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Modelling Asthma Treatment Dynamics: Insights from the g-Formula
Irene Mommers1, Job F M van Boven2,3, Jens H J Bos1
1Pharmacotherapy, -Epidemiology and -Economics, University of Groningen, Groningen, The Netherlands.
Clinical Epidemiology
|March 18, 2025
Summary
The g-formula accurately simulates asthma treatment switching behavior, revealing that older age, male sex, and certain comorbidities reduce treatment changes. These findings highlight patient subgroups that may require closer monitoring for optimal asthma management.
Area of Science:
- Pharmacology
- Epidemiology
- Health Services Research
Background:
- Asthma treatment requires long-term management, with dynamic changes in medication often necessary.
- Analyzing treatment trajectories is complex due to time-varying factors.
- The g-formula presents a robust statistical method for causal inference in longitudinal studies.
Purpose of the Study:
- To evaluate the g-formula's capability in simulating real-world asthma treatment switching patterns.
- To identify patient characteristics associated with differences in asthma treatment switching behavior.
Main Methods:
- Retrospective cohort study using the IADB.nl pharmacy dispensing database (1994-2021).
- Inclusion of individuals aged 16-45 years initiating inhaled asthma medication.
- Application of the g-formula with logistic regression to predict treatment trajectories and analyze associations with patient factors (age, sex, comorbidities).
Main Results:
- G-formula simulations closely matched real-world asthma treatment switching: 76% predicted vs. 77% observed switchers.
- Older individuals (45 vs. 16 years) switched less frequently but earlier.
- Comorbidities such as atopic diseases (ATD), cardiovascular diseases (CVD), mental health problems (MHP), and gastroesophageal reflux disease (GERD) were associated with reduced treatment switching.
Conclusions:
- The g-formula is effective for simulating dynamic asthma treatment pathways.
- Factors like older age, male sex, and specific comorbidities are linked to decreased treatment switching.
- Identifying these subgroups can inform targeted interventions and improve asthma care by addressing potential treatment delays.
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