Using costing to facilitate policy making towards Universal Health Coverage: findings and recommendations from country-level experiences
Bibliographic record
Abstract
As countries progress towards universal health coverage (UHC), they frequently develop explicit packages of health services compatible with UHC goals. As part of the Disease Control Initiative 3 Country Translation project, a systematic survey instrument was developed and used to review the experience of five low-income and lower-middle-income countries-Afghanistan, Ethiopia, Pakistan, Somalia and Sudan-in estimating the cost of their proposed packages. The paper highlights the main results of the survey, providing information about how costing exercises were conducted and used and what country teams perceived to be the main challenges. Key messages are identified to facilitate similar exercises and improve their usefulness. Critical challenges to be addressed include inconsistent application of costing methods, measurement errors and data reliability issues, the lack of adequate capacity building, and the lack of integration between costing and budgeting. The paper formulates four recommendations to address these challenges: (1) developing more systematic guidance and standard ways to implement costing methodologies, particularly regarding the treatment of health systems-related common costs, (2) acknowledging ranges of uncertainty of costing results and integrating sensitivity analysis, (3) building long-term capacity at the local level and institutionalising the costing process in order to improve both reliability and policy relevance, and (4) closely linking costing exercises to public budgeting.
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 | 0.045 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".