Progress toward eliminating TB and HIV deaths in Brazil, 2001–2015: a spatial assessment

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.Electronic supplementary materialThe online version of this article (10.1186/s12916-018-1131-6) contains supplementary material, which is available to authorized users.

Determinants associated with areas with higher tuberculosis mortality rates: an ecological study

“…Educational level is an important predictor of increase in mortality rates due to TB [13,19,25]. In our study, municipalities with a lower percentage of the population aged 18 years or more without complete primary education and in informal occupations were the ones that are more likely to increase their TB mortality rate.…”

“…Multivariate analysis obtained a robust statistical model with a high discriminative capacity and consistent with theoretical and historical investigations of TB mortality [9,19,25,26]. It identified the variable 'HIV testing' as an expressive odds ratio with municipalities that provided the most for HIV testing as reference category.…”

“…Ecological mapping of the outcome variable showed that the southeastern and southern centre of Sergipe were affected by high TB mortality rates as well as high disease incidence rates, which suggests the need for health care reinforcement in specific municipalities. This demonstrates the role of geographic detail in estimating disease burden to guide accurate public health responses in priority areas, in order to achieve overall health goals and improve health equity . The associated factors were mapped and divided by tercile and showed oscillations in their proportions regarding the geographic area.…”

The burden of tuberculosis and attributable risk factors in Brazil, 1990–2017: results from the Global Burden of Disease Study 2017

“…The GBD 2017 data sources and analytical process for general mortality and morbidity estimates and for specific analysis of TB burden among individuals who were HIV-positive and HIV-negative have been detailed elsewhere [ 3 , 10 , 12 , 13 ]. For Brazil, the main mortality data source used in GBD study was the Brazilian Mortality Information System ( Sistema de Informações sobre Mortalidade – SIM in Portuguese) database, adjusted by other national and international sources [ 18 – 21 ]. Vital registration data were adjusted and corrected for mortality completeness and redistribution of garbage codes (assignment of causes of death that could not or should not be classified as the underlying cause of death, including ill-defined codes and the use of intermediate causes) to more precise underlying causes of death using GBD algorithms and misclassified HIV deaths (i.e., deaths caused by HIV being assigned to other underlying causes of death, such as TB or diarrhea, because of stigma or misdiagnosis) [ 3 , 10 , 13 ].…”

Bayesian hierarchical modeling of joint spatiotemporal risk patterns for Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) in Kenya

“…There was strong evidence of disease-time, disease-space and space-time interactions. Studies by [68] and [69] also found a strong association on the joint risks of HIV and TB, they used bivariate maps to show that the joint distribution for both TB and HIV diseases was spatially heterogeneous across Brazil.…”