004

Abstract

Background

Currently in the U.S. it is recommended that tuberculosis screening and treatment programs be targeted at high-risk populations. While a strategy of targeted testing and treatment of persons most likely to develop tuberculosis is attractive, it is uncertain how best to accomplish this goal. In this study we seek to identify geographical areas where on-going tuberculosis transmission is occurring by linking Geographic Information Systems (GIS) technology with molecular surveillance.

Methods

This cross-sectional analysis was performed on data collected on persons newly diagnosed with culture positive tuberculosis at the Tarrant County Health Department (TCHD) between January 1, 1993 and December 31, 2000. Clinical isolates were molecularly characterized using IS6110-based RFLP analysis and spoligotyping methods to identify patients infected with the same strain. Residential addresses at the time of diagnosis of tuberculosis were geocoded and mapped according to strain characterization. Generalized estimating equations (GEE) analysis models were used to identify risk factors involved in clustering.

Results

Evaluation of the spatial distribution of cases within zip-code boundaries identified distinct areas of geographical distribution of same strain disease. We identified these geographical areas as having increased likelihood of on-going transmission. Based on this evidence we plan to perform geographically based screening and treatment programs.

Conclusion

Using GIS analysis combined with molecular epidemiological surveillance may be an effective method for identifying instances of local transmission. These methods can be used to enhance targeted screening and control efforts, with the goal of interruption of disease transmission and ultimately incidence reduction.