Evidence map›Paper›PMID 40098628›Full record

ArticleClinical epidemiology2025

Modelling Asthma Treatment Dynamics: Insights from the g-Formula.

Irene Mommers, Job F M van Boven, Jens H J Bos, Sumaira Mubarik, Eelko Hak, Maarten J Bijlsma

Abstract read
In one paragraph

Article in Clinical epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Irene MommersPharmacotherapy, -Epidemiology and -Economics, University of Groningen, Groningen, The Netherlands.ORCID 0000-0002-4185-4323
Job F M van BovenGroningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, Groningen, The Netherlands.ORCID 0000-0003-2368-2262
Jens H J BosPharmacotherapy, -Epidemiology and -Economics, University of Groningen, Groningen, The Netherlands.ORCID 0000-0001-9209-7564
Sumaira MubarikPharmacotherapy, -Epidemiology and -Economics, University of Groningen, Groningen, The Netherlands.ORCID 0000-0001-6041-1061
Eelko HakPharmacotherapy, -Epidemiology and -Economics, University of Groningen, Groningen, The Netherlands.ORCID 0000-0003-0849-7210
Maarten J BijlsmaPharmacotherapy, -Epidemiology and -Economics, University of Groningen, Groningen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The g-formula offers a promising approach to analyze long-term dynamic asthma treatment trajectories. This study investigates whether the g-formula can simulate real-world asthma treatment trajectories and predicts subgroup differences in switching behavior. Patients and Methods: This retrospective cohort study identified individuals aged 16- to 45 years who initiated inhaled asthma medication in the Netherlands between 1994 and 2021, from the IADB.nl pharmacy dispensing database. We used the g-formula combined with logistic regression to predict treatment trajectories and their associations with various patient characteristics, such as age, sex, chronic drug treatment for atopic diseases (ATD), cardiovascular diseases (CVD), thyroid diseases, arthritis, diabetes, gastroesophageal reflux disease (GERD), mental health problems (MHP), and immunosuppressants. Results: The simulations predicted 76% of individuals to switch treatment, on average 2.3 times, with the first switch occurring on average after 8.3 months, which agrees with the real-world observations (77%, 2.3 times and 7.9 months, respectively). Fewer 45-year-olds switched treatment compared to 16-year-olds (74% vs 78%, p < 0.001), but they switched earlier (8.1 vs 8.6 months, p < 0.001) and more frequently (2.4 vs 2.3 times, p < 0.001). Women were more likely to switch compared to men. Patients with ATD, CVD, MHP, or GERD switched significantly less often (p < 0.05). Conclusion: The g-formula effectively simulates asthma treatment trajectories and found higher age, male sex, ATD, CVD, MHP, and GERD to decrease overall switching behavior. These patients might benefit from earlier intervention or closer monitoring to reduce delays in treatment progression.

Indexed as

asthmag-formulainhaled medicationpredictiontrajectoriestreatment steps

Identifiers

PMID40098628
PMCPMC11913031

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.