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Record W4296116604 · doi:10.1016/j.eclinm.2022.101646

Time trends in tuberculosis mortality across the BRICS: an age-period-cohort analysis for the GBD 2019

2022· article· en· W4296116604 on OpenAlex
Zhiyong Zou, Guangqi Liu, Simon I Hay, Saurav Basu, Uzma Belgaumi, Arkadeep Dhali, Sameer Dhingra, Ginenus Fekadu, Mahaveer Golechha, Nitin Joseph, Kewal Krishan, Francisco Rogerlândio Martins‐Melo, Sumaira Mubarik, Osaretin Christabel Okonji, Mahesh P. A, Priya Rathi, Ranjitha S Shetty, Paramdeep Singh, Surjit Singh, Pugazhenthan Thangaraju, Ziyue Wang, Михаил Сергеевич Застрожин, Christopher J L Murray, Hmwe Hmwe Kyu, Yangmu Huang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEClinicalMedicine · 2022
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of ChinaChandigarh UniversityDepartment of Anthropology, McMaster UniversityManipal Academy of Higher Education
KeywordsMedicineTuberculosisDemographyCohortPopulationChinaMortality rateCohort studyEnvironmental healthGeographySurgeryInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.080
GPT teacher head0.451
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it