Spatial patterns of pulmonary tuberculosis incidence and their relationship to socio-economic status in Vitoria, Brazil

• Published in: The international journal of tuberculosis and lung disease Vol. 14; no. 11; pp. 1395 - 1402
• Main Authors: MACIEL, E. L. N, PAN, W, DIETZE, R, PERES, R. L, VINHAS, S. A, RIBEIRO, F. K, PALACI, M, RODRIGUES, R. R, ZANDONADE, E, GOLUB, J. E
• Format: Journal Article
• Language: English
• Published: Paris, France IUATLD 01.11.2010; International Union against Tuberculosis and Lung Disease
• Subjects: Bacterial diseases; Bacterial diseases of the respiratory system; Bayes Theorem; Biological and medical sciences; Brazil - epidemiology; Cluster Analysis; Epidemiologic Surveillance; Human bacterial diseases; Humans; Incidence; Infectious diseases; Medical sciences; Models, Statistical; Mycobacterium; Pneumology; Poisson Distribution; Prevention Control; Respiratory system : syndromes and miscellaneous diseases; Retrospective Studies; Socioeconomic Factors; Spatial Distribution; Tuberculosis; Tuberculosis and atypical mycobacterial infections; Tuberculosis, Pulmonary - epidemiology; South America; Brazil; America; Brazil, Espirito Santo; Lung disease; Respiratory disease; Pulmonary tuberculosis; tuberculosis; Economic status; Mycobacterial infection; Epidemiology; Incidence; Infection; Prevention; prevention control; Control; Spatial distribution; Surveillance; Bacteriosis; epidemiologic surveillance; Pneumology
• ISSN: 1027-3719; 1815-7920

OBJECTIVE: To investigate spatial patterns of the incidence of pulmonary tuberculosis (TB) and its relationship with socio-economic status in Vitoria, Espirito Santo, Brazil.DESIGN: In a 4-year, retrospective, territory-based surveillance study of all new pulmonary TB cases conducted in Vitoria between 2002 and 2006, spatial patterns of disease incidence were compared using spatial clustering statistics (Anselin's local indicators of spatial association [LISA] and Getis-Ord Gi* statistics), smoothed empirical Bayes estimates and model-predicted incidence rates. Spatial Poisson models were fit to examine the relationship between socio-economic status and TB incidence.RESULTS: A total of 651 TB cases were reported across 78 neighborhoods, with rates ranging from 0 to 129 cases per 100 000 population. Moran's I indicated strong spatial autocorrelation among incidence rates (0.399, P < 0.0001), and four areas of high incidence were identified by LISA and Gi* statistics. Smoothed spatial empirical Bayes estimates demonstrate that two of these areas range from 70 to 90 cases/100 000, while the other two range from 40 to 70 cases/100 000. TB incidence and socio-economic status had a significant curvilinear relationship (P = 0.02).CONCLUSIONS: Data derived from these spatial statistical tools will help TB control programs to allocate TB resources to those populations most at risk of increasing TB rates and to target areas where TB control efforts need to be concentrated.