Global Patterns and Trends in Total Burden of Hepatitis B from 1990 to 2019 and Predictions to 2030
Bibliographic record
Abstract
Background and Aims: Despite widespread vaccination against hepatitis B and availability of antiviral drugs, hepatitis B remained a major global public health problem. Therefore, an improved understanding of the burden of hepatitis B was required to help design strategies for global intervention. Methods: Data on hepatitis B was collected by the Global Burden of Disease (GBD) 2019 database from 1990 to 2019. Age-standardized incidence rates (ASIR), mortality rates (ASMR) and disability-adjusted life year rates (ASDR) for hepatitis B were extracted from GBD 2019 and stratified by age, level of regionals and country. Estimated annual percentage changes (EAPC) of ASIR, ASMR and ASDR were calculated to quantify the temporal trends in hepatitis B. Results: Globally, ASIR showed a continuous downward trend, from 1552.2 in 1990 to 1010.0 per 100,000 persons in 2019, with an annual decrease of 1.52% (95% CI -1.66--1.38). ASMR showed a persistent decline, declining by nearly half in 2019 compared to 1990 (6.7 vs 12.4 per 100,000 persons), with an annual decrease of 2.55% (95% CI -2.74--2.35). ASDR showed a continuing downward trend, and the EAPC was -2.55% (95% CI -2.74--2.35). This decreasing pattern was heterogeneous across regions and countries. Hepatitis B related deaths increased significantly in high socio-demographic index countries such as UK, USA, and Canada. The ARIMA model estimates a 36.14% and 6.00% decrease in ASIR and ASMR, respectively, by 2030 compared to 2015. Conclusion: Global hepatitis B morbidity and mortality rates decreased significantly from 1990 to 2019, but with a high degree of heterogeneity among regions and countries. It was still far from achieving the WHO goal of elimination of viral hepatitis by 2030, especially mortality rate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".