Nationwide economic analysis of pulmonary tuberculosis in the Brazilian healthcare system over seven years (2015-2022): a population-based study

Abstract

Background: Tuberculosis (TB) remains a global challenge and disproportionately affecting vulnerable populations. This study analyses the economic burden of pulmonary TB in Brazil, focusing on direct healthcare costs. It also evaluates the cost-effectiveness of the Directly Observed Treatment (DOT) strategy and the economic effort required to achieve a 90% probability of cure.

Methods: A nationwide retrospective study utilized data from the Brazilian Information System for Notifiable Diseases (SINAN) between 2015 and 2022. The cost per pulmonary TB case was estimated, encompassing expenses related to healthcare professionals, medication, laboratory exams, and the duration of treatment reported in SINAN. The population was stratified based on the presence of social vulnerabilities or a history of previous anti-TB treatment. Number Needed to Treat (NNT) analyses assessed the effectiveness of DOT implementation. Additionally, the study calculated the cost needed to achieve a 90% probability of cure through binomial regression models.

Findings: The total direct cost for pulmonary TB in Brazil during the seven years exceeded $1.3 billion, with retreatment cases accounting for $23.5 million. The lowest NNT of DOT were homeless (3.0), people who use drugs (3.72), and retreatment (4.56) subpopulations. These groups also presented the highest cost to achieve a 90% probability of cure.

Interpretation: This study highlights the economic impact of pulmonary TB on the Brazilian healthcare system. It underscores the effectiveness of DOT across various patient groups, regardless of their vulnerabilities or previous anti-TB treatment history. NNT analyses highlighted retreatment, homeless, and people who use drugs subpopulations as the most effective for DOT implementation.

Funding: Intramural Research Program-Oswaldo Cruz Foundation.

Keywords: Cost; Direct observed treatment; Retreatment; Treatment outcome; Tuberculosis; Vulnerable populations.

Fig. 1

Flow chart of the TB cases notified in Brazil between 2015 and 2022 included in the study. Abbreviations: SINAN: Brazilian Information System for Notifiable Diseases; PTB: pulmonary tuberculosis; EPTB: extrapulmonary tuberculosis, ATT: anti-tuberculosis treatment; HCU: healthcare unit.

Fig. 2

Overview of the total cost of PTB in Brazil between 2015 and 2022. (a) The total cost of PTB in Brazil shows the cumulative economic burden over the seven years. (b) Cost based on the history of ATT, distinguishing between new cases and retreatment cases. (c–d) Cost analysis according to social vulnerability. The cost per case was calculated considering the duration from the start to the completion or abandonment of treatment. Abbreviations: TB: tuberculosis; people who use drugs: People who use drugs; PDL: People deprived of liberty; HCW: Health Care Workers.

Fig. 3

Bar plot depicts the impact of DOT implementation on ATT outcomes among the overall study population between 2015 and 2022. Abbreviations: DOT: Direct Observed treatment; ATT: anti-tuberculosis treatment; PTB: pulmonary tuberculosis.

Fig. 4

Bar plot depicts the impact of DOT implementation on ATT outcomes among PTB cases according to the history of ATT between 2015 and 2022. Abbreviations: DOT: Direct Observed treatment; ATT: anti-tuberculosis treatment; PTB: pulmonary tuberculosis.

Fig. 5

Bar plot depicts the impact of DOT implementation on ATT outcomes among PTB cases according to the social vulnerability between 2015 and 2022. Abbreviations: DOT: Direct Observed treatment; ATT: anti-tuberculosis treatment; PTB: pulmonary tuberculosis.

Fig. 6

Binomial regression models illustrating the cost per case to increase the probability of cure during ATT. (a) Depicts the cost per case in the overall PTB cases. (b) Represents the cost per case based on the history of ATT. (c) Illustrates the cost per case based on the presence or absence of social vulnerabilities. (d) Stratifies the cost per case based on each social vulnerability in the study. Abbreviations: PTB: Pulmonary Tuberculosis; ATT: Anti-Tuberculosis Treatment; PUD: People who use drugs; PDL: People deprived of liberty; HCW: Health Care Worker.

Fig. 7

Binomial regression models depict the cost per case to increase the probability of cure during ATT among vulnerable and non-vulnerable subgroups. Abbreviations: ATT: anti-tuberculosis treatment; PUD: People who use drugs; PDL: People deprived of liberty; HCW: Health Care Worker.

Fig. 8

Binomial regression models depict the cost per case to increase the probability of cure during ATT among vulnerable and non-vulnerable subgroups stratified according to the ATT history. Abbreviations: ATT: anti-tuberculosis treatment.