Time trends in tuberculosis mortality across the BRICS: an age-period-cohort analysis for the GBD 2019
Bibliographic record
Abstract
Background: Tuberculosis is the leading cause of death from a single infectious agent among the HIV-negative population and ranks first among the HIV-positive population. However, few studies have assessed tuberculosis trends in Brazil, Russia, India, China and South Africa (BRICS) or with an emphasis on HIV status. This study assesses the time trends of tuberculosis mortality across the BRICS with an emphasis on HIV status from 1990 to 2019. Methods: We obtained tuberculosis data from the Global Burden of Disease 2019 study (GBD 2019). We calculated the relative proportion of tuberculosis to all communicable, maternal, neonatal, and nutritional diseases by HIV status across the BRICS. We used age-period-cohort modelling to estimate cohort and period effects in tuberculosis from 1990 to 2019, and calculated net drift (overall annual percentage change), local drift (annual percentage change in each age group), longitudinal age curves (expected longitudinal age-specific rate), and period (cohort) relative risks. Findings: There were 549,522 tuberculosis deaths across the BRICS in 2019, accounting for 39.3% of global deaths. Among HIV-negative populations, the age-standardised mortality rate (ASMR) of tuberculosis in BRICS remained far higher than that of high-income Asia Pacific countries, especially in India (36.1 per 100 000 in 2019, 95% UI [30.7, 42.6]) and South Africa (40.1 per 100 000 in 2019, 95% UI [36.8, 43.7]). China had the fastest ASMR reduction across the BRICS, while India maintained the largest tuberculosis death numbers with an annual decrease much slower than China's (-4.1 vs -8.0%). Among HIV-positive populations, the ASMR in BRICS surged from 0.24 per 100 000 in 1990 to 5.63 per 100 000 in 2005, and then dropped quickly to 1.70 per 100 000 in 2019. Brazil was the first country to reverse the upward trend of HIV/AIDS-tuberculosis (HIV-TB) mortality in 1995, and achieved the most significant reduction (-3.32% per year). The HIV-TB mortality in South Africa has realised much progress since 2006, but still has the heaviest HIV-TB burden across the BRICS (ASMR: 70.0 per 100 000 in 2019). We also found unfavourable trends among HIV-negative middle-aged (35-55) adults of India, men over 50 in the HIV-negative population and whole HIV-positive population of South Africa, and women aged 45-55 years of Russia. China had little progress in its HIV-positive population with worsening period risks from 2010 to 2019, and higher risks in the younger cohorts born after 1980. Interpretation: BRICS' actions on controlling tuberculosis achieved positive results, but the overall improvements were less than those in high-income Asia Pacific countries. BRICS and other high-burden countries should strengthen specified public health approaches and policies targeted at different priority groups in each country. Funding: National Natural Science Foundation of China (82073573; 72074009), Peking University Global Health and Infectious Diseases Group.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".