Viral Hepatitis Strategic Information to Achieve Elimination by 2030: Key Elements for HIV Program Managers
Bibliographic record
Abstract
Evidence documenting the global burden of disease from viral hepatitis was essential for the World Health Assembly to endorse the first Global Health Sector Strategy (GHSS) on viral hepatitis in May 2016. The GHSS on viral hepatitis proposes to eliminate viral hepatitis as a public health threat by 2030. The GHSS on viral hepatitis is in line with targets for HIV infection and tuberculosis as part of the Sustainable Development Goals. As coordination between hepatitis and HIV programs aims to optimize the use of resources, guidance is also needed to align the strategic information components of the 2 programs. The World Health Organization monitoring and evaluation framework for viral hepatitis B and C follows an approach similar to the one of HIV, including components on the following: (1) context (prevalence of infection), (2) input, (3) output and outcome, including the cascade of prevention and treatment, and (4) impact (incidence and mortality). Data systems that are needed to inform this framework include (1) surveillance for acute hepatitis, chronic infections, and sequelae and (2) program data documenting prevention and treatment, which for the latter includes a database of patients. Overall, the commonalities between HIV and hepatitis at the strategic, policy, technical, and implementation levels justify coordination, strategic linkage, or integration, depending on the type of HIV and viral hepatitis epidemics. Strategic information is a critical area of this alignment under the principle of what gets measured gets done. It is facilitated because the monitoring and evaluation frameworks for HIV and viral hepatitis were constructed using a similar approach. However, for areas where elimination of viral hepatitis requires data that cannot be collected through the HIV program, collaborations are needed with immunization, communicable disease control, tuberculosis, and hepatology centers to ensure collection of information for the remaining indicators.
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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 | 0.111 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.031 | 0.031 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.014 | 0.028 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".