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Vol. 17. Issue 2.
Pages 218-233 (March - April 2013)
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Vol. 17. Issue 2.
Pages 218-233 (March - April 2013)
Special article
Open Access
Tuberculosis in Brazil: last ten years analysis – 2001–2010
Visits
3742
Gisele Pinto de Oliveira
Corresponding author
giselepoliveira@gmail.com

Corresponding author at: Tuberculosis National Control Program, Secretariat of Health Surveillance, Ministry of Health of Brazil, Setor Comercial Sul, Quadra 4, Bloco A, Edifício Principal, 1° andar, Brasilia, DF, 70304-000, Brazil.
, Ana Wieczorek Torrens, Patrícia Bartholomay, Draurio Barreira
Tuberculosis National Control Program, Secretariat of Health Surveillance, Ministry of Health of Brazil, Brasília, DF, Brazil
Related content
Braz J Infect Dis. 2013;17:123-410.1016/j.bjid.2013.01.002
Eduardo Martins Netto
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Statistics
Figures (2)
Tables (8)
Table 1. TCP coverage and case detection – Brasil, 2001–2010.
Table 2. Number of new cases and tuberculosis crude incidence rate (Sinan-TB) – Brazil and state of residence, 2001–2010.
Table 3. Tuberculosis cases profile according to sex, age, race, education, input in the information system and institutionalization status (Sinan-TB) – Brazil, 2001–2010.
Table 4. Diagnosis and treatment variables analysis of new cases (Sinan-TB) – Brazil, 2001–2010.
Table 5. New cases outcome (Sinan-TB) – Brazil and state of residence, 2001–2010.
Table 6. Diagnosis and treatment variables analysis of retreatment cases (Sinan-TB) – Brazil, 2001–2010.
Table 7. Hospital admissions duo to tuberculosis (SIH-SUS) – Brazil and state of residence, 2001–2010.
Table 8. Number of deaths and crude mortality rate (SIM) – Brazil and state of residence, 2001–2010.
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Abstract
Objective

To describe tuberculosis epidemiological situation in Brazil, as well as program performance indicators in 2001–2010 period, and discuss the relationship between changes observed and control measures implemented in this century first decade.

Methods

It is a descriptive study, data source was the Information System for Notifiable Diseases (Sinan), Mortality Information System (SIM), Unified Health System Hospital Information System (SIH/SUS) and TB Multidrug-resistant Surveillance System (MDR-TB/SS). Indicators analyzed were organized into four major groups: TB control program (TCP) coverage and case detection; morbidity; treatment and TCP performance; and mortality.

Results

In the years analyzed there was a decrease in the number of new cases and incidence rate, mortality reduction (relative and absolute), and improvement in TB detection and diagnosis, as well in TB/HIV coinfection and drug resistance. However, little progress was found in contact investigation, diagnosis in primary care and TB cure rate.

Discussion

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.

Keywords:
Tuberculosis
Epidemiology
Surveillance
Information system
Data quality
Full Text
Introduction

Although tuberculosis (TB) has an effective treatment for decades, with the resurgence of the disease in the 80s and 90s, as a result of the AIDS epidemic, the World Health Organization (WHO) established TB as a global public health emergency in 1993.

At the time, it was estimated a total of 7–8 million incident cases of TB and 1.3–1.6 million deaths per year worldwide.1 Likewise, recognizing TB as a major global health problem, the United Nations (UN) included tuberculosis in the Millennium Development Goals in 2000. TB is present in the sixth goal and the global targets set for 2015 include reducing the incidence and mortality of the disease by 50% when compared to 1990.

Brazil is of the 22 countries with high burden of the disease worldwide. The number of TB incident cases has decreased on average 1.3% per year in the world since 2002 and mortality was reduced by a third since 1990. If these trends continue, global targets for TB control could be achieved. Brazil has a decreasing trend in incidence rate and according to WHO estimates has reached the goal of start reducing mortality.1

As the main strategy for tuberculosis control, in order to reduce default and death from TB and increase cure, WHO adopted the Directly Observed Treatment Short-Course (DOTS). The strategy includes six components: political commitment, case detection by microscopy sputum smear, standardized treatment, directly observed treatment (DOTS), regular and uninterrupted standardized drugs supply and reporting case system.2 This strategy importance is to make treatment outcome not only a patient responsibility, but also a compromise between them and health care system from diagnosis to discharge. Government should make TB control a political priority giving all logistics and strategic conditions necessary in the way.

As tuberculosis became a priority inside the Health Ministry (HM) DOTS strategy and decentralization of TB control to primary care began to strengthen. The increasing national budget, the presence of TB in different instruments of agreement between federal government, states and municipalities, provided increased visibility to TB, both technical and political.

Over the last decade, TB National Control Program (NTP) has been engaged in disseminating morbidity and mortality data from their information systems in publications as the Brazil Health Series, epidemiological bulletins and scientific articles. The intention is subsidize decision-making and adoption of public policies in the three levels of management with information generated from surveillance data. This study aims to describe TB epidemiological and controlling situation in Brazil, in the 2001–2010 period, and discuss the relationship between changes observed and control measures proposed in this century first decade.

Methods

It is a descriptive study of TB notified cases, hospitalizations and deaths occurred in Brazil in the 2001–2010 period.

Data sources used were the Information System for Notifiable Diseases called Sinan-TB (updated on November 2011), the Mortality Information System called SIM, the Unified Health System Hospital Information System called SIH/SUS, the Multidrug-Resistant Tuberculosis Surveillance System called MDR-TB/SS, the Health Establishment National Register and the population bases from the Informatic Department of Unified Health System called Datasus.

The definition of new TB case followed the guidelines included in the Recommendations Manual for Tuberculosis Control in Brazil.3 Qualifications on TB records in Sinan were made by states and municipalities, through out surveillance routines performed, and by national level by checks on information available on national basis.3

Epidemiological and operational TB data were analyzed for the period of 2001–2010, and were aggregated by year of diagnosis, Brazil and Federal Units (FU) of residence. The variables “institutionalized”, “contacts investigated” and “supervised treatment performed” were inserted in Sinan in 2007. For this reason, they were only described after this year.

For data analysis were used the softwares EpiInfo 3.5.2, Microsoft Excel® 2010 and Microsoft Access® 2003. The indicators analyzed were organized into four major groups: TB control programs (TCP) coverage and case detection; morbidity; treatment and PCT performance; and mortality.

TCP coverage and case detection

  • -

    Percentage of municipalities which diagnosed TB cases. Case notification was used as a proxy of diagnosis;

  • -

    Percentage of TB cases diagnosed in primary care facilities (PCF);

  • -

    DOTS coverage in health facilities. The variable “supervised treatment performed” was used to analyze this indicator and WHO's recommended concept of DOTS coverage in the health unit in which the health unit with at least one case in DOTS was accounted for in analysis; and

  • -

    TB detection rate for all forms of the disease. WHO's estimate number of cases in Brazil was used for comparison.

Morbidity

  • -

    Crude incidence rate per 100,000 inhabitants;

  • -

    Percentage of TB cases by type input in the information system (new, retreatment and transfers);

  • -

    Percentage of new cases by sex, age, race, education, and institutionalization;

  • -

    Percentage of new cases according to clinical form;

  • -

    Number of cases of MDR-TB; and

  • -

    Percentage of TB/HIV cases by total of new cases.

Treatment and TCP performance

  • -

    Percentage of smear tests performed by total of new pulmonary cases;

  • -

    Percentage of new cases tested for HIV (only the positive and negative cases were accounted, “in process” were discarded);

  • -

    Percentage of contacts investigated among contacts identified;

  • -

    Percentage of new cases regarding the closer situation;

  • -

    Percentage of retreatment cases with sputum culture performed;

  • -

    Percentage of new cases on DOTS by total new cases, and

  • -

    Number of TB hospitalizations and average admission cost.

Mortality

  • -

    Crude TB mortality rate per 100,000 inhabitants. For this indicator analysis were included only deaths that had TB as a primary cause of death.

ResultsTCP coverage and case detection

In 2010, 62.2% of Brazilian municipalities diagnosed at least one case, while in 2001 this figure was 48.9%. In 2001, primary care units notified 50.2% (19,181) of new smear positive cases. In 2010, this proportion rose to 56.3% (22,983), representing an annual increase of 2.1% on average in Brazil.

The variable “directly observed treatment performed” was included in Sinan in 2007. For this reason, DOTS coverage was analyzed from this year on. The number of health facilities that perform DOTS in Brazil increased from 1608 in 2006, which represented 30.1% of all units that have reported cases in the country, to 4745 (75.2%) in 2010. This represents an increase of 40.9% on average in the years studied.

The case detection rate in 2001 was 65% while 2010 showed the best value in the series, 88% (Table 1).

Table 1.

TCP coverage and case detection – Brasil, 2001–2010.

  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010 
TCP coverage
Percentage of municipalities which diagnosed TB cases  48.9  60.1  62.1  62.2  64  64.2  63.1  64.1  63.7  62.2 
Percentage of TB cases diagnosed in primary care facilities  50.2  52.0  54.1  55.1  55.6  56.9  56.2  55.6  56.7  56.3 
DOTS coverage in health facilities  –  –  –  –  –  30.1  69.6  71.1  72.4  75.2 
Case detection
TB detection rate  65  77  74  83  81  79  78  82  86  88 

Source: Sinan-TB, WHO.

