Prevalence and risk factors for atopic dermatitis in Greenlandic children
Bibliographic record
Abstract
BACKGROUND: The epidemiology of atopic dermatitis (AD) in Greenland has been sparsely investigated. AIM: To examine the point and overall prevalence, cumulative incidence at different ages, and associated risk factors for AD among children in Greenland. METHODS: Between 2019 and 2020, three towns in Greenland, representing 48% of the total population, were visited. A cross-sectional study was conducted, including children aged 0-7 years attending daycare centres. Parents completed a questionnaire with questions on AD and related risk factors. A diagnosis of AD was based on the UK Working Party's criteria along with a clinical examination. RESULTS: In total, 839 children aged 0-7 years were included. The overall prevalence of AD was 35% according to physician's diagnosis and assessment. The point prevalence was 28% and peaked among 1-year-old children (36%) and declined with age. The cumulative incidence at ages 1-6 years varied between 29% and 41% and was highest in 1-year-old children and showed a slight decline with increasing age. In the fully adjusted multivariate model, AD was associated with being of Inuit descent [odds ratio (OR) 1.7, 95% confidence interval (CI) 1.1-2.8]; food allergy in the child (OR 3.6, 95% CI 2.3-5.6); ear infection in the child (OR 1.4, 95% CI 1.0-1.9); having a mother with a high educational level (OR 1.5, 95% CI 1.0-2.3); maternal atopy (OR 1.4, 95% CI 1.1-2.0); and paternal atopy (OR 2.0, 95% CI 1.5-2.8). No environmental risk factors were identified. CONCLUSION: The overall prevalence of AD in children in Greenland is high and has likely increased over the past 20 years. The point prevalence was highest in the youngest children indicating early onset of disease. Inuit descent, family atopy predisposition and having a higher socioeconomic status (based on parental educational level and housing) increased the risk of AD. Insight into possible Inuit-specific genetic predisposition is needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".