Global burden of disease due to rifampicin-resistant tuberculosis: a mathematical modeling analysis.
Nicolas A MenziesBrian W AllwoodAnna S DeanPeter J DoddRein M G J HoubenLyndon P JamesGwenan Mary KnightJamilah MeghjiLinh N NguyenAndrea RachowSamuel G SchumacherFuad MirzayevTed CohenPublished in: Nature communications (2023)
In 2020, almost half a million individuals developed rifampicin-resistant tuberculosis (RR-TB). We estimated the global burden of RR-TB over the lifetime of affected individuals. We synthesized data on incidence, case detection, and treatment outcomes in 192 countries (99.99% of global tuberculosis). Using a mathematical model, we projected disability-adjusted life years (DALYs) over the lifetime for individuals developing tuberculosis in 2020 stratified by country, age, sex, HIV, and rifampicin resistance. Here we show that incident RR-TB in 2020 was responsible for an estimated 6.9 (95% uncertainty interval: 5.5, 8.5) million DALYs, 44% (31, 54) of which accrued among TB survivors. We estimated an average of 17 (14, 21) DALYs per person developing RR-TB, 34% (12, 56) greater than for rifampicin-susceptible tuberculosis. RR-TB burden per 100,000 was highest in former Soviet Union countries and southern African countries. While RR-TB causes substantial short-term morbidity and mortality, nearly half of the overall disease burden of RR-TB accrues among tuberculosis survivors. The substantial long-term health impacts among those surviving RR-TB disease suggest the need for improved post-treatment care and further justify increased health expenditures to prevent RR-TB transmission.
Keyphrases
- mycobacterium tuberculosis
- pulmonary tuberculosis
- healthcare
- public health
- risk factors
- hiv aids
- palliative care
- cardiovascular disease
- climate change
- human immunodeficiency virus
- mental health
- hepatitis c virus
- hiv infected
- antiretroviral therapy
- health information
- machine learning
- quality improvement
- risk assessment
- social media
- electronic health record
- drug induced
- deep learning
- artificial intelligence
- health promotion