Morbidity

TB incidence in Brazil started to decline in 2003. It occurred a small increase in 2008, and continued to decline after words as seen in Fig. 1.

Fig. 1.

Tuberculosis crude incidence rate (Sinan-TB) – Brazil, 2001–2010. Source: Sinan-TB.

(0.08MB).

The incidence rate decreased on average 1.4% annually from 2001 to 2010. This decrease, however, did not occur evenly throughout the period, between regions or FS. In 2001, North and Northeast regions showed the highest incidence rates in the country, 51.2/100,000 inhab. and 46.0/100,000 inhab., respectively. With the exception of southern Brazil, all other regions showed a decline in the incidence rate over the 10 years of study. In 2010, Northern region showed the highest incidence rate in the country (45.7/100,000 inhab.) followed by Southeast (40.7/100,000 inhab.) (Table 2).

Table 2.

Number of new cases and tuberculosis crude incidence rate (Sinan-TB) – Brazil and state of residence, 2001–2010.

Federate unit  Number of cases
  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010 
Missing  540  682  748  887  821  31  50  59  57  56 
North Region  6776  6890  6888  7117  6942  6893  6953  7014  7321  7252 
Rondônia  561  536  548  532  541  448  473  481  571  477 
Acre  325  305  305  278  267  352  282  274  322  307 
Amazonas  2273  2105  2035  2135  2085  2164  2274  2380  2278  2360 
Roraima  131  145  161  185  130  122  121  136  132  129 
Pará  3024  3278  3410  3544  3477  3343  3351  3338  3597  3601 
Amapá  194  252  211  224  230  230  244  233  220  192 
Tocantins  268  269  218  219  212  234  208  172  201  186 
Northeast Region  22,228  21,561  22,775  22,877  23,157  20,980  20,250  20,568  20,688  19,622 
Maranhão  2637  2725  2623  2668  2760  2544  2478  2212  2163  2112 
Piauí  1168  1103  1035  1102  1088  992  848  804  851  813 
Ceará  3545  3593  3915  3855  3997  3525  3497  3838  3871  3631 
Rio Grande do Norte  1041  1080  1128  1169  1083  997  926  1020  971  910 
Paraíba  1137  1150  1186  1219  1214  991  1009  1074  1067  1061 
Pernambuco  3810  4043  4309  4465  4433  4067  4081  4209  4202  4128 
Alagoas  1141  1146  1196  1183  1258  1141  1177  1204  1187  1154 
Sergipe  434  457  527  491  676  594  504  589  571  518 
Bahia  7315  6264  6856  6725  6648  6129  5730  5618  5805  5295 
Southeast Region  32,638  36,269  35,645  34,742  33,514  32,820  32,714  33,776  32,919  32,724 
Minas Gerais  1187  5029  5152  5189  5044  4691  4686  4545  4254  3867 
Espírito Santo  1335  1333  1321  1276  1270  1201  1259  1378  1274  1298 
Rio de Janeiro  13,670  13,584  13,279  12,943  12,329  11,582  11,554  11,848  11,633  11,310 
São Paulo  16,446  16,323  15,893  15,334  14,871  15,346  15,215  16,005  15,758  16,249 
South Region  8203  8913  9214  8975  8741  8308  8748  8996  9151  9095 
Paraná  2635  2800  2872  2616  2676  2437  2592  2540  2406  2393 
Santa Catarina  1352  1526  1576  1516  1485  1540  1579  1670  1650  1730 
Rio Grande do Sul  4216  4587  4766  4843  4580  4331  4577  4786  5095  4972 
Center-West Region  3412  3181  3336  3096  3293  3181  3110  3185  3054  3181 
Mato Grosso do Sul  838  767  880  863  895  778  825  888  897  820 
Mato Grosso  1217  1055  1049  955  1119  1152  1017  1099  985  1186 
Goiás  1012  1014  1034  935  921  873  860  844  887  884 
Distrito Federal  345  345  373  343  358  378  408  354  285  291 
Brazil  73,797  77,496  78,606  77,694  76,468  72,213  71,825  73,598  73,190  71,930 
Federate unit  Incidence rate
  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010 
Missing  –  –  –  –  –  –  –  –  –  – 
North Region  51.2  51.0  50.0  50.6  47.2  45.9  45.3  46.3  47.7  45.7 
Rondônia  39.8  37.4  37.6  35.9  35.3  28.7  29.7  32.2  38.0  30.5 
Acre  56.6  52.0  50.8  45.3  39.9  51.3  40.1  40.3  46.6  41.9 
Amazonas  78.4  71.1  67.1  68.9  64.5  65.4  67.1  71.2  67.1  67.7 
Roraima  38.8  41.8  45.1  50.3  33.2  30.2  29.1  32.9  31.3  28.6 
Pará  47.7  50.8  51.9  52.9  49.9  47.0  46.2  45.6  48.4  47.5 
Amapá  38.9  48.8  39.5  40.5  38.7  37.4  38.3  38.0  35.1  28.7 
Tocantins  22.6  22.3  17.7  17.5  16.2  17.6  15.3  13.4  15.6  13.4 
Northeast Region  46.0  44.1  46.1  45.9  45.4  40.7  38.8  38.7  38.6  37.0 
Maranhão  46.0  47.0  44.7  44.9  45.2  41.1  39.6  35.1  34.0  32.1 
Piauí  40.7  38.1  35.4  37.4  36.2  32.7  27.7  25.8  27.1  26.1 
Ceará  47.0  46.9  50.5  49.0  49.4  42.9  42.0  45.4  45.3  43.0 
Rio Grande do Norte  37.0  37.9  39.1  40.0  36.1  32.8  30.0  32.8  30.9  28.7 
Paraíba  32.8  32.9  33.7  34.4  33.8  27.4  27.6  28.7  28.3  28.2 
Pernambuco  47.6  50.0  52.8  54.2  52.7  47.8  47.5  48.2  47.7  46.9 
Alagoas  39.9  39.7  41.0  40.1  41.7  37.4  38.2  38.5  37.6  37.0 
Sergipe  23.9  24.8  28.1  25.8  34.4  29.7  24.8  29.5  28.3  25.0 
Bahia  55.4  47.0  51.0  49.6  48.1  43.9  40.7  38.7  39.7  37.8 
Southeast Region  44.4  48.7  47.3  45.5  42.7  41.3  40.6  42.1  40.7  40.7 
Minas Gerais  6.5  27.4  27.8  27.7  26.2  24.1  23.8  22.9  21.2  19.7 
Espírito Santo  42.3  41.6  40.6  38.7  37.3  34.7  35.8  39.9  36.5  36.9 
Rio de Janeiro  93.9  92.3  89.2  86.1  80.1  74.4  73.4  74.6  72.7  70.7 
São Paulo  43.7  42.8  41.1  39.1  36.8  37.4  36.5  39.0  38.1  39.4 
South Region  32.2  34.6  35.4  34.1  32.4  30.4  31.6  32.7  33.0  33.2 
Paraná  27.2  28.6  29.0  26.1  26.1  23.5  24.7  24.0  22.5  22.9 
Santa Catarina  24.8  27.6  28.1  26.7  25.3  25.8  26.1  27.6  27.0  27.7 
Rio Grande do Sul  40.9  44.1  45.3  45.6  42.2  39.5  41.3  44.1  46.7  46.5 
Center-West Region  28.7  26.3  27.1  24.7  25.3  24.0  23.0  23.3  22.0  22.6 
Mato Grosso do Sul  39.7  35.8  40.6  39.3  39.5  33.9  35.4  38.0  38.0  33.5 
Mato Grosso  47.5  40.5  39.6  35.4  39.9  40.3  34.9  37.2  32.8  39.1 
Goiás  19.8  19.5  19.5  17.3  16.4  15.2  14.7  14.4  15.0  14.7 
Distrito Federal  16.4  16.1  17.0  15.4  15.3  15.9  16.8  13.8  10.9  11.3 
Brazil  42.8  44.4  44.4  43.4  41.5  38.7  37.9  38.8  38.2  37.7 

Source: Sinan-TB and Datasus.

While incidence declined 5.0% on average per year in Tocantins, there was an average increase of 1.7% annually in Sergipe. 2001 was excluded for Minas Gerais state due to migration error in databases in Sinan on that year.

These rates also fluctuated substantially over the period studied. Almost all FS had fluctuations greater than 10% from one year to another, with the exception of Amazonas, Pará, Rio Grande do Norte, Pernambuco, Minas Gerais, Rio de Janeiro, São Paulo and Rio Grande do Sul. In 2010, Amazonas, Espírito Santo, São Paulo, Paraná, Santa Catarina and Distrito Federal showed opposite trends from the remaining states.

In 2010, the highest incidence rates occurred in Rio de Janeiro (70.7), Amazonas (67.7), Pará (47.5), Pernambuco (46.9) and Rio Grande do Sul (46.5) states. In that same year, the difference between the highest and the lowest rate registered in Rio de Janeiro (70.7 per 100,000 inhabitants) and Distrito Federal (11 per 100,000 inhabitants) was higher than six times (Table 2).

As can be seen in Table 3, new cases represented 82.7% (71,930) of all reported cases in 2010. That figure was 84.6% (73,797) in 2001. Compared to 2001, the observed values in 2010 decreased in almost all FS. In 2010, the proportion of new cases among all cases notified ranged from 89.8% (1624) in São Paulo to 76.2% (1061) in Paraíba.

Table 3.

