What Determines HIV Prevention Costs at Scale? Evidence from the Avahan Programme in India
Bibliographic record
Abstract
Expanding essential health services through non-government organisations (NGOs) is a central strategy for achieving universal health coverage in many low-income and middle-income countries. Human immunodeficiency virus (HIV) prevention services for key populations are commonly delivered through NGOs and have been demonstrated to be cost-effective and of substantial global public health importance. However, funding for HIV prevention remains scarce, and there are growing calls internationally to improve the efficiency of HIV prevention programmes as a key strategy to reach global HIV targets. To date, there is limited evidence on the determinants of costs of HIV prevention delivered through NGOs; and thus, policymakers have little guidance in how best to design programmes that are both effective and efficient. We collected economic costs from the Indian Avahan initiative, the largest HIV prevention project conducted globally, during the first 4 years of its implementation. We use a fixed-effect panel estimator and a random-intercept model to investigate the determinants of average cost. We find that programme design choices such as NGO scale, the extent of community involvement, the way in which support is offered to NGOs and how clinical services are organised substantially impact average cost in a grant-based payment setting.
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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 | 0.009 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".