<scp>Real‐world</scp> clinical, psychosocial and economic burden of atopic dermatitis: Results from a multicountry study
Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis (AD), a relapsing, inflammatory skin disease, is associated with pruritus that can negatively affect patients' quality of life. Understanding the burden of AD is critical for informing and tailoring treatment and disease management to improve patient outcomes. This study characterized global treatment patterns and the clinical, psychosocial and economic burden of moderate-to-severe AD. METHODS: MEASURE-AD was a cross-sectional 28-country study in patients with physician-confirmed moderate-to-severe AD who were either receiving or eligible for systemic therapy for AD. Patients ≥12 years were enrolled between December 2019 and December 2020 while attending routine office or clinic visit. Primary outcomes included Worst Pruritus Numeric Rating Scale (WP-NRS; range: 0-10) and Dermatology Life Quality Index (DLQI; range: 0-30) and Children's DLQI (CDLQI; range: 0-30). Secondary outcomes included physician- and patient-reported clinical, psychosocial and economic burden. RESULTS: Of the 1591 patients enrolled, 1558 (1434 adults and 124 adolescents) fulfilled all patient selection criteria and were included in this analysis. Almost all patients (98.4%) in the total population were using AD medications and more than half (56%) were receiving systemic medication (15% systemic monotherapy). The most used systemic therapies were dupilumab (56.3%), systemic glucocorticoids (18.1%) and methotrexate (16.2%). Mean WP-NRS was 5.3 in the total population, and most patients (≥55%) reported moderate-to-severe pruritus (WP-NRS ≥4). Mean DLQI was 10.8 and mean CDLQI was 9.6. Secondary endpoints demonstrated substantial clinical, psychosocial, and economic burden of disease. Subgroup analysis demonstrated that patients receiving systemic therapy had lower disease burden than those not taking systemic medications. CONCLUSIONS: While systemic therapy lowers overall disease burden, patients with moderate-to-severe AD continue to have substantial multidimensional disease burden and uncontrolled disease. Overall, there is a need for effective disease management, including effective treatments that improve patients' psychosocial outcomes and reduce the economic burden of AD.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".