Spatial analysis of tuberculosis cure in primary care in Rio de Janeiro, Brazil
José Carlos Prado Junior1, Roberto de Andrade Medronho2
1Centro de Estudos Estratégicos, Fundação Oswaldo Cruz, Avenida Brasil 4036, 10° andar, Prédio da Expansão, Manguinhos, Rio de Janeiro, RJ, 21040-361, Brazil.
Background:
Tuberculosis (TB) presents a high burden of disease and is considered a global emergency by the World Health Organization (WHO), as the leading cause of death from infectious disease in adults. TB incidence is related directly to access to health services and socioeconomic determinants and inequality. Providing primary care settings can lead to improved access, shorter waiting times for patients, and enhanced TB case detection. The article aims to identify the spatial and temporal risk areas for TB and the relationship between TB cure and primary healthcare coverage from 2012 to 2014 in Rio de Janeiro, Brazil.
Methods:
A cross-sectional study was conducted in Rio de Janeiro, Brazil. All cases of TB reported to the Information System on Diseases of Notification (SINAN) from 2012 to 2014 were included. Socioeconomic variables from the 2010 Brazilian national census were also added. Socioeconomic variables were selected from multivariate analysis using principal factors analysis. Spatial association was verified with generalized additive model (GAM). It was possible to identify areas at higher risk of failure to cure TB.
Results:
TB rates showed strong positive spatial autocorrelation. TB cure rate varied according to schooling (individuals with complete secondary schooling had higher cure rates than illiterate individuals; OR 1.72, 95% CI 1.30-2.29), alcohol consumption (OR 0.47, 95% CI 0.35-0.64), contact investigation (OR 2.00, 95% CI 1.56-2.57), positive HIV serology (OR 0.31, 95% CI 0.23-0.42), and census tracts with higher elderly rates (OR 9.39, 95% CI 1.03-85.26). Individuals who had been covered by primary healthcare (PHC) for 35 to 41 months had 1.64 higher odds of cure, compared to those with no PHC coverage (95% CI 1.07-2.51).
Conclusion:
A comprehensive risk map was developed, allowing public health interventions. Spatial analysis allowed identifying areas with lower odds of TB cure in the city of Rio de Janeiro. TB cure was associated statistically with time of coverage by primary healthcare. TB cure rate also varied according to sociodemographic factors like schooling, alcohol abuse, and population density. This methodology can be generalized to other areas and/or other public health problems.
Highlights:
We studied standardized municipal TB cure rates in an area of social inequality in Brazil. TB rates showed strong positive spatial autocorrelation. Higher rates were associated with population density and socioeconomic conditions. Illiterate individuals were less likely to achieve TB cure. TB cure was less likely in individuals with HIV and alcohol abuse. TB cure was greater in areas with high primary healthcare coverage.
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