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.
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.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".