Essential Medicines in Universal Health Coverage: A Scoping Review of Public Health Law Interventions and How They Are Measured in Five Middle-Income Countries
Bibliographic record
Abstract
Very few studies exist of legal interventions (national laws) for essential medicines as part of universal health coverage in middle-income countries, or how the effect of these laws is measured. This study aims to critically assess whether laws related to universal health coverage use five objectives of public health law to promote medicines affordability and financing, and to understand how access to medicines achieved through these laws is measured. This comparative case study of five middle-income countries (Ecuador, Ghana, Philippines, South Africa, Ukraine) uses a public health law framework to guide the content analysis of national laws and the scoping review of empirical evidence for measuring access to medicines. Sixty laws were included. All countries write into national law: (a) health equity objectives, (b) remedies for users/patients and sanctions for some stakeholders, (c) economic policies and regulatory objectives for financing (except South Africa), pricing, and benefits selection (except South Africa), (d) information dissemination objectives (ex. for medicines prices (except Ghana)), and (e) public health infrastructure. The 17 studies included in the scoping review evaluate laws with economic policy and regulatory objectives (n = 14 articles), health equity (n = 10), information dissemination (n = 3), infrastructure (n = 2), and sanctions (n = 1) (not mutually exclusive). Cross-sectional descriptive designs (n = 8 articles) and time series analyses (n = 5) were the most frequent designs. Change in patients’ spending on medicines was the most frequent outcome measure (n = 5). Although legal interventions for pharmaceuticals in middle-income countries commonly use all objectives of public health law, the intended and unintended effects of economic policies and regulation are most frequently investigated.
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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 | 0.037 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 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".