Tuberculosis cases profile according to sex, age, race, education, input in the information system and institutionalization status (Sinan-TB) – Brazil, 2001–2010.

TB cases profile  20012002200320042005
  n  n  n  n  n 
Input in information system                     
New case  73,797  84.6  77,496  83.5  78,606  83.8  77,694  83.6  76,468  83.8 
Retreatment  11,661  13.4  11,930  12.8  11,100  11.8  10,761  11.6  10,116  11.1 
Transfer from another unit  1579  1.8  3160  3.4  3708  4.0  4191  4.5  4488  4.9 
Sex                     
Male  47,133  63.9  49,545  63.9  50,235  63.9  49,947  64.3  49,369  64.6 
Female  26,584  36.0  27,877  36.0  28,361  36.1  27,735  35.7  27,067  35.4 
Age                     
1–4 years old  1362  1.8  1345  1.7  1334  1.7  1217  1.6  1080  1.4 
5–14 years old  2005  2.7  2113  2.7  2035  2.6  1931  2.5  1965  2.6 
15–34 years old  30,460  41.3  31,277  40.4  31,816  40.5  31,560  40.7  30,972  40.5 
35–64 years old  33,558  45.5  35,792  46.3  36,373  46.3  36,058  46.5  35,598  46.6 
65 and plus  6306  8.6  6856  8.9  6937  8.8  6841  8.8  6790  8.9 
Education                     
Illiterate  8207  11.1  8886  11.5  8344  10.6  7818  10.1  7547  9.9 
Up to 8 years  30,023  40.7  32,230  41.6  34,471  43.9  34,541  44.5  33,814  44.2 
More than 8 years  11,013  14.9  14,570  18.8  16,792  21.4  18,052  23.2  17,895  23.4 
Race                     
Missing  65,414  88.6  49,404  63.8  29,477  37.5  23,781  30.6  22,472  29.4 
White  3391  4.6  12,266  15.8  19,903  25.3  21,173  27.3  20,347  26.6 
Black  863  1.2  4281  5.5  7542  9.6  8381  10.8  8038  10.5 
Yellow  96  0.1  428  0.6  796  1.0  855  1.1  682  0.9 
Brown  3715  5.0  10,532  13.6  20,135  25.6  22,836  29.4  24,318  31.8 
Indian  318  0.4  585  0.8  753  1.0  668  0.9  610  0.8 
Institutionalization                     
Missing  –  –  –  –  –  –  –  –  –  – 
Not institutionalized  –  –  –  –  –  –  –  –  –  – 
Jail  –  –  –  –  –  –  –  –  –  – 
Institutionalized but not in jail  –  –  –  –  –  –  –  –  –  – 
TB cases profile  20062007200820092010
  n  n  n  n  n 
Input in information system                     
New case  72,213  83.6  71,825  83.5  73,598  83.7  73,190  83.7  71,930  82.7 
Retreatment  9884  11.4  9903  11.5  10,127  11.5  10,020  11.5  10,405  12.0 
Transfer from another unit  4128  4.8  4291  5.0  4173  4.7  4167  4.8  4625  5.3 
Sex                     
Male  46,761  64.8  46,930  65.3  48,271  65.7  48,056  66.1  47,546  64.8 
Female  25,449  35.2  24,893  34.7  25,315  34.3  25,131  33.9  24,383  35.2 
Age                     
1–4 years old  1002  1.4  1034  1.4  965  1.3  1015  1.4  907  1.3 
5–14 years old  1708  2.4  1773  2.5  1806  2.5  1752  2.4  1536  2.1 
15–34 years old  29,045  40.3  28,903  40,3  29,872  40.6  29,895  40.9  29,173  40.6 
35–64 years old  33,778  46.8  33,553  46.8  34,408  46.8  33,997  46.5  33,654  46.9 
65 and plus  6595  9.1  6443  9.0  6447  8.8  6434  8.8  6545  9.1 
Education                     
Illiterate  5872  8.1  2985  4.2  3540  4.8  3584  4.9  3478  4.8 
Up to 8 years  26,483  36.7  31,443  43.8  28,814  39.2  2789  38  26,322  36.6 
More than 8 years  13,653  18.9  9651  13.4  11,944  16.2  12,841  17.5  12,757  17.7 
Race                     
Missing  17,660  24.5  13,361  18.6  11,649  15.8  7769  10.6  6732  9.4 
White  20,532  28.4  22,567  31.4  24,150  32.8  25,346  34.6  25,231  35.1 
Black  8210  11.4  8462  11.8  8948  12.2  9420  12.9  9176  12.8 
Yellow  767  1.1  820  1.1  776  1.1  746  1.0  646  0.9 
Brown  24,392  33.8  25,785  35.9  27,283  37.1  29,093  39.7  29,366  40.8 
Indian  622  0.9  830  1.2  792  1.1  816  1.1  779  1.1 
Institutionalization                     
Missing  –  –  23,870  33.2  17,292  23.5  3959  5.4  3565  5.0 
Not institutionalized  –  –  42,924  59.8  50,571  68.7  62,533  85.4  61,664  85.7 
Jail  –  –  2726  3.8  3445  4.7  4407  6.0  4643  6.5 
Institutionalized but not in jail  –  –  2305  3.2  2290  3.1  2291  3.1  2058  2.9 

Source: Sinan-TB.

In 2010, ten Brazilian FS concentrated more than 80% (57,806) of new TB cases in the country, São Paulo, Rio de Janeiro, Bahia, Rio Grande do Sul, Pernambuco, Minas Gerais, Ceará, Pará, Amazonas and Paraná. Rio de Janeiro and São Paulo themselves were responsible for 38.3% (27,559) of all new cases in the country in that same year.

Regarding demographic variables, it is observed that TB affects all population groups with predominance in males on working age. Men accounted for 63.9% (47,133) of all new cases in 2001. This proportion gradually increased until reached 66.1% (48,056) in 2009 and dropped again to 64.8% (47,546) in 2010. Tow age groups, 15–34 and 35–64 years old, concentrated more than 85% of new TB cases in the country in all the years studied.

The high number of missing records in variable “race/color” until 2006 made difficult to analyze this variable in the early years of the study. For this reason this variable was described from 2007 on. In 2010 when color was registered on more than 90% of cases, 53.6% (38,542) of new cases were brown or black and 35.1% (25,231) were white.

Regarding education, in 2001 about half the cases, 51.8% (38,230), had studied less than 8 years. Throughout the period the proportion of new cases illiterate and up to 8 years of study decreased on average 6.9% and 0.7% respectively between the years studied, while the category over 8 years of study showed an increase of 3.4% annually on average. It should be considered, however, the improvement in education among the whole Brazilian population in this period.

Table 3 shows that the proportion of new cases institutionalized in prisons increased from 3.8% (2726) in 2007 to 6.5% (4643) in 2010, an annual increase of 19.7% on average in the period.

The number of multidrug-resistant tuberculosis (MDR-TB) cases in 2010 was 607. This figure was 334 in 2001. This represents an annual increase of 8.1% on average in the number of MDR-TB cases in Brazil in the 10 years studied. This increase was particularly high between 2004–2005 and 2009–2010, with 22.6% and 47.3% increase from one year to another, respectively (Fig. 2). It is important to consider that in this last period NTP began to prioritize culture and sensitivity testing for all retreatment cases and for the most vulnerable populations.

Fig. 2.

Number of multidrug resistant tuberculosis cases – Brazil, 2001–2010. Source: Multidrug Resistant Surveillance System (TBMR/SS).

(0.09MB).

The proportion of new TB cases HIV positive was 9.9% (7096) in 2010. Compared to 2001, which recorded 7.5% (5508) HIV-positive cases among all TB cases, there was an average annual increase of 3.2% in coinfection during the period studied (Table 4), reflecting the increase on HIV testing in recent years.

Table 4.

Diagnosis and treatment variables analysis of new cases (Sinan-TB) – Brazil, 2001–2010.

New cases  20012002200320042005
  n  n  n  n  n 
Pulmonary  63,336  85.8  66,256  85.5  67,209  86  66,423  85.5  65,684  85.9 
Extrapulmonary  10,461  14.2  11,240  14.5  11,397  14  11,270  14.5  10,784  14.1 
Sputum smear performed  52,245  82.5  54,705  82.6  55,732  83  55,129  83.0  55,490  84.5 
Bacilliferous  39,460  62.3  41,416  62.5  42,044  63  41,471  62.4  41,801  63.6 
Tested for HIV  19,034  25.8  21,967  28.3  24,175  31  25,633  33.0  28,274  37.0 
HIV positive  5508  7.5  5941  7.7  6066  5830  7.5  5806  7.6 
Investigated contacts  –  –  –  –  –  –  –  –  –  – 
Cure  49,954  67.7  52,688  68.0  55,137  70  54,885  70.6  55,579  72.7 
Default  8137  11.0  7649  9.9  7453  7182  9.2  6881  9.0 
Transfer from another unit  5003  6.8  5599  7.2  6237  5981  7.7  5769  7.5 
Death  58  0.1  54  0.1  83  95  0.1  273  0.4 
Missing  6274  8.5  6670  8.6  4705  4689  6.0  3069  4.0 
MDR TB  27  0.0  62  0.1  55  81  0.1  76  0.1 
Cases under DOTS  –  –  –  –  –  –  –  –  –  – 
New cases  20062007200820092010
  n  n  n  n  n 
Pulmonary  62,006  85.9  61,529  85.7  62,994  85.6  62,707  85.7  61,784  85.9 
Extrapulmonary  10,201  14.1  10,290  14.3  10,588  14.4  10,464  14.3  10,128  14.1 
Sputum smear performed  52,691  85.0  52,753  85.7  54,116  85.9  53,866  85.9  53,440  86.5 
Bacilliferous  40,442  65.2  40,341  65.6  41,276  65.5  40,667  64.9  40,820  66.1 
Tested for HIV  29,646  41.1  33,542  46.7  37,346  50.7  40,127  54.8  42,056  58.5 
HIV positive  5701  7.9  6415  8.9  6648  9.0  6815  9.3  7096  9.9 
Investigated contacts  –  –  114,218  57.6  127,205  56.8  139,741  61.7  130,948  57.9 
Cure  52,092  72.1  51,853  72.2  53,075  72.1  51,984  71.0  44,527  61.9 
Default  6548  9.1  6799  9.5  7130  9.7  7324  10.0  5888  8.2 
Transfer from another unit  4843  6.7  4638  6.5  4962  6.7  5343  7.3  5741  8.0 
Death  1336  1.9  2543  3.5  2397  3.3  2309  3.2  2196  3.1 
Missing  3353  4.6  2928  4.1  3075  4.2  2971  4.1  10,643  14.8 
MDR TB  76  0.1  119  0.2  99  0.1  163  0.2  108  0.2 
Cases under DOTS  –  –  28,744  33.4  31,135  35.4  32,716  37.4  36,736  42.2 

