Biologic medicine inclusion in 138 national essential medicines lists
Bibliographic record
Abstract
Abstract Background Essential medicines lists (EMLs) are intended to reflect the priority health care needs of populations. We hypothesized that biologic disease-modifying antirheumatic drugs (DMARDs) are underrepresented relative to conventional DMARDs in existing national EMLs. We aimed to survey the extent to which biologic DMARDs are included in EMLs, to determine country characteristics contributing to their inclusion or absence, and to contrast this with conventional DMARD therapies. Methods We searched 138 national EMLs for 10 conventional and 14 biologic DMARDs used in the treatment of childhood rheumatologic diseases. Via regression modelling, we determined country characteristics accounting for differences in medicine inclusion between national EMLs. Results Eleven countries (7.97%) included all 10 conventional DMARDs, 115 (83.33%) ≥5, and all countries listed at least one. Gross domestic product (GDP) per capita was associated with the total number of conventional DMARDs included (β 1 1.02 [95% CI 0.39, 1.66]; P = 0.00279). Among biologic DMARDs, 3 countries (2.2%) listed ≥10, 15 (10.9%) listed ≥5, and 47 (34.1%) listed at least one. Ninety-one (65.9%) of countries listed no biologic DMARDs. European region (β 1 1.30 [95% CI 0.08, 2.52]; P = 0.0367), life expectancy (β 1 –0.70 [95% CI -1.22, − 0.18]; P = 0.0085), health expenditure per capita (β 1 1.83 [95% CI 1.24, 2.42]; P < 0.001), and conventional DMARDs listed (β 1 0.70 [95% CI 0.33, 1.07]; P < 0.001) were associated with the total number of biologic DMARDs included. Conclusion Biologic DMARDs are excluded from most national EMLs. By comparison, conventional DMARDs are widely included. Countries with higher health spending and longer life expectancy are more likely to list biologics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".