Predictors of inappropriate and excessive use of reliever medications in asthma: a 16-year population-based study
Bibliographic record
Abstract
BACKGROUND: Understanding factors associated with the inappropriate or excessive use of short-acting beta agonists (SABA) can help develop better policies. METHODS: We used British Columbian (BC)'s administrative health data (1997-2014) to create a retrospective cohort of asthma patients aged between 14 and 55 years. The primary and secondary outcomes were, respectively, inappropriate and excessive use of SABA based on a previously validated definition. Exposures were categorised into groups comprising socio-demographic variables, indicators of type and quality of asthma care, and burden of comorbid conditions. RESULTS: 343,520 individuals (56.3% female, average age 30.5) satisfied the asthma case definition, contributing 2.6 million person-years. 7.3% of person-years were categorised as inappropriate SABA use and 0.9% as excessive use. Several factors were associated with lower likelihood of inappropriate use, including female sex, higher socio-economic status, higher continuity of care, having received pulmonary function test in the previous year, visited a specialist in the previous year, and the use of inhaled corticosteroids in the previous year. An asthma-related outpatient visit to a general practitioner in the previous year was associated with a higher likelihood of inappropriate SABA use. Similar associations were found for excessive SABA use with the exception that visit to respirologist and the use of systemic corticosteroids were associated with increased likelihood of excessive use. CONCLUSIONS: Despite proven safety issues, inappropriate SABA use is still prevalent. Several factors belonging to patients' characteristics and type/quality of care were associated with inappropriate use of SABAs and can be used to risk-stratify patients for targeted attempts to reduce this preventable cause of adverse asthma outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".