Source: Sinan-TB.

New pulmonary cases represented approximately 85% of the total cases reported in 2001 and these values remained almost constant until 2010 (Table 4).

Treatment and TCP performance

In 2001, 82.5% (52,245) of new pulmonary cases underwent microscopy sputum smear. This percentage has increased gradually until reached 86.5% (53,440) in 2010. New smear-positive cases accounted for 62.3% (39,460) of all new pulmonary cases in 2001 and there was a slight increase in this figure over the period, reaching a value of 66.1% (40,820) in 2010 (Table 4).

With an inverse behavior from cure rate, the proportion of default decreased from 11.0% (8137) in 2001 to 9.0% (6881) in 2005. Then it remained almost constant until 2009, recording 10.0% (7324) that year. In 2010, default rate was 8.2% (5888), although outcome had 14.8% (10,643) of missing data in that year.

However, this trend was not homogeneous between federal states. While default decreased on average 8.8% annually in Distrito Federal, there was an average annual increase in treatment default of 14.0% in Roraima. In 2010, default rate ranged between 2.1% (6) in Distrito Federal and 10.6% (527) in Rio Grande do Sul. Among Brazilian states, Distrito Federal, Tocantins, Piauí and Acre, showed less than 5% of default in 2009. That same year, Minas Gerais, São Paulo, Pernambuco, Rondônia, Rio Grande do Sul, Maranhão and Rio de Janeiro showed default rates greater than 10%.

The proportion of treatment site transfers increased 2.1% annually on average between 2001 and 2010. In the years studied, São Paulo registered an average annual decrease of 12.4% and Acre an average annual increase of 59.4%. The proportion of treatment site transfers ranged from 0.9% (149) in São Paulo and 25.5% (49) in Amapá in 2010 (Table 5).

Table 5.

New cases outcome (Sinan-TB) – Brazil and state of residence, 2001–2010.

Federate unit  20012002200320042005
  Cure  Default  Transfer  Cure  Default  Transfer  Cure  Default  Transfer  Cure  Default  Transfer  Cure  Default  Transfer 
Missing  64.6  18.7  10.6  69.1  11.6  11.9  74.2  10.4  8.6  73.1  8.0  9.6  70.9  12.2  7.8 
Rondônia  72.5  12.5  9.1  77.2  10.4  7.3  69.9  11.9  12.0  67.5  10.7  16.4  73.2  7.8  13.1 
Acre  83.7  11.4  0.9  76.7  10.5  6.6  70.5  14.4  9.8  77.7  9.0  7.9  80.1  8.2  6.4 
Amazonas  80.2  10.6  1.2  79.5  10.2  3.9  75.4  9.1  8.1  74.8  10.3  7.8  69.9  11.6  5.6 
Roraima  82.4  4.6  6.9  81.4  4.8  6.9  83.2  2.5  8.1  85.4  2.7  6.5  83.1  3.8  6.2 
Para  71.8  11.4  11.0  73.6  11.4  9.9  70.8  11.1  11.4  72.8  10.2  10.4  73.0  10.2  9.4 
Amapá  64.9  16.0  11.9  61.9  15.1  10.3  63.5  10.4  10.4  65.6  11.2  11.6  60.4  10.0  13.5 
Tocantins  69.4  8.6  14.2  73.6  11.2  10.8  67.4  8.7  20.6  74.9  6.8  15.5  72.2  5.7  16.5 
Maranhão  70.4  12.3  10.6  71.7  12.3  9.9  68.3  11.9  12.6  68.3  10.8  14.6  71.4  6.7  15.6 
Piauí  72.7  5.0  17.2  68.3  3.7  22.7  75.3  4.1  13.8  64.9  3.8  24.6  68.6  4.3  19.9 
Ceara  73.3  6.3  4.2  61.8  6.6  4.8  72.0  7.8  6.7  72.9  7.4  5.2  74.6  7.7  6.6 
Rio Grande do Norte  77.7  11.5  4.9  78.0  11.1  3.9  69.4  9.2  15.6  68.3  9.8  17.0  67.9  9.2  18.3 
Paraíba  72.6  11.8  11.3  71.1  8.0  13.0  75.3  7.0  12.9  68.4  8.2  16.2  73.1  8.1  13.4 
Pernambuco  64.1  15.4  8.0  65.3  12.5  12.2  64.4  11.0  13.9  67.1  10.4  12.5  67.4  10.4  12.4 
Alagoas  76.2  11.7  6.3  71.9  10.4  12.0  72.2  9.6  12.3  75.1  10.7  7.7  78.5  9.4  4.9 
Sergipe  81.6  10.1  3.7  83.8  6.6  2.4  82.5  5.9  5.9  78.6  10.6  5.5  70.9  6.5  14.8 
Bahia  63.5  8.7  9.5  66.1  8.2  14.1  67.1  7.3  14.9  71.4  7.6  10.2  72.1  6.9  10.3 
Minas Gerais  67.3  16.2  5.2  73.8  10.5  5.2  72.5  10.5  5.1  71.0  9.8  7.3  73.7  8.9  6.5 
Espírito Santo  74.5  6.6  11.2  79.7  4.8  9.8  79.4  4.3  8.6  79.4  5.0  8.2  83.4  5.6  3.9 
Rio de Janeiro  51.8  11.9  4.8  49.5  10.2  4.3  57.7  10.5  5.3  57.1  11.0  4.8  66.1  11.2  5.4 
São Paulo  72.5  12.2  5.3  73.7  10.9  4.4  76.8  9.9  3.4  78.8  9.0  2.9  77.9  9.6  2.8 
Paraná  73.9  10.6  5.4  75.7  7.8  6.7  73.9  7.5  7.9  70.7  8.1  9.8  75.5  6.6  7.7 
Santa Catarina  71.4  10.4  4.4  74.6  7.7  6.7  75.1  8.7  6.9  76.8  9.7  5.6  77.3  7.1  7.3 
Rio Grande do Sul  69.7  8.9  8.9  70.7  9.3  7.5  71.8  9.8  7.6  72.6  8.4  8.2  71.4  8.8  8.4 
Mato Grosso do Sul  75.5  11.5  6.6  70.4  11.5  9.0  74.1  9.4  5.8  71.3  7.8  7.2  75.4  6.1  6.8 
Mato Grosso  80.0  8.8  5.8  76.8  7.9  8.2  77.6  9.2  7.3  76.3  10.3  7.6  77.2  8.4  7.1 
Goiás  71.1  10.0  11.1  74.2  10.3  8.6  69.3  10.3  10.8  65.5  10.3  13.3  68.9  9.2  12.1 
Distrito Federal  86.4  7.0  3.2  85.2  6.1  1.7  84.7  5.9  2.7  86.0  4.4  2.9  83.5  5.6  5.6 
Brazil  67.7  11.0  6.8  68.0  9.9  7.2  70.1  9.5  7.9  70.6  9.2  7.7  72.7  9.0  7.5 
Federate unit  20062007200820092010
  Cure  Default  Transfer  Cure  Default  Transfer  Cure  Default  Transfer  Cure  Default  Transfer  Cure  Default  Transfer 
Missing  48.4  9.7  16.1  50.0  12.0  26.0  37.3  8.5  28.8  29.8  12.3  33.3  41.1  7.1  28.6 
Rondônia  71.9  10.7  9.2  73.6  8.2  8.5  73.8  10.6  8.9  67.4  10.7  16.6  60.6  8.6  13.0 
Acre  79.0  2.8  7.4  86.9  4.3  2.5  85.8  7.7  1.8  90.4  4.3  1.6  82.1  6.5  1.6 
Amazonas  72.9  10.5  7.2  66.6  10.3  8.6  68.0  9.4  7.9  72.7  9.8  6.7  68.6  9.1  8.1 
Roraima  67.2  5.7  9.0  88.4  2.5  5.0  79.4  5.1  5.1  82.6  8.3  3.8  78.3  4.7  6.2 
Para  71.6  10.4  6.9  73.1  11.8  7.9  71.3  11.9  8.3  71.3  9.9  9.1  65.4  7.7  11.2 
Amapá  56.5  16.1  11.7  68.9  12.3  13.9  63.9  11.2  17.2  65.0  10.0  19.1  47.9  9.9  25.5 
Tocantins  76.1  1.3  16.2  74.5  4.3  11.5  75.0  4.7  10.5  72.1  4.0  11.9  58.1  2.2  15.1 
Maranhão  70.1  7.4  13.2  72.2  6.8  14.2  73.7  8.6  10.8  70.8  11.4  10.0  61.9  9.4  10.1 
Piauí  67.7  3.8  19.0  69.2  4.1  17.5  66.3  4.1  20.0  61.1  3.1  16.0  51.8  3.6  14.9 
Ceara  76.4  7.2  6.8  78.5  7.7  6.6  76.6  8.1  7.2  72.5  8.7  9.3  59.1  7.4  8.4 
Rio Grande do Norte  67.4  14.3  12.3  71.9  8.9  11.0  71.4  8.9  10.5  70.1  9.2  10.9  52.2  5.3  13.5 
Paraíba  79.5  7.5  4.9  71.7  10.2  11.6  63.8  12.8  14.9  63.0  8.0  17.7  49.1  6.6  23.2 
Pernambuco  68.9  8.1  12.2  68.8  9.2  10.1  65.2  11.3  11.6  60.4  10.4  12.9  47.8  8.3  12.9 
Alagoas  78.9  8.9  4.1  77.2  8.3  4.6  74.1  10.0  6.9  68.5  10.0  9.1  57.0  8.6  13.2 
Sergipe  71.9  9.8  12.3  77.8  13.3  3.6  74.9  14.1  3.9  74.3  9.8  5.6  75.7  7.7  5.0 
Bahia  66.9  6.2  8.9  70.6  6.9  9.0  71.6  6.7  9.1  68.6  6.6  11.9  55.7  5.3  13.8 
Minas Gerais  72.8  8.8  7.5  74.2  9.0  5.8  74.8  8.8  5.8  73.6  10.1  5.5  64.9  7.4  7.9 
Espírito Santo  77.7  7.2  6.2  80.3  5.3  5.9  80.6  5.7  6.2  78.6  7.4  5.9  71.4  7.0  7.2 
Rio de Janeiro  67.8  12.0  6.2  64.7  12.6  5.0  65.4  11.6  6.3  67.2  14.0  6.2  48.7  9.2  6.1 
São Paulo  76.1  10.5  0.9  75.9  10.5  1.2  77.8  10.3  1.1  77.4  10.3  1.3  76.1  9.3  0.9 
Paraná  73.5  7.0  7.3  73.2  7.1  8.9  73.5  8.4  7.7  71.9  7.4  7.5  65.9  6.6  8.1 
Santa Catarina  76.7  6.1  8.1  75.2  6.8  8.9  73.3  8.2  8.7  75.0  7.1  7.8  67.1  5.7  12.9 
Rio Grande do Sul  70.9  7.5  8.5  70.3  9.6  8.0  68.1  10.4  9.4  66.2  10.7  10.4  59.3  10.6  11.7 
Mato Grosso do Sul  77.4  5.8  5.5  74.3  8.2  6.1  73.5  7.0  5.1  69.0  8.4  5.0  57.9  6.7  4.4 
Mato Grosso  75.7  6.6  8.3  78.3  4.8  8.5  76.9  7.6  9.6  72.9  7.6  9.9  54.1  7.1  12.3 
Goiás  65.2  8.9  10.9  70.3  8.6  11.2  73.0  7.9  9.4  70.9  8.7  7.7  57.6  6.0  10.2 
Distrito Federal  81.7  3.2  6.6  85.5  2.5  5.1  82.2  3.7  9.9  86.3  2.5  5.3  76.3  2.1  8.6 
Brazil  72.1  9.1  6.7  72.2  9.5  6.5  72.1  9.7  6.7  71.0  10.0  7.3  61.9  8.2  8.0 

