Association of Countries’ Atopic Dermatitis Burden and Sociodemographic Index with Topical Calcineurin Inhibitor Utilization
Bibliographic record
Abstract
Abstract: Background: There is limited knowledge on international trends in topical calcineurin inhibitor (TCI) utilization. Objective: To describe international TCI utilization trends from 2012 to 2019 and evaluate the relationship of country-level economic status, geographic location, and atopic dermatitis (AD) disease burden with drug utilization. Methods: We used IQVIA MIDAS ® pharmaceutical quarterly sales data to attain country-level purchasing of TCIs in grams from 2012 to 2019. A multivariable linear regression estimated the association between countries’ sociodemographic index (SDI), AD disability-adjusted life year (DALY) rates, and geographic location with TCI utilization. Results: A total of 68 countries were included in our analysis. From 2012 to 2019, overall TCI utilization increased by 66% but remained 11.2 times higher in high-sociodemographic compared with low-middle/low-sociodemographic countries. SDI and geographic location were associated with greater TCI utilization in multivariable analyses, whereas AD DALY rates were not. High-SDI countries used 21,476 grams (95% confidence interval [CI]: 11,915 to 31,036) and high-middle SDI countries used 9,403 grams (95% CI: −393 to 19,200) more TCIs per 100,000 people compared with low-middle/low-SDI countries, respectively. Northern hemisphere countries used 8,588 grams more TCIs per 100,000 people (95% CI: 612 to 16,564). Conclusions: We demonstrated greater TCI utilization among high-SDI compared with lower SDI countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".