Comorbidities in childhood atopic dermatitis: A population-based study.

BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disease that is associated with allergic comorbidities. However, studies examining comorbidities in childhood AD are incomplete, which may contribute to suboptimal care. OBJECTIVE: The objective was to compare the risk of developing different allergic and non-allergic comorbidities among children with AD to that of a matched non-AD reference cohort in Sweden. METHODS: This was a nationwide population-based cohort study using longitudinal data from primary and specialist care registers. Patients with AD were identified by confirmed diagnosis in primary or specialist care. The non-AD reference cohort was randomly drawn from the general population and matched 1:1 with the AD patients. The risk of developing the following conditions was evaluated: hypersensitivity and allergic disorders, neurological disorders, psychiatric disorders, infections, immunological and inflammatory disorders, Type 1 diabetes (T1D), endocrine and metabolic disorders, skeletal disorders, ocular disorders and malignancies. RESULTS: This study included 165,145 patients with AD (mild-to-moderate [n = 126,681] and severe [n = 38,464]) and an equally sized reference cohort. Patients with AD displayed a higher risk of developing comorbid conditions for all investigated categories, except for T1D and skeletal disorders, compared with the reference cohort. The highest risk compared with the reference cohort was observed for hypersensitivity and allergic disorders (hazard ratio [HR]: 3.87), followed by malignancies (HR: 2.53) and immunological and inflammatory disorders (HR: 2.36). Patients with AD also had higher risk of developing multiple comorbidities (≥2). The risk of comorbidity onset increased alongside AD severity and patients with active AD were associated with increased risk of comorbidity onset compared with patients in remission. CONCLUSIONS: The clinical burden of AD is substantial for children with AD and patients are at an increased risk of developing several comorbid conditions extending beyond the atopic march. Our results also showed a positive association between worsening severity of AD and an increased risk of comorbidity onset.

Validation of Patient Identification Algorithms for Atopic Dermatitis Using Healthcare Databases.

INTRODUCTION: The use of real-world data offers a possibility to perform large-scale epidemiological studies in actual clinical settings. Despite their many advantages, administrative databases were not designed to be used in research, and the validation of diagnoses and treatments in administrative databases is needed. The primary objective of this study was to validate an existing algorithm based on dispensed prescriptions and diagnoses of skin conditions to identify pediatric patients with atopic dermatitis (AD), using a diagnosis of AD in primary care as a gold standard. METHODS: Retrospective observational data were collected from nation-wide secondary care and pharmacy-dispensed medication databases and two regional primary care databases in Sweden. An existing algorithm and a Modified algorithm, using skin-specific diagnoses from secondary care and/or pharmacy-dispensed prescriptions to identify patients with AD, were assessed. To verify the presence of AD, diagnoses from primary care were used in the base case and complemented with diagnoses from secondary care in a sensitivity analysis. RESULTS: The sensitivity (30.0%) and positive predictive value (PPV) (40.7%) of the existing algorithm were low in the pediatric patient population when using primary care data only but increased when secondary care visits were also included in the Modified algorithm (sensitivity, 62.1%; PPV, 66.3%). The specificity of the two algorithms was high in both the base case and sensitivity analysis (95.1% and 94.1%). In the adult population, sensitivity and PPV were 20.4% and 8.7%, respectively, and increased to 48.3% and 16.9% when secondary care visits were also included in the Modified algorithm. CONCLUSION: The Modified algorithm can be used to identify pediatric AD populations using primary and secondary administrative data with acceptable sensitivity and specificity, but further modifications are needed to accurately identify adult patients with AD.