Tuberculosis in Brazil: last ten years analysis – 2001–2010

Oliveira, Gisele Pinto de; Torrens, Ana; Bartholomay, Patrícia; Barreira, Dráurio

doi:10.1016/j.bjid.2013.01.005

Cited by 51 publications

(76 citation statements)

References 11 publications

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“…Case-detection rate of SINAN reporting range was estimated to be 88 % in 2010. 9 Without these census and survey data, DALY-gap measurements would not have been possible to calculate. While countries like India and Kenya have made efforts to conduct census and define geographic boundaries of their urban informal settlements, 25,26 other countries with mega-cities have yet to do so.…”

Section: Discussionmentioning

confidence: 99%

“…Quality assessments of this database have been performed routinely since its establishment, making it a recognized reliable source of disease data. 9 SINAN works in three levels. First, the case is reported to the municipality health department where the data are entered into the SINAN database.…”

Section: Study Population and Census Tract Designationmentioning

confidence: 99%

“…[5][6][7][8] These conditions engender other adverse health outcomes unrelated to TB. Despite significant allocation of resources to combat TB with a rigorous national TB control program, 9 the city of Rio de Janeiro reported the second highest TB incidence rates in the country in 2010. 10 In addition, Rio de Janeiro has Brazil's largest population of slum dwellers, as defined by the Brazilian National 2010 Census Report.…”

Section: Introductionmentioning

confidence: 99%

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Tuberculosis DALY-Gap: Spatial and Quantitative Comparison of Disease Burden Across Urban Slum and Non-slum Census Tracts

et al. 2015

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To quantitatively assess disease burden due to tuberculosis between populations residing in and outside of urban informal settlements in Rio de Janeiro, Brazil, we compared disability-adjusted life years (DALYs), or BDALY-gap.^Using the 2010 Brazilian census definition of informal settlements as aglomerados subnormais (AGSN), we allocated tuberculosis (TB) DALYs to AGSN vs non-AGSN census tracts based on geocoded addresses of TB cases reported to the Brazilian Information System for Notifiable Diseases in 2005 and 2010. DALYs were calculated based on the 2010 Global Burden of Disease methodology. DALY-gap was calculated as the difference between age-adjusted DALYs/100,000 population between AGSN and non-AGSN. Total TB DALY in Rio in 2010 was 16,731 (266 DALYs/100,000). DALYs were higher in AGSN census tracts (306 vs 236 DALYs/100,000), yielding a DALY-gap of 70 DALYs/100,000. Attributable DALY fraction for living in an AGSN was 25.4 %. DALY-gap was highest for males 40-59 years of age (501 DALYs/100,000) and in census tracts with G60 % electricity (12,327 DALYs/100,000). DALY-gap comparison revealed spatial and quantitative differences in TB burden between slum vs non-slum census tracts that were not apparent using traditional measures of incidence and mortality. This metric could be applied to compare TB burden or burden for other diseases in mega-cities with large informal settlements for more targeted resource allocation and evaluation of intervention programs.

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Section: Discussionmentioning

confidence: 99%

Section: Study Population and Census Tract Designationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tuberculosis DALY-Gap: Spatial and Quantitative Comparison of Disease Burden Across Urban Slum and Non-slum Census Tracts

et al. 2015

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“…Overall, the incidence rate of TB in Brazil has been steadily decreasing (12) , whereas in the State of Amazonas, there has only been a slight decrease; therefore, the State of Amazonas remains as 1 of 2 states with the highest incidence rates in Brazil. The highest incidence rate tends to alternate between the States of Amazonas and Rio de Janeiro (9) .…”

Section: Introductionmentioning

confidence: 97%

“…Both the relative and absolute number of TB deaths in Brazil decreased between 2001 and 2010 (12) . However, in a study that examined the underreporting of tuberculosis in the SIM in Brazil, by comparing it to the SINAN, the percentage of unreported deaths in the country was 39.4%, and in Amazonas, the percentage was 36.9% (14) , suggesting a much greater mortality burden than has been reported for the state.…”

Section: Introductionmentioning

confidence: 99%

Temporal distribution of tuberculosis in the State of Amazonas, Brazil

Garrido

Bührer-Sékula

Souza

et al. 2015

Rev. Soc. Bras. Med. Trop.

