Characterizing adult asthma: a cross-sectional epidemiologic study from the canadian primary care sentinel surveillance network
Bibliographic record
Abstract
National asthma prevalence data in Canada typically come from health surveys or administrative records. Since most asthma care is provided by family physicians, primary care electronic medical records (EMRs) may offer valuable insights into asthma epidemiology and treatment patterns. This study aimed to estimate the prevalence of adult asthma across Canada using national EMR data, examine the demographics and comorbidities of asthma patients, and review national prescribing practices. We used a validated EMR case definition for adult asthma applied to the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) database, which includes data from 12 networks across Canada. We identified patients with at least one encounter in a two-year period and estimated asthma prevalence, stratified by age, sex, and BMI. Comorbidity rates and medication prescriptions were assessed in patients with asthma. Among 854,567 adults, 94,410 were identified with confirmed/suspected asthma (11% prevalence). Asthma was more common in females (12 vs. 10%, p < 0.0001), across all age brackets except 18-29 years old. A chi-square test for trend showed a decrease in prevalence with increasing age (p < 0.0001). Females with asthma had a higher prevalence of ≥4 comorbidities than males (33 vs. 30%, p < 0.0001). Additionally, 13% of asthma patients were prescribed only as-needed short-acting bronchodilators, without a controller. The 11% asthma prevalence found in this study aligns with national survey estimates, providing support for the use of EMRs in disease and practice surveillance. Future efforts should focus on integrating tools within EMR to support asthma diagnosis and treatment adherence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".