Source: Sinan-TB.

13.4% (11,661) of all cases reported in 2001 were retreatment. Half of those were relapse and half readmission after default, representing 6.8% (5957) and 6.5% (5704) respectively. These values remained almost constant over the period, and in 2010 the proportion of retreatment was 12% (10,405).

Sputum culture in retreatment cases showed an average annual increase of 10.4% during the study period. The percentage of sputum culture tests conducted among retreatment cases in 2010 was 30.1% (2932) and in 2001 was 12.5% (1353) (Table 6).

Table 6.

Diagnosis and treatment variables analysis of retreatment cases (Sinan-TB) – Brazil, 2001–2010.

Retreatment  20012002200320042005
  n  n  n  n  n 
Relapse  5957  6.8  6293  6.8  5863  5626  6.1  5325  5.8 
Readmission after default  5704  6.5  5637  6.1  5237  5135  5.5  4791  5.2 
Culture performed  1353  12.5  1412  12.8  1457  14.2  1497  15.0  1582  16.9 
Cure  5957  51.1  6016  50.4  5819  52  5636  52.4  5512  54.5 
Default  2523  21.6  2495  20.9  2410  22  2313  21.5  2159  21.3 
Death  11  0.1  10  0.1  12  27  0.3  72  0.7 
Transfer from another unit  944  8.1  1001  8.4  1100  10  942  8.8  904  8.9 
Missing  1334  11.4  1501  12.6  849  1001  9.3  661  6.5 
MDR TB  36  0.3  55  0.5  55  66  0.6  77  0.8 
Retreatment  20062007200820092010
  n  n  n  n  n 
Relapse  5488  6.4  5202  6.0  5181  5.9  5037  5.8  5251  6.0 
Readmission after default  4396  5.1  4701  5.5  4946  5.6  4983  5.7  5154  5.9 
Culture performed  1846  20.1  2104  22.9  2300  24.5  2383  25.5  2932  30.1 
Cure  5436  55.0  5186  52.4  5202  51.4  4799  47.9  4161  40.0 
Default  2172  22.0  2335  23.6  2497  24.7  2561  25.6  2158  20.7 
Death  277  2.8  451  4.6  432  4.3  455  4.5  355  3.4 
Transfer from another unit  781  7.9  780  7.9  863  8.5  985  9.8  1156  11.1 
Missing  580  5.9  622  6.3  635  6.3  653  6.5  2030  19.5 
MDR TB  90  0.9  132  1.3  121  1.2  132  1.3  164  1.6 

Source: Sinan-TB.

Regarding retreatment cases outcome, in 2001, 51.1% (5957) cured, 21.6% (2523) were default, 8.1% (944) were transferred to another treatment site, and 0.3% (36) developed MDR-TB. These values remained almost constant over the period, with the exception of MDR-TB who presented an average annual increase of 21.3%. The proportion of missing data on closure got down 4.6% on average between 2001 and 2009, falling from 11.4% (1334) in 2001 to 6.5% (653) in 2009. In 2010, the proportion of missing data regarding closure was 19.5% (2030).

As can be seen in Table 4, the proportion of cases contained in the national database submitted to DOTS increased from 33.4% (28,744) in 2007 to 42.2% (36,763) in 2010. This represents an annual increase in the proportion of cases under DOTS of 8.2% on average.

In the 10 years studied, there were 180,363 hospital admissions duo to TB in Brazil, and this represented a 206 million dollars in hospital charges. In 2010, 16,153 hospital admissions were recorded in Brazil duo to all forms of TB, compared to 18,523 in 2001, representing an annual decrease of 1.0% on average. However, this trend was not uniform throughout the period, nor between FS. While São Paulo experienced an average annual decrease of 13.0% in TB hospitalizations during the study period, with 2020 admissions for TB in 2010, Sergipe had an average annual increase of 169.6%, with 43 admissions for TB in 2010. Santa Catarina, Paraná and Goiás also showed an average increase of more than 20% in hospital admissions for TB during the study period.

In 2001, São Paulo and Rio de Janeiro states alone concentrated 54.1% (10,027) of all admissions in the country for TB. In 2010, these states accounted for 27.5% (4200) of TB admissions. This decrease was mainly a decrease in the number of hospitalizations in the state of São Paulo. Paraná, Minas Gerais, Bahia, Pernambuco and Rio Grande do Sul in 2010 contributed over 5% each in the total of hospital admissions for TB in the country.

The average cost of hospital admissions duo to TB also varied over the years studied and between federal states. In 2001, R$ 751.14 was the average cost for this kind of hospitalization in the country, and in 2010 that figure raised up to R$ 1478.93. There was an average annual increase of 8.2% on the average cost of hospitalization for TB in Brazil in the period. Sergipe, Goiás and Amazonas had an average annual increase of 25.3%, 21.9% and 19.3%, respectively, on the average cost of hospitalization due to TB (Table 7).

Table 7.

Hospital admissions duo to tuberculosis (SIH-SUS) – Brazil and state of residence, 2001–2010.

