AI Insight
Researchers developed LEAP (Lifetime Exposures and Asthma outcomes Projection), a microsimulation-based health policy model for asthma in Canada, designed to project asthma-related outcomes at the individual level from 2001 onwards. The model integrates five modules covering demographics, risk factors, asthma occurrence, asthma outcomes, and payoffs, drawing on national surveys, administrative databases, and a longitudinal birth cohort. Validation assessments showed that LEAP's demographic projections and asthma prevalence estimates closely matched real-world data from administrative sources and published literature.
Why it matters
LEAP provides health policymakers with a standardized, open-source framework to evaluate and compare early interventions for asthma in Canada, potentially informing more cost-effective public health strategies. Its public availability in both Julia and Python makes it accessible to a broad research and policy community, facilitating reproducibility and future refinement.
Understand the Science
by Tae Yoon Lee, John Petkau, Kate M. Johnson, Stuart E. Turvey, Amin Adibi, Padmaja Subbarao, Ainsleigh Hill, Mark Ewert, Mohsen Sadatsafavi
Objectives
To address the need for health policy planning focused on early interventions for asthma, we developed the Lifetime Exposures and Asthma outcomes Projection (LEAP), a reference policy model for asthma in Canada.
Methods
Based on a previously developed concept map, we used an open-population microsimulation design to deal with the multidimensionality of risk factors and the need for modelling realistic aspects of adopting health technologies. The model consists of five intertwined modules: demographics, risk factors, asthma occurrence, asthma outcomes, and payoffs. The demographics module was based on sex- and age-specific estimates and projections from national surveys. For the first version of the model, we concentrated on key risk factors from the concept map: age, sex, family history of asthma at birth, and exposure to antibiotics in the first year of life. The distributions of risk factors were estimated from population-based administrative databases and a population-based longitudinal birth cohort. Quantitative evidence synthesis provided estimated parameters for the asthma occurrence and outcomes modules. Costs and utility weights were obtained from the literature. We conducted face validity, calibration, and internal validity assessments.
Results
LEAP is capable of modelling asthma-related health outcomes at the individual level from 2001 onwards. Age-sex stratified demographic projections from the model closely matched projections, while asthma prevalence and control levels matched their respective estimates from the administrative data and estimates from the literature. The LEAP model is publicly accessible as open-source software: https://github.com/tyhlee/LEAP.jl (Julia) and https://resplab.github.io/leap/ (Python).
Conclusions
LEAP is the first reference Canadian asthma policy model that emerged from identified needs for health policy planning for early interventions in asthma. It can provide a unified framework under which different interventions and policies can be consistently compared to identify those with the highest value proposition. The model needs to be gradually updated to accommodate other risk factors, and its validity should be independently examined.
Source: Development of an asthma policy model for Canada: Lifetime Exposures and Asthma outcomes Projection