Geo-Epidemiologic and Molecular Characterization to Identify Social, Cultural, and Economic Factors Where Targeted Tuberculosis Control Activities Can Reduce Incidence in Maryland, 2004–2010. (November 2013)

• Record Type: Journal Article
• Authors: Prussing, Catharine; Castillo-Salgado, Carlos; Baruch, Nancy; Cronin, Wendy A.
• Is Part Of: Public health reports. Volume 128:Number 6(2013)Supplement 3
• Page Start: 104
• Page End: 114
• Publication Date: 2013-11
• Subjects: Public health -- United States -- Periodicals; 614.0973
• DOI: 10.1177/00333549131286S314
• Languages: English
• ISSNs: 0033-3549

Abstract

Objectives: Tuberculosis (TB) disproportionately affects members of socioeconomically disadvantaged and minority populations in the U.S. We describe the geospatial distribution of TB cases in Maryland, identify areas at high risk for TB, and compare the geospatial clustering of cases with genotype clustering and demographic, socioeconomic, and TB risk-factor information. Methods: Addresses of culture-positive, genotyped TB cases reported to the Maryland Department of Health and Mental Hygiene from January 1, 2004, to December 31, 2010, were geocoded and aggregated to census tracts. Geospatial clusters with higher-than-expected case numbers were identified using Poisson spatial cluster analysis. Case distribution and geospatial clustering information were compared with ( 1 ) genotype clustering (spoligotypes and 12-locus MIRU-VNTR), ( 2 ) individual-level risk and demographic data, and ( 3 ) census tract-level demographic and socioeconomic data. Results: We genotoyped 1, 384 (98%) isolates from 1, 409 culture-positive TB cases. Two geospatial clusters were found: one in Baltimore City and one in Montgomery and Prince George's counties. Cases in these geospatial clusters were equally or less likely to share genotypes than cases outside the geospatial clusters. The two geospatial clusters had poverty and crowding in common but differed significantly by risk populations and behaviors. Conclusions: Genotyping results indicated that recent transmission did not explain most geospatial clustering, suggesting that geospatial clustering is largely mitigated by social determinants. Analyses combining geospatial, genotyping, and epidemiologic data can help characterize populations most at risk for TB and inform the design of targeted interventions.