Potential Impact of the US President's Emergency Plan for AIDS Relief on the Tuberculosis/HIV Coepidemic in Selected Sub-Saharan African Countries
Bibliographic record
Abstract
BACKGROUND: There are limited data measuring the impact of expanded human immunodeficiency virus (HIV) prevention activities on the tuberculosis epidemic at the country level. Here, we characterized the potential impact of the US President's Emergency Plan for AIDS Relief (PEPFAR) on the tuberculosis epidemic in sub-Saharan Africa. METHODS: We selected 12 focus countries (countries receiving the greatest US government investments) and 29 nonfocus countries (controls). We used tuberculosis incidence and mortality rates and relative risks to compare time periods before and after PEPFAR's inception, and a tuberculosis/HIV indicator to calculate the rate of change in tuberculosis incidence relative to the HIV prevalence. RESULTS: Comparing the periods before and after PEPFAR's implementation, both tuberculosis incidence and mortality rates have diminished significantly and to a higher degree in focus countries. The relative risk for developing tuberculosis, comparing those with and without HIV, was 22.5 for control and 20.0 for focus countries. In most focus countries, the tuberculosis epidemic is slowing down despite some regions still experiencing an increase in HIV prevalence. CONCLUSIONS: This ecological study showed that PEPFAR had a more consistent and substantial effect on HIV and tuberculosis in focus countries, highlighting the likely link between high levels of HIV investment and broader effects on related diseases such as tuberculosis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".