<p>Gender Differences in Inhaled Pharmacotherapy Utilization in Patients with Obstructive Airway Diseases (OADs): A Population-Based Study</p>
Bibliographic record
Abstract
Purpose: Gender differences in the incidence, susceptibility and severity of many obstructive airway diseases (OADs) have been well recognized. However, gender differences in the inhaled pharmacotherapy profile are not well characterized. Methods: -agonist (SABA or LABA), ICS/LABA, short-or long-acting muscarinic antagonist (SAMA or LAMA) among patients with asthma, COPD or asthma-COPD overlap (ACO). We used Clinical Practice Research Datalink to identify OAD patients, 18 years and older, who were new-users (1-year washout period) from 01-January-1998 to 31-July-2018. Multivariable logistic regression was used to examine gender differences in each of the inhaled pharmacotherapies after controlling for potential confounders. Results: A total of 242,079 new-users (asthma: 84.93%; COPD: 10.19%; ACO: 4.88%) of inhaled pharmacotherapies were identified. The multivariable analyses showed that males with COPD were more likely to be a new user of a LABA (odds ratio [OR] 1.29; 95% confidence interval [CI], 1.12-1.49), LAMA (OR 1.21; 95% CI 1.10-1.33), SAMA (OR 1.11; 95% CI 1.01-1.21) and less likely to be a new user of a SABA (OR 0.84; 95% CI, 0.80-0.89) compared to females. Similar patterns were also observed for patients with ACO; males were more likely to be prescribed with LABA (OR 1.26; 95% CI 1.03-1.55), LAMA (OR 1.28; 95% CI 1.11-1.48), SAMA (OR 1.28; 95% CI 1.11-1.48), and less likely to be a new user of a SABA (OR 0.89; 95% CI, 0.82-0.96). Also, males with asthma were more likely to be a new-user of ICS/LABA (OR 1.15; 95% CI, 1.08-1.23) and less likely to start an ICS (OR 0.97; 95% CI, 0.95-0.99) in comparison with females. Conclusion: Our study showed significant gender differences in new-users of inhaled pharmacotherapies among OAD patients. Adjusting for proxies of disease severity, calendar year, smoking and socioeconomic status did not change the association by gender.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".