The economics of scaling up: cost estimation for HIV/AIDS interventions
Bibliographic record
Abstract
BACKGROUND: The scaling up of HIV/AIDS programming has been one of the most extensive undertakings in international public health. Yet decision-makers are encountering significant uncertainties about financing and the need to understand programming costs at different scales of delivery. OBJECTIVES: To review the economic methodologies for examining costs and variation by scale. To summarize and synthesize the current evidence related to the provision of HIV/AIDS interventions and scaling up. METHODS: We used a review of economic methodologies to generate a conceptual framework for classifying existing data, looking at both short-run and long-run perspectives. A review of the literature was performed using PubMed and available grey literature. Factors facilitating comparison and generalizability are highlighted. RESULTS: There is growing evidence of scale variation among the costs of HIV/AIDS interventions. Scale variation has been found to explain 26-70% of cost variation across locations for similar interventions. Average costs may become larger or smaller as the volume of services expands, depending on the level of coverage and type of intervention. Key constraints to scaling up include infrastructure investments and cost results need to be interpreted in this light. CONCLUSIONS: Evidence to date suggests that cost efficiencies associated with scale may reflect different ways of delivering services at higher volumes, including lower quality outputs. There is still, however, an extremely limited economic evidence base and mechanisms to integrate economic analyses into routine programme monitoring are recommended.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".