ArticleSkin health and disease2025
Harmonizing epidemiological research methodology for atopic dermatitis research: protocol for the EPISTAR international consensus exercise.
Article in Skin health and disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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21 authors.
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Abstract
Background: Epidemiological studies of atopic dermatitis lack standardization in key areas, including how the burden is collected and reported, diagnostic criteria, sociodemographic factors and measurement of disease severity. Therefore, direct cross-study comparisons, recognition of population differences and pooled analyses are challenging or not possible. Consequently, the burden of atopic dermatitis remains difficult to assess and address. The Epidemiological Study Designs for Atopic Dermatitis Research (EPISTAR) initiative aims to reach consensus on which domain items should be recommended for future population-based epidemiological studies on atopic dermatitis and how they should be assessed. Methods: In phase 1, a steering group consisting of experts from dermatology and epidemiology as well as patient representatives will generate an initial list of items constituting key variables to measure. This list will be created by reviewing the existing literature, prioritizing evidence from systematic reviews where available. Phase 2 will include an international consensus exercise, conducted through eDelphi methodology. Phase 3 will involve an online consensus conference. In each Delphi round, international participants from diverse stakeholder groups will be invited to assess each item by rating their level of agreement with the item and methods by which it can be measured. Items that reach consensus will be removed after each round. Data analysis will follow predefined consensus criteria, with raw numbers, means and frequencies reported. Discussion: This harmonized approach has the potential to transform the field of atopic dermatitis epidemiology by addressing gaps in data quality and comparability, facilitating meta-analyses, and ultimately informing evidence-based policy and clinical guidelines.
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