Development and Validation of an Asthma Policy Model for Canada: Lifetime Exposures and Asthma outcomes Projection (LEAP)
Bibliographic record
Abstract
Abstract Purpose To develop Lifetime Exposures and Asthma outcomes Projection (LEAP), a reference policy model for evaluating health outcomes and costs of asthma interventions and policies for the Canadian population. Methods Following the best practice guidelines for development, we first created a conceptual map with a steering committee of clinician experts and economic modelers through a modified Delphi-process. Following the committee’s recommendations and given the multidimensionality of risk factors and the need for modeling realistic aspects (e.g., gradual market penetration) of adopting health technologies, we opted for an open-population microsimulation design. For the first version of the model, we concentrated on several key risk factors (age, sex, family history of asthma at birth, and exposure to antibiotics in the first year of life) from the concept map. The model consists of five intertwined modules: 1) demographic, 2) risk factors, 3) asthma occurrence, 4) asthma outcomes, and 5) payoffs. The demographic module, including birth, mortality, immigration, and emigration, was based on sex– and age-specific estimates and projections from Statistics Canada. The distributions of risk factors, including family history of asthma and exposure to antibiotics, were estimated from population-based administrative databases and a population-based longitudinal birth cohort. To estimate parameters in the asthma occurrence (prevalence, incidence, reassessment) and asthma outcomes (severity, symptom control, exacerbations) modules, we performed quantitative evidence synthesis. Costs and utility weights were obtained from the literature. We conducted multiple face and internal validation assessments. Results LEAP is capable of modeling asthma-related health outcomes at the individual and aggregate levels from 2001 onwards. Face validity was confirmed by checking the structure, equations, codes, and results. We calibrated and internally validated the age-sex stratified demographic projections to the estimates and projections from Statistics Canada, the age-sex stratified asthma prevalence to the administrative data, and the asthma control levels and exacerbation rates to the estimates from the literature. Conclusions LEAP is the first reference Canadian asthma policy model that emerged from identified needs for health policy planning for early interventions in asthma. As an open-source and open-access platform, LEAP can provide a unified framework under which different interventions and policies can be consistently compared to identify those with the highest value proposition. Funding This study was funded by a research grant from the Canadian Institutes of Health Research and Genome Canada (274CHI). The funders had no role in any aspect of this study and were not aware of the results. Ethics This study was approved by the institutional review board of the University of British Columbia, Vancouver (H22-00571).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".