Federate unit  20012002200320042005
  n  Average value  n  Average value  n  Average value  n  Average value  n  Average value 
Rondônia  169  0.9  455.3  120  0.6  437.3  175  0.8  448.6  158  0.8  489.6  132  0.7  584.7 
Acre  104  0.6  478.3  139  0.7  598.4  136  0.6  729.4  107  0.5  774.4  106  0.6  679.6 
Amazonas  291  1.6  504.1  277  1.4  524.8  661  3.2  733.3  884  4.3  866.8  874  4.7  1014.2 
Roraima  60  0.3  465.4  55  0.3  528.7  54  0.3  555.3  42  0.2  663.4  40  0.2  634.4 
Pará  640  3.5  501.2  591  3.0  518.5  595  2.8  540.0  627  3.1  646.7  463  2.5  693.3 
Amapá  91  0.5  458.7  87  0.4  467.0  47  0.2  541.1  59  0.3  524.7  57  0.3  630.5 
Tocantins  48  0.3  431.0  41  0.2  529.5  45  0.2  641.2  85  0.4  549.2  80  0.4  606.2 
Maranhão  397  2.1  467.1  339  1.7  554.0  330  1.6  549.7  318  1.6  654.5  316  1.7  691.3 
Piauí  151  0.8  431.1  271  1.4  561.0  254  1.2  549.9  175  0.9  577.8  236  1.3  649.7 
Ceará  367  2.0  645.3  714  3.6  867.2  555  2.6  945.8  487  2.4  838.8  498  2.7  759.4 
Rio Grande do Norte  236  1.3  570.1  287  1.5  653.8  281  1.3  623.1  302  1.5  697.8  238  1.3  903.0 
Paraíba  414  2.2  546.3  492  2.5  615.0  555  2.6  719.0  526  2.6  723.9  525  2.8  697.3 
Pernambuco  1057  5.7  717.9  1103  5.6  735.7  1235  5.9  598.6  1720  8.4  611.8  1308  7.1  976.1 
Alagoas  102  0.6  464.8  258  1.3  570.0  307  1.5  639.1  281  1.4  846.9  326  1.8  898.1 
Sergipe  0.0  400.0  29  0.1  902.5  35  0.2  1271.3  30  0.1  769.2  23  0.1  762.9 
Bahia  895  4.8  590.9  802  4.1  702.5  820  3.9  643.9  1084  5.3  681.5  1364  7.4  839.4 
Minas Gerais  1021  5.5  789.6  1481  7.5  879.7  1493  7.1  958.8  1470  7.2  1101.5  1459  7.9  1145.3 
Espírito Santo  150  0.8  527.0  109  0.6  507.5  240  1.1  432.6  174  0.9  443.8  120  0.6  577.5 
Rio de Janeiro  2491  13.4  819.4  2291  11.6  857.6  2288  10.9  839.7  2563  12.5  890.5  2279  12.3  896.8 
São Paulo  7536  40.7  863.2  7197  36.4  880.1  6991  33.4  920.8  5780  28.3  945.0  5008  27.1  1005.4 
Paraná  463  2.5  888.8  725  3.7  1101.2  833  4.0  1103.2  856  4.2  1275.4  654  3.5  1170.9 
Santa Catarina  133  0.7  575.4  291  1.5  1051.0  276  1.3  1063.9  181  0.9  850.9  185  1.0  908.1 
Rio Grande do Sul  759  4.1  733.2  1229  6.2  903.5  1608  7.7  975.9  1492  7.3  1051.6  1373  7.4  1056.1 
Mato Grosso do Sul  297  1.6  766.7  284  1.4  788.0  403  1.9  795.0  340  1.7  823.9  317  1.7  838.4 
Mato Grosso  222  1.2  511.1  200  1.0  561.8  199  0.9  534.7  176  0.9  581.8  110  0.6  705.1 
Goiás  221  1.2  532.1  221  1.1  643.9  321  1.5  722.2  308  1.5  656.7  282  1.5  746.6 
Distrito Federal  206  1.1  535.6  139  0.7  568.7  211  1.0  544.3  199  1.0  566.9  126  0.7  623.8 
Brazil  18,523  100.0  751.1  19,772  100.0  814.6  20,948  100.0  832.8  20,424  100.0  869.2  18,499  100.0  938.7 
Federate unit  20062007200820092010
  n  Average value  n  Average value  n  Average value  n  Average value  n  Average value 
Rondônia  104  0.6  630.3  98  0.6  632.7  62  0.3  105.5  93  0.6  239.0  117  0.7  148.8 
Acre  95  0.6  637.1  144  0.9  680.2  94  0.5  202.3  121  0.8  377.5  80  0.5  450.4 
Amazonas  359  2.1  701.0  277  1.8  886.1  284  1.6  495.3  300  1.9  572.4  453  2.8  1437.7 
Roraima  41  0.2  679.1  39  0.3  756.2  36  0.2  223.6  28  0.2  254.5  50  0.3  444.4 
Pará  428  2.5  635.2  379  2.4  693.4  449  2.5  786.5  475  3.1  1219.2  399  2.5  1231.2 
Amapá  47  0.3  663.4  24  0.2  734.2  68  0.4  263.1  52  0.3  140.4  44  0.3  146.9 
Tocantins  114  0.7  658.4  102  0.7  538.4  111  0.6  421.0  91  0.6  1065.7  87  0.5  877.0 
Maranhão  290  1.7  654.1  263  1.7  651.3  327  1.8  524.5  175  1.1  288.8  167  1.0  445.2 
Piauí  150  0.9  596.2  156  1.0  583.7  142  0.8  544.9  127  0.8  675.7  142  0.9  759.0 
Ceará  607  3.6  729.8  562  3.6  739.1  556  3.0  839.1  700  4.5  1329.0  708  4.4  1121.4 
Rio Grande do Norte  358  2.1  1039.5  299  1.9  1102.6  354  1.9  939.9  403  2.6  1507.6  428  2.6  1638.6 
Paraíba  607  3.6  684.7  582  3.8  694.0  444  2.4  1279.0  536  3.5  1548.5  724  4.5  1598.3 
Pernambuco  965  5.7  1006.7  999  6.5  1087.7  1648  9.0  1717.4  1354  8.8  1790.5  1470  9.1  1685.9 
Alagoas  186  1.1  835.2  221  1.4  854.6  178  1.0  455.1  223  1.4  694.5  250  1.5  1168.9 
Sergipe  72  0.4  1173.2  59  0.4  829.1  36  0.2  475.6  26  0.2  1243.3  43  0.3  724.4 
Bahia  1255  7.4  909.3  1254  8.1  1084.8  1090  6.0  968.2  1161  7.5  1574.0  1369  8.5  1604.5 
Minas Gerais  1485  8.8  1154.5  1203  7.8  1338.9  1461  8.0  1136.5  1384  9.0  1347.1  1302  8.1  1426.1 
Espírito Santo  120  0.7  695.4  127  0.8  705.5  128  0.7  882.7  167  1.1  1114.9  143  0.9  1321.3 
Rio de Janeiro  2166  12.8  932.6  2233  14.4  937.7  2243  12.3  776.5  2191  14.2  980.9  2180  13.5  1173.1 
São Paulo  4584  27.1  973.7  4020  26.0  938.9  2715  14.9  1248.1  2050  13.3  1529.1  2020  12.5  1590.7 
Paraná  633  3.7  1246.4  551  3.6  1328.8  2037  11.2  1265.5  961  6.2  2119.0  913  5.7  2150.8 
Santa Catarina  252  1.5  1135.7  184  1.2  1170.3  330  1.8  1181.3  412  2.7  1607.9  422  2.6  1523.4 
Rio Grande do Sul  1048  6.2  1022.1  952  6.1  995.1  1907  10.5  1232.3  1639  10.6  1773.9  1805  11.2  1782.7 
Mato Grosso do Sul  367  2.2  964.5  323  2.1  867.8  313  1.7  1460.9  233  1.5  1793.5  279  1.7  1587.1 
Mato Grosso  118  0.7  653.3  86  0.6  697.1  121  0.7  931.5  73  0.5  885.1  83  0.5  1478.0 
Goiás  301  1.8  877.1  219  1.4  684.9  970  5.3  594.3  299  1.9  1520.9  346  2.1  1482.7 
Distrito Federal  154  0.9  812.0  131  0.8  634.5  142  0.8  345.7  131  0.9  571.5  129  0.8  280.2 
Brazil  16,906  100.0  940.1  15,487  100.0  962.3  18,246  100.0  1074.7  15,405  100.0  1416.8  16,153  100.0  1478.9 

Source: Unified Health System Hospital Information System (SIH/SUS).

Mortality

Brazil has experienced an average annual decline in TB mortality rate of 2.9% between 2001 and 2010. In 2010, TB mortality rate was 2.4 deaths per 100,000 inhabitants. As the incidence rate, this trend was not uniform across states. While Paraná showed an annual decrease of 6.5% on average on mortality rate, Paraíba had an average annual increase of 10.9% in their rate.

Just as hospital admissions, São Paulo and Rio de Janeiro concentrated the majority of TB deaths in the country, accounting together for 43.3% (2349) of all deaths duo to TB in the country in 2001. This proportion has decreased over the study period, falling to 37.8% (1740) in 2010 (Table 8).

Table 8.

Number of deaths and crude mortality rate (SIM) – Brazil and state of residence, 2001–2010.

