Abstract

BackgroundBrazil has high burdens of tuberculosis (TB) and HIV, as previously estimated for the 26 states and the Federal District, as well as high levels of inequality in social and health indicators. We improved the geographic detail of burden estimation by modelling deaths due to TB and HIV and TB case fatality ratios for the more than 5400 municipalities in Brazil.MethodsThis ecological study used vital registration data from the national mortality information system and TB case notifications from the national communicable disease notification system from 2001 to 2015. Mortality due to TB and HIV was modelled separately by cause and sex using a Bayesian spatially explicit mixed effects regression model. TB incidence was modelled using the same approach. Results were calibrated to the Global Burden of Disease Study 2016. Case fatality ratios were calculated for TB.ResultsThere was substantial inequality in TB and HIV mortality rates within the nation and within states. National-level TB mortality in people without HIV infection declined by nearly 50% during 2001 to 2015, but HIV mortality declined by just over 20% for males and 10% for females. TB and HIV mortality rates for municipalities in the 90th percentile nationally were more than three times rates in the 10th percentile, with nearly 70% of the worst-performing municipalities for male TB mortality and more than 75% for female mortality in 2001 also in the worst decile in 2015. The same municipality ranking metric for HIV was observed to be between 55% and 61%. Within states, the TB mortality rate ratios by sex for municipalities in the worst decile versus the best decile varied from 1.4 to 2.9, and HIV varied from 1.4 to 4.2. The World Health Organization target case fatality rate for TB of less than 10% was achieved in 9.6% of municipalities for males versus 38.4% for females in 2001 and improved to 38.4% and 56.6% of municipalities for males versus females, respectively, by 2014.ConclusionsMortality rates in municipalities within the same state exhibited nearly as much relative variation as within the nation as a whole. Monitoring the mortality burden at this level of geographic detail is critical for guiding precision public health responses.

Highlights

  • Brazil has high burdens of tuberculosis (TB) and human immunodeficiency virus (HIV), as previously estimated for the 26 states and the Federal District, as well as high levels of inequality in social and health indicators

  • The International Statistical Classification of Diseases (ICD) convention is for TB deaths in persons living with HIV infection (PLHIV) to be assigned to HIV as the underlying cause, which can hide the contribution of TB to these deaths if only a single cause of death is reported in vital registration [30]. We address these challenges by utilising comprehensive cause of death assignment and small area estimation to conduct a nationwide analysis of TB and HIV mortality at fine geographic scale

  • National age-standardised mortality due to TB in persons without HIV decreased by nearly 50% from 6.7 (95% uncertainty interval (UI) 6.5–6.9) deaths per 100,000 in 2001 to 3.5 (95% UI 3.4–3.6) deaths per 100,000 in 2015 among males, and from 2.3 (95% UI 2.2–2.4) deaths per 100,000 in 2001 to 1.2 (95% UI 1.1–1.2) deaths per 100,000 in 2015 among females

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Summary

Introduction

Brazil has high burdens of tuberculosis (TB) and HIV, as previously estimated for the 26 states and the Federal District, as well as high levels of inequality in social and health indicators. We improved the geographic detail of burden estimation by modelling deaths due to TB and HIV and TB case fatality ratios for the more than 5400 municipalities in Brazil. Brazil is a high-burden country for tuberculosis (TB) and human immunodeficiency virus (HIV)-TB co-infection [1] and characterised by high levels of inequality in social and health indicators [2,3,4]. The national strategy to end TB in Brazil prescribes TB control strategies based on local epidemiology; fine-scale mapping of TB and HIV burden can provide information to prioritise additional programmatic investments toward improving health [16]

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