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The validity of register data to identify children with atopic dermatitis, asthma or allergic rhinoconjunctivitis
Lone Graff Stensballe1,2, Lotte Klansø1, Andreas Jensen1
1The Child and Adolescent Clinic 4072, Juliane Marie Centret, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
Insights
Validated algorithms accurately identify children with atopic dermatitis, asthma, and allergic rhinoconjunctivitis using register data. These tools are valuable for population-level research on increasing allergic disease incidence.
Area of Science:
- Pediatric Allergy and Immunology
- Public Health Research
- Epidemiology
Background:
- Rising incidence of atopic dermatitis, wheezing, asthma, and allergic rhinoconjunctivitis necessitates robust research methods.
- Register-based studies are crucial for studying specific disease subpopulations and identifying causes.
- Existing algorithms for identifying these conditions in children using register data require validation.
Purpose of the Study:
- To validate algorithms designed to identify children with atopic dermatitis, asthma, or allergic rhinoconjunctivitis using electronic health records.
- To compare algorithm performance against a gold standard of in-depth caretaker interviews regarding physician diagnoses.
Main Methods:
- Algorithms utilized register data on disease-specific prescribed medications and hospital contacts.
- A gold standard was established through detailed telephone interviews with caretakers of 454 Danish children (born 1997-2003).
- Sensitivity, specificity, and 95% confidence intervals were calculated to assess algorithm validity.
Main Results:
- Atopic dermatitis algorithm: Sensitivity 74.1%, Specificity 73.0%.
- Asthma algorithm: High sensitivity (84.1%) and specificity (81.6%) for asthmatic bronchitis; Sensitivity 83.3% but lower specificity (66.0%) for physician-diagnosed asthma.
- Allergic rhinoconjunctivitis algorithm: Sensitivity 84.4%, Specificity 81.6%.
Conclusions:
- The developed algorithms demonstrate validity for identifying children with atopic dermatitis, asthma, and allergic rhinoconjunctivitis.
- These algorithms are valuable tools for population-level research utilizing register data.
- The findings support the use of these algorithms in epidemiological studies and intervention evaluations.
Background:
The incidence of atopic dermatitis, wheezing, asthma and allergic rhinoconjunctivitis has been increasing. Register-based studies are essential for research in subpopulations with specific diseases and facilitate epidemiological studies to identify causes and evaluate interventions. Algorithms have been developed to identify children with atopic dermatitis, asthma or allergic rhinoconjunctivitis using register information on disease-specific dispensed prescribed medication and hospital contacts, but the validity of the algorithms has not been evaluated. This study validated the algorithms vs gold standard deep telephone interviews with the caretaker about physician-diagnosed atopic dermatitis, wheezing, asthma or allergic rhinoconjunctivitis in the child.
Methods:
The algorithms defined each of the three atopic diseases using register-based information on disease-specific hospital contacts and/or filled prescriptions of disease-specific medication. Confirmative answers to questions about physician-diagnosed atopic disease were used as the gold standard for the comparison with the algorithms, resulting in sensitivities and specificities and 95% confidence intervals. The interviews with the caretaker of the included 454 Danish children born 1997-2003 were carried out May-September 2015; the mean age of the children at the time of the interview being 15.2 years (standard deviation 1.3 years).
Results:
For the algorithm capturing children with atopic dermatitis, the sensitivity was 74.1% (95% confidence interval: 66.9%-80.2%) and the specificity 73.0% (67.3%-78.0%). For the algorithm capturing children with asthma, both the sensitivity of 84.1% (78.0%-88.8%) and the specificity of 81.6% (76.5%-85.8%) were high compared with physician-diagnosed asthmatic bronchitis (recurrent wheezing). The sensitivity remained high when capturing physician-diagnosed asthma: 83.3% (74.3%-89.6%); however, the specificity declined to 66.0% (60.9%-70.8%). For allergic rhinoconjunctivitis, the sensitivity was 84.4% (78.0-89.2) and the specificity 81.6% (75.0-84.4).
Conclusion:
The algorithms are valid and valuable tools to identify children with atopic dermatitis, wheezing, asthma or allergic rhinoconjunctivitis on a population level using register data.
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