Federate unit  Number of deaths
  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010 
Rondônia  35  37  46  32  30  28  25  34  20  27 
Acre  26  19  21  18  27  23  28  16  16  15 
Amazonas  117  106  102  88  104  107  96  113  133  110 
Roraima  10 
Pará  175  129  152  170  152  155  169  179  180  169 
Amapá  11  10  11  11  11  13 
Tocantins  13  14  13  15  19  11  14  12 
Maranhão  121  125  116  159  181  179  168  196  192  186 
Piauí  56  79  71  64  73  72  78  84  81  71 
Ceará  256  232  191  214  232  264  253  269  276  239 
Rio Grande do Norte  67  48  46  47  52  42  70  71  53  63 
Paraíba  53  86  113  79  142  109  67  75  80  86 
Pernambuco  422  401  427  436  398  379  418  403  397  354 
Alagoas  79  89  89  70  76  83  85  95  99  91 
Sergipe  34  26  30  39  41  43  35  35  45  39 
Bahia  429  470  418  412  375  440  428  434  406  377 
Minas Gerais  293  312  308  333  319  298  298  306  315  285 
Espírito Santo  68  64  71  70  51  67  67  73  70  61 
Rio de Janeiro  1030  961  889  910  789  848  825  870  815  889 
São Paulo  1319  1158  1120  1053  928  970  921  910  922  851 
Paraná  212  192  203  191  169  176  141  152  122  118 
Santa Catarina  57  57  59  56  51  54  46  59  65  61 
Rio Grande do Sul  308  314  276  281  277  242  275  290  273  258 
Mato Grosso do Sul  58  63  62  68  66  57  48  59  67  66 
Mato Grosso  94  95  70  76  86  80  87  78  82  98 
Goiás  59  57  68  68  70  65  59  50  57  47 
Distrito Federal  23  19  19  22  15  10  18  13 
Brazil  5425  5162  4987  4981  4735  4823  4735  4881  4797  4603 
Federate unit  Mortality rate
  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010 
Rondônia  2.5  2.6  3.2  2.2  2.0  1.8  1.6  2.3  1.3  1.7 
Acre  4.5  3.2  3.5  2.9  4.0  3.3  4.0  2.4  2.3  2.0 
Amazonas  4.0  3.6  3.4  2.8  3.2  3.2  2.8  3.4  3.9  3.2 
Roraima  3.0  1.7  2.0  1.4  1.8  1.5  0.0  0.7  0.5  0.9 
Pará  2.8  2.0  2.3  2.5  2.2  2.2  2.3  2.4  2.4  2.2 
Amapá  2.2  1.9  1.1  1.1  1.9  1.8  1.7  1.1  1.4  1.9 
Tocantins  1.1  0.6  0.6  1.1  1.0  1.1  1.4  0.9  1.1  0.9 
Maranhão  2.1  2.2  2.0  2.7  3.0  2.9  2.7  3.1  3.0  2.8 
Piauí  1.9  2.7  2.4  2.2  2.4  2.4  2.5  2.7  2.6  2.3 
Ceará  3.4  3.0  2.5  2.7  2.9  3.2  3.0  3.2  3.2  2.8 
Rio Grande do Norte  2.4  1.7  1.6  1.6  1.7  1.4  2.3  2.3  1.7  2.0 
Paraíba  1.5  2.5  3.2  2.2  3.9  3.0  1.8  2.0  2.1  2.3 
Pernambuco  5.3  5.0  5.2  5.3  4.7  4.5  4.9  4.6  4.5  4.0 
Alagoas  2.8  3.1  3.1  2.4  2.5  2.7  2.8  3.0  3.1  2.9 
Sergipe  1.9  1.4  1.6  2.0  2.1  2.1  1.7  1.8  2.2  1.9 
Bahia  3.2  3.5  3.1  3.0  2.7  3.2  3.0  3.0  2.8  2.7 
Minas Gerais  1.6  1.7  1.7  1.8  1.7  1.5  1.5  1.5  1.6  1.5 
Espírito Santo  2.2  2.0  2.2  2.1  1.5  1.9  1.9  2.1  2.0  1.7 
Rio de Janeiro  7.1  6.5  6.0  6.1  5.1  5.4  5.2  5.5  5.1  5.6 
São Paulo  3.5  3.0  2.9  2.7  2.3  2.4  2.2  2.2  2.2  2.1 
Paraná  2.2  2.0  2.0  1.9  1.6  1.7  1.3  1.4  1.1  1.1 
Santa Catarina  1.0  1.0  1.1  1.0  0.9  0.9  0.8  1.0  1.1  1.0 
Rio Grande do Sul  3.0  3.0  2.6  2.6  2.6  2.2  2.5  2.7  2.5  2.4 
Mato Grosso do Sul  2.7  2.9  2.9  3.1  2.9  2.5  2.1  2.5  2.8  2.7 
Mato Grosso  3.7  3.6  2.6  2.8  3.1  2.8  3.0  2.6  2.7  3.2 
Goiás  1.2  1.1  1.3  1.3  1.2  1.1  1.0  0.9  1.0  0.8 
Distrito Federal  1.1  0.9  0.9  1.0  0.6  0.4  0.7  0.4  0.2  0.5 
Brazil  3.1  3.0  2.8  2.8  2.6  2.6  2.5  2.6  2.5  2.4 
                     
                     

Source: Mortality Information System (SIM).

Discussion

According to key epidemiological and operational TB indicators analysis made in this article, many advances on tuberculosis control in Brazil were achieved in the last 10 years. It is important to say that Sinan database is updated monthly for HM. For this reason, indicators analyzed in this study may have significant change in value at the time of publication.

There was an increase in the number of municipalities that diagnosed and reported TB cases in the surveillance system. This result may infer the expansion of TB control programs coverage in the country, since diagnosis and reporting are primary activities of an implemented program. However, attention should be paid to about 40% of municipalities with no known cases of the disease, pointing to the existence of silent municipalities. The state programs should be aware of municipalities with this behavior so that disease surveillance failures can be identified and corrected.

In recent years Brazil showed a significant improvement in case detection rate when compared to WHO estimates. TB control decentralization to primary care can be a facilitator to diagnosis and information access. However, it must be consider that WHO's method of calculating estimated cases has changed over the series analyzed, which may have influenced this indicator improvement.4

The incidence rate is an indicator that measures the risk of illness of a given population in a given location and time. For TB, a chronic and difficult to treat disease, control requires actions shared with sectors outside health sector, which may explain the slight drop in annual incidence. This indicator behavior tends to be different between regions and states in the country, because it is influenced by implementation stage of TB control actions in the locality. Places where control actions are more consolidated tend to have more significant reduction. Political issues influence must also be raised, since successive changes in administrations, particularly in cities, leads to discontinuation in efforts and causes changes in TB indicators. However, fluctuations more than 10% from one year to another should be investigated, since it may indicate cases underreporting and compromise disease surveillance quality.

The highest TB incidence among males and young adults is a reality worldwide.1 This profile, besides having the highest incidence, is the one with grater treatment default. Because most patients are in working age, access to diagnosis and treatment is complicated because working and health facilities opening hours usually match. To minimize this problem municipalities must create different strategies, such as alternative hours for primary care function and partnerships with patients’ workplaces.

Analysis of “race,” “education” and “closure” variables were hampered by missing fields. This problem was highlighted in several studies5–7 as a limiting factor of any epidemiological analysis. Analysis of field completeness in Sinan should be a routine activity in surveillance to ensure variables reliability.

The collecting process of information of the variable “race,” jeopardizes data reliability. In some places this variable is self-reported, while in others it is biased by health workers opinion who writes down information without patient knowledge. Even with the described limitations, black and brown colors accounted for the largest quantity of cases, as already demonstrated in literature.8 Significant increase in cases of white color should be considered when analyzing data, suggesting an increased risk of illness over the years analyzed. Although in lesser extent, only approximately 1.1% of cases, Indian race is a cause of concern due to its high risk of illness and difficult diagnosis and treatment access.3

Variable “education status” is perhaps the only variable in Sinan that can be used as proxy of patient's socioeconomic status. Although it was not this study subject, an additional concern, beyond this group higher risk of getting ill, is that people with less education also have an increased risk of unfavorable outcomes, such as treatment default and death. Local strategies of social support through food baskets distribution and offset help aim to improve treatment adherence.

Recognition that prison people are more vulnerable to TB when compared to general population was important to raise the need of direct recommendations to this population group. Incorporation of the variable “incarcerated” in Sinan in 2007 already showed concern in quantifying this problem magnitude. Global Fund TB Project implementation in Brazil, with a working component directed to prison system, supported TNP to spread this topic importance, as well as training professionals in states and municipalities. This work result can be seen in figures, since gradual increase in incarcerated reported cases in Sinan suggests the problem has been recognized and worked more systematically in recent years. However, the link between Health and Justice Sectors remains a major challenge for disease control in the country.

TB/HIV cases require special attention, since they have higher risk of unfavorable treatment outcomes.9 Increase in reported cases of coinfection seems to be related to increase on HIV testing among TB cases, which doubled over the years analyzed, although co-infection percentage did not increase in that same proportion. These data support the hypothesis that a few years ago, only one group of TB cases, perhaps the one possessing greatest risk on health workers judgment, were tested for HIV. Delay on returning test results to the health units and also on updating the surveillance system may be responsible for HIV testing figures lower than reality. The introduction of rapid HIV testing in health care system may have contributed to minimize this problem, since result comes out in minutes, allowing health workers to know almost immediately the patient's HIV status.

MDR-TB cases have higher probability of unfavorable outcomes, as well the possibility of adverse effects, beyond longer treatment when compared to sensitives.1,10 Increase in number of MDR TB cases in the years studied appears to be associated with increase in culture testing in the same period, particularly in retreatment cases. MH recommends culture and sensitivity testing for all retreatment cases in order to identify drug resistance early, although culture testing is still very low. 30% of retreatment cases had culture done in 2010 and it has doubled when compared to 2001.

