INTRODUCTION

As a high prevalent condition, asthma generates a substantial economic burden to the society and health care systems (1,2). Allergic asthma (AA) is the most frequent phenotype and is defined by the presence of sensitization to environmental allergens. Patients with AA experience a considerable burden in terms of poor health-related quality-of-life, productivity loss and healthcare resource utilization (HCRU) that increases with severity (3).
Inhaled corticosteroids (ICS) are the first line therapy recommended for the control of asthma symptoms due to their high anti-inflammatory effect. Despite its proven cost-effectiveness, their efficacy depends on its constant use over time and some patients remain with persistent symptoms resulting in adverse events in the long-term (4,5). New strategies with potential disease modifying effects should be evaluated in terms of their clinical and economic implications. Allergen immunotherapy (AIT) is the only therapy that can modify the progression of allergic diseases by inducing immune tolerance (6). It is associated with reduction of HCRU and protective effect that can translate in potential cost-savings (7–9). Previous research suggests that AIT may be cost-effective for the treatment of patients with asthma (10,11). However, studies evaluating the cost-effectiveness of SCIT with HDM have been frequently based in randomized controlled trials (RCTs), and the use of relevant outcomes like exacerbations and medication step down is lacking. Potential differences in the cost-effectiveness across populations (e.g., children vs adults, patients with AA-only vs patients with AA and allergic rhinitis [AR]) have also been underexplored (10,12).
Model-based cost-effectiveness evaluations allows the combination of multiple sources of evidence, extrapolating results beyond the study length of clinical trials and converting treatment effects into policy-relevant outcomes (10,12). In this study, we sought to evaluate the cost-effectiveness of SCIT + ICS vs ICS for pediatric and adult patients with AA and AA with AR in Colombia through a decision-analytic modeling approach and multiple data sources, including parameters from real-world studies.