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63www.scielo.br/rsbmt I www.rsbmt.org.br Revista da Sociedade Brasileira de Medicina Tropical 48(Suppl I):63-69, 2015http://dx.doi. org/10.1590/0037-8682-0055-2014 Review Article ABSTRACT Tuberculosis (TB) is one of the infectious diseases that contributes most to the morbidity and mortality of millions of people worldwide. Brazil is one of 22 countries that accounts for 80% of the tuberculosis global burden. The highest incidence rates in Brazil occur in the States of Amazonas and Rio de Janeiro. The aim of this study was to describe the temporal distribution of TB in the State of Amazonas. Between 2001 and 2011, 28,198 cases of tuberculosis were reported in Amazonas, distributed among 62 municipalities, with the capital Manaus reporting the highest (68.7%) concentration of cases. Tuberculosis was more prevalent among males (59.3%) aged 15 to 34 years old (45.5%), whose race/color was predominantly pardo (64.7%) and who had pulmonary TB (84.3%). During this period, 81 cases of multidrug-resistant TB were registered, of which the highest concentration was reported from 2008 onward (p = 0.002). The municipalities with the largest numbers of indigenous individuals affected were São Gabriel da Cachoeira (93%), Itamarati (78.1%), and Santa Isabel do Rio Negro (70.1%). The future outlook for this region includes strengthening the TB control at the primary care level, by expanding diagnostic capabilities, access to treatment, research projects developed in collaboration with the Dr. Heitor Vieira Dourado Tropical Medicine Foundation [Fundação de Medicina Tropical Dr. Heitor Vieira Dourado (FMT-HVD)] and fi nancing institutions, such as the project for the expansion of the Clinical Research Center and the creation of a hospital ward for individuals with transmissible respiratory diseases, including TB.

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Epidemiological Models Incorporating Mobility, Behavior, and Time Scales

Brauer

Castillo‐Chavez

Feng

2019

Texts in Applied Mathematics

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The work of Eubank et al. [24], Sara del Valle et al. [44], Chowell et al. [7, 18], and Castillo-Chavez and Song [13] have highlighted the impact of modified modeling approaches that incorporate heterogeneous modes of mobility within variable environments in order to study their impact on the dynamics of infectious diseases. Castillo-Chavez and Song [13], for example, proceeded to highlight a Lagrangian perspective, that is, the use of models that keep track at all times of the identity of each individual. This approach was used to study the consequences of deliberate efforts to transmit smallpox in a highly populated city, involving transient subpopulations and the availability of massive modes of public transportation. Here, a multi-group epidemic Lagrangian framework where mobility and the risk of infection are functions of patch residence time and local environmental risk is introduced. This Lagrangian approach has been used within classical contact epidemiological (that is, transmission is due to "contacts" between individuals) formulations in the context of a possible deliberate release of biological agents [2, 13]. The Lagrangian approach is introduced here as a modeling approach that explicitly avoids the assignment of heterogeneous contact rates to individuals. The use of contacts or activity levels and the view that transmission is due to collisions between individuals has a long history and it is conceptually consistent with the way we envision disease transmission between susceptible and infectious individuals. However, contacts are hard to define and consequently, at least in the context of communicable diseases, impossible to measure in various settings. Is it possible to capture interactions of individual mathematically in a way different from the notion of contacts? The approach that is proposed focuses on the use of modeling frameworks that involve patches/environments defined or characterized by risks of infection that are functions of the time spent in each environment/patch. These patches/environments may or may not have permanent hosts and they may be used to account for places of "transitory" residence like mass transportation systems or

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Tuberculosis in Brazil: last ten years analysis – 2001–2010

Abstract: Results showed many advances in tuberculosis control in the 10 years analyzed, but it also points to serious obstacles that need to be solved so Brazil can eliminate tuberculosis as a public health problem.

Cited by 51 publications

References 11 publications

Tuberculosis DALY-Gap: Spatial and Quantitative Comparison of Disease Burden Across Urban Slum and Non-slum Census Tracts

Tuberculosis DALY-Gap: Spatial and Quantitative Comparison of Disease Burden Across Urban Slum and Non-slum Census Tracts

Temporal distribution of tuberculosis in the State of Amazonas, Brazil

Epidemiological Models Incorporating Mobility, Behavior, and Time Scales

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