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Geographical Distribution and Surveillance of Tuberculosis (TB) Using Spatial Statistics

Geographical Distribution and Surveillance of Tuberculosis (TB) Using Spatial Statistics

Ila Agnihotri, PK Joshi, Neeraj Tiwari
Copyright: © 2013 |Volume: 4 |Issue: 2 |Pages: 15
ISSN: 1947-9654|EISSN: 1947-9662|EISBN13: 9781466632363|DOI: 10.4018/jagr.2013040103
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MLA

Agnihotri, Ila, et al. "Geographical Distribution and Surveillance of Tuberculosis (TB) Using Spatial Statistics." IJAGR vol.4, no.2 2013: pp.39-53. http://doi.org/10.4018/jagr.2013040103

APA

Agnihotri, I., Joshi, P., & Tiwari, N. (2013). Geographical Distribution and Surveillance of Tuberculosis (TB) Using Spatial Statistics. International Journal of Applied Geospatial Research (IJAGR), 4(2), 39-53. http://doi.org/10.4018/jagr.2013040103

Chicago

Agnihotri, Ila, PK Joshi, and Neeraj Tiwari. "Geographical Distribution and Surveillance of Tuberculosis (TB) Using Spatial Statistics," International Journal of Applied Geospatial Research (IJAGR) 4, no.2: 39-53. http://doi.org/10.4018/jagr.2013040103

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Abstract

Socio-demographic and health indices vary across the administrative units in a country. Thus, reported morbidity and mortality figures vary and inter/intra state comparison becomes a challenge. To handle such issues and administer a centralized health management system, identifying disease clusters and providing services to high risk population become important. Exploring a small part of the immense potential of geographic information systems (GIS) in centralized health management, this study presents a method of generating effective information for proper health management at local level. Such information is important for infectious diseases like tuberculosis (TB). The present paper discusses quarterly GIS mapping and assessment of TB in 1,965 villages of Almora district, Uttarakhand, India from 2003 to 2008. The values for Morbidity Rate (MBR) are depicted in risk maps for each quarter. Moran’s I indices were used to estimate the global spatial autocorrelation between the morbidity rates. Local Moran’s I (LISA) was used to detect spatial clusters and outliers, and for the prediction of hotspots of the disease. The result of this study has the potential to reflect a realistic assessment of the disease situation at the local level. Future work on this study can be utilized for planning and policy framework related to TB and other diseases.

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