Increase on pulmonary cases that performed sputum smear on the evaluated years is a program quality indicator since as a consequence a smaller volume of cases will be treated without bacteriological confirmation. However, increase in active tuberculosis cases percentage cause concern, since they are responsible for the transmission chain maintenance and disease perpetuation. Diagnosing these cases early is an essential activity for TB control.

According to Freire,11 the risk of case contacts developing TB, in a five years follow-up study, was 2300 cases per 100,000 contacts (4.6/1000 contacts/year). This finding reinforces the recommendation that all contacts should be investigated after a case diagnosis for other patients early identification and future cases prevention. Despite the variable “contacts investigated” had been inserted in Sinan in 2007, their inclusion did not have the same effect as the inclusion of the variable “institutionalized”, since there was not a increase in contacts investigation in the 10 years analyzed. Some limiting factors such as fail in fulfilling the Record Books, fail in updating the information system with follow-up information and health workers misunderstandings about the concept of a contact investigated must be taken into consideration.

Cure and default rates are subject of major national and international targets. However, rates closest to reality may be only found in approximately 1.5 years after case diagnosis. Because treatment is long, deficiencies in following-up cases and as consequence in follow-up bulletins that update Sinan can be identified as possible causes of cases without closure maintenance. Some states are known to have, historically, rates equal or above of those recommended by WHO, but it is not a national reality. Variations between federal states can be express by health care models adopted, diagnosed cases complexity, health services organization and surveillance quality.

Treatment default is a major challenge in TB control today. Men, alcohol and drugs users, diabetics, coinfection cases, institutionalized cases and homeless people are recognized as vulnerable groups to default. For them, alternative strategies for follow-up should be performed. Aiming to contribute in reducing default and preventing MDR TB, MH changed his therapeutic regimen from three to four drugs and adopted the so-called fixed-dose combination (FDC) or “4 in 1”, where four drugs are gathered into the same pill. This event marked a milestone for disease control in the country and it is expected that in a near future results can be measured.

Several studies have demonstrated DOTS effectiveness in TB cases.12,13 The two indicators about DOTS analyzed tended to increase over the study period, but some points should be taken into account when interpreting these figures. Until 2010, health workers responsible for TB treatment interpreted DOTS concept in several different ways. Therefore, NTP has developed a more specific rule to consider a case to be under DOTS, and published in his manual of recommendations.3 This change on DOTS concept should result in this indicator reduction over the next year making it closer to reality. In addition, in all cases DOTS is automatically filled by the system as performed, requiring upgrade if not performed. This procedure in Sinan may be overestimating these values.

Although in a small amount, the number of hospital admissions duo to TB decreased from 2001 to 2010. Hospitalizations duo TB may be associated with delay in diagnosis and irregular treatment, as well as cases that tend to develop more severe forms of the disease.14,15 The increase in family health strategy coverage may be influencing reduction in hospitalizations, duo to expansion of access to diagnosis and treatments. Despite this national trend, some states had their hospitalizations increased. A possible explanation for Santa Catarina and Paraná states is the high number of TB/HIV coinfection cases when compared to other Brazilian states, which can cause serious complications leading to hospitalization. States that have high default rates also tend to have more hospitalizations due the disease, since these cases do not have treatment under control.

Regarding mortality from TB analysis, the country shows declining trend for over a decade, more pronounced until 2006. The cooling on the mortality drop can be explained by Ministry of Health strategy to reduce deaths due to unknown causes or poorly defined in that year. Due to this activity about 300 deaths each year have been attributed to TB after investigation. In 2011, Brazil achieved the STOP TB Partnership target to reduce mortality by 50% when compared to 1990. However, when analyzing mortality we should be alert to TB as associated cause in death, once in cases of coinfection, for example, AIDS remains the primary cause of death because criteria in causes of death classification. Underreporting deaths duo to or with TB in Sinan is a problem already explained in literature and need to be worked by states and municipalities.15–17 The implementation of deaths duo to or with TB investigation routine may help reduce this problem since done systematically and with well-defined criteria.

Further advances can be described when we analyze the last 10 years of TB control in the country. The maintenance of TB as a priority on government political agenda, as well as maintaining epidemiological and operational TB indicators in major national agreements should be highlighted. The creation of metropolitan committees for fighting against TB as spaces of link between civil society and government in 11 metropolitan areas has allowed the expansion of partnerships for control actions. In the laboratory field, the introduction of real time molecular biology test, rapid test (validation in real conditions still undergoing) can provide greater agility in diagnosis.

For many years WHO took a expectancy position regarding tuberculosis control in Brazil, given the poor results obtained and the reluctance on the country's behavior to adopt WHO's recommendations. This attitude contrasted with recognition given to National STD/AIDS (DST/AIDS-NP) and Immunization (NIP) programs as international models. Since 2003, however, with tuberculosis control prioritization and its election as one of the Ministry of Health (MoH) priorities, WHO has demonstrated its recognition regarding national efforts.

Despite significant advances, many challenges must be overcome so eliminating TB as a public health problem goal can be achieved. When assessing the past we must say that improvement in indicators cannot be explained only by tuberculosis control program efforts. We must also consider TB social causes and prioritize mitigation of factors that increase some population segments vulnerability to the disease and promote actions that facilitate diagnosis access and treatment adherence.

Partnership with social movements and interaction with other sectors, particularly with social welfare, justice and institutions that work in promoting human rights, racial equality, combating the abuse of licit drugs (such as tobacco and alcohol) and illicit (especially crack), as well as liaison with legislature, to enable projects that benefit patients with tuberculosis and their families, with social support measures and inclusion in social programs, and facilitate access to health services. These steps are essential for more consistent results to be achieved in the medium and long term.

Conflict of interest

All authors declare to have no conflict of interest.

References
[1]
World Health Organization.
Global tuberculosis control 2011: WHO report 2011.
WHO, (2011),
[2]
World Health Organization.
Report 2008. Global tuberculosis control: surveillance, planning, financing.
WHO Library Cataloguing-in-Publication Data, (2008),
[3]
Brasil.
Manual de recomendações para o controle da tuberculose no Brasil.
Ministério da Saúde, Secretaria de Vigilância em Saúde. Departamento de Vigilância Epidemiológica, (2011),
[4]
World Health Organization.
Taxa de Incidência e Mortalidade dos países.
(2010),
[5]
C.M. Moreira, E.L. Maciel.
Completeness of tuberculosis control program records in the case registry database of the state of Espírito Santo, Brazil: analysis of the 2001–2005.
J Bras Pneumol, 34 (2008), pp. 225-229
[6]
T.A. Malhao, G.P. Oliveira, S.B. Codenotti, F. Moherdaui.
Avaliação da completitude do sistema de informação de agravos de notificação da tuberculose, Brasil (2001–2006).
Epidemiol Serv Saúde, 19 (2010), pp. 245-256
[7]
D.E. Romero, C.B. Cunha.
Evaluation of quality of epidemiological and demographic variables in the Live Births Information System, 2002.
Cad Saúde Pública, 23 (2007), pp. 701-714
[8]
A.M.B. Menezes, J.D. da Costa, H. Gonçalves.
Incidência e fatores de risco para tuberculose em Pelotas, uma cidade do Sul do Brasil.
Rev Bras Epidemiol, 1 (1998),
[9]
C.A.S. Schmaltz, F.M. Sant’Anna, S.C. Neves.
Influence of HIV infection on mortality in a cohort of patients treated for tuberculosis in the context of wide access to HAART, in Rio de Janeiro, Brazil.
J Acquir Immune Defic Syndr, 52 (2009),
[10]
J.C. Johnston, N.C. Shahidi, M. Sadatsafavi, J.M. Fitzgerald.
Treatment outcomes of multidrug-resistant tuberculosis: a systematic review and meta-analysis.
[11]
D.N. Freire, A.M. Bonametti, T. Matsuo.
Diagnóstico precoce e progressão da tuberculose em contatos.
Epidemiol Serv Saúde, 16 (2007), pp. 155-163
[12]
A.A. Vieira, S.A. Ribeiro.
Abandono do tratamento de tuberculose utilizando-se as estratégias tratamento auto-administrado ou tratamento supervisionado no Programa Municipal de Carapicuíba, São Paulo, Brasil.
J Bras Pneumol, 34 (2008), pp. 159-166
[13]
S.P. Ntshangaa, R. Rustomjeea, M.L.H. Mabaso.
Royal Society of Tropical Medicine and Hygiene, (2009),
[14]
R.A. Arcencio, M.F. Oliveira, T.C.S. Villa.
Internações por tuberculose pulmonar no Estado de São Paulo no ano de 2004.
Ciência Saúde Coletiva, 2 (2007), pp. 409-417
[15]
L.M.O. Sousa, R.S. Pinheiro.
Óbitos e internações por tuberculose não notificados no município do Rio de Janeiro.
Rev Saúde Pública, 45 (2011), pp. 31-39
[16]
L.B. Selig.
Óbitos atribuídos à tuberculose no estado do Rio de Janeiro.
J Bras Pneumol, 30 (2004), pp. 417-424
[17]
Oliveira GP. Subnotificação dos óbitos por tuberculose: associação com indicadores socioeconômicos e de desempenho dos programas municipais de controle. MSc dissertation. Rio de Janeiro: Universidade Federal do Rio de Janeiro; 2010.
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