Assessing atopic disease in children two to six years old: reliability of a revised questionnaire

Torbjørn Øien1, Ola Storrø, Roar Johnsen

  • 1Department of Public Health and General Practice, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. torbjorn.oien@ntnu.no

Insights

This study found that a revised questionnaire reliably identified asthma diagnosis, allergy testing, and antibiotic use in young children. However, questions about medical treatments for eczema and infections showed lower reliability, potentially causing bias.

Area of Science:

  • Pediatric Allergy and Immunology
  • Epidemiological Research Methods
  • Child Health

Background:

  • A study in Trondheim initiated in 2002 aimed to reduce childhood allergic diseases through interventions like decreasing secondhand smoke (SHS) and indoor dampness.
  • No validated questionnaires were available for assessing atopic diseases in the target age group (2-6 years).

Purpose of the Study:

  • To assess the reliability of a newly adapted questionnaire for studying atopic diseases in children aged two to six years.
  • To evaluate the questionnaire's suitability for research in pediatric allergy and environmental health.

Main Methods:

  • A revised questionnaire, adapted from the International Study of Asthma and Allergies in Childhood (ISAAC) protocol, was administered to 77 families.
  • Agreement between questionnaire responses and medical records was analyzed using Kappa statistics and proportional agreement.

Main Results:

  • Excellent agreement (kappa > 0.80) was observed for doctor-diagnosed asthma, allergy testing, and antibiotic use.
  • Good agreement (kappa 0.45-0.59) was found for asthma treatment, eczema symptoms, and past infections.

Conclusions:

  • The revised questionnaire demonstrated high reliability for assessing asthma diagnosis, allergy testing, and antibiotic use in young children.
  • Lower reliability for questions on medical treatment of eczema, allergic rhinoconjunctivitis, and infections indicates a potential for information bias and misclassification.
Abstract