A Longitudinal Assessment of Acute Care Utilization in the Year Prior to Deaths in Canadian Patients With Asthma
Bibliographic record
Abstract
Abstract Rationale: Despite recent advances in novel therapeutics, mortality in patients with asthma continues to occur. Identifying patients at high-risk for death is critical for early intervention. We aimed to assess trends in asthma mortality over the past 20 years in Ontario, Canada and identify patients who may be at higher risk. Methods: We assembled cross-sectional cohorts of individuals with prevalent asthma (aged 0-99 years) using population-based health administrative databases from 2003-2022. We focused on trends up to 2019, prior to the COVID-19 pandemic period. All-cause of death were counted from the provincial Registered Persons Database. Emergency Department (ED) visits and hospitalizations for asthma in the 1-year prior to death were assembled from the National Ambulatory Care Reporting System (NACRS) and hospital discharge abstracts. Using logistic regression, we compared the ED visit and hospitalization rates between those who died with those who stayed alive with relative risk (RR) and 95% confidence intervals (CI). RRs will be further stratified by age groups, rural residence and deprivation index (on-going analysis). Results: A total of 2,219,623 individuals with prevalent asthma were included in this study with 97,963 all-cause deaths observed from 2003 to 2022. Rates of all-cause of death per 1,000 asthma prevalence increased from 2.5 (2003) to 3.1 (2019) per 1000 representing a 23% increase over time. Over this same time-period asthma ED visits and hospitalizations decreased. In 2019, the asthma ED visit rate in the 1-year prior was nearly 2-fold higher in the asthma cohort that died (19.1 per 1000) compared to those who did not (10.7 per 1000) (RR=1.79, 95%CI: 1.78-1.81, p<0.00001). Furthermore, in the 1-year prior to death, the asthma hospitalization rate among those who died (10.3 per 1000) was nearly 7-fold higher compared to those who did not (1.54 per 1000) (RR=6.70, 95%CI: 6.61-6.80, p<0.00001, Figure 1). Conclusions: All-cause mortality rates in patients with asthma have increased over the past 20 years in Ontario, Canada despite decreasing rates of acute care utilization. Those with ED visits, and particularly hospitalizations for asthma are a group at higher risk for death in the year following these visits, which may represent an opportunity for intervention. Ongoing analyses will assess whether this risk persists in groups stratified by age, rural residence and deprivation index.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".