Literature DB >> 22094150

Including the third dimension: a spatial analysis of TB cases in Houston Harris County.

Marsha L Feske1, Larry D Teeter, James M Musser, Edward A Graviss.   

Abstract

To reach the tuberculosis (TB) elimination goals established by the Institute of Medicine (IOM) and the Centers for Disease Control and Prevention (CDC), measures must be taken to speed the currently stagnant TB elimination rate and curtail a future peak in TB incidence. Increases in TB incidence have historically coincided with immigration, poverty, and joblessness; all situations that are currently occurring worldwide. Effective TB elimination strategies will require the geographical elucidation of areas within the U.S. that have endemic TB, and systematic surveillance of the locations and location-based risk factors associated with TB transmission. Surveillance data was used to assess the spatial distribution of cases, the yearly TB incidence by census tract, and the statistical significance of case clustering. The analysis revealed that there are neighborhoods within Houston/Harris County that had a heavy TB burden. The maximum yearly incidence varied from 245/100,000-754/100,000 and was not exclusively dependent of the number of cases reported. Geographically weighted regression identified risk factors associated with the spatial distribution of cases such as: poverty, age, Black race, and foreign birth. Public transportation was also associated with the spatial distribution of cases and census tracts identified as high incidence were found to be irregularly clustered within communities of varied SES.
Copyright © 2011 Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 22094150     DOI: 10.1016/j.tube.2011.10.006

Source DB:  PubMed          Journal:  Tuberculosis (Edinb)        ISSN: 1472-9792            Impact factor:   3.131


  9 in total

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Authors:  Marsha L Feske; Larry D Teeter; James M Musser; Edward A Graviss
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3.  Plan Beta for tuberculosis: it's time to think seriously about poorly ventilated congregate settings.

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Journal:  Int J Tuberc Lung Dis       Date:  2016-01       Impact factor: 2.373

4.  Spatio-temporal analysis of smear-positive tuberculosis in the Sidama Zone, southern Ethiopia.

Authors:  Mesay Hailu Dangisso; Daniel Gemechu Datiko; Bernt Lindtjørn
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5.  A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States.

Authors:  Abolfazl Mollalo; Liang Mao; Parisa Rashidi; Gregory E Glass
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7.  Spatial-temporal distribution of genotyped tuberculosis cases in a county with active transmission.

Authors:  Saroochi Agarwal; Duc T Nguyen; Larry D Teeter; Edward A Graviss
Journal:  BMC Infect Dis       Date:  2017-05-31       Impact factor: 3.090

8.  Identifying geographical heterogeneity of pulmonary tuberculosis in southern Ethiopia: a method to identify clustering for targeted interventions.

Authors:  Mesay Hailu Dangisso; Daniel Gemechu Datiko; Bernt Lindtjørn
Journal:  Glob Health Action       Date:  2020-12-31       Impact factor: 2.640

9.  Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review.

Authors:  Debebe Shaweno; Malancha Karmakar; Kefyalew Addis Alene; Romain Ragonnet; Archie Ca Clements; James M Trauer; Justin T Denholm; Emma S McBryde
Journal:  BMC Med       Date:  2018-10-18       Impact factor: 8.775

  9 in total

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