Burden, Treatment Patterns and Real-World Barriers to Prescribing Advanced Treatments in Adults with Atopic Dermatitis in Brazil and Colombia
Bibliographic record
Abstract
INTRODUCTION: This study aimed to explore the relationship between atopic dermatitis (AD) disease severity and impact on patients and to describe AD treatment patterns and barriers to treatment in Brazil and Colombia. METHODS: Data were drawn from the Adelphi AD Disease Specific Programme™, a cross-sectional survey of physicians and their patients with AD, conducted in Brazil and Colombia between May 2022 and July 2023. Physicians provided data on demographics, clinical characteristics, treatments, and reasons for not prescribing targeted therapy (TT). Patients completed various patient-reported outcome (PRO) questionnaires: Patient-Oriented Eczema Measure, Dermatology Life Quality Index, EuroQol five-dimensional, and Work Productivity and Activity Impairment. Data were analysed descriptively. Comparisons between current severity were made using analysis of variance (ANOVA) or Kruskal-Wallis tests. RESULTS: Overall, 100 physicians provided data for 624 adult patients with AD from Brazil (n = 328) and Colombia (n = 296): 47% currently mild, 43% moderate, and 10% severe. For all the PRO measures analysed in Colombia and for the majority in Brazil, the impact on patients increased with increasing disease severity (p < 0.05). In total, 85% of patients with AD in Brazil and 73% in Colombia were treated with topical therapies, with 51% in Brazil and 77% in Colombia receiving systemic therapies, which included biologics (Brazil 12%; Colombia 33%) and oral Janus kinase inhibitors (Brazil 5% and Colombia 10%). The main barriers to TT in Brazil were patients being unable to pay for treatment and treatment not being covered by health insurance. In Colombia, the main barriers were formulary restrictions and patients being very recently diagnosed. CONCLUSIONS: Increased AD disease severity was associated with a greater patient impact and reduced quality of life, with healthcare costs and formulary restrictions hindering optimal treatment across Brazil and Colombia.
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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 | 0.000 |
| 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".