Development of a Four-Language Questionnaire to Investigate Environmental Risk Factors for the Development of Canine Atopic Dermatitis and to Monitor Disease Course and Progression.

Background: The chronic and multifactorial character of canine atopic dermatitis (cAD) often leads to poor disease control and treatment dissatisfaction. Environmental factors are likely to contribute to the disease development and may play a more important role than assumed previously. This opens new research directions that require an appropriate tool to obtain useful data from large representative study populations.

Hypothesis/objectives: A tool such as a questionnaire is suitable for obtaining high-quality data to investigate the pathogenesis of cAD and monitor the disease.

Materials and methods: To assure the tool's validity and reliability, the development process of this four-language questionnaire (original language German) included two pilot tests (with owners' interviews and questionnaire evaluation sheets), test-retest assessment, content validity evaluation and a structured translation and back-translation into three languages (English, Italian and French).

Results: The development process took place between June 2024 and December 2024. The preliminary questionnaire comprised 107 questions. The pilot tests (round 1 = four participants, round 2 = two participants) resulted in a revision of 31 questions and the deletion of three. The test-retest assessment revealed an Intraclass Correlation Coefficient of 0.80. The panel of (six) experts evaluated the questionnaire with a content validity index of 0.99. The translation and back-translation process revealed that only minor adjustments were sufficient to guarantee the validity and reliability across languages.

Conclusions and clinical relevance: The comprehensive development process ensures high validity and reliability of the questionnaire, indicating that such a process can not only positively impact the quality of the developed tool, but also create a reliable basis for the generation of accurate and less biased data.