Essential diabetes medicines and health outcomes in 127 countries
Bibliographic record
Abstract
AIM: Diabetes is the ninth leading cause of death. Improving access to diabetes medicines may decrease mortality. Diabetes medicines on national essential medicines lists (NEMLs) vary considerably. We examine the association between diabetes population health outcomes relating to mortality and the listing of diabetes medicines on national essential medicine lists for 127 countries. MATERIALS AND METHODS: We conducted a cross-sectional study. We determined the number of diabetes medicines on NEMLs and used multiple linear regression to analyse the association between diabetes health outcomes and the number of medicines on NEMLs. We used linear regression to assess the association between diabetes health outcomes and the listing of or not listing of medicines that were listed by 25-75% of countries. Diabetes prevalence, gross domestic product (GDP) per capita and mean expenditure per person with diabetes were controlled for in all analyses. RESULTS: The total number of diabetes medicines listed on NEMLs ranged from 0 to 16 (median: 4; interquartile range: 3-6). Diabetes health outcome scores were associated with the number of diabetes medicines on NEMLs [1.3-point increase (95% confidence interval, 95% CI 0.5-2.1) for every additional medicine on NEMLs; P = .002] and GDP per capita [19.5-point increase (95% CI 5.4-33.6) for every 10-fold increase in GDP; P = .003]. Diabetes expenditure was not associated with health outcome scores (P = .23). Increases in diabetes health outcomes score were associated with the listing of glimepiride (7.9-point increase, 95% CI 2.3-13.5, P = .006) and glipizide (5.8-point increase, 95% CI 0.03-11.6, P = .049) on NEMLs. CONCLUSIONS: Listing of diabetes medicines on NEMLs has the potential to improve population health outcomes related to mortality in countries with diverse incomes and diabetes prevalence without necessarily increasing diabetes health expenditure.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".