Stensballe Lone Graff, Klansø Lotte, Jensen Andreas, Haerskjold Ann, Thomsen Simon Francis, Simonsen Jacob
The Child and Adolescent Clinic 4072, Juliane Marie Centret, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark.
Pediatr Allergy Immunol. 2017 Sep;28(6):535-542. doi: 10.1111/pai.12743. Epub 2017 Jul 27.
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.
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).
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).
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.
特应性皮炎、喘息、哮喘和变应性鼻结膜炎的发病率一直在上升。基于登记处的研究对于特定疾病亚人群的研究至关重要，并有助于开展流行病学研究以确定病因和评估干预措施。已开发出算法，利用特定疾病的配药处方和医院就诊记录信息来识别患有特应性皮炎、哮喘或变应性鼻结膜炎的儿童，但这些算法的有效性尚未得到评估。本研究将这些算法与金标准进行了验证，即与儿童监护人就医生诊断的儿童特应性皮炎、喘息、哮喘或变应性鼻结膜炎进行深度电话访谈。
这些算法利用基于登记处的特定疾病医院就诊记录信息和/或特定疾病药物的处方记录来定义这三种特应性疾病中的每一种。关于医生诊断的特应性疾病问题的肯定回答被用作与算法进行比较的金标准，从而得出敏感性、特异性及95%置信区间。对纳入的454名1997年至2003年出生的丹麦儿童的监护人进行的访谈于２０１５年５月至９月进行；访谈时儿童的平均年龄为15.2岁（标准差1.3岁）。
对于识别患有特应性皮炎儿童的算法，敏感性为74.1%（95%置信区间：66.9%-80.2%），特异性为73.0%（67.3%-78.0%）。对于识别患有哮喘儿童的算法，与医生诊断的喘息性支气管炎（复发性喘息）相比，敏感性为84.1%（78.0%-88.8%），特异性为81.6%（76.5%-85.8%），两者均较高。在识别医生诊断的哮喘时，敏感性仍然很高：83.3%（74.3%-89.6%）；然而，特异性降至66.0%（60.9%-70.8%）。对于变应性鼻结膜炎，敏感性为84.4%（78.0-89.2），特异性为81.6%（75.0-84.4）。
这些算法是利用登记数据在人群层面识别患有特应性皮炎、喘息、哮喘或变应性鼻结膜炎儿童的有效且有价值的工具。
