Trends in global glucose lowering medication consumption: Insights from pharmaceutical sales data (2010–2021)
Bibliographic record
Abstract
Diabetes imposes a substantial global burden. Examining consumption trends of glucose lowering medications can help facilitate cross-country comparisons and uncover areas of unmet need. Leveraging IQVIA MIDAS data, our analysis spans 72 countries and 2 regions from 2010 to 2021, employing defined daily dose (DDD) as a consumption metric. We assessed consumption trends across income tiers and individual country rankings, exploring WHO essential medicines, insulin, new drug classes and specific drugs, with further analyses on their correlation with treatment guideline releases. Global glucose lowering medication consumption rate increased from 39.2 to 54.0 DDD per thousand inhabitants per day (DDD/TID) between 2010 and 2021. Across the same period, median consumption rates were 60.1 DDD/TID [IQR, 46.5-70.6] in high-income countries (HICs), 26.9 DDD/TID [IQR, 8.0-51.3] in upper-middle income countries (UMICs) and 10.8 DDD/TID [IQR, 6.5-18.5] in low- and lower-middle income countries (LMICs). While the most significant consumption changes occurred in UMICs and LMICs such as Bosnia, China and Indonesia, HICs such as Finland, Canada and USA consistently showed the highest consumption rates. Over the study period, the median consumption rates for essential medicines, insulin and new drug classes increased, except for intermediate-acting insulin and soluble insulin/biosimilars. HICs drove the consumption of fast-acting and long-acting insulin and new drug classes, whereas UMICs and LMICs drove the consumption of intermediate-acting insulin. This study sheds light on the global variations in glucose lowering medication consumption, providing insights to address access gaps, particularly in UMICs and LMICs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".