Pattern, Determinants, and Impact of HIV Spending on Care and Treatment in 38 High-Burden Low- and Middle-Income Countries
Bibliographic record
Abstract
UNLABELLED: : Achieving the 90-90-90 targets by 2020 requires increased focus, resources, and efficiency to provide earlier access to antiretroviral therapy (ART). METHODS: We used 2009 to 2013 National AIDS Spending Assessment data to assess HIV care and treatment spending in 38 high-burden, low- and middle-income countries (LMICs). RESULTS: In 2013, 23 of the 38 high-burden countries spent less than 50% of total HIV spending on care and treatment. HIV spending on ART per people living with HIV (PLHIV; adjusted) averaged US$299 (US$32-US$2463). During 2009 to 2013, a 10% increase in average spending on care and treatment per PLHIV was associated with an increase in ART coverage of 2.4% and a decrease in estimated AIDS-related death rate of 2.4 per 1000 PLHIV. DISCUSSION: HIV spending in high-burden LMICs does not consistently reflect the new science around the preventative and clinical benefits of earlier HIV diagnosis and ART initiation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".